Latest Articles Include:
- In praise of maize
- Nat Genet 42(12):1031 (2010)
Nature Genetics | Editorial In praise of maize Journal name:Nature GeneticsVolume: 42,Page:1031Year published:(2010)DOI:doi:10.1038/ng1210-1031Published online24 November 2010 The field of genetics owes its existence and most of its methods to agriculture. This year, genomic strategies and tools have notably begun to pay back the favor. Crop plants may be not only the discipline's most readily translated applications but also its most fruitful model organisms. View full text Additional data - Evolutionary genetics quantified
- Nat Genet 42(12):1033 (2010)
Evolutionary genetics is the study of how genetic processes such as mutation and recombination are affected by the evolutionary forces of selection, genetic drift and migration to produce patterns of genetic variation at segregating loci and for quantitative traits, within and between populations and over short and long timescales. This theory can then be applied to observed distributions of allele frequencies and the genetic variance of quantitative traits to infer which regions of the genome are evolving as expected from a balance between selectively neutral mutations and genetic drift or to show signatures of departure from the neutral expectation. - LincRNAs join the pluripotency alliance
- Nat Genet 42(12):1035-1036 (2010)
A new study shows that somatic cell reprogramming is accompanied by changes in the expression of large intergenic non-coding RNAs (lincRNAs). Some of these reprogramming-induced lincRNAs are directly targeted by key pluripotency factors and regulate reprogramming, implicating lincRNAs in the reinstatement and maintenance of pluripotency. - New mutations and intellectual function
- Nat Genet 42(12):1036-1038 (2010)
Exome-based sequencing is a powerful approach for studying rare genetic diseases. A new study now applies this technology to demonstrate an important role for de novo mutations in sporadic mental retardation. - FTO gains function
- Nat Genet 42(12):1038-1039 (2010)
Previous genome-wide association studies have identified a strong association between FTO and human obesity, although the mechanism by which FTO affects obesity remains unknown. A new study suggests that the obesity risk alleles are gain-of-function. - Research highlights
- Nat Genet 42(12):1041 (2010)
- RNA sequencing shows no dosage compensation of the active X-chromosome
- Nat Genet 42(12):1043-1047 (2010)
Nature Genetics | Analysis RNA sequencing shows no dosage compensation of the active X-chromosome * Yuanyan Xiong1, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoshu Chen1, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Zhidong Chen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Xunzhang Wang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Suhua Shi1 Search for this author in: * NPG journals * PubMed * Google Scholar * Xueqin Wang2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jianzhi Zhang4jianzhi@umich.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Xionglei He1, 5hexiongl@mail.sysu.edu.cn Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 42,Pages:1043–1047Year published:(2010)DOI:doi:10.1038/ng.711Published online21 November 2010 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 Mammalian cells from both sexes typically contain one active X chromosome but two sets of autosomes. It has previously been hypothesized that X-linked genes are expressed at twice the level of autosomal genes per active allele to balance the gene dose between the X chromosome and autosomes (termed 'Ohno's hypothesis'). This hypothesis was supported by the observation that microarray-based gene expression levels were indistinguishable between one X chromosome and two autosomes (the X to two autosomes ratio (X:AA) ~1). Here we show that RNA sequencing (RNA-Seq) is more sensitive than microarray and that RNA-Seq data reveal an X:AA ratio of ~0.5 in human and mouse. In Caenorhabditis elegans hermaphrodites, the X:AA ratio reduces progressively from ~1 in larvae to ~0.5 in adults. Proteomic data are consistent with the RNA-Seq results and further suggest the lack of X upregulation at the protein level. Together, our findings reject Ohno's hypothesis, necessitating a major revisio! n of the current model of dosage compensation in the evolution of sex chromosomes. View full text Figures at a glance * Figure 1: Comparison of gene expressions measured by microarray and RNA-Seq6, 11, 12, 13. Human liver is considered unless otherwise noted. () Estimation variation measured by the fold difference of microarray intensities of two same-target probesets or of RNA-Seq signals from two halves of the same gene. () Identical to , except that mouse liver is considered here. () Comparison of the internal