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- No second thoughts about data access
- Nat Genet 43(5):389 (2011)
Nature Genetics | Editorial No second thoughts about data access Journal name:Nature GeneticsVolume: 43,Page:389Year published:(2011)DOI:doi:10.1038/ng.827Published online27 April 2011 More data than we can handle is no excuse to give up our efforts to promote data access, but it may make us think about new ways to make it sustainable. View full text Additional data - Defining rare variants by their frequencies in controls may increase type I error
- Nat Genet 43(5):391-392 (2011)
Nature Genetics | Correspondence Defining rare variants by their frequencies in controls may increase type I error * Mathieu Lemire1Journal name:Nature GeneticsVolume: 43,Pages:391–392Year published:(2011)DOI:doi:10.1038/ng.818Published online27 April 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. To the Editor: In the August 2010 edition of Nature Genetics, Johansen et al.1 reported a genome-wide association study for hypertriglyceridemia (HTG) and re-sequenced four genes that showed association with HTG at genome-wide significant levels in a subset of 438 individuals with HTG (cases) and 327 controls. Focusing on rare protein-modifying variants (those with a minor allele frequency of at most 1% in the controls), they observed nearly twice as many carriers of at least one rare allele among the cases (28.1%) compared to the controls (15.3%; P = 2.6 × 10−5). We want to point out some methodological issues that arise when aggregation-based methods are applied to a set of rare variants defined by frequency thresholds calculated from the controls. Because this procedure leaves the frequency of variants unbounded in the cases while inherently imposing an upper bound in the controls, this selection bias can result in inflated type I errors, even when no true genetic effect is present i! n the sequenced regions. To illustrate this, we simulated data for 380 SNPs, in linkage equilibrium for simplicity, whose frequencies were sampled from an infinite population according to Wright's formula2, 3; a large majority of these SNPs had very low allele frequency. We simulated genotypes for 770 individuals according to the rules of Hardy-Weinberg equilibrium. We arbitrarily labeled 330 individuals as controls and the remaining 440 individuals as cases; no true genetic effect was simulated. On average, only 94 SNPs were polymorphic in the 770 individuals and 80 of them had allele frequencies in the controls of less than 1%; these numbers are similar to the number of variants discovered and the number of variants labeled as rare in Johansen et al.1. We compared the count of carriers of at least one minor allele across the rare SNPs, among the cases and the controls, to the count of non-carriers using Fisher's exact test. Table 1 shows that the odds ratio estimated from these counts is close to ! 1.15. The true type I error of the test is 0.66% at a nominal P < 0.001 for a test known to be conservative. We note that this deviation from the null is aggravated when a larger set of rare variants enters the analysis from, for example, sequencing larger regions, and also more dramatically when the region is sequenced in a smaller sample (Table 1). A permutation procedure that consists of comparing the observed test statistic to the ones obtained from random permutations of the cases and controls would lead to a correct estimate of the significance level, as long as the set of rare variants upon which the test statistic is based is recomputed with each replicate (from the full set of polymorphic SNPs, not just the SNPs that entered the original analysis); otherwise, the selection bias would not be carried over from one replicate to the next, and its effect would not be correctly accounted for. Table 1: Testing for the aggregation of rare alleles in cases Full table The advantage of working with a set of rare variants defined as those with a frequency calculated in the controls below a certain threshold (as opposed to, say, a frequency estimated from the combined sample of cases and controls) is that this procedure imposes no bounds on how high the frequency may get in the cases, which is a desirable effect. Working with a seemingly unbiased set of rare variants defined as those below a certain frequency threshold in either the cases or the controls still resulted in inflated type I errors, whereas using estimates from the combined set of cases and controls results in expected, albeit generally conservative, type I errors (Table 1); however, these two strategies could potentially lead to a higher loss of power if, in a given region, both rare susceptibility and rare protective variants coexist3, 4. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Ontario Institute for Cancer Research, MaRS Centre, Toronto, Ontario, Canada. * Mathieu Lemire Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Mathieu Lemire Author Details * Mathieu Lemire Contact Mathieu Lemire Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Bias due to selection of rare variants using frequency in controls
- Nat Genet 43(5):392-393 (2011)
Nature Genetics | Correspondence Bias due to selection of rare variants using frequency in controls * Richard D Pearson1Journal name:Nature GeneticsVolume: 43,Pages:392–393Year published:(2011)DOI:doi:10.1038/ng.816Published online27 April 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. To the Editor: Johansen et al.1 report an excess of rare variants in individuals with hyper-triglyceridemia (HTG) (cases) compared to controls. The definition of rare variants as those having low minor allele frequency in controls (as opposed to in all samples) biases the selection of rare variants in favor of those that have higher frequencies in cases. We used a simulation study to show that, although this bias is unlikely to alter the main conclusions of the Johansen et al.1 study, using such an approach in studies with different population allele frequencies could lead to erroneous conclusions. The bias can be appreciated intuitively by considering two SNPs, A and B, both having a true frequency of 1% in both cases and controls. Suppose that in the samples observed, the frequency of A is 0.9% in cases and 1.1% in controls (caused by noise), whereas the frequency of B is 1.1% in cases and 0.9% in controls. SNP A will be removed from further consideration because the frequency in the controls is >1%. SNP B will be included in the analysis and will show a greater frequency in cases as compared to controls. Johansen et al.1 reported 154 rare variants in 438 individuals with HTG and 53 rare variants in 327 controls. This gives a case to control frequency ratio of 2.17. In an attempt to create a dataset with similar allele frequencies, we simulated 180 variants with allele frequencies at regular intervals from 0.00005–0.095%, 10 variants with frequencies at regular intervals from 0.1–1% and 13 variants at regular frequencies from 2–14%. We simulated each variant using the same frequency in 438 cases samples and 327 control samples. We then excluded variants that had a frequency of (i) >1% in controls and (ii) >1% in cases and controls combined. We repeated the simulations 10,000 times. After we excluded variants that had a frequency of >1% in controls the mean number of singleton variants across 10,000 simulations was 56, similar to the 54 reported by Johansen et al.1. The mean number of rare variants with >10 alleles was 2.03, similar to the 2 reported by Johansen et al.1.! The mean total number of rare variant alleles was 199, similar to the 207 reported by Johansen et al.1. We excluded a mean of 14.5 variants, as they had allele frequencies >1%, similar to the 14 reported by Johansen et al.1. We conclude that the distribution of allele frequencies of the variants simulated was similar to the distribution of allele frequencies in the Johansen et al.1 study. For each simulation, we calculated the case to control frequency ratios. When variants were excluded that had a frequency of >1% in controls, the median case to control frequency ratio was 1.049. When variants were excluded that had a frequency of >1% in cases and controls combined, the median case to control frequency ratio was 1.003. The bias introduced by setting a frequency threshold in controls only results in a significant difference in case to control frequency ratio (two sided t test P = 2.2 × 10−16). It is important to stress that the case to control frequency ratios seen in these simulations are much lower than the 2.16 reported by Johansen et al.1. As such, it is highly unlikely that the bias introduced by using a frequency threshold based on controls only will alter the main conclusions in that study. We were interested in better understanding the extent of bias introduced by setting a rare variant cutoff threshold on controls only in studies with different underlying variant allele frequencies. We performed further simulations of 161 variants with allele frequencies at regular intervals from (i) 0–1%, (ii) 0.5–2% and (iii) 1–5%. Figure 1 shows the distributions of case to control frequency ratios from 10,000 simulations of allele frequencies designed to be similar to those in the Johansen et al.1 study, allele frequencies (i) to (iii), and for rare variant thresholds set on controls only or in cases and controls combined. The blue vertical line shows the true underlying case to control frequency ratio (1) in these simulations. It can be seen that when a rare variant threshold based on frequency in controls only is used (first column), the large majority of simulations show an estimated case to control frequency ratio higher than 1, with the effect being more pronou! nced when the underlying true frequencies are higher. When the variants are what might be considered 'low frequency' (1–5%) rather than 'rare' (below 1%), it would be more likely to observe a case to control frequency ratio of 2 than it would be to observe the true case to control frequency of 1. When a rare variant threshold based on a combined frequency in cases and controls is used (second column), it can be seen that estimated case to control frequency ratios are centered around 1, as would be expected. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. * Richard D Pearson Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Richard D Pearson Author Details * Richard D Pearson Contact Richard D Pearson Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Bias due to selection of rare variants using frequency in controls
- Nat Genet 43(5):394-395 (2011)
Nature Genetics | Correspondence Bias due to selection of rare variants using frequency in controls * Christopher T Johansen1, 2 * Jian Wang1, 2 * Robert A Hegele1, 2 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:394–395Year published:(2011)DOI:doi:10.1038/ng.817Published online27 April 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Johansen et al. reply: High-throughput sequencing technologies have enabled identification of rare genomic variants in large study samples, although standards for appropriate design, quality control, statistical analysis and interpretation are not yet established. An important issue that directly influences design of resequencing studies is how best to define rare variants. In our recent study in Nature Genetics1, we defined rare variants using a frequency threshold of <1% in healthy controls to eliminate variants with low probability of deleterious effects; this identified a significant excess of rare non-synonymous variants in triglyceride-associated genes gleaned from a genome-wide association study of patients with hypertriglyceridemia (HTG). Two separate correspondences reported in this issue, from Mathieu Lemire2 and Richard Pearson3, use simulated data to show that defining rare variants this way might overestimate their accumulation in cases. However, our own post hoc analysis of our data ! now shows that alternate definitions of rare variants do not effect either our results or our interpretation (Table 1). We believe these differences are probably caused by certain properties of simulated and real biological datasets in addition to essential hypotheses underlying our study design. Table 1: Rare variant accumulation in genes identified by a genome-wide association study of hypertriglyceridemia is robust to rare variant definition or analysis strategy Full table The simulations presented by both Lemire2 and Pearson3 appear to be sensitive to the underlying frequency distribution of simulated variants. For instance, overestimation of variant counts and allele frequencies introduces selection bias into permutation-based analyses when variant frequencies are recalculated after each permutation. Variants with multiple occurrences are more prone to frequency changes, as more variants are available for permutation among cases and controls, leading to variant inclusion when their frequencies are <1% in controls but exclusion when their frequencies are >1% in controls. Higher frequency variants are more likely to introduce such bias, whereas singleton variants are immune to such bias. Indeed, the analysis of Pearson shows minimal bias is introduced when the variant frequency distribution is nearly identical to our sample, whereas increasing variant frequencies systematically introduce more bias3. Similarly, the analysis of Lemire shows that! selection bias is exaggerated with increasing numbers of rare variants, as more variants are available for permutation, and smaller sample sizes, where simulated variant frequencies are more liable to deviate from the 'true' population frequency2. These instances of selection bias are only problematic when a restriction is placed upon rare variant inclusion in cases or controls alone, whereas a combined frequency threshold imposing no restriction upon either group protects against potential inflation of test statistics. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Department of Medicine, Schulich School of Medicine and Dentistry, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada. * Christopher T Johansen, * Jian Wang & * Robert A Hegele * Department of Biochemistry, Schulich School of Medicine and Dentistry, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada. * Christopher T Johansen, * Jian Wang & * Robert A Hegele Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Robert A Hegele Author Details * Christopher T Johansen Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Robert A Hegele Contact Robert A Hegele Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Cattle gain stature
- Nat Genet 43(5):397-398 (2011)
Nature Genetics | News and Views Cattle gain stature * Peter M Visscher1 * Michael E Goddard2 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:397–398Year published:(2011)DOI:doi:10.1038/ng.819Published online27 April 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Identifying causal variants for complex traits and understanding their function remain arduous tasks. A new study combines the advantages of gene mapping in livestock with elegant genetic and functional analyses to address these challenges and identifies candidate regulatory variants affecting stature in cattle. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Peter M. Visscher is at the Queensland Institute of Medical Research, Brisbane, Queensland, Australia. * Michael E. Goddard is at the Department of Primary Industries, Melbourne, Victoria, Australia and the Department of Agriculture and Food Systems, University of Melbourne, Melbourne, Victoria, Australia. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Peter M Visscher or * Michael E Goddard Author Details * Peter M Visscher Contact Peter M Visscher Search for this author in: * NPG journals * PubMed * Google Scholar * Michael E Goddard Contact Michael E Goddard Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - High-throughput identification of genetic interactions in HIV-1
- Nat Genet 43(5):398-400 (2011)
Nature Genetics | News and Views High-throughput identification of genetic interactions in HIV-1 * Daniel M Weinreich1Journal name:Nature GeneticsVolume: 43,Pages:398–400Year published:(2011)DOI:doi:10.1038/ng.820Published online27 April 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. A study characterizes the in vitro replicative capacity of over 70,000 clinical isolates of HIV-1 in the absence of drugs, or in the presence of one of 15 individual drugs. The largest survey of the effects of mutations on fitness undertaken in any organism, this study finds extensive pairwise interactions among over 1,800 variable sites identified through sequencing the protease and reverse transcriptase genes. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Daniel M. Weinreich is in the Department of Ecology and Evolutionary Biology and the Center for Computational Molecular Biology at Brown University, Providence, Rhode Island, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Daniel M Weinreich Author Details * Daniel M Weinreich Contact Daniel M Weinreich Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Estimating the mutation rate of Mycobacterium tuberculosis during infection
- Nat Genet 43(5):400-401 (2011)
Nature Genetics | News and Views Estimating the mutation rate of Mycobacterium tuberculosis during infection * David R Sherman1 * Sebastien Gagneux2 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:400–401Year published:(2011)DOI:doi:10.1038/ng.815Published online27 April 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Current models of Mycobacterium tuberculosis latency presume very low mycobacterial replication and mutation rates. In contrast to these models, a study reporting whole-genome sequencing of mycobacteria isolated from infected macaques shows that the mutational capacity of M. tuberculosis during latency is not reduced, a finding with important implications for tuberculosis research and control. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * David R. Sherman is at the Seattle Biomedical Research Institute and the University of Washington, Seattle, Washington, USA. * Sebastien Gagneux is at the Swiss Tropical and Public Health Institute and University of Basel, Basel, Switzerland. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David R Sherman Author Details * David R Sherman Contact David R Sherman Search for this author in: * NPG journals * PubMed * Google Scholar * Sebastien Gagneux Search for this author in: * NPG journals * PubMed * Google Scholar Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Research highlights
- Nat Genet 43(5):403 (2011)
Nature Genetics | Research Highlights Research highlights Journal name:Nature GeneticsVolume: 43,Page:403Year published:(2011)DOI:doi:10.1038/ng.829Published online27 April 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. iPS cell integrity Correction of genetic defects in patient-specific induced pluripotent stem (iPS) cells is a potential therapeutic approach for many human diseases. However, recent work has shown that iPS cells generally carry several point mutations relative to the parental somatic cells. James Thomson and colleagues assessed the genomic integrity of an iPS cell line after reprogramming, gene targeting and removal of a selection cassette (Proc. Natl. Acad. Sci. USA published online, doi:10.1073/pnas.1103388108, 4 April 2011). The authors isolated iPS cells from an individual with gyrate atrophy carrying a deleterious mutation in OAT, the gene encoding ornithine-δ-aminotransferase, and corrected the mutation using homologous recombination. Analysis of both the parental fibroblast line and the iPS cell line showed that the mutational load at the time of initial reprogramming was fairly substantial, although they performed the subsequent genetic correction and removal of the cassette with min! imal further changes. Specifically, they found two deletions, one amplification and nine protein-coding mutations in the initial iPS cell clone, but they observed no other mutations or copy number variants after the subsequent events. Further research is needed to determine if the mutational load differs between starting somatic cell types or whether it increases with aging. PC De novo mutations and intellectual disability Neuronal synaptic glutamate receptor complexes are involved in synaptic plasticity, learning and memory and have been implicated in neurocognitive diseases. Now, Jacques Michaud and colleagues report a systematic search for de novo mutations in glutamate receptor complexes in non-syndromic intellectual disability (NSID) (Am. J. Hum. Genet.88, 306–316, 2011). The authors sequenced 197 genes encoding glutamate receptors and their known interacting proteins in 95 individuals with sporadic NSID and identified 646 unique variants which were further tested for parental transmission. They identified ten de novo truncating, deletion, splicing or missense mutations in seven genes. Six of these mutations are in SYNGAP1, STXBP1 and SHANK3, genes previously implicated in neurocognitive diseases. The remaining four candidate genes, KIF1A, GRIN1, EPB41L1 and CACNG2, were sequenced in 50 additional sporadic NSID cases and 285 controls. This led to the identification of one additional dup! lication mutation in GRIN1. The authors documented functional effects of the mutations in KIF1A, GRIN1, EPB41L1 and CACNG2 in cell culture systems, but identification of mutations in additional patients will be needed to confirm their role in NSID. EN View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Genetics for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data - Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature
- Nat Genet 43(5):405-413 (2011)