consistency of RNA-Seq data and microarray data. The expression differences from one-half of the nucleotides (RNA-Seq) or a probeset (microarray) are shown for 1,000 randomly picked gene pairs each with twofold ± 0.01-fold expression difference from the other half of nucleotides (RNA-Seq) or from the other probeset (microarray). The central bold line shows the median, the box encompasses 50% of data points and the error bars include 90% of data points. () Pearson's correlation (r) of microarray and RNA-Seq expression signals (gray) and of RNA-Seq signals from two independent experiments (black). A certain fraction of genes (x axis) with the highest expression according t! o one of the RNA-Seq datasets are examined. Error bars show 95% confidence intervals estimated by bootstrapping. () Microarray consistently underestimates expression differences between genes. The microarray expression differences of 1,000 randomly picked gene pairs each with x-fold (x = 2 ± 0.01, 4 ± 0.02, 8 ± 0.04, 16 ± 0.08, 32 ± 0.16, and 64 ± 0.32) RNA-Seq expression difference are shown. The central bold line shows the median, the box encompasses 50% of data points and the error bars include 90% of data points. () Relative liver expressions of 55 mouse genes, measured by RNA-Seq, microarray and qRT-PCR. * Figure 2: Comparisons of RNA-Seq gene expression levels between the X chromosome and autosomes in 12 human tissues and 3 mouse tissues11, 12, 13, 16. () The median expression levels of X-linked genes (closed diamonds) and autosomal genes (open circles) are compared. Median expressions of autosomal genes were normalized to 1. Error bars show 95% bootstrap confidence intervals. Sex information is listed in the parantheses after the tissue names (M, male; F, female; NA, unknown). () X:AA ratios of median expressions from the human liver when X is compared to individual autosomes. Error bars show 95% bootstrap confidence intervals. * Figure 3: Comparison of RNA-Seq gene expression levels of the X chromosome and autosomes in C. elegans19. () X:AA expression ratios at four developmental stages estimated by Miller's jackknife method. Error bars show 95% confidence intervals. () Gene expression levels of later developmental stages relative to L2. The overall expressions of autosomal genes at different stages relative to L2 are largely the same, with the medians being 0.98, 0.93 and 0.97 for L3/L2, L4/L2 and adult/L2 C. elegans, respectively. X-linked genes show an overall approximate twofold downregulation, with the median relative expressions being 0.71, 0.55 and 0.43 for L3/L2, L4/L2 and adult/L2 C. elegans, respectively. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE12946 * GSE13652 * GSE3413 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Yuanyan Xiong & * Xiaoshu Chen Affiliations * State Key Laboratory of Biocontrol, College of Life Sciences, Sun Yat-sen University, Guangzhou, China. * Yuanyan Xiong, * Xiaoshu Chen, * Zhidong Chen, * Xunzhang Wang, * Suhua Shi & * Xionglei He * School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, China. * Xueqin Wang * Zhongshan Medical School, Sun Yat-sen University, Guangzhou, China. * Xueqin Wang * Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA. * Jianzhi Zhang * State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China. * Xionglei He Contributions X.H. and J.Z. conceived the study. Y.X., X.C. and Z.C. produced data. X.H., X.C., Y.X., J.Z., Xunzhang Wang, S.S. and Xueqin Wang analyzed data. X.H., Xunzhang Wang and S.S. provided reagents. X.H. and J.Z. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Xionglei He (hexiongl@mail.sysu.edu.cn) or * Jianzhi Zhang (jianzhi@umich.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Tables 1–15 and Supplementary Figures 1–5. Additional data - Fine-mapping at three loci known to affect fetal hemoglobin levels explains additional genetic variation
- Nat Genet 42(12):1049-1051 (2010)
Nature Genetics | Brief Communication Fine-mapping at three loci known to affect fetal hemoglobin levels explains additional genetic variation * Geneviève Galarneau1 Search for this author in: * NPG journals * PubMed * Google Scholar * Cameron D Palmer2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Vijay G Sankaran4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart H Orkin4, 5, 6, 5, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Joel N Hirschhorn2, 3, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Guillaume Lettre1, 8guillaume.lettre@mhi-humangenetics.org Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1049–1051Year published:(2010)DOI:doi:10.1038/ng.707Received13 July 2010Accepted13 October 2010Published online07 November 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We used resequencing and genotyping in African Americans with sickle cell anemia (SCA) to characterize associations with fetal hemoglobin (HbF) levels at the BCL11A, HBS1L-MYB and β-globin loci. Fine-mapping of HbF association signals at these loci confirmed seven SNPs with independent effects and increased the explained heritable variation in HbF levels from 38.6% to 49.5%. We also identified rare missense variants that causally implicate MYB in HbF production. View full text Author information * Author information * Supplementary information Affiliations * Montreal Heart Institute, Montréal, Québec, Canada. * Geneviève Galarneau & * Guillaume Lettre * Divisions of Genetics and Endocrinology and Program in Genomics, Children's Hospital Boston, Boston, Massachusetts, USA. * Cameron D Palmer & * Joel N Hirschhorn * Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. * Cameron D Palmer & * Joel N Hirschhorn * Division of Hematology and Oncology, Children's Hospital Boston, Boston, Massachusetts, USA. * Vijay G Sankaran & * Stuart H Orkin * Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Vijay G Sankaran & * Stuart H Orkin * Howard Hughes Medical Institute, Boston, Massachusetts, USA. * Stuart H Orkin * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Joel N Hirschhorn * Départment de Médecine, Université de Montréal, Montréal, Québec, Canada. * Guillaume Lettre Contributions V.G.S., S.H.O., J.N.H. and G.L. conceived and designed the experiment. G.G., C.D.P. and G.L. performed the experiments. G.G., C.D.P. and G.L. analyzed the data. G.G., C.D.P., V.G.S., S.H.O., J.N.H. and G.L. contributed reagents, materials and/or analysis tools. G.G. and G.L. wrote the paper with contributions from all authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Guillaume Lettre (guillaume.lettre@mhi-humangenetics.org) Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 2 (256K) DNA sequence variants identified at the BCL11A, HBS1L-MYB and A-globin loci by DNA re-sequencing in the HapMap Northern European (CEU) and West African (YRI) founders, and in 70 sickle cell anemia (SCA) patients from the Cooperative Study of Sickle Cell Disease (CSSCD) * Supplementary Table 3 (36K) Association results with HbF levels for all 95 common SNPs (minor allele frequency A1%) genotyped in 1,032 African-American sickle cell anemia (SCA) patients from the Cooperative Study of Sickle Cell Disease (CSSCD) * Supplementary Table 6 (100K) Association results to fetal hemoglobin (HbF) levels for imputed SNPs PDF files * Supplementary Text and Figures (580K) Supplementary Tables 1–10 and Supplementary Figures 1 and 2 Additional data - Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection
- Nat Genet 42(12):1053-1059 (2010)
Nature Genetics | Article Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection * Hon-Ming Lam1, 6honming@cuhk.edu.hk Search for this author in: * NPG journals * PubMed * Google Scholar * Xun Xu2, 3, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Xin Liu1, 2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Wenbin Chen2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Guohua Yang2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Fuk-Ling Wong1 Search for this author in: * NPG journals * PubMed * Google Scholar * Man-Wah Li1 Search for this author in: * NPG journals * PubMed * Google Scholar * Weiming He2 Search for this author in: * NPG journals * PubMed * Google Scholar * Nan Qin2 Search for this author in: * NPG journals * PubMed * Google Scholar * Bo Wang2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Min Jian2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Wang2 Search for this author in: * NPG journals * PubMed * Google Scholar * Guihua Shao1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Wang2, 5wangj@genomics.cn Search for this author in: * NPG journals * PubMed * Google Scholar * Samuel Sai-Ming Sun1ssun@cuhk.edu.hk Search for this author in: * NPG journals * PubMed * Google Scholar * Gengyun Zhang2, 3zhanggengyun@genomics.cn Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 42,Pages:1053–1059Year published:(2010)DOI:doi:10.1038/ng.715Received21 June 2010Accepted22 October 2010Published online14 November 2010 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 We report a large-scale analysis of the patterns of genome-wide genetic variation in soybeans. We re-sequenced a total of 17 wild and 14 cultivated soybean genomes to an average of approximately ×5 depth and >90% coverage using the Illumina Genome Analyzer II platform. We compared the patterns of genetic variation between wild and cultivated soybeans and identified higher allelic diversity in wild soybeans. We identified a high level of linkage disequilibrium in the soybean genome, suggesting that marker-assisted breeding of soybean will be less challenging than map-based cloning. We report linkage disequilibrium