Nature Genetics | Article Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature * Latifa Karim1, 5 * Haruko Takeda1, 5 * Li Lin1, 5 * Tom Druet1, 5 * Juan A C Arias2 * Denis Baurain1 * Nadine Cambisano1 * Stephen R Davis3 * Frédéric Farnir1 * Bernard Grisart1 * Bevin L Harris2 * Mike D Keehan2 * Mathew D Littlejohn4 * Richard J Spelman2 * Michel Georges1 * Wouter Coppieters1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:405–413Year published:(2011)DOI:doi:10.1038/ng.814Received23 August 2010Accepted30 March 2011Published online24 April 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We report mapping of a quantitative trait locus (QTL) with a major effect on bovine stature to a ~780-kb interval using a Hidden Markov Model–based approach that simultaneously exploits linkage and linkage disequilibrium. We re-sequenced the interval in six sires with known QTL genotype and identified 13 clustered candidate quantitative trait nucleotides (QTNs) out of >9,572 discovered variants. We eliminated five candidate QTNs by studying the phenotypic effect of a recombinant haplotype identified in a breed diversity panel. We show that the QTL influences fetal expression of seven of the nine genes mapping to the ~780-kb interval. We further show that two of the eight candidate QTNs, mapping to the PLAG1-CHCHD7 intergenic region, influence bidirectional promoter strength and affect binding of nuclear factors. By performing expression QTL analyses, we identified a splice site variant in CHCHD7 and exploited this naturally occurring null allele to exclude CHCHD7 as single! causative gene. View full text Figures at a glance * Figure 1: Linkage mapping of a body size QTL in an HF × J F2 cross. () Location scores obtained across the entire bovine genome for weight (blue lines) and height (red line) using a line-cross model16. The black dotted line corresponds to the 5% genome-wide significance threshold. () Location scores for BTA14 when analyzing weight (blue lines) and height (red line) using a paternal half-sibling pedigree model implemented with HSQM17. The black dotted line corresponds to the 5% chromosome-wide significance threshold. The red horizontal bar corresponds to the 95% CI for the QTL location (live weight). The black horizontal bar shows the position of the 1.1-Mb critical interval defined in a previous study24. () Highest chromosome-wide log10 (1/P) values for each of the six sire families for height (red) and live weight (blue). The corresponding map positions are given above the bars. The black dotted line marks the 5% chromosome-wide significance threshold. The black arrows point toward the four sire families segregating for the QTL. () Sire-spe! cific allele substitution effects on weight (blue bars) and height (red bar) expressed in kg and mm, respectively. Family-specific allele substitution effects were determined at the most significant QTL position in the across-family analysis. Error bars, 95% CI of the slope estimate (β1), computed using standard procedures (β1 ± t (0.025, n – 2) × SEβ1). Chrom, chromosome. * Figure 2: Linkage and LD fine-mapping of the body size QTL. (–) The x axes correspond to chromosomal positions in bp. The black horizontal lines correspond to the QTL candidate interval previously defined24. The blue horizontal lines correspond to the 780-kb segment sequenced in the present study. Every graph shows the results of all analyses in gray watermarks to facilitate cross comparison. () The red line shows a linkage-based QTL analysis of live weight in the HF × J intercross using HSQM17 and 56 microsatellites (see Fig. 1b) (F values, right axis). Red dots show single-point linkage plus LD analysis of live weight in the HF × J intercross using Phasebook25 and 925 SNPs plus 56 microsatellites (likelihood ratio test, left axis). () The red line shows haplotype-based linkage plus LD analysis of live weight in the HF × J intercross using Phasebook and 925 SNPs plus 56 microsatellites (likelihood ratio test, left axis). () The red line shows haplotype-based linkage plus LD analysis of breeding value for live weight in 3,570 pr! ogeny-tested sires from the NZ outbred dairy cattle population using Phasebook and 293 SNPs from the Illumina BovineSNP50 assay26 spanning a 15-Mb BTA14 segment (likelihood ratio test, left axis). () The red dots show single-point linkage plus LD analysis of live weight in the HF × J intercross using Phasebook and 11 candidate QTN identified by resequencing the 780-kb candidate interval in the six F1 sires. * Figure 3: Annotated genes and markers within the re-sequenced ~780-kb QTL interval. The 'Genes' track shows the organization of the nine genes mapping to the ~780-kb critical region (LYN, RPS20, MOS, PLAG1, CHCHD7, SDR16C5 (RDHE2), SDR16C6, PENK and IMPAD1). The 'SNPs' track shows (i) in blue, the position of 36 out of the 925 SNP panel used to fine map the QTL by combined linkage plus LD mapping, (ii) in red, the position of the 14 candidate QTN with segregation vector matching the QTL genotypes of the six F1 sires, and (iii) in green, the CHCHD7 ss319607409 splice site variant. * Figure 4: Effect of QTN genotype on the expression level of nine positional candidate genes in fetal liver, bone, muscle and brain. Blue bars, quantitative RT-PCR; red bars, allelic imbalance test using 3′ UTR SNPs; green bars, allelic imbalance test using an intronic SNP. The x axis measures the slope of the regression (quantitative RT-PCR) or the ratio of the Q allele over the q allele (allelic imbalance tests) on a log2 scale. The vertical black lines correspond to the absence of an effect of QTN genotype on expression. #, P < 0.10; *, 0.01 < P < 0.05; **, P < 0.01; ND, not done; NE, no detectable expression. * Figure 5: Effects of ss319607405 and ss319607406 on bidirectional promoter strength using a luciferase reporter assay. () Schematic representation of the supposedly bidirectional promoter driving expression of the head-to-head oriented PLAG1 and CHCHD7 genes (blue), with corresponding Phastcons conservation scores (green) and multispecies sequence alignment of a segment encompassing the ss319607405 and ss319607406 candidate QTNs. The arrows mark the positions of the 'long' and 'short' fragments cloned in the pGL4 luciferase reporter vector in the 'forward' (toward PLAG1) and 'reverse' (toward CHCHD7) orientation. () Ratios of firefly to renilla luminescence obtained after transfection of COS-1 cells with (i) a promoterless pGL4 vector (NP), (ii) two distinct, sequence-verified preparations of the pGL4 vector endowed with the thymidine kinase promoter (TK), (iii) pairs of sequence-verified preparations of the pGL4 vector endowed with the q or Q version of the long or short fragment cloned either in forward (reddish) or reverse (bluish) orientation. Error bars correspond to the s.e.m. computed! from replicates. () Schematic representation of the recombinant 'Q-q' and 'q-Q' promoter fragments that were generated by swapping the Q and q residues at the ss319607405 and ss319607406 sites as shown. () Ratios of firefly to renilla luminescence obtained with the non-recombinant 'Q-Q' promoter as well as recombinant 'Q-q' and 'q-Q' promoters cloned in forward and reverse orientation relative to the cognate non-recombinant 'qq' promoter. Error bars correspond to s.e.m. * Figure 6: Effect of QTL genotype on binding of trans-acting nuclear factors. (–) Representative results of EMSA experiments conducted with radiolabeled 29-mer (sQ and sq) and 74-mer or 80-mer probes (LQ and Lq) spanning ss319607405 and ss319607406. "sCtrl" corresponds to an unrelated 25-mer control duplex used as control competitor. Results shown were obtained with nuclear extracts derived from fetal bone () and C2C12 cells (,). Complexes with differential affinity for the Q and q allele are marked by the gray (short probe; Q - High conservation of transcription factor binding and evidence for combinatorial regulation across six Drosophila species
- Nat Genet 43(5):414-420 (2011)
Nature Genetics | Article High conservation of transcription factor binding and evidence for combinatorial regulation across six Drosophila species * Qiye He1, 4 * Anaïs F Bardet2, 4 * Brianne Patton1 * Jennifer Purvis1 * Jeff Johnston1 * Ariel Paulson1 * Madelaine Gogol1 * Alexander Stark2 * Julia Zeitlinger1, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:414–420Year published:(2011)DOI:doi:10.1038/ng.808Received20 December 2010Accepted21 March 2011Published online10 April 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The binding of some transcription factors has been shown to diverge substantially between closely related species. Here we show that the binding of the developmental transcription factor Twist is highly conserved across six Drosophila species, revealing strong functional constraints at its enhancers. Conserved binding correlates with sequence motifs for Twist and its partners, permitting the de novo discovery of their combinatorial binding. It also includes over 10,000 low-occupancy sites near the detection limit, which tend to mark enhancers of later developmental stages. These results suggest that developmental enhancers can be highly evolutionarily constrained, presumably because of their complex combinatorial nature. View full text Figures at a glance * Figure 1: Evolutionary constraints on Twist binding across six Drosophila species. () Overview of the comparative ChIP-Seq pipeline. We directly translated the genomic coordinates of matched reads to D. melanogaster for peak calling and analysis (see Supplementary Tables 3456 for alternatives). () Twist binding at the tin enhancer52 is highly similar across six Drosophila species. () Conservation of D. melanogaster Twist (left) and Snail (right) binding sites across Drosophila species (red; two independent biological replicates per species) compared to a biological replicate in D. melanogaster (*) and a control that assessed the background conservation rate by offsetting all D. melanogaster peaks by 20 kb (gray). Note that conservation levels varied with the ChIP enrichments; for example, conservation levels are lower than expected for D. erecta. (,) Quantitative changes of Twist binding increase with the evolutionary distance. () The number of Twist binding peaks with ≥fourfold changes in height (normalized read density) increased approximately linearly! with the phylogenetic distance (y = 0.24x + 0.09; R2 = 0.86). Percentages are based on 8,796 peaks called independently in at least one ChIP experiment. Note that one D. erecta replicate is an outlier because of lower ChIP enrichments. () Invariant peaks are consistent between species comparisons. Seventy-five percent (2,968 of 3,949) of the invariant peaks (≤twofold change) between D. melanogaster and D. pseudoobscura are also invariant between D. melanogaster and D. yakuba, which corresponds to a highly significant overlap (P = 10−26). The overlaps of invariant peaks were also highly significant between all other species pairs; numbers indicate percentage of overlap (with binomial P values all ≤ 4 × 10−13). D.xxx, any non-melanogaster Drosophila species: D.mel, D. melanogaster; D.sim, D. simulans; D.yak, D. yakuba; D.ere, D. erecta; D.ana, D. ananassae; D.pse, D. pseudoobscura. * Figure 2: High conservation of functional Twist binding across six Drosophila species. () Preferential conservation of peaks near genes that are downregulated at least twofold in twist mutant embryos (red) compared to control genes that do not change (gray; data from a previous study10); the fraction of D. melanogaster peaks that are conserved across all six species was significantly different, with binomial P < 10−3. () Preferential conservation of peaks near genes in Gene Ontology categories associated with Twist function (red; (1) dorsoventral axis specification, (2) gastrulation, (3) mesodermal cell fate determination, (4) muscle fiber development) or Gene Ontology categories not related to Twist function (gray; (5) carbohydrate metabolic process, (6) amino acid metabolic process, (7) mRNA metabolic process). The difference between all genes in the combined functional versus Twist-independent categories was significant, with a binomial P < 10−21. For an overview of all Gene Ontology categories, see Supplementary Table 13. * Figure 3: Preferential conservation of clustered binding peaks. () Conservation rates (percent of D. melanogaster peaks that are conserved across all six species) for peaks in different genomic regions. CDS, coding-sequence; UTR, untranslated region. The number of D. melanogaster peaks in each region is shown on top. () Conservation rates are as in but are dependent on the distances of the peak summits to the nearest gene transcription start sites (TSS). () Conservation rates are as in but dependent on the distances between two neighboring peak summits (independent of the conservation of either peak). Isolated peaks are significantly less highly conserved (P < 10−45 compared to the leftmost bin). Note that the 0–0.5-kb bin is not populated because of the width of the peaks. * Figure 4: Twist binding depends on the sequence motifs of Twist and its partner transcription factors. () Twist binding peaks shared across all species (conserved) or D. melanogaster–specific (D.mel-spec) peaks have similar overall phastCons scores (left; Wilcoxon P = 0.39) and nucleotide conservation (middle; Wilcoxon P < 10−4) but different conservation rates for the Twist motif (hypergeometric P < 10−17). () Sequence changes (in percent) that cause motif and peak loss (Supplementary Fig. 17). () At top, quantitative changes of peak height correlate with Twist motif quality (MAST score). Peaks that are ≥fourfold lower in a second species compared to D. melanogaster (left) contain more motifs with lower scores in that species than in D. melanogaster (P < 10−13 for all). The reverse is true for peaks that are ≥fourfold higher (right; P < 10−4 for all except the D. erecta 2 replicate, which had P = 0.43). Circles and diamonds represent the fraction of changed motifs in each pairwise comparison, and bar heights indicate the median values. At bottom, an example of ! a quantitative change of Twist binding at the gap1 gene locus that correlates with Twist motif quality (red, mismatches to the consensus motif). () Motifs of Twist partner transcription factors correlate with Twist binding. Shown are the top non-Twist motifs6 that are conserved in fully conserved Twist peaks but not D. melanogaster–specific peaks (fold improvement between motif conservation rates). () Loss of Twist binding in D. ananassae despite a conserved Twist motif correlates with the loss of a Dorsal motif in the vn (vein) intron (read density scales are identical across species). * Figure 5: Conservation of low-occupancy peaks. () The similarity of Twist binding (blue) extends beyond the peak (black bar) at the btsz (bitesize) locus (left). Read densities are similar across species (black) even when excluding peak regions (gray). D. mel*, biological replicate; control, independence is simulated by reverting the read density. () Several thousand peaks are detectably bound across species. Shown is the fold enrichment (ChIP/WCE) at the position aligned to the D. melanogaster peak summit (median of 500 peaks per bin; D.mel*, biological replicate; control, D. melanogaster peaks shifted by 20 kb). () Several thousand peaks contain Twist motifs that are specifically conserved. Top, at any rank, peaks (solid red) contained more Twist motifs than expected given shifted peaks (dashed red), randomized motifs (solid gray; all P < 10−144 for high-occupancy peaks and P < 10−57 for low-occupancy peaks). Bottom, Twist motifs in peaks at any rank (bins of 500) were more often conserved across all species than e! xpected given the average conservation of the peak region (randomized motifs) or the genome-wide conservation of the Twist motif (shifted; all P < 10−3 for high-occupancy peaks and P < 10−5 for low-occupancy peaks). () The conservation rate of low-occupancy peaks dropped with increasing distance to the nearest high-occupancy peak (P < 10−8 between the outermost bins). () Low-occupancy peaks overlapped increasingly with ChIP-chip data30 from later time points (top), whereas high-occupancy peaks showed the opposite trend. To account for different numbers of ChIP-chip peaks at different time points, we calculated the enrichments against shifted peak locations (all P < 10−20). Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions ArrayExpress * E-MTAB-376 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Qiye He & * Anaïs F Bardet Affiliations * Stowers Institute for Medical Research, Kansas City, Missouri, USA. * Qiye He, * Brianne Patton, * Jennifer Purvis, * Jeff Johnston, * Ariel Paulson, * Madelaine Gogol & * Julia Zeitlinger * Research Institute of Molecular Pathology (IMP), Vienna, Austria. * Anaïs F Bardet & * Alexander Stark * Department of Pathology, University of Kansas Medical School, Kansas City, Missouri, USA. * Julia Zeitlinger Contributions Q.H. performed the ChIP experiments and library preparation, and J.J., A.P., M.G. and J.Z. established the ChIP-Seq pipeline. B.P. and J.P. raised the different Drosophila species, harvested the embryos and staged them, A.F.B. and A.S. analyzed the data, and Q.H., A.F.B., A.S. and J.Z. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Alexander Stark or * Julia Zeitlinger Author Details * Qiye He Search for this author in: * NPG journals * PubMed * Google Scholar * Anaïs F Bardet Search for this author in: * NPG journals * PubMed * Google Scholar * Brianne Patton Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer Purvis Search for this author in: * NPG journals * PubMed * Google Scholar * Jeff Johnston Search for this author in: * NPG journals * PubMed * Google Scholar * Ariel Paulson Search for this author in: * NPG journals * PubMed * Google Scholar * Madelaine Gogol Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Stark Contact Alexander Stark Search for this author in: * NPG journals * PubMed * Google Scholar * Julia Zeitlinger Contact Julia Zeitlinger Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 7 (401K) Conservation of D. melanosgaster Twist binding peaks in six Drosophila species * Supplementary Table 11 (2M) Quantitative changes of Twist binding peaks in all six Drosophila species * Supplementary Table 12 (459K) GO analysis of invariant vs. variant peaks * Supplementary Table 13 (602K) GO analysis of genes near Twist binding peaks PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–19 and Supplementary Tables 1–6, 8–10 and 14–16. Additional data - Glycerol-3-phosphate is a critical mobile inducer of systemic immunity in plants
- Nat Genet 43(5):421-427 (2011)
Nature Genetics | Article Glycerol-3-phosphate is a critical mobile inducer of systemic immunity in plants * Bidisha Chanda1, 4 * Ye Xia1, 4 * Mihir Kumar Mandal1 * Keshun Yu1 * Ken‐Taro Sekine1, 5 * Qing-ming Gao1 * Devarshi Selote1 * Yanling Hu2 * Arnold Stromberg2 * Duroy Navarre3 * Aardra Kachroo1 * Pradeep Kachroo1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:421–427Year published:(2011)DOI:doi:10.1038/ng.798Received17 August 2010Accepted02 February 2011Published online27 March 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Glycerol-3-phosphate (G3P) is an important metabolite that contributes to the growth and disease-related physiologies of prokaryotes, plants, animals and humans alike. Here we show that G3P serves as the inducer of an important form of broad-spectrum immunity in plants, termed systemic acquired resistance (SAR). SAR is induced upon primary infection and protects distal tissues from secondary infections. Genetic mutants defective in G3P biosynthesis cannot induce SAR but can be rescued when G3P is supplied exogenously. Radioactive tracer experiments show that a G3P derivative is translocated to distal tissues, and this requires the lipid transfer protein, DIR1. Conversely, G3P is required for the translocation of DIR1 to distal tissues, which occurs through the symplast. These observations, along with the fact that dir1 plants accumulate reduced levels of G3P in their petiole exudates, suggest that the cooperative interaction of DIR1 and G3P orchestrates the induction of SAR ! in plants. View full text Figures at a glance * Figure 1: Impaired SAR in gly1 and gli1 plants correlates with a defect in G3P metabolism but not fatty acid (FA) or lipid flux. () Relative levels of fatty acids in 4-week-old Col-0, gly1, gli1 or act1 leaves. The values are presented as means of six to eight replicates. Asterisks denote a significant difference with wild type (t-test, P < 0.05). FW indicates fresh weight. () Total lipid levels in indicated genotypes. DW indicates dry weight. Asterisks denote a significant difference with wild type (t-test, P < 0.05). () SAR in Col-0, gly1, gli1 or act1 plants. We inoculated primary leaves with MgCl2 (gray bars) or Pseudomonas syringae expressing avrRpt2 (black bars) and the distal leaves 48 h later with a virulent strain of P. syringae. () SAR in Col-0, act1 gly1, act1 gli1 or gly1 gli1 plants. The error bars in – represent s.d. * Figure 2: G3P levels increase in response to pathogen inoculation. () SAR in Col-0, G3Pdh knockout (KO) lines. We inoculated primary leaves with MgCl2 (gray bars) or P. syringae expressing avrRpt2 (black bars) and the distal leaves with a virulent strain of P. syringae. () G3P levels in local or distal leaves of Col-0 (wild-type) plants. () G3P levels in petiole exudates of Col-0, gly1 and gli1 plants at 0, 6, 24 and 48 h post inoculation with the avr pathogen (avrRpt2). Asterisks in and denote significant differences (t-test, P < 0.05). The error bars in – represent s.d. * Figure 3: Exogenous application of G3P restores defective SAR in gly1 and gli1 plants, and G3P-conferred SAR is dependent on SID2. () SAR response in Col-0 (wild-type), gly1 and gli1 plants. We inoculated primary leaves with MgCl2, avrRpt2, G3P or avrRpt2 + G3P and the distal leaves 24 h later with a virulent strain of P. syringae. (,) SAR response in Col-0 () and gly1 () plants infiltrated with exudates collected from Col-0, gly1 or gli1 plants that were treated either with MgCl2 (EXMgCl2) or avrRpt2 (EXavrRpt2). () SAR response in Col-0 and sid2 plants infiltrated with extudates collected from Col-0 plants. Exudates were collected post inoculations with MgCl2 (EXMgCl2) or avrRpt2 (EXavrRpt2). () SAR in Col-0 plants after infiltrating primary leaves with MgCl2, EXMgCl2, MgCl2 + G3P or EXMgCl2 + G3P. EXMgCl2 was collected from the wild-type (Col-0) plants. We inoculated the distal leaves with virulent pathogen at 12, 24 and 48 h post infiltration of primary leaves. The error bars in – represent s.d. EX, exudates. * Figure 4: G3P-conferred SAR is dependent on DIR1. () SAR in Col-0 plants infiltrated with EXMgCl2, EXMgCl2 + G3P, total protein extracted from petiole exudates (EX-protein), EX-protein + G3P and EX-protein pretreated with proteinase K (p-k) before addition of G3P. () SAR response in WS-0 (wild-type) and dir1 plants infiltrated with exudates collected from WS-0 or dir1 plants that were treated either with EXMgCl2, EXMgCl2 + G3P, EXavrRpt2 or EXavrRpt2 + G3P. () SAR response in Col-0 (wild-type) plants infiltrated with MgCl2 or MgCl2 containing G3P and/or DIR1. The error bars in – represent s.d. EX, exudates. * Figure 5: G3P and DIR1 are dependent on each other for translocation into distal tissues. () Quantification of radioactivity in local (infiltrated) and distal tissues of leaves infiltrated with 14C-G3P or 14C-G3P + DIR1. DPM, disintegrations per minute. () Autoradiograph showing images of distal leaves collected from plants that were infiltrated with 14C-G3P or 14C-G3P + DIR1. We sampled the leaves were 24 h post treatments. () G3P levels in petiole exudates of WS-0 (wild-type) and dir1 plants collected 24 h after mock (M) or avrRpt2 (A) inoculations. () SAR response in Col-0 (wild-type) and gly1 gli1 plants infiltrated with MgCl2 or MgCl2+ DIR1 in the local leaves. () Immunoblot showing translocation of DIR1-GFP into distal tissues of G3P-treated Nicotiana benthamiana plants. I and D indicate infiltrated and distal tissues, respectively. Control indicates untreated wild-type plants. () Confocal micrograph showing localization of DIR1-GFP in N. benthamiana plants expressing RFP-tagged nuclear histone protein H2B (see also Supplementary Fig. 12c,d). Arrow indicate! s nucleus, arrowhead indicates endoplasmic reticulum. Scale bar, 5 μM. The micrograph shown is a RFP and GFP overlay image. () Confocal micrographs showing co-localization of DIR1-RFP and Tobacco Mosaic Virus movement protein (MP) 30-GFP in N. benthamiana plants. The punctate fluorescence signals indicated by arrows are plasmodesmata. Scale bar, 5 μM. () Autoradiograph of extracts from infiltrated (I) and distal (D) leaves of plants infiltrated with 14C-G3P or 14C-G3P + DIR1. We prepared extracts with or without phosphatase inhibitor, normalized for protein content and run on a cellulose thin-layer chromatography plate. Arrow indicates direction of the run. () SAR in indicated genotypes treated with water or glycerol 6 h before mock (MgCl2) or avrRpt2 inoculations. Water or glycerol was infiltrated in same leaves that were later inoculated with MgCl2 or avrRpt2. The error bars in , , and represent s.d. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Bidisha Chanda & * Ye Xia Affiliations * Department of Plant Pathology, University of Kentucky, Lexington, Kentucky, USA. * Bidisha Chanda, * Ye Xia, * Mihir Kumar Mandal, * Keshun Yu, * Ken‐Taro Sekine, * Qing-ming Gao, * Devarshi Selote, * Aardra Kachroo & * Pradeep Kachroo * Department of Statistics, University of Kentucky, Lexington, Kentucky, USA. * Yanling Hu & * Arnold Stromberg * US Department of Agriculture, Agricultural Research Service, Washington State University, Prosser, Washington, USA. * Duroy Navarre * Present address: Iwate Biotechnology Research Center, Iwate, Japan. * Ken‐Taro Sekine Contributions B.C. and Y.X. carried out Arabidopsis SAR experiments in parallel with contributions from K.‐T.S. and Q.-m.G. Soybean SAR experiments were carried out by D.S. G3P estimations were carried out by B.C. with contributions from Y.X. Generation of G3Pdh knockout lines and their analysis was carried out by B.C. GLY1-GFP transgenic lines were generated by K.‐T.S. DIR1 protein purification, binding, translocation assays and confocal microscopy were carried out by M.K.M. with contributions from D.S. TLC and G3P translocation assays were carried out by B.C. and M.K.M. with contributions from P.K. RNA blot and RT-PCR analyses were carried out by B.C. and Q.-m.G. Y.H. and A.S. analyzed microarray data with contributions from M.K.M. D.N. estimated salicylic acid levels. K.Y., B.C. and P.K. analyzed azeliac acid and jasmonic acid levels. K.Y. developed GC-MS–based protocol for detection and quantification of glycerol. P.K. and A.K. supervised the project and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Pradeep Kachroo Author Details * Bidisha Chanda Search for this author in: * NPG journals * PubMed * Google Scholar * Ye Xia Search for this author in: * NPG journals * PubMed * Google Scholar * Mihir Kumar Mandal Search for this author in: * NPG journals * PubMed * Google Scholar * Keshun Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Ken‐Taro Sekine Search for this author in: * NPG journals * PubMed * Google Scholar * Qing-ming Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Devarshi Selote Search for this author in: * NPG journals * PubMed * Google Scholar * Yanling Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Arnold Stromberg Search for this author in: * NPG journals * PubMed * Google Scholar * Duroy Navarre Search for this author in: * NPG journals * PubMed * Google Scholar * Aardra Kachroo Search for this author in: * NPG journals * PubMed * Google Scholar * Pradeep Kachroo Contact Pradeep Kachroo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–15 and Supplementary Tables 1–3 Additional data - Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease
- Nat Genet 43(5):429-435 (2011)
Nature Genetics | Letter Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease * Paul Hollingworth1, 110 * Denise Harold1, 110 * Rebecca Sims1, 110 * Amy Gerrish1, 110 * Jean-Charles Lambert2, 3, 4, 110 * Minerva M Carrasquillo5, 110 * Richard Abraham1 * Marian L Hamshere1 * Jaspreet Singh Pahwa1 * Valentina Moskvina1 * Kimberley Dowzell1 * Nicola Jones1 * Alexandra Stretton1 * Charlene Thomas1 * Alex Richards1 * Dobril Ivanov1 * Caroline Widdowson1 * Jade Chapman1 * Simon Lovestone6, 7 * John Powell7 * Petroula Proitsi7 * Michelle K Lupton7 * Carol Brayne8 * David C Rubinsztein9 * Michael Gill10 * Brian Lawlor10 * Aoibhinn Lynch10 * Kristelle S Brown11 * Peter A Passmore12 * David Craig12 * Bernadette McGuinness12 * Stephen Todd12 * Clive Holmes13 * David Mann14 * A David Smith15 * Helen Beaumont15 * Donald Warden15 * Gordon Wilcock16 * Seth Love17 * Patrick G Kehoe17 * Nigel M Hooper18 * Emma R L C Vardy14, 18, 19 * John Hardy20, 21 * Simon Mead22 * Nick C Fox22 * Martin Rossor22 * John Collinge22 * Wolfgang Maier23, 24 * Frank Jessen23 * Eckart Rüther24, 25, 26 * Britta Schürmann23, 26 * Reiner Heun23, 27 * Heike Kölsch23 * Hendrik van den Bussche28 * Isabella Heuser29 * Johannes Kornhuber30 * Jens Wiltfang31 * Martin Dichgans32, 33 * Lutz Frölich34 * Harald Hampel35 * John Gallacher36 * Michael Hüll37 * Dan Rujescu37 * Ina Giegling36 * Alison M Goate38, 39, 40 * John S K Kauwe41 * Carlos Cruchaga38 * Petra Nowotny38 * John C Morris39 * Kevin Mayo38 * Kristel Sleegers42, 43 * Karolien Bettens42, 43 * Sebastiaan Engelborghs42, 44 * Peter P De Deyn42, 44 * Christine Van Broeckhoven42, 43 * Gill Livingston45 * Nicholas J Bass45 * Hugh Gurling45 * Andrew McQuillin45 * Rhian Gwilliam46 * Panagiotis Deloukas46 * Ammar Al-Chalabi47 * Christopher E Shaw47 * Magda Tsolaki48 * Andrew B Singleton49 * Rita Guerreiro49 * Thomas W Mühleisen50, 51 * Markus M Nöthen25, 50, 51 * Susanne Moebus52 * Karl-Heinz Jöckel52 * Norman Klopp53 * H-Erich Wichmann53, 54, 55 * V Shane Pankratz56 * Sigrid B Sando57, 58 * Jan O Aasly57, 58 * Maria Barcikowska59 * Zbigniew K Wszolek60 * Dennis W Dickson5 * Neill R Graff-Radford5, 60 * Ronald C Petersen61, 62 * the Alzheimer's Disease Neuroimaging Initiative63 * Cornelia M van Duijn64, 65 * Monique M B Breteler64, 65 * M Arfan Ikram64, 65 * Anita L DeStefano66, 67 * Annette L Fitzpatrick68 * Oscar Lopez69, 70 * Lenore J Launer71 * Sudha Seshadri67, 72 * CHARGE consortium * Claudine Berr73 * Dominique Campion74 * Jacques Epelbaum75 * Jean-François Dartigues76 * Christophe Tzourio77 * Annick Alpérovitch77 * Mark Lathrop78, 79 * EADI1 consortium * Thomas M Feulner80 * Patricia Friedrich80 * Caterina Riehle80 * Michael Krawczak81, 82, 83 * Stefan Schreiber82, 83 * Manuel Mayhaus80 * S Nicolhaus83 * Stefan Wagenpfeil84 * Stacy Steinberg85 * Hreinn Stefansson85 * Kari Stefansson86 * Jon Snædal87 * Sigurbjörn Björnsson87 * Palmi V Jonsson87 * Vincent Chouraki2, 3, 4 * Benjamin Genier-Boley2, 3, 4 * Mikko Hiltunen88 * Hilkka Soininen88 * Onofre Combarros89, 90 * Diana Zelenika91 * Marc Delepine91 * Maria J Bullido90, 92 * Florence Pasquier4, 93 * Ignacio Mateo89, 90 * Ana Frank-Garcia90, 94 * Elisa Porcellini95 * Olivier Hanon96 * Eliecer Coto97 * Victoria Alvarez97 * Paolo Bosco98 * Gabriele Siciliano99 * Michelangelo Mancuso99 * Francesco Panza100 * Vincenzo Solfrizzi100 * Benedetta Nacmias101 * Sandro Sorbi101 * Paola Bossù102 * Paola Piccardi103 * Beatrice Arosio104 * Giorgio Annoni105 * Davide Seripa106 * Alberto Pilotto106 * Elio Scarpini107 * Daniela Galimberti107 * Alexis Brice108 * Didier Hannequin109 * Federico Licastro95 * Lesley Jones1 * Peter A Holmans1 * Thorlakur Jonsson85 * Matthias Riemenschneider80 * Kevin Morgan11 * Steven G Younkin5 * Michael J Owen1 * Michael O'Donovan1 * Philippe Amouyel2, 3, 4, 92 * Julie Williams1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:429–435Year published:(2011)DOI:doi:10.1038/ng.803Received09 September 2010Accepted10 March 2011Published online03 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We sought to identify new susceptibility loci for Alzheimer's disease through a staged association study (GERAD+) and by testing suggestive loci reported by the Alzheimer's Disease Genetic Consortium (ADGC) in a companion paper. We undertook a combined analysis of four genome-wide association datasets (stage 1) and identified ten newly associated variants with P ≤ 1 × 10−5. We tested these variants for association in an independent sample (stage 2). Three SNPs at two loci replicated and showed evidence for association in a further sample (stage 3). Meta-analyses of all data provided compelling evidence that ABCA7 (rs3764650, meta P = 4.5 × 10−17; including ADGC data, meta P = 5.0 × 10−21) and the MS4A gene cluster (rs610932, meta P = 1.8 × 10−14; including ADGC data, meta P = 1.2 × 10−16) are new Alzheimer's disease susceptibility loci. We also found independent evidence for association for three loci reported by the ADGC, which, when combined, showed genome! -wide significance: CD2AP (GERAD+, P = 8.0 × 10−4; including ADGC data, meta P = 8.6 × 10−9), CD33 (GERAD+, P = 2.2 × 10−4; including ADGC data, meta P = 1.6 × 10−9) and EPHA1 (GERAD+, P = 3.4 × 10−4; including ADGC data, meta P = 6.0 × 10−10). View full text Figures at a glance * Figure 1: GERAD+ study design. *Data for rs744373 and rs3818361 in the CHARGE consortium have been presented elsewhere15, as has data for rs381861 in the EADI2 samples4; as such these SNPs were not included in stage 3. * Figure 2: Schematic of the associated variants reported in reference to (a) ABCA7 and (b) chromosomal region chr11: 59.81Mb–60.1Mb harboring members of the MS4A gene cluster. Chromosome positions are shown at the top of the schematics (UCSC Feb 2009). Gene schematic: horizontal arrows indicate directions of transcription, black boxes indicate gene exons and the untranslated region. The −log10P of the SNPs analyzed in stage 1 are shown in the chart and graph. The GERAD+ stage 1, 2 and 3 meta-analysis P values for rs3764650 (ABCA7), rs610932 (MS4A6A) and rs670139 (MS4A4E) are indicated by the red lines. The D′ LD block structure of ABCA7 plus the surrounding region and chr11: 59.81Mb–60.1Mb according to the CEPH HapMap data are provided at the bottom of each schematic with lines indicating where each SNP genotyped on the Illumina 610-quad chip is represented. * Figure 3: Forest plots showing association in the different datasets for SNPs at the ABCA7 (rs3764650) and MS4A (rs610932 and rs670139) loci. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Paul Hollingworth, * Denise Harold, * Rebecca Sims, * Amy Gerrish, * Jean-Charles Lambert & * Minerva M Carrasquillo Affiliations * Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Neurosciences and Mental Health Research Institute, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK. * Paul Hollingworth, * Denise Harold, * Rebecca Sims, * Amy Gerrish, * Richard Abraham, * Marian L Hamshere, * Jaspreet Singh Pahwa, * Valentina Moskvina, * Kimberley Dowzell, * Nicola Jones, * Alexandra Stretton, * Charlene Thomas, * Alex Richards, * Dobril Ivanov, * Caroline Widdowson, * Jade Chapman, * Lesley Jones, * Peter A Holmans, * Michael J Owen, * Michael O'Donovan & * Julie Williams * INSERM U744, F-59019 Lille, France. * Jean-Charles Lambert, * Vincent Chouraki, * Benjamin Genier-Boley & * Philippe Amouyel * Institut Pasteur de Lille, F-59019, Lille, France. * Jean-Charles Lambert, * Vincent Chouraki, * Benjamin Genier-Boley & * Philippe Amouyel * Université de Lille Nord de France, F-59000 Lille, France. * Jean-Charles Lambert, * Vincent Chouraki, * Benjamin Genier-Boley, * Florence Pasquier & * Philippe Amouyel * Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, Florida, USA. * Minerva M Carrasquillo, * Dennis W Dickson, * Neill R Graff-Radford & * Steven G Younkin * National Institute for Health Research Biomedical Research Centre for Mental Health at the South London and Maudsley National Health Service Foundation Trust and Institute of Psychiatry, Kings College, London, UK. * Simon Lovestone * Department of Neuroscience, Institute of Psychiatry, Kings College, London, UK. * Simon Lovestone, * John Powell, * Petroula Proitsi & * Michelle K Lupton * Institute of Public Health, University of Cambridge, Cambridge, UK. * Carol Brayne * Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. * David C Rubinsztein * Mercer's Institute for Research on Aging, St. James Hospital and Trinity College, Dublin, Ireland. * Michael Gill, * Brian Lawlor & * Aoibhinn Lynch * Institute of Genetics, Queen's Medical Centre, University of Nottingham, Nottingham, UK. * Kristelle S Brown & * Kevin Morgan * Ageing Group, Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, Belfast, UK. * Peter A Passmore, * David Craig, * Bernadette McGuinness & * Stephen Todd * Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, UK. * Clive Holmes * Neurodegeneration and Mental Health Research Group, School of Community Based Medicine, University of Manchester, Hope Hospital, Stott Lane, Salford, Manchester, UK. * David Mann & * Emma R L C Vardy * Oxford Project to Investigate Memory and Ageing (OPTIMA), University of Oxford, John Radcliffe Hospital, Oxford, UK. * A David Smith, * Helen Beaumont & * Donald Warden * Nuffield Department of Clinical Medicine, Medical Sciences Division. University of Oxford, Headington, Oxford, UK. * Gordon Wilcock * Dementia Research Group, University of Bristol Institute of Clinical Neurosciences, Frenchay Hospital, Bristol, UK. * Seth Love & * Patrick G Kehoe * Institute of Molecular and Cellular Biology, Faculty of Biological Sciences, LIGHT Laboratories, University of Leeds, Leeds, UK. * Nigel M Hooper & * Emma R L C Vardy * Cerebral Function Unit, Salford Royal National Health Service (NHS) Trust, Stott Lane, Salford, UK. * Emma R L C Vardy * Department of Molecular Neuroscience, Institute of Neurology, London, UK. * John Hardy * Reta Lilla Weston Laboratories, Institute of Neurology, London, UK. * John Hardy * Department of Neurodegenerative Disease, University College London, Institute of Neurology, London, UK. * Simon Mead, * Nick C Fox, * Martin Rossor & * John Collinge * Department of Psychiatry, University of Bonn, Bonn, Germany. * Wolfgang Maier, * Frank Jessen, * Britta Schürmann, * Reiner Heun & * Heike Kölsch * German Centre for Neurodegenerative Diseases, Bonn, Bonn, Germany. * Wolfgang Maier & * Eckart Rüther * Institute for Molecular Psychiatry, University of Bonn, Bonn, Germany. * Eckart Rüther & * Markus M Nöthen * Department of Psychiatry, University of Göttingen, Göttingen, Germany. * Eckart Rüther & * Britta Schürmann * Department of Psychiatry, Royal Derby Hospital, Derby, UK. * Reiner Heun * Institute of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. * Hendrik van den Bussche * Department of Psychiatry, Charité Berlin, Berlin, Germany. * Isabella Heuser * Department of Psychiatry and Psychotherapy, University of Erlangen, Nuremberg, Germany. * Johannes Kornhuber * Landschaftsverband Rheinland-Hospital Essen, Department of Psychiatry and Psychotherapy, University Duisburg-Essen, Essen, Germany. * Jens Wiltfang * Department of Neurology, Klinikum der Universität München, Munich, Germany. * Martin Dichgans * Institute for Stroke and Dementia Research, Klinikum der Universität München, Munich, Germany. * Martin Dichgans * Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany. * Lutz Frölich * Department of Psychiatry, Psychsomatic Medicine and Psychotherapy, Johann Wolfgang Goethe-University, Frankfurt, Germany. * Harald Hampel * Department of Primary Care and Public Health, School of Medicine, Cardiff University, Cardiff, UK. * John Gallacher & * Ina Giegling * Centre for Geriatric Medicine and Section of Gerontopsychiatry and Neuropsychology, University of Freiburg, Freiburg, Germany. * Michael Hüll & * Dan Rujescu * Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, USA. * Alison M Goate, * Carlos Cruchaga, * Petra Nowotny & * Kevin Mayo * Department of Neurology, Washington University School of Medicine, St Louis, Missouri, USA. * Alison M Goate & * John C Morris * Department of Genetics, Washington University School of Medicine, St Louis, Missouri, USA. * Alison M Goate * Department of Biology, Brigham Young University, Provo, Utah, USA. * John S K Kauwe * Institute Born-Bunge, University of Antwerp, Antwerpen, Belgium. * Kristel Sleegers, * Karolien Bettens, * Sebastiaan Engelborghs, * Peter P De Deyn & * Christine Van Broeckhoven * Neurodegenerative Brain Diseases group, Department of Molecular Genetics, Vlaams Interuniversitair Instituut voor Biotechnologie, Antwerpen, Belgium. * Kristel Sleegers, * Karolien Bettens & * Christine Van Broeckhoven * Memory Clinic and Department of Neurology, ZiekenhuisNetwerk Antwerpen, Middelheim, Antwerpen, Belgium. * Sebastiaan Engelborghs & * Peter P De Deyn * Department of Mental Health Sciences, University College London, London UK. * Gill Livingston, * Nicholas J Bass, * Hugh Gurling & * Andrew McQuillin * The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * Rhian Gwilliam & * Panagiotis Deloukas * MRC Centre for Neurodegeneration Research, Department of Clinical Neuroscience, King's College London, Institute of Psychiatry, London, UK. * Ammar Al-Chalabi & * Christopher E Shaw * Third Department of Neurology, Aristotle University of Thessaloniki, Thessaloniki, Greece. * Magda Tsolaki * Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA. * Andrew B Singleton & * Rita Guerreiro * Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany. * Thomas W Mühleisen & * Markus M Nöthen * Institute of Human Genetics, University of Bonn, Bonn, Germany. * Thomas W Mühleisen & * Markus M Nöthen * Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, University Duisburg-Essen, Essen, Germany. * Susanne Moebus & * Karl-Heinz Jöckel * Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. * Norman Klopp & * H-Erich Wichmann * Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany. * H-Erich Wichmann * Klinikum Grosshadern, Munich, Germany. * H-Erich Wichmann * Division of Biomedical Statistics and Informatics, Mayo Clinic and Mayo Foundation, Rochester, Minnesota, USA. * V Shane Pankratz * Department of Neurology, St. Olav's Hospital, Trondheim, Norway. * Sigrid B Sando & * Jan O Aasly * Department of Neuroscience, Norwegian University of Science and Technology, NTNU, 7491 Trondheim Norway. * Sigrid B Sando & * Jan O Aasly * Department of Neurodegenerative Disorders, Medical Research Centre, Polish Academy of Sciences, Warsaw, Poland. * Maria Barcikowska * Department of Neurology, Mayo Clinic College of Medicine, Jacksonville, Florida, USA. * Zbigniew K Wszolek & * Neill R Graff-Radford * Department of Neurology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. * Ronald C Petersen * Mayo Alzheimer Disease Research Center, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. * Ronald C Petersen * Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (see URLs). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. See URLs for a complete listing of the ADNI investigators. * Department of Epidemiology, Erasmus Medical Center, University Medical Center, Rotterdam, The Netherlands. * Cornelia M van Duijn, * Monique M B Breteler & * M Arfan Ikram * Netherlands Consortium for Healthy Aging, Leiden, The Netherlands. * Cornelia M van Duijn, * Monique M B Breteler & * M Arfan Ikram * Departments of Neurology and Biostatistics, Boston University School of Medicine, Boston, Massachusetts, USA. * Anita L DeStefano * The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA. * Anita L DeStefano & * Sudha Seshadri * Department of Epidemiology, University of Washington, Seattle, Washington, USA. * Annette L Fitzpatrick * Department of Neurology, The Alzheimer's Disease Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. * Oscar Lopez * Department of Psychiatry, The Alzheimer's Disease Research Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. * Oscar Lopez * Neuroepidemiology Section, Laboratory of Epidemiology, Demography and Biometry (LJL), National Institute on Aging, Washington DC, USA. * Lenore J Launer * Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA. * Sudha Seshadri * INSERM U888, Hôpital La Colombière, Montpellier, France. * Claudine Berr * INSERM U614, Faculté de Médecine-Pharmacie de Rouen, Rouen, France. * Dominique Campion * Unité Mixte de Recherche 894, Inserm Faculté de Médecine, Université Paris Descartes, Paris, France. * Jacques Epelbaum * INSERM U897, Victor Segalen University, Bordeaux, France. * Jean-François Dartigues * INSERM U708, Paris, France. * Christophe Tzourio & * Annick Alpérovitch * Centre National de Genotypage, Institut Genomique, Commissariat à l'énergie Atomique, Evry, France. * Mark Lathrop * The Foundation Jean Dausset-Centre d'Etude du Polymorphisme Humain, Paris, France. * Mark Lathrop * Department of Psychiatry and Psychotherapy, Universitätsklinikum des Saarlandes, Universität des Saarlandes Saarbruecken Germany. * Thomas M Feulner, * Patricia Friedrich, * Caterina Riehle, * Manuel Mayhaus & * Matthias Riemenschneider * Institute of Medical Informatics and Statistics, Christian-Albrechts-University, Kiel, Germany. * Michael Krawczak * Biobank Popgen, Institute of Experimental Medicine, Section of Epidemiology, Christian-Albrechts-University, Kiel, Germany. * Michael Krawczak & * Stefan Schreiber * Institute for Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany. * Michael Krawczak, * Stefan Schreiber & * S Nicolhaus * Institute of Medical Statistics and Epidemiology, Klinikum Rechts der Isar, Technische Universität, München, Germany. * Stefan Wagenpfeil * deCODE Genetics, Reykjavik, Iceland. * Stacy Steinberg, * Hreinn Stefansson & * Thorlakur Jonsson * deCODE Genetics and University of Iceland, Faculty of Medicine, Reykjavik, Iceland. * Kari Stefansson * Faculty of Medicine, University of Iceland, Reykjavik, Iceland. * Jon Snædal, * Sigurbjörn Björnsson & * Palmi V Jonsson * Department of Neurology, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland. * Mikko Hiltunen & * Hilkka Soininen * Neurology Service, 'Marqués de Valdecilla' University Hospital (University of Cantabria), Santander, Spain. * Onofre Combarros & * Ignacio Mateo * Centro Investigacion Biomedica en Red Enfermedades Neurodegenerativas 'Marqués de Valdecilla' University Hospital (University of Cantabria), Santander, Spain. * Onofre Combarros, * Maria J Bullido, * Ignacio Mateo & * Ana Frank-Garcia * Centre National de Genotypage, Institut Genomique, Commissariat à l'énergie Atomique, Evry, France. * Diana Zelenika & * Marc Delepine * Centro de Biologia Molecular Severo Ochoa, Universidad Autonoma, Campus de Cantoblanco, Madrid, Spain. * Maria J Bullido & * Philippe Amouyel * Centre Hospitalier Régional Universitaire de Lille, Lille, France. * Florence Pasquier * Servicio de Neurologia, Hospital Universitario La Paz (UAM), Madrid, Spain. * Ana Frank-Garcia * Department of Experimental Pathology, School of Medicine, University of Bologna, Bologna, Italy. * Elisa Porcellini & * Federico Licastro * University Paris Descartes, Department of Geriatrics, Broca Hospital, Paris, France. * Olivier Hanon * Genetic Molecular Unit, Hospital Universitario Central de Asturias, Oviedo, Spain. * Eliecer Coto & * Victoria Alvarez * Istituto Di Ricovero e Cura a Carattere Scientifico Oasi Maria SS, Troina, Italy. * Paolo Bosco * Department of Neuroscience, Neurological Clinic, University of Pisa, Italy. * Gabriele Siciliano & * Michelangelo Mancuso * Department of Geriatrics, Center for Aging Brain, Memory Unit, University of Bari, Policlinico, Bari, Italy. * Francesco Panza & * Vincenzo Solfrizzi * Department of Neurological and Psychiatric Sciences, University of Florence, Florence, Italy. * Benedetta Nacmias & * Sandro Sorbi * Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Roma, Italy. * Paola Bossù * Lab of Molecular Genetics, Section of Clinical Pharmacology, Department of Neuroscience, University of Cagliari, Cagliari, Italy. * Paola Piccardi * Department of Internal Medicine, Università degli Studi di Milano, Fondazione IRCCS, Ospedale Maggiore, Mangiagalli e Regina Elena, Milan, Italy. * Beatrice Arosio * Department of Clinical Medicine and Prevention, University of Milano-Bicocca, Monza, Italy. * Giorgio Annoni * Geriatric Unit and Gerontology-Geriatric Research Laboratory, Department of Medical Science, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy. * Davide Seripa & * Alberto Pilotto * Department of Neurological Sciences, Università degli Studi di Milano, Fondazione IRCCS, Ospedale Maggiore, Mangiagalli e Regina Elena, Milan, Italy. * Elio Scarpini & * Daniela Galimberti * INSERM, UMR_S679, Hopital de la salpétirère, Paris, France. * Alexis Brice * INSERM U614, Faculté de Médecine-Pharmacie de Rouen, Rouen, France. * Didier Hannequin Consortia * the Alzheimer's Disease Neuroimaging Initiative * CHARGE consortium * EADI1 consortium Contributions J. Williams directed this study, assisted by M.J.O. and M.O.D. and was also helped by P.H., R.S., A.G., R.A., L.J. and D. Harold. J. Williams, P.H. and D.H. took primary responsibility for drafting the manuscript, assisted by R.S., A.G., R.A., M.O.D. and M.J.O. All authors contributed to the sample collection, sample preparation, genotyping and/or conduct of the GWAS upon which this study is based. J. Williams, R.A., P.H., R.S., A.G., C.W., J. Chapman, K.D., N.J., A.S., C. Thomas, S. Lovestone, J.P., P. Proitsi, M.K.L., C. Brayne, D.C.R., M.G., B.L., A.L., K. Morgan, K.S.B., P.A.P., D. Craig, B.M., S.T., C.H., D.M., A.D.S., S. Love, P.G.K., J.H., S. Mead, N.C.F., M. Rossor, J. Collinge, W.M., F.J., B.S., E.R., R.H., H.K., H.v.d.B., I.H., J.K., J. Wiltfang, M. Dichgans, L.F., H.H., M. Hüll, J.G., A.M.G., D.R., I.G., J.S.K.K., C.C., P.N., J.C.M., K. Mayo, K. Sleegers, K.B., S.E., P.P.D., C.V.B., G.L., N.J.B., H.G., A.M., M.T., T.W.M., M.M.N., S. Moebus, K.-H.J., N.K. and H.-E! .W. contributed to clinical sample collection, ascertainment, diagnosis and preparation of samples from the independent GERAD2 sample genotyped as part of this study. R.S., D. Harold, A.G., D.R. and I.G. were responsible for procedures related to genotyping the GERAD2 sample. V.C., B.G.-B., M. Hiltunen, O.C., D.Z., M. Delepine, M.J.B., F. Pasquier, I.M., A.F.-G., E.P., O.H., E. Coto, V.A., P. Bosco, G.S., M. Mancuso, F. Panza, B.N., S. Sorbi, P. Bossù, P. Piccardi, B.A., G.A., D.S., E.S., D.G., A.B., D. Hannequin, F.L., H. Soininen, J.-C.L. and P.A. were responsible for sample collection, sample preparation, genotyping and analysis of the EADI2 Sample. S.S., A.L.D., O.L. and L.J.L., as well as M.A.I., C.M.v.D. and M.M.B.B. contributed clinical and genotypic data to the CHARGE GWAS. J.-C.L. and P.A. contributed clinical and genotypic data. M.M.C. played a leading role, along with H.B., D.W., G.W., N.M.H., E.R.L.C.V., S.B.S., J.O.A., M.B., Z.K.W., D.W.D., N.R.G.R., R.C.P., K! . Morgan and S.G.Y. in sample collection, sample preparation, ! genotyping and analysis of the Mayo2 sample. M. Riemenschneider, T.M.F., P.F., C.R., M.K., S. Schreiber, M. Mayhaus, S.N. and S.W. were responsible for sample collection, conduct and analysis of the AD-IG GWAS. S. Steinberg, T.J., H. Stefansson, K. Stefansson, J.S., S.B. and P.V.J were responsible for sample collection, conduct and analysis of the deCODE GWAS. D. Harold and P.H. completed statistical quality control and produced association statistics under the supervision of J. Williams and P.A.H. All authors discussed the results and approved the manuscript. Competing financial interests The authors have applied for an International PCT (Patent Co-operation Treaty) patent filing (European Office). 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Author information * Supplementary information Excel files * Supplementary Table 6 (98K) Results for SNPs at the CD2AP, EPHA1, ARID5B and CD33 loci. * Supplementary Table 7 (78K) Sample size tested for each SNP * Supplementary Table 8 (295K) Summary statistics for each dataset PDF files * Supplementary Text and Figures (541K) Supplementary Note and Supplementary Tables 1–5 Additional data - Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease
- Nat Genet 43(5):436-441 (2011)
Nature Genetics | Letter Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease * Adam C Naj1, 115 * Gyungah Jun2, 3, 4, 115 * Gary W Beecham1, 5 * Li-San Wang6 * Badri Narayan Vardarajan3 * Jacqueline Buros3 * Paul J Gallins1 * Joseph D Buxbaum7, 8, 9 * Gail P Jarvik10, 11 * Paul K Crane12 * Eric B Larson13 * Thomas D Bird14 * Bradley F Boeve15 * Neill R Graff-Radford16, 17 * Philip L De Jager18, 19 * Denis Evans20 * Julie A Schneider21, 22 * Minerva M Carrasquillo16 * Nilufer Ertekin-Taner16, 17 * Steven G Younkin16 * Carlos Cruchaga23 * John S K Kauwe24 * Petra Nowotny23 * Patricia Kramer25, 26 * John Hardy27 * Matthew J Huentelman28 * Amanda J Myers29 * Michael M Barmada30 * F Yesim Demirci30 * Clinton T Baldwin3 * Robert C Green3, 31, 32 * Ekaterina Rogaeva33 * Peter St George-Hyslop33, 34 * Steven E Arnold35 * Robert Barber36 * Thomas Beach37 * Eileen H Bigio38 * James D Bowen39 * Adam Boxer40 * James R Burke41 * Nigel J Cairns42 * Chris S Carlson43 * Regina M Carney44 * Steven L Carroll45 * Helena C Chui46 * David G Clark47 * Jason Corneveaux28 * Carl W Cotman48 * Jeffrey L Cummings49 * Charles DeCarli50 * Steven T DeKosky51 * Ramon Diaz-Arrastia52 * Malcolm Dick48 * Dennis W Dickson16 * William G Ellis53 * Kelley M Faber54 * Kenneth B Fallon45 * Martin R Farlow55 * Steven Ferris56 * Matthew P Frosch57 * Douglas R Galasko58 * Mary Ganguli59 * Marla Gearing60, 61 * Daniel H Geschwind62 * Bernardino Ghetti63 * John R Gilbert1, 5 * Sid Gilman64 * Bruno Giordani65 * Jonathan D Glass66 * John H Growdon67 * Ronald L Hamilton68 * Lindy E Harrell47 * Elizabeth Head69 * Lawrence S Honig70 * Christine M Hulette71 * Bradley T Hyman67 * Gregory A Jicha72 * Lee-Way Jin53 * Nancy Johnson73 * Jason Karlawish74 * Anna Karydas40 * Jeffrey A Kaye26, 75 * Ronald Kim76 * Edward H Koo58 * Neil W Kowall31, 77 * James J Lah66 * Allan I Levey66 * Andrew P Lieberman78 * Oscar L Lopez79 * Wendy J Mack80 * Daniel C Marson47 * Frank Martiniuk81 * Deborah C Mash82 * Eliezer Masliah58, 83 * Wayne C McCormick12 * Susan M McCurry84 * Andrew N McDavid43 * Ann C McKee31, 77 * Marsel Mesulam85, 86 * Bruce L Miller40 * Carol A Miller87 * Joshua W Miller53 * Joseph E Parisi88, 89 * Daniel P Perl90 * Elaine Peskind91 * Ronald C Petersen15 * Wayne W Poon48 * Joseph F Quinn26 * Ruchita A Rajbhandary1 * Murray Raskind91 * Barry Reisberg56, 92 * John M Ringman49 * Erik D Roberson47 * Roger N Rosenberg52 * Mary Sano8 * Lon S Schneider46, 93 * William Seeley40 * Michael L Shelanski94 * Michael A Slifer1, 5 * Charles D Smith72 * Joshua A Sonnen95 * Salvatore Spina63 * Robert A Stern31 * Rudolph E Tanzi67 * John Q Trojanowski6 * Juan C Troncoso96 * Vivianna M Van Deerlin6 * Harry V Vinters49, 97 * Jean Paul Vonsattel98 * Sandra Weintraub85, 86 * Kathleen A Welsh-Bohmer41, 99 * Jennifer Williamson70 * Randall L Woltjer100 * Laura B Cantwell6 * Beth A Dombroski6 * Duane Beekly101 * Kathryn L Lunetta2 * Eden R Martin1, 5 * M Ilyas Kamboh30, 79 * Andrew J Saykin54, 102 * Eric M Reiman28, 103, 104, 105 * David A Bennett22, 106 * John C Morris42, 107 * Thomas J Montine95 * Alison M Goate23 * Deborah Blacker108, 109 * Debby W Tsuang91 * Hakon Hakonarson110 * Walter A Kukull111 * Tatiana M Foroud54 * Jonathan L Haines112, 113 * Richard Mayeux70, 114 * Margaret A Pericak-Vance1, 5 * Lindsay A Farrer2, 3, 4, 31, 32 * Gerard D Schellenberg6 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:436–441Year published:(2011)DOI:doi:10.1038/ng.801Received27 September 2010Accepted10 March 2011Published online03 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study of late-onset Alzheimer disease using a three-stage design consisting of a discovery stage (stage 1) and two replication stages (stages 2 and 3). Both joint analysis and meta-analysis approaches were used. We obtained genome-wide significant results at MS4A4A (rs4938933; stages 1 and 2, meta-analysis P (PM) = 1.7 × 10−9, joint analysis P (PJ) = 1.7 × 10−9; stages 1, 2 and 3, PM = 8.2 × 10−12), CD2AP (rs9349407; stages 1, 2 and 3, PM = 8.6 × 10−9), EPHA1 (rs11767557; stages 1, 2 and 3, PM = 6.0 × 10−10) and CD33 (rs3865444; stages 1, 2 and 3, PM = 1.6 × 10−9). We also replicated previous associations at CR1 (rs6701713; PM = 4.6 × 10−10, PJ = 5.2 × 10−11), CLU (rs1532278; PM = 8.3 × 10−8, PJ = 1.9 × 10−8), BIN1 (rs7561528; PM = 4.0 × 10−14, PJ = 5.2 × 10−14) and PICALM (rs561655; PM = 7.0 × 10−11, PJ = 1.0 × 10−10), but not at EXOC3L2, to late-on! set Alzheimer's disease susceptibility1, 2, 3. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Adam C Naj & * Gyungah Jun Affiliations * The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA. * Adam C Naj, * Gary W Beecham, * Paul J Gallins, * John R Gilbert, * Ruchita A Rajbhandary, * Michael A Slifer, * Eden R Martin & * Margaret A Pericak-Vance * Department of Biostatistics, Boston University, Boston, Massachusetts, USA. * Gyungah Jun, * Kathryn L Lunetta & * Lindsay A Farrer * Department of Medicine (Genetics Program), Boston University, Boston, Massachusetts, USA. * Gyungah Jun, * Badri Narayan Vardarajan, * Jacqueline Buros, * Clinton T Baldwin, * Robert C Green & * Lindsay A Farrer * Department of Ophthalmology, Boston University, Boston, Massachusetts, USA. * Gyungah Jun & * Lindsay A Farrer * Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miami, Florida, USA. * Gary W Beecham, * John R Gilbert, * Michael A Slifer, * Eden R Martin & * Margaret A Pericak-Vance * Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Li-San Wang, * John Q Trojanowski, * Vivianna M Van Deerlin, * Laura B Cantwell, * Beth A Dombroski & * Gerard D Schellenberg * Department of Neuroscience, Mount Sinai School of Medicine, New York, New York, USA. * Joseph D Buxbaum * Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA. * Joseph D Buxbaum & * Mary Sano * Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York, USA. * Joseph D Buxbaum * Department of Genome Sciences, University of Washington, Seattle, Washington, USA. * Gail P Jarvik * Department of Medicine (Medical Genetics), University of Washington, Seattle, Washington, USA. * Gail P Jarvik * Department of Medicine, University of Washington, Seattle, Washington, USA. * Paul K Crane & * Wayne C McCormick * Group Health Research Institute, Seattle, Washington, USA. * Eric B Larson * Department of Neurology, University of Washington, Seattle, Washington, USA. * Thomas D Bird * Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA. * Bradley F Boeve & * Ronald C Petersen * Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. * Neill R Graff-Radford, * Minerva M Carrasquillo, * Nilufer Ertekin-Taner, * Steven G Younkin & * Dennis W Dickson * Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA. * Neill R Graff-Radford & * Nilufer Ertekin-Taner * Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Philip L De Jager * Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. * Philip L De Jager * Rush Institute for Healthy Aging, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois, USA. * Denis Evans * Department of Pathology (Neuropathology), Rush University Medical Center, Chicago, Illinois, USA. * Julie A Schneider * Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA. * Julie A Schneider & * David A Bennett * Department of Psychiatry and Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St. Louis, Missouri, USA. * Carlos Cruchaga, * Petra Nowotny & * Alison M Goate * Department of Biology, Brigham Young University, Provo, Utah, USA. * John S K Kauwe * Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA. * Patricia Kramer * Department of Neurology, Oregon Health and Science University, Portland, Oregon, USA. * Patricia Kramer, * Jeffrey A Kaye & * Joseph F Quinn * Institute of Neurology, University College London, Queen Square, London, UK. * John Hardy * Neurogenomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA. * Matthew J Huentelman, * Jason Corneveaux & * Eric M Reiman * Department of Psychiatry and Behavioral Sciences, University of Miami, Miami, Florida, USA. * Amanda J Myers * Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Michael M Barmada, * F Yesim Demirci & * M Ilyas Kamboh * Department of Neurology, Boston University, Boston, Massachusetts, USA. * Robert C Green, * Neil W Kowall, * Ann C McKee, * Robert A Stern & * Lindsay A Farrer * Department of Epidemiology, Boston University, Boston, Massachusetts, USA. * Robert C Green & * Lindsay A Farrer * Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, Canada. * Ekaterina Rogaeva & * Peter St George-Hyslop * Cambridge Institute for Medical Research and Department of Clinical Neurosciences, University of Cambridge, Cambridge, Massachusetts, UK. * Peter St George-Hyslop * Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Steven E Arnold * Department of Pharmacology and Neuroscience, University of Texas Southwestern, Fort Worth, Texas, USA. * Robert Barber * Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Phoenix, Arizona, USA. * Thomas Beach * Department of Pathology, Northwestern University, Chicago, Illinois, USA. * Eileen H Bigio * Swedish Medical Center, Seattle, Washington, USA. * James D Bowen * Department of Neurology, University of California San Francisco, San Francisco, California, USA. * Adam Boxer, * Anna Karydas, * Bruce L Miller & * William Seeley * Department of Medicine, Duke University, Durham, North Carolina, USA. * James R Burke & * Kathleen A Welsh-Bohmer * Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA. * Nigel J Cairns & * John C Morris * Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. * Chris S Carlson & * Andrew N McDavid * Department of Psychiatry, Vanderbilt University, Nashville, Tennessee, USA. * Regina M Carney * Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Steven L Carroll & * Kenneth B Fallon * Department of Neurology, University of Southern California, Los Angeles, California, USA. * Helena C Chui & * Lon S Schneider * Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA. * David G Clark, * Lindy E Harrell, * Daniel C Marson & * Erik D Roberson * Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, California, USA. * Carl W Cotman, * Malcolm Dick & * Wayne W Poon * Department of Neurology, University of California Los Angeles, Los Angeles, California, USA. * Jeffrey L Cummings, * John M Ringman & * Harry V Vinters * Department of Neurology, University of California Davis, Sacramento, California, USA. * Charles DeCarli * University of Virginia School of Medicine, Charlottesville, Virginia, USA. * Steven T DeKosky * Department of Neurology, University of Texas Southwestern, Dallas, Texas, USA. * Ramon Diaz-Arrastia & * Roger N Rosenberg * Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA. * William G Ellis, * Lee-Way Jin & * Joshua W Miller * Department of Medical and Molecular Genetics, Indiana University, Indianapolis, Indiana, USA. * Kelley M Faber, * Andrew J Saykin & * Tatiana M Foroud * Department of Neurology, Indiana University, Indianapolis, Indiana, USA. * Martin R Farlow * Department of Psychiatry, New York University, New York, New York, USA. * Steven Ferris & * Barry Reisberg * C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital, Charlestown, Massachusetts, USA. * Matthew P Frosch * Department of Neurosciences, University of California San Diego, La Jolla, California, USA. * Douglas R Galasko, * Edward H Koo & * Eliezer Masliah * Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Mary Ganguli * Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, USA. * Marla Gearing * Emory Alzheimer's Disease Center, Emory University, Atlanta, Georgia, USA. * Marla Gearing * Neurogenetics Program, University of California Los Angeles, Los Angeles, California, USA. * Daniel H Geschwind * Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, Indiana, USA. * Bernardino Ghetti & * Salvatore Spina * Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA. * Sid Gilman * Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA. * Bruno Giordani * Department of Neurology, Emory University, Atlanta, Georgia, USA. * Jonathan D Glass, * James J Lah & * Allan I Levey * Department of Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA. * John H Growdon, * Bradley T Hyman & * Rudolph E Tanzi * Department of Pathology (Neuropathology), University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Ronald L Hamilton * Department of Molecular and Biomedical Pharmacology, University of California Irvine, Irvine, California, USA. * Elizabeth Head * Taub Institute on Alzheimer's Disease and the Aging Brain, Department of Neurology, Columbia University, New York, New York, USA. * Lawrence S Honig, * Jennifer Williamson & * Richard Mayeux * Department of Pathology, Duke University, Durham, North Carolina, USA. * Christine M Hulette * Department of Neurology, University of Kentucky, Lexington, Kentucky, USA. * Gregory A Jicha & * Charles D Smith * Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, Illinois, USA. * Nancy Johnson * Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Jason Karlawish * Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon, USA. * Jeffrey A Kaye * Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, California, USA. * Ronald Kim * Department of Pathology, Boston University, Boston, Massachusetts, USA. * Neil W Kowall & * Ann C McKee * Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA. * Andrew P Lieberman * University of Pittsburgh Alzheimer's Disease Research Center, Pittsburgh, Pennsylvania, USA. * Oscar L Lopez & * M Ilyas Kamboh * Department of Preventive Medicine, University of Southern California, Los Angeles, California, USA. * Wendy J Mack * Department of Medicine-Pulmonary, New York University, New York, New York, USA. * Frank Martiniuk * Department of Neurology, University of Miami, Miami, Florida, USA. * Deborah C Mash * Department of Pathology, University of California San Diego, La Jolla, California, USA. * Eliezer Masliah * School of Nursing Northwest Research Group on Aging, University of Washington, Seattle, Washington, USA. * Susan M McCurry * Alzheimer's Disease Center, Northwestern University, Chicago, Illinois, USA. * Marsel Mesulam & * Sandra Weintraub * Cognitive Neurology, Northwestern University, Chicago, Illinois, USA. * Marsel Mesulam & * Sandra Weintraub * Department of Pathology, University of Southern California, Los Angeles, California, USA. * Carol A Miller * Department of Anatomic Pathology, Mayo Clinic, Rochester, Minnesota, USA. * Joseph E Parisi * Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA. * Joseph E Parisi * Department of Pathology, Mount Sinai School of Medicine, New York, New York, USA. * Daniel P Perl * Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA. * Elaine Peskind, * Murray Raskind & * Debby W Tsuang * Alzheimer's Disease Center, New York University, New York, New York, USA. * Barry Reisberg * Department of Psychiatry, University of Southern California, Los Angeles, California, USA. * Lon S Schneider * Department of Pathology, Columbia University, New York, New York, USA. * Michael L Shelanski * Department of Pathology, University of Washington, Seattle, Washington, USA. * Joshua A Sonnen & * Thomas J Montine * Department of Pathology, Johns Hopkins University, Baltimore, Maryland, USA. * Juan C Troncoso * Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA. * Harry V Vinters * Taub Institute Department of Pathology, Columbia University, New York, New York, USA. * Jean Paul Vonsattel * Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA. * Kathleen A Welsh-Bohmer * Department of Pathology, Oregon Health and Science University, Portland, Oregon, USA. * Randall L Woltjer * National Alzheimer's Coordinating Center, University of Washington, Seattle, Washington, USA. * Duane Beekly * Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, Indiana, USA. * Andrew J Saykin * Department of Psychiatry, University of Arizona, Phoenix, Arizona, USA. * Eric M Reiman * Arizona Alzheimer's Consortium, Phoenix, Arizona, USA. * Eric M Reiman * Banner Alzheimer's Institute, Phoenix, Arizona, USA. * Eric M Reiman * Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA. * David A Bennett * Department of Neurology, Washington University, St. Louis, Missouri, USA. * John C Morris * Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Deborah Blacker * Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA. * Deborah Blacker * Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Hakon Hakonarson * Department of Epidemiology, University of Washington, Seattle, Washington, USA. * Walter A Kukull * Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA. * Jonathan L Haines * Vanderbilit Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA. * Jonathan L Haines * Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA. * Richard Mayeux Contributions J.D. Buxbaum, G.P.J., P.K.C., E.B.L., T.D.B., B.F.B., N.R.G.-R., P.L.D., D.E., J.A. Schneider, M.M.C., N.E.-T., S.G.Y., C.C., J.S.K.K., P.N., P.K., J.H., M.J.H., A.J.M., M.M.B., F.Y.D., C.T.B., R.C.G., E.R., P.S.G.-H., S.E.A., R.B., T.B., E.H.B., J.D. Bowen, A.B., J.R.B., N.J.C., C.S.C., S.L.C., H.C.C., D.G.C., J.C., C.W.C., J.L.C., C.D., S.T.D., R.D.-A., M.D., D.W.D., W.G.E., K.M.F., K.B.F., M.R.F., S.F., M.P.F., D.R.G., M. Ganguli, M. Gearing, D.H.G., B. Ghetti, J.R.G., S.G., B. Giordani, J.D.G., J.H.G., R.L.H., L.E.H., E.H., L.S.H., C.M.H., B.T.H., G.A.J., L.-W.J., N.J., J.K., A.K., J.A.K., R.K., E.H.K., N.W.K., J.J.L., A.I.L., A.P.L., O.L.L., W.J.M., D.C. Marson, F.M., D.C. Mash, E.M., W.C.M., S.M.M., A.N.M., A.C.M., M.M., B.L.M., C.A.M., J.W.M., J.E.P., D.P.P., E.P., R.C.P., W.W.P., J.F.Q., M.R., B.R., J.M.R., E.D.R., R.N.R., M.S., L.S.S., W.S., M.L.S., M.A.S., C.D.S., J.A. Sonnen, S.S., R.A.S., R.E.T., J.Q.T., J.C.T., V.M.V., H.V.V., J.P.V., S.W., K.A.W.-B., J.W., R.L.! W., L.B.C., B.A.D., D. Beekly, M.I.K., A.J.S., E.M.R., D.A.B., A.M.G., W.A.K., T.M.F., J.L.H., R.M., M.A.P.-V., L.A.F. L.B.C., D. Beekly, D.A.B., J.C.M., T.J.M., A.M.G., D. Blacker, D.W.T., H.H., W.A.K., T.M.F., J.L.H., R.M., M.A.P.-V., L.A.F., G.D.S. A.C.N., G.J., G.W.B., L.-S.W., B.N.V., J.B., P.J.G., R.M.C., R.A.R., M.A.S., K.L.L., E.R.M., J.L.H., M.A.P.-V., L.A.F. A.C.N., G.J., G.W.B., L.-S.W., B.N.V., J.B., P.J.G., R.A.R., M.A.S., K.L.L., E.R.M., M.I.K., A.J.S., E.M.R., D.A.B., J.C.M., T.J.M., A.M.G., D. Blacker, D.W.T., H.H., W.A.K., T.M.F., J.L.H., R.M., M.A.P.-V., L.A.F., G.D.S. A.C.N., G.J., G.W.B., L.-S.W., B.N.V., J.B., P.J.G., J.L.H., R.M., M.A.P.-V., L.A.F., G.D.S. D.A.B., J.C.M., T.J.M., A.M.G., D. Blacker, D.W.T., H.H., W.A.K., T.M.F., J.L.H., R.M., M.A.P.-V., L.A.F., G.D.S. Competing financial interests T.D.B. received licensing fees from and is on the speaker's bureau of Athena Diagnostics, Inc. M.R.F. receives research funding from Bristol-Myers Squibb Company, Danone Research, Elan Pharmaceuticals, Inc., Eli Lilly and Company, Novartis Pharmaceuticals Corporation, OctaPharma AG, Pfizer Inc. and Sonexa Therapeutics, Inc; receives honoraria as scientific consultant from Accera, Inc., Astellas Pharma US Inc., Baxter, Bayer Pharmaceuticals Corporation, Bristol-Myers Squibb, Eisai Medical Research, Inc., GE Healthcare, Medavante, Medivation, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corp., Pfizer, Inc., Prana Biotechnology Ltd., QR Pharma., Inc., the Sanofi-aventis Group and Toyama Chemical Co., Ltd.; and is speaker for Eisai Medical Research, Inc., Forest Laboratories, Pfizer Inc. and Novartis Pharmaceuticals Corporation. A.M.G. has research funding from AstraZeneca, Pfizer and Genentech and has received remuneration for giving talks at Pfizer and Genentech. R.C.P. i! s on the Safety Monitoring Committee of Pfizer, Inc. (Wyeth) and a consultant to the Safety Monitoring Committee at Janssen Alzheimer's Immunotherapy Program (Elan), to Elan Pharmaceuticals, and to GE Healthcare. R.E.T. is a consultant to Eisai, Japan in the area of Alzheimer's genetics and a shareholder in and consultant to Pathway Genomics, Inc, San Diego, California, USA. 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Figures (885K) Supplementary Tables 1–4 and 8 and 9, Supplementary Figures 1 and 2 and Supplementary Note Additional data - Exome sequencing identifies GRIN2A as frequently mutated in melanoma
- Nat Genet 43(5):442-446 (2011)