block location and distribution, and we identified a set of 205,614 tag SNPs that may be useful for QTL mapping and association studies. The data here provide a valuable resource for the analysis of wild soybeans and to facilitate future breeding and quantitative trait analysis. View full text Figures at a glance * Figure 1: Analysis of the phylogenetic relationship, population structure and LD decay of wild and cultivated soybeans. () A neighbor-joining phylogenetic tree constructed using SNP data. () Principal component analysis of cultivated (red) and wild (blue) soybeans. () Bayesian clustering (STRUCTURE, K = 5) of soybean accessions. () LD decay determined by squared correlations of allele frequencies (r2) against distance between polymorphic sites in cultivated (red) and wild (blue) soybeans. * Figure 2: Summary of resequencing data of 17 wild and 14 cultivated soybean accessions. The average genome coverage is ~90%. Concentric circles show the different features that were drawn using the Circos program39. The 20 chromosomes are portrayed along the perimeter of each circle. () Insertion or deletion in the reference cultivated soybean genome5 (unique genome in blue) and the wild accession W05 (unique genome in green). () QTLs of domestication-related traits25 (blue blocks). () Genomic diversity (θπ) of wild soybeans (blue) and cultivated soybeans (red). () FST value of wild versus cultivated soybeans (red, >0.4; blue, <0.03). () LD blocks (>50 kb) of wild soybeans (blue) and cultivated soybeans (red). () Introgression of wild genomic regions (red) into cultivated soybean accessions. () A graphical view of duplicated annotated genes is indicated by connections between segments. * Figure 3: Patterns of LD blocks in two genomic regions. (–) LD blocks in chromosome 5 (~6.2–6.4 Mb). (–) LD blocks in chromosome 10 (~42.6–42.8 Mb). Location of LD blocks for wild (blue segments) and cultivated (red segments) soybeans, FST value (black line), and genomic diversity of wild (blue dotted line) and cultivated (red dotted line) soybeans are shown in and . Red and white spots indicate strong (r2 = 1) and weak (r2 = 0) LD, respectively, for wild ( and ) and cultivated ( and ) soybeans. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * SRA020131 * ss244318098 * ss250607844 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hon-Ming Lam, * Xun Xu, * Xin Liu, * Wenbin Chen & * Guohua Yang Affiliations * State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. * Hon-Ming Lam, * Xin Liu, * Fuk-Ling Wong, * Man-Wah Li, * Guihua Shao & * Samuel Sai-Ming Sun * BGI-Shenzhen, Shenzhen, China. * Xun Xu, * Xin Liu, * Wenbin Chen, * Guohua Yang, * Weiming He, * Nan Qin, * Bo Wang, * Jun Li, * Min Jian, * Jian Wang, * Jun Wang & * Gengyun Zhang * Key Laboratory of Genomics, Ministry of Agriculture, BGI-Shenzhen, China. * Xun Xu & * Gengyun Zhang * Institute of Crop Sciences, The Chinese Academy of Agricultural Sciences, Beijing, China. * Guihua Shao * Department of Biology, University of Copenhagen, Copenhagen, Denmark. * Jun Wang Contributions H.-M.L., G.Z., S.S.-M.S. and Jun Wang managed the project. H.-M.L., X.X., X.L, N.Q. and G.Y. designed the experiments and led the data analysis. W.H., B.W., J.L., W.C., M.J. and Jian Wang contributed to DNA sequencing and bioinformatics. F.-L.W., M.-W.L. and G.S. prepared samples and contributed to data analysis. H.-M.L., X.X. and X.L. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Gengyun Zhang (zhanggengyun@genomics.cn) or * Samuel Sai-Ming Sun (ssun@cuhk.edu.hk) or * Jun Wang (wangj@genomics.cn) or * Hon-Ming Lam (honming@cuhk.edu.hk) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (848K) Supplementary Figures 1–13 and Supplementary Tables 1–7 Additional data - The developmental dynamics of the maize leaf transcriptome
- Nat Genet 42(12):1060-1067 (2010)
Nature Genetics | Article The developmental dynamics of the maize leaf transcriptome * Pinghua Li1, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Lalit Ponnala2, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Neeru Gandotra3 Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Wang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yaqing Si4 Search for this author in: * NPG journals * PubMed * Google Scholar * S Lori Tausta3 Search for this author in: * NPG journals * PubMed * Google Scholar * Tesfamichael H Kebrom1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas Provart5, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Rohan Patel5, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher R Myers2 Search for this author in: * NPG journals * PubMed * Google Scholar * Edwin J Reidel7 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Turgeon7 Search for this author in: * NPG journals * PubMed * Google Scholar * Peng Liu4 Search for this author in: * NPG journals * PubMed * Google Scholar * Qi Sun2 Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy Nelson3 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas P Brutnell1, 7tpb8@cornell.