Nature Genetics | Letter Exome sequencing identifies GRIN2A as frequently mutated in melanoma * Xiaomu Wei1 * Vijay Walia1, 12 * Jimmy C Lin2, 12 * Jamie K Teer3 * Todd D Prickett1 * Jared Gartner1 * Sean Davis4 * NISC Comparative Sequencing Program5 * Katherine Stemke-Hale6 * Michael A Davies6, 7 * Jeffrey E Gershenwald8, 9 * William Robinson10 * Steven Robinson10 * Steven A Rosenberg11 * Yardena Samuels1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:442–446Year published:(2011)DOI:doi:10.1038/ng.810Received04 October 2010Accepted24 March 2011Published online15 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The incidence of melanoma is increasing more than any other cancer, and knowledge of its genetic alterations is limited. To systematically analyze such alterations, we performed whole-exome sequencing of 14 matched normal and metastatic tumor DNAs. Using stringent criteria, we identified 68 genes that appeared to be somatically mutated at elevated frequency, many of which are not known to be genetically altered in tumors. Most importantly, we discovered that TRRAP harbored a recurrent mutation that clustered in one position (p. Ser722Phe) in 6 out of 167 affected individuals (~4%), as well as a previously unidentified gene, GRIN2A, which was mutated in 33% of melanoma samples. The nature, pattern and functional evaluation of the TRRAP recurrent mutation suggest that TRRAP functions as an oncogene. Our study provides, to our knowledge, the most comprehensive map of genetic alterations in melanoma to date and suggests that the glutamate signaling pathway is involved in this di! sease. View full text Figures at a glance * Figure 1: Distribution of new non-synonymous recurrent mutations. Seven new non-synonymous recurrent mutations identified in this study are presented on relevant protein schematics. Black arrows indicate locations of recurrent mutations and conserved protein functional domains are indicated as colored boxes (1, immunoglobulin I-set domain; 2, fibronectin type III domain; 3, neogenin C terminus; 4, phosphoinositide-specific phospholipase C, efhand-like; 5, phosphatidylinositol-specific phospholipase C, X domain; 6, phosphatidylinositol-specific phospholipase C, Y domain; 7, C2 domain; 8, PDZ domain; 9, nitric oxide synthase, oxygenase domain; 10, flavodoxin; 11, FAD binding domain; 12, oxidoreductase NAD-binding domain; 13, LRRNT, leucine rich repeat N-terminal domain; 14, leucine rich repeat; 15, immunoglobulin I-set domain; 16, fibronectin type III domain; 17, major facilitator superfamily). * Figure 2: Effect of mutant TRRAP on colony formation and apoptosis. () Mutant TRRAP induces cell transformation. Foci formation of NIH 3T3 cells transfected with the indicated constructs or empty vector control. Ras p.Gly12Val was included as a positive control for cell transformation. () Detection of TRRAP and KRas protein expression in lysates of transiently transfected NIH 3T3 cells by immunoblot analysis. () Immunoblot of cell lysates from HEK 293T cells transiently transfected with either control vector or shRNAs that target TRRAP. For normalization, lysates were analyzed in parallel by anti–α-tubulin immunoblotting. NS, non specific. () Immunoblot of melanoma cells transduced with shRNA targeting TRRAP and immunoblotted with anti-TRRAP. (–) TRRAP mutation confers resistance to apoptosis. Apoptosis quantification of melanoma cell lines transduced with shRNA control or shRNAs targeting TRRAP by Hoechst 33258-staining. Cells were grown in growth medium containing 2.5% serum for the indicated times. Error bars, s.d. (,) Immunoblot ana! lysis of representative melanoma lines presented in – using the indicated antibodies to assess PARP cleavage. WT, wild type. * Figure 3: Location of somatic mutations in GRIN2A. A schematic of human GRIN2A is presented, with conserved functional domains indicated as colored blocks. Somatic mutations are indicated with arrows and amino acid changes. Recurrent mutations and nonsense mutations are indicated as orange arrows and black boxes, respectively. Conserved domains: SP, signal peptide; PBP1_iGluR_NMDA_NR2, N-terminal leucine/isoleucine/valine-binding protein LIVBP-like domain of the NR2 subunit of NMDA receptor family; PBPb, bacterial periplasmic substrate-binding protein; Lig_chan, ligand-gated ion channel; NMDAR2_C, N-methyl D-aspartate receptor 2B3 C terminus. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * NM_001075.4 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Vijay Walia & * Jimmy C Lin Affiliations * The Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health (NIH), Bethesda, Maryland, USA. * Xiaomu Wei, * Vijay Walia, * Todd D Prickett, * Jared Gartner & * Yardena Samuels * Ludwig Center for Cancer Genetics and Therapeutics and Howard Hughes Medical Institute at the Johns Hopkins Kimmel Cancer Center, Baltimore, Maryland, USA. * Jimmy C Lin * Genetic Disease Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland, USA. * Jamie K Teer * The Genetics Branch, National Cancer Institute, NIH, Bethesda, Maryland, USA. * Sean Davis * NIH Intramural Sequencing Center, National Human Genome Research Institute, NIH, Bethesda, Maryland, USA. * NISC Comparative Sequencing Program * Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Katherine Stemke-Hale & * Michael A Davies * Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Michael A Davies * Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Jeffrey E Gershenwald * Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Jeffrey E Gershenwald * Division of Medical Oncology, University of Colorado Denver School of Medicine, Aurora, Colorado, USA. * William Robinson & * Steven Robinson * The Surgery Branch, National Cancer Institute, NIH, Bethesda, Maryland, USA. * Steven A Rosenberg Consortia * NISC Comparative Sequencing Program Contributions X.W., V.W., J.C.L., J.K.T., T.D.P. and Y.S. designed the study; K.S.-H., M.A.D., J.E.G., W.R., S.R. and S.A.R. collected and analyzed the melanoma samples; X.W., J.K.T., J.G., J.C.L., S.D. and the NISC Comparative Sequencing Program analyzed the genetic data; V.W. and T.D.P. performed and analyzed the functional data. All authors contributed to the final version of the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yardena Samuels Author Details * Xiaomu Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Vijay Walia Search for this author in: * NPG journals * PubMed * Google Scholar * Jimmy C Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Jamie K Teer Search for this author in: * NPG journals * PubMed * Google Scholar * Todd D Prickett Search for this author in: * NPG journals * PubMed * Google Scholar * Jared Gartner Search for this author in: * NPG journals * PubMed * Google Scholar * Sean Davis Search for this author in: * NPG journals * PubMed * Google Scholar * Katherine Stemke-Hale Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Davies Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey E Gershenwald Search for this author in: * NPG journals * PubMed * Google Scholar * William Robinson Search for this author in: * NPG journals * PubMed * Google Scholar * Steven Robinson Search for this author in: * NPG journals * PubMed * Google Scholar * Steven A Rosenberg Search for this author in: * NPG journals * PubMed * Google Scholar * Yardena Samuels Contact Yardena Samuels Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 1 (40K) Score cutoff for determination of melanoma somatic mutations * Supplementary Table 2 (1M) Somatic mutations identified in the Discovery Screen * Supplementary Table 3 (408K) Significance of the observed mutation rate over the expected mutation rate PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–7, Supplementary Tables 4–9 and Supplementary Note. Additional data - A genome-wide association study identifies three loci associated with susceptibility to uterine fibroids
- Nat Genet 43(5):447-450 (2011)
Nature Genetics | Letter A genome-wide association study identifies three loci associated with susceptibility to uterine fibroids * Pei-Chieng Cha1 * Atsushi Takahashi2 * Naoya Hosono3 * Siew-Kee Low1 * Naoyuki Kamatani2 * Michiaki Kubo3 * Yusuke Nakamura1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:447–450Year published:(2011)DOI:doi:10.1038/ng.805Received24 November 2010Accepted14 March 2011Published online03 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Uterine fibroids are a common benign tumor of the female genital tract. We conducted a genome-wide association study in which 457,044 SNPs were analyzed in 1,607 individuals with clinically diagnosed uterine fibroids and 1,428 female controls. SNPs showing suggestive associations (P < 5 × 10−5) were further genotyped in 3,466 additional cases and 3,245 female controls. Three loci on chromosomes 10q24.33, 22q13.1 and 11p15.5 revealed genome-wide significant associations with uterine fibroids. The SNPs showing the most significant association in a combination analysis at each of these loci were rs7913069 (P = 8.65 × 10−14, odds ratio (OR) = 1.47), rs12484776 (P = 2.79 × 10−12, OR = 1.23) and rs2280543 (P = 3.82 × 10−12, OR = 1.39), respectively. Subsequent fine mapping of these regions will be necessary to pinpoint the causal variants. Our findings should shed light on the pathogenesis of uterine fibroids. View full text Figures at a glance * Figure 1: Manhattan plot for the GWAS of uterine fibroids indicating −log10P of the Cochran-Armitage trend test for 457,044 SNPs plotted against their respective positions on each chromosome. * Figure 2: Regional association plots and recombination rates of the three loci associated with uterine fibroids at chromosomes 10q24.33 (SLK and OBFC1) (a), 22q13.1 (TNCRB6) (b) and 11p15.5 (ODF3-BET1L-RIC8A-SIRT3) (c). For each plot, −log10P of SNPs in the GWAS were plotted against relative chromosomal locations. The diamond and circle signs represent genotyped and imputed SNPs, respectively. All SNPs are color coded as red (r2 = 0.8–1.0), orange (r2 = 0.5–0.8), yellow (r2 = 0.2–0.5) and white (r2 < 0.2) according to their pairwise r2 to the marker SNP. The marker SNP is indicated by an arrow, and the combined P value of each marker SNP is represented by a blue diamond sign. SNP positions followed NCBI build 36 coordinates. Estimated recombination rates (cM/Mb) are plotted as a dark blue line. Author information * Author information * Supplementary information Affiliations * Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. * Pei-Chieng Cha, * Siew-Kee Low & * Yusuke Nakamura * Laboratory for Statistical Analysis, RIKEN Center for Genomic Medicine, Yokohama, Japan. * Atsushi Takahashi & * Naoyuki Kamatani * Laboratory for Genotyping Development, RIKEN Center for Genomic Medicine, Yokohama, Japan. * Naoya Hosono & * Michiaki Kubo Contributions Y.N. initiated the recruitment of samples, obtained financial support and conceived the study. Y.N., M.K. and P.-C.C. designed the study. P.-C.C. performed SNP selection, genotyping, data analysis and prepared the manuscript. A.T. performed quality assessment and statistical analysis of GWAS data. M.K. and N.H. supervised and conducted the GWAS. S.-K.L. helped with preparation of figures. N.K. supervised the statistical analysis. M.K. and Y.N. critically reviewed and edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yusuke Nakamura Author Details * Pei-Chieng Cha Search for this author in: * NPG journals * PubMed * Google Scholar * Atsushi Takahashi Search for this author in: * NPG journals * PubMed * Google Scholar * Naoya Hosono Search for this author in: * NPG journals * PubMed * Google Scholar * Siew-Kee Low Search for this author in: * NPG journals * PubMed * Google Scholar * Naoyuki Kamatani Search for this author in: * NPG journals * PubMed * Google Scholar * Michiaki Kubo Search for this author in: * NPG journals * PubMed * Google Scholar * Yusuke Nakamura Contact Yusuke Nakamura Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 2 (82K) Associations of the 46 SNPs examined in GWAS, replication study, and combined analysis PDF files * Supplementary Text and Figures (877K) Supplementary Table 1 and Supplementary Figures 1–7. Additional data - Genome-wide association study identifies a common variant associated with risk of endometrial cancer
- Nat Genet 43(5):451-454 (2011)
Nature Genetics | Letter Genome-wide association study identifies a common variant associated with risk of endometrial cancer * Amanda B Spurdle1 * Deborah J Thompson2 * Shahana Ahmed3, 29 * Kaltin Ferguson1, 29 * Catherine S Healey3, 29 * Tracy O'Mara1, 4, 29 * Logan C Walker1 * Stephen B Montgomery5 * Emmanouil T Dermitzakis5 * The Australian National Endometrial Cancer Study Group1, 6 * Paul Fahey1 * Grant W Montgomery1 * Penelope M Webb1 * Peter A Fasching7 * Matthias W Beckmann8 * Arif B Ekici8 * Alexander Hein9, 10 * Diether Lambrechts11, 12 * Lieve Coenegrachts13 * Ignace Vergote13 * Frederic Amant13 * Helga B Salvesen14, 15 * Jone Trovik14, 15 * Tormund S Njolstad14, 15 * Harald Helland14 * Rodney J Scott16, 17, 18, 19 * Katie Ashton16, 17 * Tony Proietto20 * Geoffrey Otton20 * National Study of Endometrial Cancer Genetics Group6, 21 * Ian Tomlinson21 * Maggie Gorman21 * Kimberley Howarth21 * Shirley Hodgson22 * Montserrat Garcia-Closas23, 24 * Nicolas Wentzensen23 * Hannah Yang23 * Stephen Chanock23 * Per Hall25 * Kamila Czene25 * Jianjun Liu26 * Jingmei Li25, 26 * Xiao-Ou Shu27 * Wei Zheng27 * Jirong Long27 * Yong-Bing Xiang28 * Mitul Shah3 * Jonathan Morrison2 * Kyriaki Michailidou2 * Paul D Pharoah2, 3 * Alison M Dunning3 * Douglas F Easton2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:451–454Year published:(2011)DOI:doi:10.1038/ng.812Received01 December 2010Accepted24 March 2011Published online17 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Endometrial cancer is the most common malignancy of the female genital tract in developed countries. To identify genetic variants associated with endometrial cancer risk, we performed a genome-wide association study involving 1,265 individuals with endometrial cancer (cases) from Australia and the UK and 5,190 controls from the Wellcome Trust Case Control Consortium. We compared genotype frequencies in cases and controls for 519,655 SNPs. Forty seven SNPs that showed evidence of association with endometrial cancer in stage 1 were genotyped in 3,957 additional cases and 6,886 controls. We identified an endometrial cancer susceptibility locus close to HNF1B at 17q12 (rs4430796, P = 7.1 × 10−10) that is also associated with risk of prostate cancer and is inversely associated with risk of type 2 diabetes. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Shahana Ahmed, * Kaltin Ferguson, * Catherine S Healey & * Tracy O'Mara Affiliations * Division of Genetics and Population Health, Queensland Institute of Medical Research, Brisbane, Queensland, Australia. * The Australian National Endometrial Cancer Study Group, * Amanda B Spurdle, * Kaltin Ferguson, * Tracy O'Mara, * Logan C Walker, * Paul Fahey, * Grant W Montgomery & * Penelope M Webb * Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK. * Deborah J Thompson, * Jonathan Morrison, * Kyriaki Michailidou, * Paul D Pharoah & * Douglas F Easton * Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK. * Shahana Ahmed, * Catherine S Healey, * Mitul Shah, * Paul D Pharoah, * Alison M Dunning & * Douglas F Easton * Hormone Dependent Cancer Program, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. * Tracy O'Mara * Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland. * Stephen B Montgomery & * Emmanouil T Dermitzakis * A full list of members is provided in the Supplementary Note. * The Australian National Endometrial Cancer Study Group & * National Study of Endometrial Cancer Genetics Group * University of California at Los Angeles, David Geffen School of Medicine, Department of Medicine, Division of Hematology and Oncology, Los Angeles, California, USA. * Peter A Fasching * Institute of Human Genetics, Friedrich Alexander University Erlangen Nürnberg, Erlangen, Germany. * Matthias W Beckmann & * Arif B Ekici * University Hospital Erlangen, Department of Gynecology and Obstetrics, Friedrich Alexander University Erlangen Nürnberg, Erlangen, Germany. * Alexander Hein * University Gynecologic Oncology Center, Comprehensive Cancer Centre, Erlangen Nürnberg, Erlangen, Germany. * Alexander Hein * Vesalius Research Center, Vlaams Instituut voor Biotechnologie, Leuven, Belgium. * Diether Lambrechts * Vesalius Research Center, University of Leuven, Leuven, Belgium. * Diether Lambrechts * Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University Hospitals Leuven, Leuven, Belgium. * Lieve Coenegrachts, * Ignace Vergote & * Frederic Amant * Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway. * Helga B Salvesen, * Jone Trovik, * Tormund S Njolstad & * Harald Helland * Department of Clinical Medicine, The University of Bergen, Bergen, Norway. * Helga B Salvesen, * Jone Trovik & * Tormund S Njolstad * The Centre for Information Based Medicine and the Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, New South Wales (NSW), Australia. * Rodney J Scott & * Katie Ashton * The Hunter Medical Research Institute, John Hunter Hospital, NSW, Australia. * Rodney J Scott & * Katie Ashton * Division of Genetics, Hunter Area Pathology Service, John Hunter Hospital, Newcastle, NSW, Australia. * Rodney J Scott * Hunter Centre for Gynaecological, John Hunter Hospital, NSW, Australia. * Rodney J Scott * School of Medicine and Public Health, Faculty of Health, University of Newcastle, NSW, Australia. * Tony Proietto & * Geoffrey Otton * Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. * National Study of Endometrial Cancer Genetics Group, * Ian Tomlinson, * Maggie Gorman & * Kimberley Howarth * Department of Clinical Genetics, St. George's Hospital Medical School, London, UK. * Shirley Hodgson * Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA. * Montserrat Garcia-Closas, * Nicolas Wentzensen, * Hannah Yang & * Stephen Chanock * Institute of Cancer Research, London, UK. * Montserrat Garcia-Closas * Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. * Per Hall, * Kamila Czene & * Jingmei Li * Human Genetics, Genome Institute of Singapore, Singapore. * Jianjun Liu & * Jingmei Li * Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA. * Xiao-Ou Shu, * Wei Zheng & * Jirong Long * Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China. * Yong-Bing Xiang Consortia * The Australian National Endometrial Cancer Study Group * National Study of Endometrial Cancer Genetics Group Contributions A.B.S., D.F.E., G.M. and P.M.W. obtained funding for the study. A.B.S. and D.F.E. designed the study. A.B.S. and D.J.T. drafted the manuscript. P.F. and K.M. conducted preliminary analysis, and D.F.E. and D.J.T. conducted the final statistical analyses. A.B.S. and P.M.W. coordinated the ANECS. P.D.P. and D.F.E. coordinated Studies of Epidemiology and Risk Factors in Cancer Heredity (SEARCH). A.B.S., K.F. and T.O. coordinated the ANECS stage 1 genotyping. A.M.D., S.A. and C.S.H. coordinated the SEARCH stage 1 genotyping. L.C.W., S.B.M. and E.T.D. conducted analyses to assess correlations between genotype and gene expression. J.M. provided data management and bioinformatics support. T.O. and K.F. coordinated the ANECS and other Brisbane-based stage 2 genotyping and assisted with data management. S.A., C.S.H. and A.M.D. coordinated the stage 2 genotyping of the SEARCH samples. M.S. coordinated overall management of data for SEARCH samples. D.L., P.H., K.C., J. Liu, J. Li, I.T.,! K.H., M.G.-C., N.W., H.Y., S.C., X.-O.S. and J. Long coordinated the stage 2 genotyping, or extraction of existing genotype data, for the LES, SASBAC, NSECG, PECS and SECGS samples. The following authors coordinated the baseline studies and/or extraction of questionnaire and clinical information for studies included in stage 2 analysis: BECS (P.A.F., M.W.B., A.H. and A.B.E.); LES (D.L., L.C., I.V. and F.A.); MoMaTEC (H.B.S., J.T., H.H. and T.S.N.); NECS (R.J.S., K.A., T.P. and G.O.); NSECG (I.T., K.H., M.G. and S.H.); PECS (M.G.-C., H.Y. and N.W.); SASBAC (P.H., K.C., and J. Li); SECGS (X.-O.S. and W.Z. (principal investigators), J. Long (principal study geneticist) and Y.-B.X. (site principal investigator at the Shanghai Cancer Institute)). All authors provided critical review of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Amanda B Spurdle Author Details * Amanda B Spurdle Contact Amanda B Spurdle Search for this author in: * NPG journals * PubMed * Google Scholar * Deborah J Thompson Search for this author in: * NPG journals * PubMed * Google Scholar * Shahana Ahmed Search for this author in: * NPG journals * PubMed * Google Scholar * Kaltin Ferguson Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine S Healey Search for this author in: * NPG journals * PubMed * Google Scholar * Tracy O'Mara Search for this author in: * NPG journals * PubMed * Google Scholar * Logan C Walker Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen B Montgomery Search for this author in: * NPG journals * PubMed * Google Scholar * Emmanouil T Dermitzakis Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Fahey Search for this author in: * NPG journals * PubMed * Google Scholar * Grant W Montgomery Search for this author in: * NPG journals * PubMed * Google Scholar * Penelope M Webb Search for this author in: * NPG journals * PubMed * Google Scholar * Peter A Fasching Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias W Beckmann Search for this author in: * NPG journals * PubMed * Google Scholar * Arif B Ekici Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Hein Search for this author in: * NPG journals * PubMed * Google Scholar * Diether Lambrechts Search for this author in: * NPG journals * PubMed * Google Scholar * Lieve Coenegrachts Search for this author in: * NPG journals * PubMed * Google Scholar * Ignace Vergote Search for this author in: * NPG journals * PubMed * Google Scholar * Frederic Amant Search for this author in: * NPG journals * PubMed * Google Scholar * Helga B Salvesen Search for this author in: * NPG journals * PubMed * Google Scholar * Jone Trovik Search for this author in: * NPG journals * PubMed * Google Scholar * Tormund S Njolstad Search for this author in: * NPG journals * PubMed * Google Scholar * Harald Helland Search for this author in: * NPG journals * PubMed * Google Scholar * Rodney J Scott Search for this author in: * NPG journals * PubMed * Google Scholar * Katie Ashton Search for this author in: * NPG journals * PubMed * Google Scholar * Tony Proietto Search for this author in: * NPG journals * PubMed * Google Scholar * Geoffrey Otton Search for this author in: * NPG journals * PubMed * Google Scholar * Ian Tomlinson Search for this author in: * NPG journals * PubMed * Google Scholar * Maggie Gorman Search for this author in: * NPG journals * PubMed * Google Scholar * Kimberley Howarth Search for this author in: * NPG journals * PubMed * Google Scholar * Shirley Hodgson Search for this author in: * NPG journals * PubMed * Google Scholar * Montserrat Garcia-Closas Search for this author in: * NPG journals * PubMed * Google Scholar * Nicolas Wentzensen Search for this author in: * NPG journals * PubMed * Google Scholar * Hannah Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen Chanock Search for this author in: * NPG journals * PubMed * Google Scholar * Per Hall Search for this author in: * NPG journals * PubMed * Google Scholar * Kamila Czene Search for this author in: * NPG journals * PubMed * Google Scholar * Jianjun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Jingmei Li Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao-Ou Shu Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Zheng Search for this author in: * NPG journals * PubMed * Google Scholar * Jirong Long Search for this author in: * NPG journals * PubMed * Google Scholar * Yong-Bing Xiang Search for this author in: * NPG journals * PubMed * Google Scholar * Mitul Shah Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Morrison Search for this author in: * NPG journals * PubMed * Google Scholar * Kyriaki Michailidou Search for this author in: * NPG journals * PubMed * Google Scholar * Paul D Pharoah Search for this author in: * NPG journals * PubMed * Google Scholar * Alison M Dunning Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas F Easton Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (236K) Supplementary Note, Supplementary Figures 1 and 2 and Supplementary Tables 1–7. Additional data - Genome-wide association study identifies a susceptibility locus for HCV-induced hepatocellular carcinoma
- Nat Genet 43(5):455-458 (2011)
Nature Genetics | Letter Genome-wide association study identifies a susceptibility locus for HCV-induced hepatocellular carcinoma * Vinod Kumar1, 2 * Naoya Kato3 * Yuji Urabe1 * Atsushi Takahashi2 * Ryosuke Muroyama3 * Naoya Hosono2 * Motoyuki Otsuka4 * Ryosuke Tateishi4 * Masao Omata4 * Hidewaki Nakagawa2 * Kazuhiko Koike4 * Naoyuki Kamatani2 * Michiaki Kubo2 * Yusuke Nakamura1, 2 * Koichi Matsuda1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:455–458Year published:(2011)DOI:doi:10.1038/ng.809Received20 July 2010Accepted23 March 2011Published online17 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To identify the genetic susceptibility factor(s) for hepatitis C virus–induced hepatocellular carcinoma (HCV-induced HCC), we conducted a genome-wide association study using 432,703 autosomal SNPs in 721 individuals with HCV-induced HCC (cases) and 2,890 HCV-negative controls of Japanese origin. Eight SNPs that showed possible association (P < 1 × 10−5) in the genome-wide association study were further genotyped in 673 cases and 2,596 controls. We found a previously unidentified locus in the 5′ flanking region of MICA on 6p21.33 (rs2596542, Pcombined = 4.21 × 10−13, odds ratio = 1.39) to be strongly associated with HCV-induced HCC. Subsequent analyses using individuals with chronic hepatitis C (CHC) indicated that this SNP is not associated with CHC susceptibility (P = 0.61) but is significantly associated with progression from CHC to HCC (P = 3.13 × 10−8). We also found that the risk allele of rs2596542 was associated with lower soluble MICA protein levels in i! ndividuals with HCV-induced HCC (P = 1.38 × 10−13). View full text Figures at a glance * Figure 1: Regional association plot at rs2596542. Above, the P values of genotyped SNPs are plotted (as −log10 values) against their physical position on chromosome 6 (NCBI Build 36). The P value for rs2596542 at the GWAS stage, replication stage and combination analysis is represented by a purple diamond, circle and diamond, respectively. Estimated recombination rates from the HapMap JPT population show the local LD structure. Inset, the SNP's colors indicate LD with rs2596542 according to a scale from r2 = 0 to r2 = 1 based on pairwise r2 values from HapMap JPT. Below, gene annotations from the UCSC genome browser. * Figure 2: Correlation between soluble MICA levels and rs2596542 genotype. The x axis shows the genotypes at rs2596542, and the y axis shows the concentration of soluble MICA in pg/ml. The number of independent samples tested in each group is shown in parentheses. Each group is shown as a box plot, and the median values are shown as thick dark horizontal lines (median values of AA = 0, AG = 43.6 and GG = 77.74). The box covers the twenty-fifth to seventy-fifth percentiles, and the whiskers outside the box extend to the highest and lowest value within 1.5 times the interquartile range. Points outside the whiskers are outliers. We tested the difference in the median values among genotypes using the Kruskal-Wallis test (P = 1.6 × 10−13). We plotted the box plots using default settings in R (see URLs). * Figure 3: Correlation between soluble MICA and HCV-related diseases. The x axis shows the disease stages after HCV infection, and the y axis shows the concentration of soluble MICA in pg/ml. The number of independent samples tested in each group is shown in parentheses. Each group is shown as a box plot, and the median values are shown as thick dark horizontal lines (median values of non-HCV = 0, CHC = 64.55, LC = 72.11 and HCC = 77.98). The box covers the twenty-fifth to seventy-fifth percentiles, and the whiskers outside the box extend to the highest and lowest value within 1.5 times the interquartile range. Points outside the whiskers are outliers. We tested the difference in the median values among the disease groups using the Wilcoxon rank test. The box plots were plotted using default settings in R. Non-HCV, individuals not exposed to HCV infection; CHC, individuals with chronic hepatitis C; LC, individuals with liver cirrhosis; HCC, individuals with hepatocellular carcinoma. Author information * Author information * Supplementary information Affiliations * Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan. * Vinod Kumar, * Yuji Urabe, * Yusuke Nakamura & * Koichi Matsuda * Center for Genomic Medicine, The Institute of Physical and Chemical Research (RIKEN), Kanagawa, Japan. * Vinod Kumar, * Atsushi Takahashi, * Naoya Hosono, * Hidewaki Nakagawa, * Naoyuki Kamatani, * Michiaki Kubo & * Yusuke Nakamura * Unit of Disease Control Genome Medicine, The Institute of Medical Science, University of Tokyo, Tokyo, Japan. * Naoya Kato & * Ryosuke Muroyama * Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan. * Motoyuki Otsuka, * Ryosuke Tateishi, * Masao Omata & * Kazuhiko Koike Contributions K.M. and Y.N. conceived of the study; Y.N., V.K., M.K. and K.M. designed the study; V.K., Y.U., R.M. and N.H. performed genotyping; V.K., Y.N. and K.M. wrote the manuscript; A.T. and N. Kamatani performed quality control at the genome-wide phase; Y.N., K.M., H.N. and M.K. managed DNA and serum samples belonging to BioBank Japan; N. Kato, R.T., M. Otsuka, M. Omata and K.K. managed replication DNA and serum samples; V.K. analyzed the data, performed VNTR genotyping, ELISA and summarized the whole results; Y.N. obtained funding for the study. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Koichi Matsuda Author Details * Vinod Kumar Search for this author in: * NPG journals * PubMed * Google Scholar * Naoya Kato Search for this author in: * NPG journals * PubMed * Google Scholar * Yuji Urabe Search for this author in: * NPG journals * PubMed * Google Scholar * Atsushi Takahashi Search for this author in: * NPG journals * PubMed * Google Scholar * Ryosuke Muroyama Search for this author in: * NPG journals * PubMed * Google Scholar * Naoya Hosono Search for this author in: * NPG journals * PubMed * Google Scholar * Motoyuki Otsuka Search for this author in: * NPG journals * PubMed * Google Scholar * Ryosuke Tateishi Search for this author in: * NPG journals * PubMed * Google Scholar * Masao Omata Search for this author in: * NPG journals * PubMed * Google Scholar * Hidewaki Nakagawa Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuhiko Koike Search for this author in: * NPG journals * PubMed * Google Scholar * Naoyuki Kamatani Search for this author in: * NPG journals * PubMed * Google Scholar * Michiaki Kubo Search for this author in: * NPG journals * PubMed * Google Scholar * Yusuke Nakamura Search for this author in: * NPG journals * PubMed * Google Scholar * Koichi Matsuda Contact Koichi Matsuda Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–10 and Supplementary Tables 1–17. Additional data - Common variation in GPC5 is associated with acquired nephrotic syndrome
- Nat Genet 43(5):459-463 (2011)
Nature Genetics | Letter Common variation in GPC5 is associated with acquired nephrotic syndrome * Koji Okamoto1, 2 * Katsushi Tokunaga2 * Kent Doi1 * Toshiro Fujita1 * Hodaka Suzuki3 * Tetsuo Katoh3 * Tsuyoshi Watanabe3 * Nao Nishida2 * Akihiko Mabuchi2 * Atsushi Takahashi4 * Michiaki Kubo5 * Shiro Maeda6 * Yusuke Nakamura7 * Eisei Noiri1, 8 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:459–463Year published:(2011)DOI:doi:10.1038/ng.792Received01 November 2010Accepted23 February 2011Published online27 March 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Severe proteinuria is a defining factor of nephrotic syndrome irrespective of the etiology. Investigation of congenital nephrotic syndrome has shown that dysfunction of glomerular epithelial cells (podocytes) plays a crucial role in this disease1. Acquired nephrotic syndrome is also assumed to be associated with podocyte injury. Here we identify an association between variants in GPC5, encoding glypican-5, and acquired nephrotic syndrome through a genome-wide association study and replication analysis (P value under a recessive model (Prec) = 6.0 × 10−11, odds ratio = 2.54). We show that GPC5 is expressed in podocytes and that the risk genotype is associated with higher expression. We further show that podocyte-specific knockdown and systemic short interfering RNA injection confers resistance to podocyte injury in mouse models of nephrosis. This study identifies GPC5 as a new susceptibility gene for nephrotic syndrome and implicates GPC5 as a promising therapeutic target ! for reducing podocyte vulnerability in glomerular disease. View full text Figures at a glance * Figure 1: Association P value plot of the GPC5 region in nephrotic syndrome and LD construction. () Genotyped SNPs shown with P values for association with nephrotic syndrome around the GPC5 region. rs16946160 is represented as a black diamond. All other SNPs are color-coded according to the strength of LD (as measured by r2) with rs16946160. () Evidence for association with nephrotic syndrome around the intron 2 region following imputation using HapMap II JPT and CHB reference panels. The plot includes pairwise D′ values from the HapMap release 22 for the JPT and CHB populations. * Figure 2: Association between rs16946160 genotype and GPC5 mRNA expression and localization of expression in human kidney. () The mRNA expression level of GPC5 in peripheral blood leukocytes from healthy individuals with different genotypes (18 G/G, 11 G/A and 8 A/A) relative to β-actin measured using real-time quantitative PCR (qPCR). Lines show quartile points. () Scheme of qPCR for screening splicing variant. No splicing variant was detected using intron-spanning RT-PCR either in blood leukocytes or the renal cortex. () Immunofluorescence staining of human kidney was visualized using confocal microscopy. GPC5 was detected as Alexa-488 fluorescence (green), and Nephrin was visualized using Alexa-633 fluorescence (blue). * Figure 3: In vitro functional analysis of GPC5. () Knockdown of Gpc5 inhibits FGF2 binding. Cells were collected and incubated with 10 nM biotinylated FGF2 ligand for 45 min at 4 °C. After staining with R-phycoerythrin-conjugated streptavidin, we counted 10,000 cells by flow cytometry (n = 6). Agarose gel electrophoresis of the products generated by RT-PCR using primers specific to rat Gpc5, Fgfr2 and β-actin. Rat cultured glomerular epithelial cells (GEC) were transfected with PBS (lane 1), negative control siRNA (lane 2) and siRNA against Gpc5 (lane 3). () Signal transduction in cells with or without knockdown in response to FGF2. We stimulated the GEC cells with 1 ng/ml FGF2 for 60 min. Cell lysates were subjected to protein blot analysis and blotted to examine the presence of Gpc5, Frs2α, phosphorylated Frs2α and actin. We calculated the phospho-Frs2α/Frs2α ratio (n = 4). () GEC cells were seeded in the absence (vehicle) or presence of 10 ng/ml FGF2 with or without knockdown. Morphology was analyzed using a micr! oscope (Eclipse, Nikon) and photographed using a digital camera. The rates of round cells were determined (n = 5). () In the knockdown group, prevalence of the G0/G1 phase was increased and that of the G2/M phase was decreased (n = 4). () Schematic depiction of the paracellular permeability influx assay. Treated cells' monolayer on type 1 collagen-coated BD BioCoat cell culture inserts were incubated for 8 h. Albumin permeability across the monolayer was then determined using the protein concentration in outer chamber relative to that in the inner chamber (n = 3 in quadruplicate). All error bars in this figure are s.e.m. * Figure 4: PAN-FGF2–induced nephrotic syndrome in wild-type sibling (sib) mice is attenuated by Gpc5 conditional knockdown. () Albumin to creatine ratio (ACR) was significantly increased in sibling control mice after PAN-FGF2 injection after day 4 (n = 8; error bars are s.e.m.; *P < 0.05, **P < 0.01). () In the sibling control group, elevated blood urea nitrogen (BUN) (mg/dl) was found on days 3, 5, 7, 10 and 14, and decreased serum albumin (g/dl) was found on days 10 and 14. We found only mild changes in knockdown mice on days 7 (BUN only), 10 and 14 (n = 8; error bars are s.e.m.; *P < 0.05, **P < 0.01). () Periodic acid-Schiff stain of the renal cortex at day 28. Focal segmental sclerosis and cast formation by Tamm-Horsfall protein were found in the sibling control group. Almost all glomeruli were unaffected in knockdown mice that received PAN-FGF2. () The sclerosis score of glomerulus in nephrotic model on day 28. The score of knockdown mice was significantly lower than those of sib control mice (n = 8; error bars are s.e.m.). () Electron microscopy conducted on day 10 revealed virtually no fo! ot-process effacement in conditional knockdown mice. Sibling control mice showed typical findings of foot-process effacement (arrow) in glomerular epithelial cells. * Figure 5: siRNA against Gpc5 ameliorates PAN-FGF2–induced nephrotic syndrome and Adriamycin-induced albuminuria. () ACR (g/gCr) was significantly decreased on days 7, 10 and 14 after Gpc5 siRNA treatment in the PAN-FGF2–induced nephrotic syndrome model (day 5) (n = 6; *P < 0.05, **P < 0.01). () In the scrambled-siRNA group, a decrease of serum albumin (Alb; g/dl) was found on days 7, 10 and 14. Decreased serum albumin on days 10 and 14 was significantly attenuated in Gpc5-siRNA–treated mice in the PAN-FGF2–induced nephrotic syndrome model (day 5) (n = 6; *P < 0.05). () ACR (mg/gCr) was significantly decreased on days 7, 10, 14 and 21 after siRNA treatment in Adriamycin-induced albuminuria model (day 5) (n = 6; *P < 0.05). () Electron microscopic quantification of foot processes at day 21 after Adriamycin injection in the albuminuria model (n = 6). All error bars in this figure are s.e.m. Author information * Author information * Supplementary information Affiliations * Department of Nephrology and Endocrinology, Department of Hemodialysis and Apheresis, University Hospital, The University of Tokyo, Tokyo, Japan. * Koji Okamoto, * Kent Doi, * Toshiro Fujita & * Eisei Noiri * Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. * Koji Okamoto, * Katsushi Tokunaga, * Nao Nishida & * Akihiko Mabuchi * Department of Nephrology, Hypertension, Diabetology, Endocrinology and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan. * Hodaka Suzuki, * Tetsuo Katoh & * Tsuyoshi Watanabe * Laboratory for Statistical Analysis, Center for Genomic Medicine, RIKEN, Yokohama, Japan. * Atsushi Takahashi * Laboratory for Genotyping Development, Center for Genomic Medicine, RIKEN, Yokohama, Japan. * Michiaki Kubo * Laboratory for Endocrinology and Metabolism, Center for Genomic Medicine, RIKEN, Yokohama, Japan. * Shiro Maeda * Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. * Yusuke Nakamura * Science and Technology Research Partnership for Sustainable Development (SATREPS), Japan Science Technology (JST), Tokyo, Japan. * Eisei Noiri Contributions K.O. performed all experimental work and data analysis and wrote the first draft of the manuscript. A.T. and M.K. obtained raw GWAS data. N.N. supported the variation screening. Y.N. planned and designed the BioBank project. K.O., S.M., K.T. and E.N. designed the experiments. T.F., S.M., A.M., K.T. and E.N. supervised the project. K.D., H.S., T.K. and T.W. collected the case data and clinical data. The manuscript was finalized by K.O. with the assistance of all authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Eisei Noiri Author Details * Koji Okamoto Search for this author in: * NPG journals * PubMed * Google Scholar * Katsushi Tokunaga Search for this author in: * NPG journals * PubMed * Google Scholar * Kent Doi Search for this author in: * NPG journals * PubMed * Google Scholar * Toshiro Fujita Search for this author in: * NPG journals * PubMed * Google Scholar * Hodaka Suzuki Search for this author in: * NPG journals * PubMed * Google Scholar * Tetsuo Katoh Search for this author in: * NPG journals * PubMed * Google Scholar * Tsuyoshi Watanabe Search for this author in: * NPG journals * PubMed * Google Scholar * Nao Nishida Search for this author in: * NPG journals * PubMed * Google Scholar * Akihiko Mabuchi Search for this author in: * NPG journals * PubMed * Google Scholar * Atsushi Takahashi Search for this author in: * NPG journals * PubMed * Google Scholar * Michiaki Kubo Search for this author in: * NPG journals * PubMed * Google Scholar * Shiro Maeda Search for this author in: * NPG journals * PubMed * Google Scholar * Yusuke Nakamura Search for this author in: * NPG journals * PubMed * Google Scholar * Eisei Noiri Contact Eisei Noiri Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Note, Supplementary Tables 1–6 and Supplementary Figures 1–10. Additional data - High-resolution characterization of a hepatocellular carcinoma genome
- Nat Genet 43(5):464-469 (2011)
Nature Genetics | Letter High-resolution characterization of a hepatocellular carcinoma genome * Yasushi Totoki1 * Kenji Tatsuno2 * Shogo Yamamoto2 * Yasuhito Arai1 * Fumie Hosoda1 * Shumpei Ishikawa3 * Shuichi Tsutsumi2 * Kohtaro Sonoda2 * Hirohiko Totsuka4 * Takuya Shirakihara1 * Hiromi Sakamoto4 * Linghua Wang2 * Hidenori Ojima5 * Kazuaki Shimada6 * Tomoo Kosuge6 * Takuji Okusaka7 * Kazuto Kato8 * Jun Kusuda9 * Teruhiko Yoshida4 * Hiroyuki Aburatani2 * Tatsuhiro Shibata1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:464–469Year published:(2011)DOI:doi:10.1038/ng.804Received24 November 2010Accepted14 March 2011Published online17 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Hepatocellular carcinoma, one of the most common virus-associated cancers, is the third most frequent cause of cancer-related death worldwide1. By massively parallel sequencing2 of a primary hepatitis C virus–positive hepatocellular carcinoma (36× coverage) and matched lymphocytes (>28× coverage) from the same individual, we identified more than 11,000 somatic substitutions of the tumor genome that showed predominance of T>C/A>G transition and a decrease of the T>C substitution on the transcribed strand, suggesting preferential DNA repair. Gene annotation enrichment analysis3 of 63 validated non-synonymous substitutions revealed enrichment of phosphoproteins. We further validated 22 chromosomal rearrangements, generating four fusion transcripts that had altered transcriptional regulation (BCORL1-ELF4) or promoter activity. Whole-exome sequencing4, 5 at a higher sequence depth (>76× coverage) revealed a TSC1 nonsense substitution in a subpopulation of the tumor cells. Th! is first high-resolution characterization of a virus-associated cancer genome identified previously uncharacterized mutation patterns, intra-chromosomal rearrangements and fusion genes, as well as genetic heterogeneity within the tumor. View full text Figures at a glance * Figure 1: Somatic substitution pattern of the liver cancer genome. () Prevalence of somatic and germline substitutions in different genome regions. () Number of each type of somatic substitution in the liver cancer genome. () Prevalence of each type of somatic substitution in different genome regions. () Number of each type of somatic substitution on the transcribed and untranscribed strands. *P < 0.05, **P < 0.01, ***P < 0.0001. * Figure 2: Characterization of rearrangements in liver cancer. () Top, schematic representation of the intra-chromosomal inversion at Xq25. Bottom left, RT-PCR analysis of the fused BCORL1-ELF4 transcript in tumor (T) and non-cancerous liver (N) tissues. We detected no ELF4-BCORL1 transcript (data not shown). Bottom right, sequence chromatography of the fusion transcript revealed an in-frame protein. Mw, molecular marker. () Schematic representation of the BCORL1-ELF4 fusion protein. BCORL1 (top) contains a CtBP1 binding domain (PXDLS sequence), a binuclear localization signal (NLS), two LXXLL nuclear receptor recruitment motifs (NR box) and tandem ankyrin repeats (ANK). ELF4 (bottom) contains an ETS (E Twenty Six) DNA binding domain and a proline-rich domain. Transactivating domains are indicated by the red bars16. The BCORL1-ELF4 chimeric protein includes most of BCORL1 (1–1,618 amino acids) lacking the NR box2 and the carboxyl-terminal portion of ELF4 containing the proline-rich domain. The number of amino acids is indicated on the! right. () Wild-type BCORL1, ELF4-CT (395–664 amino acids) and the BCORL1-ELF4 chimera were expressed as Gal4-DBD fusion proteins, and their relative transcriptional activities were compared to the Gal4-DBD protein (C) as shown. () Characterization of the CTNND1-STX5 fusion gene. Bottom left, RTPCR analysis of the fused CTNND1-STX5 transcript in tumor (T) and non-cancerous liver tissue (N). Bottom right, sequence chromatography of the fusion transcript. Data is the mean ± s.d. (n = 3). *P < 0.001. * Figure 3: Intra-tumoral genetic heterogeneity detected by exon-capture sequencing. () Specific enrichment and high sequence coverage of the target genome regions indicated by the sequence viewer (copy number (CN) status is shown above). The distribution and number of reads (black, forward read; gray, reverse read) from whole-genome sequencing (top) and whole-exome sequencing (middle) are shown. The location of the capture target regions (red box) and the exons (green box) along the genome are shown at the bottom. Note that the number of reads is dependent on copy number status. () Mutant allele frequency detected by whole-genome sequencing and whole-exome sequencing. TSC1, AACS (whose heterogeneity is shown in Supplementary Fig. 10) and DENND5A are indicated. () TSC1 mutation in the liver cancer subpopulation. Sequence chromatograms of TSC1 in lymphocytes and whole-tumor tissue are shown. Note the small peak for the mutant T allele (indicated by the arrow) in the tumor DNA. () Determination of mutant TSC1 allele frequency by digital PCR genotyping. WT, wil! d type; MUT, mutant. Author information * Author information * Supplementary information Affiliations * Division of Cancer Genomics, National Cancer Center Research Institute, Chuo-ku, Tokyo, Japan. * Yasushi Totoki, * Yasuhito Arai, * Fumie Hosoda, * Takuya Shirakihara & * Tatsuhiro Shibata * Genome Science Division, Research Center for Advanced Science and Technology, University of Tokyo, Meguro-ku, Tokyo, Japan. * Kenji Tatsuno, * Shogo Yamamoto, * Shuichi Tsutsumi, * Kohtaro Sonoda, * Linghua Wang & * Hiroyuki Aburatani * Department of Pathology, Graduate School of Medicine, University of Tokyo, Bunkyo-ku, Tokyo, Japan. * Shumpei Ishikawa * Division of Genetics, National Cancer Center Research Institute, Chuo-ku, Tokyo, Japan. * Hirohiko Totsuka, * Hiromi Sakamoto & * Teruhiko Yoshida * Division of Molecular Pathology, National Cancer Center Research Institute, Chuo-ku, Tokyo, Japan. * Hidenori Ojima * Hepatobiliary and Pancreatic Surgery Division, National Cancer Center Hospital, Chuo-ku, Tokyo, Japan. * Kazuaki Shimada & * Tomoo Kosuge * Hepatobiliary and Pancreatic Oncology Division, National Cancer Center Hospital, Chuo-ku, Tokyo, Japan. * Takuji Okusaka * Institute for Research in Humanities, Graduate School of Biostudies, Institute for Integrated Cell-Material Sciences, Kyoto University, Kyoto, Japan. * Kazuto Kato * National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan. * Jun Kusuda Contributions The study was designed by T. Shibata, H.A., T.Y. and J.K. Sequencing and data analyses were conducted by Y.T., K.T., S.Y., S.T., K. Sonoda and H.T. Allele typing and copy number analyses were performed by H.S. and S.I. Other molecular studies were done by Y.A., F.H., T. Shirakihara, and L.W.; H.O., K. Shimada, T.K., T.O. and K.K. coordinated collection of clinical sample and information. The manuscript was written by Y.T., T. Shibata, K.T., S.Y., H.A. and T.Y. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Tatsuhiro Shibata Author Details * Yasushi Totoki Search for this author in: * NPG journals * PubMed * Google Scholar * Kenji Tatsuno Search for this author in: * NPG journals * PubMed * Google Scholar * Shogo Yamamoto Search for this author in: * NPG journals * PubMed * Google Scholar * Yasuhito Arai Search for this author in: * NPG journals * PubMed * Google Scholar * Fumie Hosoda Search for this author in: * NPG journals * PubMed * Google Scholar * Shumpei Ishikawa Search for this author in: * NPG journals * PubMed * Google Scholar * Shuichi Tsutsumi Search for this author in: * NPG journals * PubMed * Google Scholar * Kohtaro Sonoda Search for this author in: * NPG journals * PubMed * Google Scholar * Hirohiko Totsuka Search for this author in: * NPG journals * PubMed * Google Scholar * Takuya Shirakihara Search for this author in: * NPG journals * PubMed * Google Scholar * Hiromi Sakamoto Search for this author in: * NPG journals * PubMed * Google Scholar * Linghua Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Hidenori Ojima Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuaki Shimada Search for this author in: * NPG journals * PubMed * Google Scholar * Tomoo Kosuge Search for this author in: * NPG journals * PubMed * Google Scholar * Takuji Okusaka Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuto Kato Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Kusuda Search for this author in: * NPG journals * PubMed * Google Scholar * Teruhiko Yoshida Search for this author in: * NPG journals * PubMed * Google Scholar * Hiroyuki Aburatani Search for this author in: * NPG journals * PubMed * Google Scholar * Tatsuhiro Shibata Contact Tatsuhiro Shibata Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Note, Supplementary Figures 1–13 and Supplementary Tables 1–9. Additional data - Mutant nucleophosmin and cooperating pathways drive leukemia initiation and progression in mice