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1060–1067Year published:(2010)DOI:doi:10.1038/ng.703Received08 April 2010Accepted05 October 2010Published online31 October 2010 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 We have analyzed the maize leaf transcriptome using Illumina sequencing. We mapped more than 120 million reads to define gene structure and alternative splicing events and to quantify transcript abundance along a leaf developmental gradient and in mature bundle sheath and mesophyll cells. We detected differential mRNA processing events for most maize genes. We found that 64% and 21% of genes were differentially expressed along the developmental gradient and between bundle sheath and mesophyll cells, respectively. We implemented Gbrowse, an electronic fluorescent pictograph browser, and created a two-cell biochemical pathway viewer to visualize datasets. Cluster analysis of the data revealed a dynamic transcriptome, with transcripts for primary cell wall and basic cellular metabolism at the leaf base transitioning to transcripts for secondary cell wall biosynthesis and C4 photosynthetic development toward the tip. This dataset will serve as the foundation for a systems biolog! y approach to the understanding of photosynthetic development. View full text Figures at a glance * Figure 1: Defining developmental and physiological characters along a B73 seedling leaf. () Nine-day-old B73 seedling. Ligules from leaf 2 and 3 were used as markers to define developmental zones. () The position of ligule 2 at '0' is the reference point for the development of leaf 3. () The sink region of leaf 3 was defined by autoradiography (Online Methods). () Leaf margins are labeled after feeding 14CO2 to leaf 2. () Tissues near the midvein are labeled after feeding 14CO2 to the exporting tip of leaf 3. (–) Transmission electron micrographs from base (), −1 cm (), +4 cm () and tip (). bs, bundle sheath cell; m, mesophyll cell. Scale bars in – represent 1 μm. * Figure 2: RNA-seq analysis of B73 leaf transcriptome. () Distribution of reads from mRNA-seq from developmental gradient. () Distribution of reads from aRNA-seq from LCM. () Distribution of reads among maize annotated genomic features. () Distribution of the reads along the length of gene models and relative to transcript abundance. () Shared and unique reads among developmental zones () and bundle sheath and mesophyll cells (). SJ, splice junction. * Figure 3: Alternative splicing of GRMZM2G147687. () Genome browser shows the alignment of reads to splice junctions (green) and exons (yellow) of GRMZM2G147687. Red arrows indicate the alternative splice junctions within the first exon, and blue arrows indicate putative intron retention events (see also Supplementary Fig. 3). () Results of qRT-PCR showing accumulation of four isoforms along the developmental gradient. * Figure 4: Dynamic progression of leaf transcriptome. () K-means clustering showing the expression profile of the maize leaf transcriptome. Eighteen clusters were identified along the four developmental zones (Base, −1 cm, +4 cm, Tip) from 16,502 differentially expressed genes. The six major clusters are presented in . Error bars show standard deviation. () Functional category enrichment (modified MapMan bins) among the six major clusters. () Functional distribution of genes in LCM analysis of bundle sheath and mesophyll cells. 16,638 genes were defined as significant from the LCM analysis. The distribution of reads among the top seven functional categories is shown (excluding 30.2% belonging to 'not assigned or unknown'). 'Others' includes 24 minor categories. () Functional category enrichment in the bundle sheath and mesophyll cells. Red, significant enrichment; white, non-significant; gray, not-detected. * Figure 5: Dynamics of transcription factor accumulation profiles. () Dendrogram of the transcription factors. Nine hundred thirty-eight significantly differentially expressed transcription factors from base, −1 cm, +4 cm and tip (RPKM > 1 in at least one segments) clustered into three lineages (G1, G2 and G3) using the Self Organization Tree Algorithm (SOTA; Online Methods). () Distribution of transcription factor families among G1, G2 and G3. () Representative functions and genes showing expression gradients from developmental zones and bundle sheath and mesophyll cells. () Distribution of the 180 transcription factor families differentially expressed between bundle sheath and mesophyll cells. In and , the number of genes in each family is shown. BS, bundle sheath; M, mesophyll. * Figure 6: eFP browser view of expression changes over leaf development. Relative gene expression values for GRMZM2G101938 (RabA, LeRab11 homolog, maximum PRKM 82.01), GRMZM2G034360 (feruloyl transferase, AT5G41040 homolog, maximum PRKM 146.26) and AC147602.5_FG003 (Sedoheptulose -1,7-bisphosphatase, maximum PRKM 1756.65) are shown. Relative gene expression was calculated from the maximum RPKM values among the developmental zones. The detailed expression patterns of genes in the each of these biosynthetic pathways is shown in Supplementary Figures 10, 11 and 12. bs, bundle sheath; m, mesophyll. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * SRA012297 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Pinghua Li & * Lalit Ponnala Affiliations * Boyce Thompson Institute, Cornell University, Ithaca, New York, USA. * Pinghua Li, * Lin Wang, * Tesfamichael H Kebrom & * Thomas P Brutnell * Computational Biology Service Unit, Cornell University, Ithaca, New York, USA. * Lalit Ponnala, * Christopher R Myers & * Qi Sun * Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, USA. * Neeru Gandotra, * S Lori Tausta & * Timothy Nelson * Department of Statistics and Statistical Laboratory, Iowa State University, Ames, Iowa, USA. * Yaqing Si & * Peng Liu * Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada. * Nicholas Provart & * Rohan Patel * Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario, Canada. * Nicholas Provart & * Rohan Patel * Department of Plant Biology, Cornell University, Ithaca, New York, USA. * Edwin J Reidel, * Robert Turgeon & * Thomas P Brutnell Contributions T.P.B., T.N. and R.T. designed the experiments. P. Li, L.P., N.G., E.J.R. and T.H.K. optimized and performed the experiments. Q.S., L.P., P. Liu, P. Li, L.W., Y.S., R.P., T.P.B., N.P., N.G., S.L.T. and C.R.M. performed data analysis. P. Li, L.P., T.N., N.G., S.L.T. and T.P.B. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Thomas P Brutnell (tpb8@cornell.edu) Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 2 (36K) Verification of RNA-seq results by qRT-PCR. * Supplementary Table 3 (4M) List of the K-means clusters. * Supplementary Table 4 (1M) List of genes exhibiting differential expression between bundle sheath and mesophyll cells. * Supplementary Table 5 (272K) Correlation between RNA-seq and proteomics data in the bundle sheath and mesophyll cells. * Supplementary Table 6 (44K) List of C4 genes and their expression changes along developmental zones. * Supplementary Table 7 (728K) List of differentially expressed transcription factors in the developmental zones and bundle sheath and mesophyll cells. * Supplemenatry Table 8 (56K) List of gene ID?s and RPKM estimates for Supplementary Figs. 8-12. PDF files * Supplementary Text and Figures (8M) Supplementary Table 1, Supplementary Figures 1–13 and Supplementary Note Additional data - Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction
- Nat Genet 42(12):1068-1076 (2010)
Nature Genetics | Article Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction * Nona Sotoodehnia1, 2, 78nsotoo@u.washington.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Aaron Isaacs3, 4, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul I W de Bakker5, 6, 7, 8, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Marcus Dörr9, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher Newton-Cheh10, 11, 12, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Ilja M Nolte13, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Pim van der Harst14, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Martina Müller15, 16, 17, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Eijgelsheim18, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Alvaro Alonso19, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew A Hicks20, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Sandosh Padmanabhan21, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Hayward22, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Albert Vernon Smith23, 24, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Ozren Polasek25, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Steven Giovannone26, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Jingyuan Fu13, 27, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Jared W Magnani12, 28 Search for this author in: * NPG journals * PubMed * Google Scholar * Kristin D Marciante2 Search for this author in: * NPG journals * PubMed * Google Scholar * Arne Pfeufer20, 29, 30 Search for this author in: * NPG journals * PubMed * Google Scholar * Sina A Gharib31 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Teumer32 Search for this author in: * NPG journals * PubMed * Google Scholar * Man Li33 Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua C Bis2 Search for this author in: * NPG