- Nat Genet 43(5):470-475 (2011)
Nature Genetics | Letter Mutant nucleophosmin and cooperating pathways drive leukemia initiation and progression in mice * George S Vassiliou1 * Jonathan L Cooper1 * Roland Rad1 * Juan Li2 * Stephen Rice1 * Anthony Uren3 * Lena Rad1 * Peter Ellis1 * Rob Andrews1 * Ruby Banerjee1 * Carolyn Grove1 * Wei Wang1 * Pentao Liu1 * Penny Wright4 * Mark Arends4 * Allan Bradley1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:470–475Year published:(2011)DOI:doi:10.1038/ng.796Received14 January 2011Accepted03 March 2011Published online27 March 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Acute myeloid leukemia (AML) is a molecularly diverse malignancy with a poor prognosis whose largest subgroup is characterized by somatic mutations in NPM1, which encodes nucleophosmin1. These mutations, termed NPM1c, result in cytoplasmic dislocation of nucleophosmin1 and are associated with distinctive transcriptional signatures2, yet their role in leukemogenesis remains obscure. Here we report that activation of a humanized Npm1c knock-in allele in mouse hemopoietic stem cells causes Hox gene overexpression, enhanced self renewal and expanded myelopoiesis. One third of mice developed delayed-onset AML, suggesting a requirement for cooperating mutations. We identified such mutations using a Sleeping Beauty3, 4 transposon, which caused rapid-onset AML in 80% of mice with Npm1c, associated with mutually exclusive integrations in Csf2, Flt3 or Rasgrp1 in 55 of 70 leukemias. We also identified recurrent integrations in known and newly discovered leukemia genes including Nf1, B! ach2, Dleu2 and Nup98. Our results provide new pathogenetic insights and identify possible therapeutic targets in NPM1c+ AML. View full text Figures at a glance * Figure 1: Conditional mouse model of type A NPM1c mutation. () N-terminal GFP-fusions of type A human (NPM1cA) and 'humanized' mouse (Npm1cA) mutants show identical sub-cellular distribution. () The conditional Npm1flox-cA allele interferes minimally with the native locus and converts to a mutant Npm1cA allele with Cre (humanized mutant exon 11 (indicated by an asterisk) in red; mouse exon 11 is homologous to human exon 12). (,) Npm1cA RNA and protein detected in post-Cre embryonic stem (ES) cells by RT-PCR () and protein blot (), respectively. () Universal lethality of Npm1flox-cA/+; Stella-Cre+ (**P = 0.0001) compared to Mendelian ratios for Npm1flox-cA/+; Mx1-Cre+ mice. P, PstI site; PuΔTK, Puro-Delta-TK cassette; K562-Npm1, K562 cells transfected with Npm1cA or Npm1WT complementary DNA; RT, reverse transcriptase; RT-PCR primers: R1, forward; R2, Npm1WT reverse; R3, Npm1cA reverse (red). * Figure 2: Hematopoietic changes and incidence of AML in Npm1cA/+ mice. () Hemopoietic expression of Npm1cA protein in Npm1cA/+ mice. () Hox gene overexpression in Npm1cA/+ versus Npm1WT lineage negative hemopoietic progenitors. (,) There were no significant differences in white cell (WCC), hemoglobin (Hb) or platelet counts (Plts), but there were higher mean red cell (MCV) and platelet (MPV) volumes, in Npm1cA/+ mice. (,) We saw no differences in marrow stem and progenitor cell compartment sizes. (,) Expansion of mature myeloid (Gr1+/Mac1+) and reduction in late (B220+/CD19+) B-cells in Npm1cA/+ compared to Npm1WT marrow. () Summary of hemopoietic changes in Npm1cA/+ mice. () Increased serial re-plating of Npm1cA/+ myeloid progenitors. () Decreased survival of Npm1cA/+ mice due to excess AML. () Example of AML showing splenomegaly due to infiltration with myeloperoxidase-positive blasts (Sp), also infiltrating the liver (Li) and blood (Bld). Error bars, s.e.m.; *P < 0.05; B,T, B or T-cell leukemia or lymphoma; Non-Hem, non-hematological; B.M., ! bone marrow. * Figure 3: Npm1cA and the GrOnc transposon synergize to cause AML. () The GrOnc transposon carrying gene-activating and -inactivating elements flanked by repeats for Sleeping Beauty (SB) and PiggyBac (PB). () Blood metaphase fluorescent in situ hybridization showing the genomic location of the GrOnc donor locus on chromosome 19. () Acceleration of leukemogenesis in Npm1cA/+ compared to Npm1+/+ mutagenized mice. () Marked increase in the proportion of AMLs and absence of T-cell tumors in Npm1cA/+ compared to Npm1+/+ mice. () DNA blot showing clonal transposon integrations in mouse leukemias. Endogenous En2 (arrowhead) and GrOnc donor locus concatamer (dots) bands indicated. () Morphology and immunohistochemistry (anti-myeloperoxidase) from an Npm1cA/+ AML. Gr1.4 LTR, Graffi1.4 MuLV long terminal repeat; SD, Lun splice donor; En2-SA and βA-SA, Engrailed 2 and Carp β-actin splice acceptor; pA, adenovirus polyadenylation signal; *P < 0.00001; B, T and U, B-cell, T-cell and undifferentiated leukemia or lymphoma; Bld, blood; Sp, spleen; Sar, my! eloid sarcoma; Ki, kidney; Li, liver; WT, wild type. * Figure 4: Common integration sites in transposon-derived leukemias. () Common integration sites in Npm1cA/+ and Npm1+/+ mice show some overlap (regular font) but are mostly different (bold). (–) Directional GrOnc integrations at the Csf2 locus, identified in 42 of 70 AMLs, were associated with the formation of two LunSD-Csf2 fusion mRNA splice variants (sv1 and sv2) and marked overexpression of Csf2 mRNA (note break in y axis). E, Csf2 enhancer. () GrOnc integrations in three individual myeloid blast colonies from one of these AMLs (AML 36) shared only two insertions, involving Csf2 and the myeloid oncogene Myst4 (also called Moz), suggesting that these were 'driver' insertions. () Directional activating integrations in intron 9 of Flt3. () Bi-directional integrations in Nup98. * Figure 5: A model for Npm1cA/+-driven leukemogenesis. () Co-occurrence table of CIS genes in 70 Npm1cA/+ AMLs shows mutually exclusive insertions in Csf2, Flt3, Rasgrp1 and Kras. () Persistence of Hox gene overexpression in Npm1cA/+ compared to Npm1+/+ AMLs, indicating that this effect of Npm1cA persists in leukemic cells (*P ≤ 0.03). () Model for Npm1cA-driven leukemogenesis supported by our data: isolated Npm1cA increases self renewal but leukemic transformation only occurs upon activation of defined proliferative pathways (type I mutation), facilitated by a permissive transcription factor (TF) mutation. Error bars, s.e.m. Localization of Npm1 protein is indicated in green. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions ArrayExpress * E-MEXP-3113 Author information * Accession codes * Author information * Supplementary information Affiliations * Mouse Genomics Team, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * George S Vassiliou, * Jonathan L Cooper, * Roland Rad, * Stephen Rice, * Lena Rad, * Peter Ellis, * Rob Andrews, * Ruby Banerjee, * Carolyn Grove, * Wei Wang, * Pentao Liu & * Allan Bradley * Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. * Juan Li * The Netherlands Cancer Institute, Amsterdam, The Netherlands. * Anthony Uren * Department of Pathology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK. * Penny Wright & * Mark Arends Contributions G.S.V. and A.B. designed the study. G.S.V. generated Npm1flox-cA mice, GrOnc mice and GFP-NPM1 constructs, managed mouse colonies, designed and validated polyclonal anti-Npm1c sera and carried out protein blots. J.L.C. and G.S.V. performed mouse genotyping, tumor processing and banking and K562 transfections. G.S.V., J.L.C., R.R. and L.R. performed mouse necropsies. G.S.V., J.L.C. and J.L. performed hemopoietic analyses. G.S.V. and C.G. performed quantitative PCR. G.S.V., P.E. and R.A. performed gene expression analysis studies. S.R., G.S.V. and R.R. performed mapping and analysis of transposon integration sites. G.S.V. and R.B. performed fluorescence in situ hybridization. W.W. and P.L. generated the Stella-Cre mice. A.U. generated the Rosaflox-SB mice. P.W. and M.A. performed histological analyses. A.B. supervised the study. All authors contributed to the writing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * George S Vassiliou or * Allan Bradley Author Details * George S Vassiliou Contact George S Vassiliou Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan L Cooper Search for this author in: * NPG journals * PubMed * Google Scholar * Roland Rad Search for this author in: * NPG journals * PubMed * Google Scholar * Juan Li Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen Rice Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony Uren Search for this author in: * NPG journals * PubMed * Google Scholar * Lena Rad Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Ellis Search for this author in: * NPG journals * PubMed * Google Scholar * Rob Andrews Search for this author in: * NPG journals * PubMed * Google Scholar * Ruby Banerjee Search for this author in: * NPG journals * PubMed * Google Scholar * Carolyn Grove Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Pentao Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Penny Wright Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Arends Search for this author in: * NPG journals * PubMed * Google Scholar * Allan Bradley Contact Allan Bradley Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–11 and Supplementary Tables 1–9. Additional data - The Arabidopsis lyrata genome sequence and the basis of rapid genome size change
- Nat Genet 43(5):476-481 (2011)
Nature Genetics | Letter The Arabidopsis lyrata genome sequence and the basis of rapid genome size change * Tina T Hu1, 15, 16 * Pedro Pattyn2, 3, 16 * Erica G Bakker4, 5, 6, 15 * Jun Cao7 * Jan-Fang Cheng8 * Richard M Clark7, 15 * Noah Fahlgren5, 9 * Jeffrey A Fawcett2, 3, 15 * Jane Grimwood8, 10 * Heidrun Gundlach11 * Georg Haberer11 * Jesse D Hollister12, 15 * Stephan Ossowski7, 15 * Robert P Ottilar8 * Asaf A Salamov8 * Korbinian Schneeberger7, 15 * Manuel Spannagl11 * Xi Wang11, 15 * Liang Yang12 * Mikhail E Nasrallah13 * Joy Bergelson4 * James C Carrington5, 9 * Brandon S Gaut12 * Jeremy Schmutz8, 10 * Klaus F X Mayer11 * Yves Van de Peer2, 3 * Igor V Grigoriev8 * Magnus Nordborg1, 14 * Detlef Weigel7 * Ya-Long Guo7 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:476–481Year published:(2011)DOI:doi:10.1038/ng.807Received13 September 2010Accepted18 March 2011Published online10 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We report the 207-Mb genome sequence of the North American Arabidopsis lyrata strain MN47 based on 8.3× dideoxy sequence coverage. We predict 32,670 genes in this outcrossing species compared to the 27,025 genes in the selfing species Arabidopsis thaliana. The much smaller 125-Mb genome of A. thaliana, which diverged from A. lyrata 10 million years ago, likely constitutes the derived state for the family. We found evidence for DNA loss from large-scale rearrangements, but most of the difference in genome size can be attributed to hundreds of thousands of small deletions, mostly in noncoding DNA and transposons. Analysis of deletions and insertions still segregating in A. thaliana indicates that the process of DNA loss is ongoing, suggesting pervasive selection for a smaller genome. The high-quality reference genome sequence for A. lyrata will be an important resource for functional, evolutionary and ecological studies in the genus Arabidopsis. View full text Figures at a glance * Figure 1: Comparison of A. lyrata and A. thaliana genomes. () Alignment of A. lyrata (Aly) and A. thaliana (Ath) chromosomes. Genomes are scaled to equal size. Only syntenic blocks of at least 500 kb are connected. () Orthology classification of genes. () Distribution of run lengths of collinear genes. The mode at 1–5 reflects frequent single-gene transpositions. () Unalignable sites can be considered as present in one species and absent in the other, as shown in the boxed sequence diagram; matches are indicated by asterisks, and mismatches are indicated by periods. The histogram on the left indicates the absolute number of unalignable sites, and the pie charts in the middle compare their relative distribution over different genomic features. See also Supplementary Table 3. () Genome composition (number of elements in parentheses). TEs, transposable elements. * Figure 2: Apparent deletions by size and annotation. A. lyrata (Aly) is always shown on top, and A. thaliana (Ath) is always shown on the bottom. TE, transposable elements. * Figure 3: Changes in genomic intervals along the A. thaliana genome. Mean ratios for all collinear gene pairs in each 100-kb window are shaded in blue, with individual values shown as light blue dots. The ratio of the absolute length of each non-overlapping 100-kb window is shown as a dark purple line. Centromeres are indicated as gray boxes. Aly, A. lyrata. * Figure 4: Change in size of collinear and rearranged regions, intergenic regions and gene families. () Size comparison of collinear regions, relative to 100-kb windows in A. thaliana. Asterisks indicate significant differences (binomial test, P < 0.001). () Relative size of intergenic intervals. () MCL clusters. () Relative size of MCL gene families. Ath, A. thaliana; Aly, A. lyrata. * Figure 5: Comparison of transposable elements. () Estimated insertion times of LTR retrotransposons based on the experimentally determined mutation rate for A. thaliana. The whiskers indicate values up to 1.5 times the interquartile range. The difference between the species was highly significant (Wilcoxon rank sum test, P < 2.2 × 10−16). () Phylogeny of Ty1/copia-like and Ty3/gypsy-like LTR retrotransposons. S. cerevisiae Ty1 and Ty3 that were used as outgroups are indicated in green. () Distances of nearest transposable elements from each gene. The difference between the two species was not simply because of fewer transposable elements in the A. thaliana genome (Supplementary Table 5 and Supplementary Fig. 4). TE, transposable element; Ath, A. thaliana; Aly, A. lyrata; MYA; million years ago. * Figure 6: Sizes and allele frequency distribution of insertions and deletions that were either fixed or still segregating in 95 A. thaliana individuals43 and that are presumed to be derived based on comparison with the A. lyrata allele. () Size distribution of fixed insertions and deletions. Insertions and deletions that are multiples of a single codon (3 bp) are overrepresented in coding regions. () Allele frequency of segregating noncoding insertion and deletion frequencies compared to that of synonymous and non-synonymous polymorphisms. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Tina T Hu & * Pedro Pattyn Affiliations * Molecular and Computational Biology, University of Southern California, Los Angeles, California, USA. * Tina T Hu & * Magnus Nordborg * Department of Plant Systems Biology, VIB, Gent, Belgium. * Pedro Pattyn, * Jeffrey A Fawcett & * Yves Van de Peer * Department of Plant Biotechnology and Genetics, Ghent University, Gent, Belgium. * Pedro Pattyn, * Jeffrey A Fawcett & * Yves Van de Peer * Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA. * Erica G Bakker & * Joy Bergelson * Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon, USA. * Erica G Bakker, * Noah Fahlgren & * James C Carrington * Department of Horticulture, Oregon State University, Corvallis, Oregon, USA. * Erica G Bakker * Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany. * Jun Cao, * Richard M Clark, * Stephan Ossowski, * Korbinian Schneeberger, * Detlef Weigel & * Ya-Long Guo * US Department of Energy Joint Genome Institute, Walnut Creek, California, USA. * Jan-Fang Cheng, * Jane Grimwood, * Robert P Ottilar, * Asaf A Salamov, * Jeremy Schmutz & * Igor V Grigoriev * Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, USA. * Noah Fahlgren & * James C Carrington * HudsonAlpha Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA. * Jane Grimwood & * Jeremy Schmutz * Munich Information Center for Protein Sequences/Institute for Bioinformatics and Systems Biology, Helmholtz Center Munich, Neuherberg, Germany. * Heidrun Gundlach, * Georg Haberer, * Manuel Spannagl, * Xi Wang & * Klaus F X Mayer * Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, California, USA. * Jesse D Hollister, * Liang Yang & * Brandon S Gaut * Department of Plant Biology, Cornell University, Ithaca, New York, USA. * Mikhail E Nasrallah * Gregor Mendel Institute, Austrian Academy of Science, Vienna, Austria. * Magnus Nordborg * Present addresses: Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA (T.T.H.), Dow AgroSciences, Portland, Oregon 97224, USA (E.G.B.), Department of Biology, University of Utah, Salt Lake City, Utah, USA (R.M.C.), Graduate University for Advanced Studies, Hayama, Kanagawa, Japan (J.A.F.), Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA (J.D.H.), Center for Genomic Regulation, Barcelona, Spain (S.O.), Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany (K.S.) and Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tübingen, Germany (X.W.). * Tina T Hu, * Erica G Bakker, * Richard M Clark, * Jeffrey A Fawcett, * Jesse D Hollister, * Stephan Ossowski, * Korbinian Schneeberger & * Xi Wang Contributions J.B., J.C.C., B.S.G., I.V.G., Y.-L.G., K.F.X.M., M.N., Y.V.d.P. and D.W. conceived the study; M.E.N. provided the biological material; J.C., J.-F.C., R.M.C., N.F., J.G. and Y.-L.G. performed the experiments; E.G.B., J.A.F., N.F., H.G., Y.-L.G., G.H., J.D.H., T.T.H., R.P.O., S.O., P.P., A.A.S., J.S., K.S., M.S., X.W. and L.Y. analyzed the data; and Y.-L.G., T.T.H., M.N. and D.W. wrote the paper with contributions from all authors. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Detlef Weigel or * Ya-Long Guo Author Details * Tina T Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Pedro Pattyn Search for this author in: * NPG journals * PubMed * Google Scholar * Erica G Bakker Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Jan-Fang Cheng Search for this author in: * NPG journals * PubMed * Google Scholar * Richard M Clark Search for this author in: * NPG journals * PubMed * Google Scholar * Noah Fahlgren Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey A Fawcett Search for this author in: * NPG journals * PubMed * Google Scholar * Jane Grimwood Search for this author in: * NPG journals * PubMed * Google Scholar * Heidrun Gundlach Search for this author in: * NPG journals * PubMed * Google Scholar * Georg Haberer Search for this author in: * NPG journals * PubMed * Google Scholar * Jesse D Hollister Search for this author in: * NPG journals * PubMed * Google Scholar * Stephan Ossowski Search for this author in: * NPG journals * PubMed * Google Scholar * Robert P Ottilar Search for this author in: * NPG journals * PubMed * Google Scholar * Asaf A Salamov Search for this author in: * NPG journals * PubMed * Google Scholar * Korbinian Schneeberger Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Spannagl Search for this author in: * NPG journals * PubMed * Google Scholar * Xi Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Mikhail E Nasrallah Search for this author in: * NPG journals * PubMed * Google Scholar * Joy Bergelson Search for this author in: * NPG journals * PubMed * Google Scholar * James C Carrington Search for this author in: * NPG journals * PubMed * Google Scholar * Brandon S Gaut Search for this author in: * NPG journals * PubMed * Google Scholar * Jeremy Schmutz Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus F X Mayer Search for this author in: * NPG journals * PubMed * Google Scholar * Yves Van de Peer Search for this author in: * NPG journals * PubMed * Google Scholar * Igor V Grigoriev Search for this author in: * NPG journals * PubMed * Google Scholar * Magnus Nordborg Search for this author in: * NPG journals * PubMed * Google Scholar * Detlef Weigel Contact Detlef Weigel Search for this author in: * NPG journals * PubMed * Google Scholar * Ya-Long Guo Contact Ya-Long Guo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Note, Supplementary Tables 1–5 and Supplementary Figures 1–4 Additional data - Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection
- Nat Genet 43(5):482-486 (2011)
Nature Genetics | Letter Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection * Christopher B Ford1, 11 * Philana Ling Lin2, 11 * Michael R Chase1 * Rupal R Shah1 * Oleg Iartchouk3 * James Galagan4, 5, 6 * Nilofar Mohaideen7 * Thomas R Ioerger8 * James C Sacchettini7 * Marc Lipsitch1, 9 * JoAnne L Flynn10 * Sarah M Fortune1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:482–486Year published:(2011)DOI:doi:10.1038/ng.811Received15 December 2010Accepted24 March 2011Published online24 April 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Tuberculosis poses a global health emergency, which has been compounded by the emergence of drug-resistant Mycobacterium tuberculosis (Mtb) strains. We used whole-genome sequencing to compare the accumulation of mutations in Mtb isolated from cynomolgus macaques with active, latent or reactivated disease. We sequenced 33 Mtb isolates from nine macaques with an average genome coverage of 93% and an average read depth of 117×. Based on the distribution of SNPs observed, we calculated the mutation rates for these disease states. We found a similar mutation rate during latency as during active disease or in a logarithmically growing culture over the same period of time. The pattern of polymorphisms suggests that the mutational burden in vivo is because of oxidative DNA damage. We show that Mtb continues to acquire mutations during disease latency, which may explain why isoniazid monotherapy for latent tuberculosis is a risk factor for the emergence of isoniazid resistance1, 2. View full text Figures at a glance * Figure 1: Experimental protocol for assessing mutational capacity in different disease states. 1) Cynomolgus macaques were infected with ~25 colony forming units (CFU) of Mtb Erdman using bronchoscopy. 2) Animals were killed in the indicated stages of disease for strain isolation. 3) Eighteen pathologic lesions were plated for bacterial colonies. Thirty-three strains were isolated for WGS. 4) Genomic DNA was isolated from these strains and then analyzed using Illumina sequencing. 5) Reads were assembled using both de novo and scaffolded approaches. Fifteen SNPs were predicted by both methodologies. Insertions and deletions were not detected using either methodology. 6) Sanger sequencing confirmed 14 of the 15 putative SNPs identified by both scaffolded and de novo analysis. * Figure 2: WGS identifies SNPs in strains isolated from animals with active, latent, and reactivated latent infection. SNPs were predicted through WGS in 33 Mtb strains isolated from nine cynomolgus macaques at various stages of disease. All SNPs predicted through WGS were confirmed with Sanger sequencing or through independent identification by WGS. Genome coverage and the original notation used to describe each animal are found in Supplementary Table 1. The total length of infection in days is listed for each animal below the animal identifier (A-I). LLL, left lower lobe; RLL, right lower lobe; RML, right middle lobe; RUL, right upper lobe; ACL, accessory lobe; CN, cranial lymph node; BN, bronchial lymph node. Coordinates (Coord.) are given for H37Rv. Inoculum (Inoc) represents the sequence at the given coordinate of the inoculating strain, Mtb Erdman. * Figure 3: The mutational capacity of strains from latency and reactivated disease is similar to that of strains from active disease or in vitro growth. (–) The mutation rate (μ) was estimated based on the number of unique SNPs (m) observed in each condition (4 active, 3 latent and 7 reactivated). This calculation was performed over a range of generation times (g, 18–240 h per generation) to allow for the uncertainty in growth rate in vivo. We determined the probability of observing μ when g is fixed at any given time to build the probability distribution function around each estimate and to define the 95% confidence intervals. We determined the single-base mutation rate of the bacterium during in vitro growth (μin vitro) by fluctuation analysis (Supplementary Fig. 1a–c) and is indicated by an arrow. In each clinical condition, μ20 (the predicted mutation rate if the generation time in vivo were as rapid as the generation time in vitro) is similar to μin vitro. Generation time in vivo is predicted to be substantially slower than in vitro, and thus the mutation rate must be proportionally higher to produce the obse! rved number of SNPs. () Given the uncertainty in generation time, a mutation rate per day can be calculated to determine the rate at which mutations occur regardless of generation time. Mutations occur at a similar rates per day regardless of the disease status of the host. Error bars represent 95% confidence intervals. * Figure 4: Mutations in Mtb isolated from macaques with latent infection and related human isolates are putative products of oxidative damage. () Ten of the 14 mutations observed in this study could be the product of oxidative damage: the deamination of cytosine (GC>AT) or the production of 7,8-dihydro-8-oxoguanine (GC>TA) by the oxidation of guanine. We saw one of each type of mutation observed in active disease (four mutations total). In contrast, eight of ten mutations observed in latent and reactivated disease are potential products of oxidative damage. There is a similar mutational spectra observed in the synonymous SNPs (sSNP) identified by WGS of a set of closely related strains from South Africa9. Susc., susceptible; MDR, multidrug resistant; XDR, extensively resistant. () These observations lead to a model of mutational pressures on Mtb during active disease and latent infection in which oxidative damage may play a central role in the generation of mutation. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Christopher B Ford & * Philana Ling Lin Affiliations * Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA. * Christopher B Ford, * Michael R Chase, * Rupal R Shah, * Marc Lipsitch & * Sarah M Fortune * Department of Pediatrics, Children's Hospital of Pittsburgh of the University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA. * Philana Ling Lin * Partners Healthcare Center for Personalized Genetic Medicine, Harvard Medical School, Cambridge, Massachusetts, USA. * Oleg Iartchouk * Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. * James Galagan * Department of Microbiology, Boston University, Boston, Massachusetts, USA. * James Galagan * The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. * James Galagan * Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas, USA. * Nilofar Mohaideen & * James C Sacchettini * Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA. * Thomas R Ioerger * Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Marc Lipsitch * Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. * JoAnne L Flynn Contributions C.B.F. performed molecular studies, conducted the data analyses, prepared the figures and drafted the manuscript; P.L.L. and J.L.F. conducted the infection of the cynomolgus macaques, determined clinical state and acquired bacterial strains on necropsy; M.R.C. analyzed sequence data and directed validation of SNPs; R.R.S. performed molecular and fluctuation analyses; O.I. oversaw sequencing of isolates sent to Partners Healthcare Center for Personalized Genetic Medicine (PHCPGM); J.G. oversaw sequencing of isolates sent to the Broad Institute; N.M., T.R.I. and J.C.S. oversaw sequencing and analysis of isolates sent to Texas A&M University; M.L. supervised and advised statistical analyses; S.M.F. initiated the project, performed molecular studies, supervised preparation and analysis of the data and drafted the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Sarah M Fortune Author Details * Christopher B Ford Search for this author in: * NPG journals * PubMed * Google Scholar * Philana Ling Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Michael R Chase Search for this author in: * NPG journals * PubMed * Google Scholar * Rupal R Shah Search for this author in: * NPG journals * PubMed * Google Scholar * Oleg Iartchouk Search for this author in: * NPG journals * PubMed * Google Scholar * James Galagan Search for this author in: * NPG journals * PubMed * Google Scholar * Nilofar Mohaideen Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas R Ioerger Search for this author in: * NPG journals * PubMed * Google Scholar * James C Sacchettini Search for this author in: * NPG journals * PubMed * Google Scholar * Marc Lipsitch Search for this author in: * NPG journals * PubMed * Google Scholar * JoAnne L Flynn Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah M Fortune Contact Sarah M Fortune Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (192K) Supplementary Figure 1 and Supplementary Tables 1 and 2. Additional data - A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase
- Nat Genet 43(5):487-489 (2011)
Nature Genetics | Letter A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase * Trevor Hinkley1 * João Martins1 * Colombe Chappey2, 3 * Mojgan Haddad2 * Eric Stawiski2, 3 * Jeannette M Whitcomb2 * Christos J Petropoulos2 * Sebastian Bonhoeffer1 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:487–489Year published:(2011)DOI:doi:10.1038/ng.795Received16 July 2010Accepted03 March 2011Published online27 March 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The development of a quantitative understanding of viral evolution and the fitness landscape in HIV-1 drug resistance is a formidable challenge given the large number of available drugs and drug resistance mutations. We analyzed a dataset measuring the in vitro fitness of 70,081 virus samples isolated from HIV-1 subtype B infected individuals undergoing routine drug resistance testing. We assayed virus samples for in vitro replicative capacity in the absence of drugs as well as in the presence of 15 individual drugs. We employed a generalized kernel ridge regression to estimate main fitness effects and epistatic interactions of 1,859 single amino acid variants found within the HIV-1 protease and reverse transcriptase sequences. Models including epistatic interactions predict an average of 54.8% of the variance in replicative capacity across the 16 different environments and substantially outperform models based on main fitness effects only. We find that the fitness landscape! of HIV-1 protease and reverse transcriptase is characterized by strong epistasis. View full text Figures at a glance * Figure 1: Analysis of predictive power. The figure shows the predictive power of the ME and MEEP models in a drug-free and 15 drug-containing environments. The predictive power is measured by the percentage deviance explained in a cross-validation dataset based on 5,000 independent virus samples. The bars represent mean, and the whiskers represent the standard errors from a sixfold cross validation. The MEEP model outperforms the ME model in all environments. The drugs used here are the protease inhibitors amprenavir (AMP), indinavir (IDV), lopinavir (LPV), nelfinavir (NFV), ritonavir (RTV) and saquinavir (SQV), the six nucleoside reverse transcriptase inhibitors abacavir (ABC), didanosine (ddI), lamivudine (3TC), stavudine (d4T), zidovudine (ZDV) and tenofovir (TFV), and the non-nucleoside reverse transcriptase inhibitors delavirdine (DLV), efavirenz (EFV) and nevirapine (NVP). * Figure 2: Analysis of predictive power of different epistatic models for four representative environments. The figure shows that most of the predictive power attributable to epistasis is in fact attributable to intragenic rather than intergenic epistatic interactions. In the non-nucleoside reverse transcriptase inhibitor environment, adding intergenic epistasis decreases predictive power. This decrease reflects that adding a large number of parameters with little or no explanatory power can reduce the predictive power of GKRR. The bars represent mean and the whiskers the standard errors from a sixfold cross validation. * Figure 3: Cumulative strength (CS) of the absolute epistatic effects in the HIV-1 protease as measured in the drug-free environment. The cumulative effect between two positions is calculated as the sum over the absolute values of all epistatic interactions between the amino acid variants at those positions as estimated by the MEEP model. We plotted CS1.5 to enhance visual clarity. The regions corresponding to the flap elbow, fulcrum and cantilever, colored in red, yellow and green, respectively, are significantly enriched in epistasis (Supplementary Fig. 1). The inset shows the structure of the HIV-1 protease (Protein Data Bank ID 1A30, rendered with PyMOL; see URLs). The region enriched in epistatic interaction, corresponding to the flap elbow, is somewhat larger than the literature description of this region19. Author information * Author information * Supplementary information Affiliations * ETH Zürich, Institute of Integrative Biology, Zürich, Switzerland. * Trevor Hinkley, * João Martins & * Sebastian Bonhoeffer * Monogram Biosciences, South San Francisco, California, USA. * Colombe Chappey, * Mojgan Haddad, * Eric Stawiski, * Jeannette M Whitcomb & * Christos J Petropoulos * Present address: Genentech, South San Francisco, California, USA. * Colombe Chappey & * Eric Stawiski Contributions T.H. developed and implemented the generalized kernel ridge regression and analyzed data. J.M. analyzed data. C.C., M.H., E.S., J.M.W. and C.J.P. generated and pre-processed the experimental data. S.B. designed the study and analyzed data. T.H. and S.B. wrote the paper. Competing financial interests C.C., M.H., E.S., J.M.W. and C.J.P. are or have been employees of Monogram BioSciences and are named inventors on US and foreign patents held by Monogram BioSciences. S.B. is a consultant of Monogram BioSciences. Corresponding authors Correspondence to: * Sebastian Bonhoeffer or * Christos J Petropoulos Author Details * Trevor Hinkley Search for this author in: * NPG journals * PubMed * Google Scholar * João Martins Search for this author in: * NPG journals * PubMed * Google Scholar * Colombe Chappey Search for this author in: * NPG journals * PubMed * Google Scholar * Mojgan Haddad Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Stawiski Search for this author in: * NPG journals * PubMed * Google Scholar * Jeannette M Whitcomb Search for this author in: * NPG journals * PubMed * Google Scholar * Christos J Petropoulos Contact Christos J Petropoulos Search for this author in: * NPG journals * PubMed * Google Scholar * Sebastian Bonhoeffer Contact Sebastian Bonhoeffer Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Note and Supplementary Figures 1–3. Additional data - A framework for variation discovery and genotyping using next-generation DNA sequencing data
- Nat Genet 43(5):491-498 (2011)
Nature Genetics | Technical Report A framework for variation discovery and genotyping using next-generation DNA sequencing data * Mark A DePristo1 * Eric Banks1 * Ryan Poplin1 * Kiran V Garimella1 * Jared R Maguire1 * Christopher Hartl1 * Anthony A Philippakis1, 2, 3 * Guillermo del Angel1 * Manuel A Rivas1, 4 * Matt Hanna1 * Aaron McKenna1 * Tim J Fennell1 * Andrew M Kernytsky1 * Andrey Y Sivachenko1 * Kristian Cibulskis1 * Stacey B Gabriel1 * David Altshuler1, 3, 4 * Mark J Daly1, 3, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:491–498Year published:(2011)DOI:doi:10.1038/ng.806Received27 August 2010Accepted17 March 2011Published online10 April 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated! in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (~4×) 1000 Genomes Project datasets. View full text Figures at a glance * Figure 1: Framework for variation discovery and genotyping from next-generation DNA sequencing. See text for a detailed description. * Figure 2: Integrative genomics viewer (IGV) visualization of alignments in region chr.1: 1,510,530–1,510,589 from the Trio NA12878 Illumina reads from the 1000 Genomes Project (a) and NA12878 HiSeq reads before (left) and after (right) multiple sequence realignment (b). Reads are depicted as arrows oriented by increasing machine cycle; highlighted bases indicate mismatches to the reference: green, A; orange, G; red, T dashes, deleted bases a coverage histogram per base is shown above the reads. Both the 4-bp indel (rs34877486) and the C/T polymorphism (rs2878874) are present in dbSNP, as are the artifactual A/G polymorphisms (rs28782535 and rs28783181) resulting from the mis-modeled indel, indicating that these sites are common misalignment errors. * Figure 3: Raw (pink) and recalibrated (blue) base quality scores for NGS paired-end read sets of NA12878 of Illumina/GA (a), Roche/454 (b) and Life/SOLiD (c) lanes from the 1000 Genomes Project and Illumina/HiSeq (d). For each technology, the top panel shows reported base quality scores compared to the empirical estimates (Online Methods); the middle panel shows the difference between the average reported and empirical quality score for each machine cycle, with positive and negative cycle values given for the first and second read in the pair, respectively; and the bottom panel shows the difference between reported and empirical quality scores for each of the 16 genomic dinucleotide contexts. For example, the AG context occurs at all sites in a read where G is the current nucleotide and A is the preceding one in the read. Root-mean-square errors (RMSE) are given for the pre- and post-recalibration curves. * Figure 4: Results of variant quality recalibration on HiSeq, exome and low-pass data sets. () Relationship in the HiSeq call set between strand bias and quality by depth for genomic locations in HapMap3 (red) and dbSNP (orange) used for training the variant quality score recalibrator (left), () and the same annotations applied to differentiate likely true positive (green) from false positive (purple) new SNPs. (–) Quality tranches in the recalibrated HiSeq (), exome () and low-pass CEU () calls beginning with (top) the highest quality but smallest call set with an estimated false positive rate among new SNP calls of <1/1000 to a more comprehensive call set (bottom) that includes effectively all true positives in the raw call set along with more false positive calls for a cumulative false positive rate of 10%. Each successive call set contains within it the previous tranche's true- and false-positive calls (shaded bars) as well as tranche-specific calls of both classes (solid bars). The tranche selected for further analyses here is indicated. * Figure 5: Variation discovered among 60 individuals from the CEPH population from the 1000 Genomes Project pilot phase plus low-pass NA12878. () Discovered SNPs by non-reference allele count in the 61 CEPH cohort, colored by known (light blue) and new (dark blue) variation, along with non-reference sensitivity to CEU HapMap3 and 1000 Genomes Project low-pass variants. () Quality and certainty of discovered SNPs by non-reference allele count. The histogram depicts the certainty of called variation broken out into 0.1%, 1% and 10% new FDR tranches. The Ti/Tv ratio is shown for known and new variation for each allele count, aggregating the new calls with allele count >74 because of their limited numbers. (,) Genotyping accuracy for NA12878 from reads alone (blue squares) and following genotype-likelihood based imputation (pink circles) called in the 61 sample call set as assessed by the NRD rate to HiSeq genotypes as a function of allele count () and sequencing depth (). * Figure 6: Sensitivity and specificity of multi-sample discovery of variation in NA12878 with increasing cohort size for low-pass NA12878 read sets processed with N additional CEPH samples. () Receiver operating characteristic (ROC) curves for SNP calls relating specificity and sensitivity to discover non-reference sites from the NA12878 HiSeq call set. The maximum callable sensitivity, 66%, is the percent of sites from the HiSeq call set where at least one read carries the alternate allele in the low-pass data for NA12878; it reflects both differences in the sequencing technologies (36–76-bp GAII for the low-pass NA12878 sample compared to 101-bp HiSeq) as well as the vagaries of sampling at 4× coverage. Because most of these missed sites are common and are consequently called in the other samples, imputation recovers ~50% of these sites. (,) Increasing power to identify strand-biased, likely false positive SNP calls with additional samples. Histograms of the strand bias annotation at raw variant calls discovered in the low-pass CEU data using NA12878 at 4× combined with one other CEU individual () and with 60 other individuals () stratified into sites pre! sent (green) and not (purple) in the 1000 Genomes Project CEU trio. Author information * Abstract * Author information * Supplementary information Affiliations * Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. * Mark A DePristo, * Eric Banks, * Ryan Poplin, * Kiran V Garimella, * Jared R Maguire, * Christopher Hartl, * Anthony A Philippakis, * Guillermo del Angel, * Manuel A Rivas, * Matt Hanna, * Aaron McKenna, * Tim J Fennell, * Andrew M Kernytsky, * Andrey Y Sivachenko, * Kristian Cibulskis, * Stacey B Gabriel, * David Altshuler & * Mark J Daly * Brigham and Women's Hospital, Boston, Massachusetts, USA. * Anthony A Philippakis * Harvard Medical School, Boston, Massachusetts, USA. * Anthony A Philippakis, * David Altshuler & * Mark J Daly * Center for Human Genetic Research, Massachusetts General Hospital, Richard B. Simches Research Center, Boston, Massachusetts, USA. * Manuel A Rivas, * David Altshuler & * Mark J Daly Contributions M.A.D., E.B., R.P., K.V.G., J.R.M., C.H., A.A.P., G.d.A., M.A.R., T.J.F., A.Y.S. and K.C. conceived of, implemented and performed analytic approaches. M.A.D., E.B., R.P., K.V.G., G.d.A., A.M.K. and M.J.D. wrote the manuscript. M.A.D., M.H. and A.M. developed Picard and GATK infrastructure underlying the tools implemented here. M.A.D., S.B.G., D.A. and M.J.D. lead the team. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mark A DePristo Author Details * Mark A DePristo Contact Mark A DePristo Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Banks Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan Poplin Search for this author in: * NPG journals * PubMed * Google Scholar * Kiran V Garimella Search for this author in: * NPG journals * PubMed * Google Scholar * Jared R Maguire Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Hartl Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony A Philippakis Search for this author in: * NPG journals * PubMed * Google Scholar * Guillermo del Angel Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel A Rivas Search for this author in: * NPG journals * PubMed * Google Scholar * Matt Hanna Search for this author in: * NPG journals * PubMed * Google Scholar * Aaron McKenna Search for this author in: * NPG journals * PubMed * Google Scholar * Tim J Fennell Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew M Kernytsky Search for this author in: * NPG journals * PubMed * Google Scholar * Andrey Y Sivachenko Search for this author in: * NPG journals * PubMed * Google Scholar * Kristian Cibulskis Search for this author in: * NPG journals * PubMed * Google Scholar * Stacey B Gabriel Search for this author in: * NPG journals * PubMed * Google Scholar * David Altshuler Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Daly Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (827K) Supplementary Figure 1, Supplementary Tables 1–7 and Supplementary Note Additional data - Corrigendum: Genome-wide association study of systemic sclerosis identifies CD247 as a new susceptibility locus
- Nat Genet 43(5):499 (2011)
Nature Genetics | Corrigendum Corrigendum: Genome-wide association study of systemic sclerosis identifies CD247 as a new susceptibility locus * Timothy R D J Radstake * Olga Gorlova * Blanca Rueda * Jose-Ezequiel Martin * Behrooz Z Alizadeh * Rogelio Palomino-Morales * Marieke J Coenen * Madelon C Vonk * Alexandre E Voskuyl * Annemie J Schuerwegh * Jasper C Broen * Piet L C M van Riel * Ruben van 't Slot * Annet Italiaander * Roel A Ophoff * Gabriela Riemekasten * Nico Hunzelmann * Carmen P Simeon * Norberto Ortego-Centeno * Miguel A González-Gay * María F González-Escribano * * Paolo Airo * Jaap van Laar * Ariane Herrick * Jane Worthington * Roger Hesselstrand * Vanessa Smith * Filip de Keyser * Fredric Houssiau * Meng May Chee * Rajan Madhok * Paul Shiels * Rene Westhovens * Alexander Kreuter * Hans Kiener * Elfride de Baere * Torsten Witte * Leonid Padykov * Lars Klareskog * Lorenzo Beretta * Rafaella Scorza * Benedicte A Lie * Anna-Maria Hoffmann-Vold * Patricia Carreira * John Varga * Monique Hinchcliff * Peter K Gregersen * Annette T Lee * Jun Ying * Younghun Han * Shih-Feng Weng * Christopher I Amos * Fredrick M Wigley * Laura Hummers * J Lee Nelson * Sandeep K Agarwal * Shervin Assassi * Pravitt Gourh * Filemon K Tan * Bobby P C Koeleman * Frank C Arnett * Javier Martin * Maureen D MayesJournal name:Nature GeneticsVolume: 43,Page:499Year published:(2011)DOI:doi:10.1038/ng0511-499aPublished online27 April 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.42, 426–429 (2010); published online 11 April 2010; corrected after print 23 March 2011 In the version of this article initially published, incorrect affiliations were published for Lorenzo Beretta and Rafaella Scorza. The correct affiliation for Lorenzo Beretta and Rafaella Scorza is "Referral Center for Systemic Autoimmune Diseases, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico and University of Milan". The error has been corrected in the HTML and PDF versions of the article. 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- Nat Genet 43(5):499 (2011)
Nature Genetics | Corrigendum Corrigendum: TTC21B contributes both causal and modifying alleles across the ciliopathy spectrum * Erica E Davis * Qi Zhang * Qin Liu * Bill H Diplas * Lisa M Davey * Jane Hartley * Corinne Stoetzel * Katarzyna Szymanska * Gokul Ramaswami * Clare V Logan * Donna M Muzny * Alice C Young * David A Wheeler * Pedro Cruz * Margaret Morgan * Lora R Lewis * Praveen Cherukuri * Baishali Maskeri * Nancy F Hansen * James C Mullikin * Robert W Blakesley * Gerard G Bouffard * * Gabor Gyapay * Susanne Rieger * Burkhard Tönshoff * Ilse Kern * Neveen A Soliman * Thomas J Neuhaus * Kathryn J Swoboda * Hulya Kayserili * Tomas E Gallagher * Richard A Lewis * Carsten Bergmann * Edgar A Otto * Sophie Saunier * Peter J Scambler * Philip L Beales * Joseph G Gleeson * Eamonn R Maher * Tania Attié-Bitach * Hélène Dollfus * Colin A Johnson * Eric D Green * Richard A Gibbs * Friedhelm Hildebrandt * Eric A Pierce * Nicholas KatsanisJournal name:Nature GeneticsVolume: 43,Page:499Year published:(2011)DOI:doi:10.1038/ng0511-499bPublished online27 April 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.43, 189–196 (2011); published online 23 January 2011; corrected after print 29 March 2011 In the version of this article initially published, the authors should have acknowledged that the work was also funded by a grant from the European Union (EU-SYSCILIA) to E.E.D., C.A.J., P.L.B. and N.K. The error has been corrected in the HTML and PDF versions of the article. 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