journals * PubMed * Google Scholar * Fernando Rivadeneira18, 34 Search for this author in: * NPG journals * PubMed * Google Scholar * Thor Aspelund23, 24 Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Köttgen35 Search for this author in: * NPG journals * PubMed * Google Scholar * Toby Johnson36, 37 Search for this author in: * NPG journals * PubMed * Google Scholar * Kenneth Rice38 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark P S Sie3 Search for this author in: * NPG journals * PubMed * Google Scholar * Ying A Wang12, 39 Search for this author in: * NPG journals * PubMed * Google Scholar * Norman Klopp17 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Fuchsberger20 Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah H Wild40 Search for this author in: * NPG journals * PubMed * Google Scholar * Irene Mateo Leach14 Search for this author in: * NPG journals * PubMed * Google Scholar * Karol Estrada34 Search for this author in: * NPG journals * PubMed * Google Scholar * Uwe Völker32 Search for this author in: * NPG journals * PubMed * Google Scholar * Alan F Wright22 Search for this author in: * NPG journals * PubMed * Google Scholar * Folkert W Asselbergs13, 14, 41 Search for this author in: * NPG journals * PubMed * Google Scholar * Jiaxiang Qu26 Search for this author in: * NPG journals * PubMed * Google Scholar * Aravinda Chakravarti42 Search for this author in: * NPG journals * PubMed * Google Scholar * Moritz F Sinner16 Search for this author in: * NPG journals * PubMed * Google Scholar * Jan A Kors43 Search for this author in: * NPG journals * PubMed * Google Scholar * Astrid Petersmann44 Search for this author in: * NPG journals * PubMed * Google Scholar * Tamara B Harris45 Search for this author in: * NPG journals * PubMed * Google Scholar * Elsayed Z Soliman46 Search for this author in: * NPG journals * PubMed * Google Scholar * Patricia B Munroe36, 37 Search for this author in: * NPG journals * PubMed * Google Scholar * Bruce M Psaty2, 47, 48, 49 Search for this author in: * NPG journals * PubMed * Google Scholar * Ben A Oostra4, 50 Search for this author in: * NPG journals * PubMed * Google Scholar * L Adrienne Cupples12, 39 Search for this author in: * NPG journals * PubMed * Google Scholar * Siegfried Perz51 Search for this author in: * NPG journals * PubMed * Google Scholar * Rudolf A de Boer14 Search for this author in: * NPG journals * PubMed * Google Scholar * André G Uitterlinden18, 34, 52 Search for this author in: * NPG journals * PubMed * Google Scholar * Henry Völzke53 Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy D Spector54 Search for this author in: * NPG journals * PubMed * Google Scholar * Fang-Yu Liu26 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Boerwinkle55, 56 Search for this author in: * NPG journals * PubMed * Google Scholar * Anna F Dominiczak21 Search for this author in: * NPG journals * PubMed * Google Scholar * Jerome I Rotter57 Search for this author in: * NPG journals * PubMed * Google Scholar * Gé van Herpen43 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Levy12, 58 Search for this author in: * NPG journals * PubMed * Google Scholar * H-Erich Wichmann15, 17, 59 Search for this author in: * NPG journals * PubMed * Google Scholar * Wiek H van Gilst14 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacqueline C M Witteman18, 52 Search for this author in: * NPG journals * PubMed * Google Scholar * Heyo K Kroemer60 Search for this author in: * NPG journals * PubMed * Google Scholar * W H Linda Kao33 Search for this author in: * NPG journals * PubMed * Google Scholar * Susan R Heckbert2, 47, 49 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Meitinger29, 30 Search for this author in: * NPG journals * PubMed * Google Scholar * Albert Hofman18, 52 Search for this author in: * NPG journals * PubMed * Google Scholar * Harry Campbell40 Search for this author in: * NPG journals * PubMed * Google Scholar * Aaron R Folsom19 Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk J van Veldhuisen14 Search for this author in: * NPG journals * PubMed * Google Scholar * Christine Schwienbacher20, 61 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher J O'Donnell12, 58 Search for this author in: * NPG journals * PubMed * Google Scholar * Claudia Beu Volpato20 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Caulfield36, 37 Search for this author in: * NPG journals * PubMed * Google Scholar * John M Connell62 Search for this author in: * NPG journals * PubMed * Google Scholar * Lenore Launer45 Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaowen Lu13 Search for this author in: * NPG journals * PubMed * Google Scholar * Lude Franke27, 63 Search for this author in: * NPG journals * PubMed * Google Scholar * Rudolf S N Fehrmann27 Search for this author in: * NPG journals * PubMed * Google Scholar * Gerard te Meerman27 Search for this author in: * NPG journals * PubMed * Google Scholar * Harry J M Groen64 Search for this author in: * NPG journals * PubMed * Google Scholar * Rinse K Weersma65 Search for this author in: * NPG journals * PubMed * Google Scholar * Leonard H van den Berg66 Search for this author in: * NPG journals * PubMed * Google Scholar * Cisca Wijmenga27 Search for this author in: * NPG journals * PubMed * Google Scholar * Roel A Ophoff67, 68 Search for this author in: * NPG journals * PubMed * Google Scholar * Gerjan Navis69 Search for this author in: * NPG journals * PubMed * Google Scholar * Igor Rudan40, 70, 71, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Harold Snieder13, 54, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * James F Wilson40, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter P Pramstaller20, 72, 73, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * David S Siscovick2, 47, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas J Wang11, 12, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Vilmundur Gudnason23, 24, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Cornelia M van Duijn3, 4, 52, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephan B Felix9, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Glenn I Fishman26, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Yalda Jamshidi54, 74, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Bruno H Ch Stricker18, 34, 43, 52, 75, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Nilesh J Samani76, 77, 78 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Kääb16, 78skaab@med.lmu.de Search for this author in: * NPG journals * PubMed * Google Scholar * Dan E Arking42, 78arking@jhmi.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 42,Pages:1068–1076Year published:(2010)DOI:doi:10.1038/ng.716Received03 May 2010Accepted19 October 2010Published online14 November 2010 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 The QRS interval, from the beginning of the Q wave to the end of the S wave on an electrocardiogram, reflects ventricular depolarization and conduction time and is a risk factor for mortality, sudden death and heart failure. We performed a genome-wide association meta-analysis in 40,407 individuals of European descent from 14 studies, with further genotyping in 7,170 additional Europeans, and we identified 22 loci associated with QRS duration (P < 5 × 10−8). These loci map in or near genes in pathways with established roles in ventricular conduction such as sodium channels, transcription factors and calcium-handling proteins, but also point to previously unidentified biologic processes, such as kinase inhibitors and genes related to tumorigenesis. We demonstrate that SCN10A, a candidate gene at the most significantly associated locus in this study, is expressed in the mouse ventricular conduction system, and treatment with a selective SCN10A blocker prolongs QRS duration.! These findings extend our current knowledge of ventricular depolarization and conduction. View full text Figures at a glance * Figure 1: Manhattan plot. Manhattan plot showing the association of SNPs with QRS interval duration in a GWAS of 40,407 individuals. The dashed horizontal line marks the threshold for genome-wide significance (P = 5 × 10−8). Twenty loci (labeled) reached genome-wide significance. Two additional loci, GOSR2 and DKK1, reached significance after genotyping of select SNPs in an additional sample of 7,170 individuals (see results section of the main text). * Figure 2: Association plots for select loci. Each SNP is plotted with respect to its chromosomal location (x axis) and its P value (y axis on the left). The tall blue spikes indicate the recombination rate (y axis on the right) at that region of the chromosome. The blue-outlined triangles indicate coding region SNPs. () Locus 1 (SCN5A-SCN10A) on chromosome 3. The six index signals are named with their rs numbers and highlighted in different colors (yellow, green, teal, blue, purple and red). Other SNPs in linkage disequilibrium with the index SNP are denoted in the same color. Color saturation indicates the degree of correlation with the index SNP. () Locus 8 (TBX5) and locus 9 (TBX3) on chromosome 12. () Locus 12 (HEATR5B-STRN) and locus 14 (CRIM1) on chromosome 2. * Figure 3: Pleiotropic associations of PR, QRS and QT loci. Electrocardiographic tracing delineating the PR, QRS and QT intervals. PR and QRS intervals reflect myocardial depolarization and conduction time through the atria and down the atrioventricular node (PR) and throughout the ventricle (QRS) and are weakly positively correlated (r = 0.09). The majority of loci that influence both PR and QRS (SCN5A, SCN10A, TBX5 and CAV1-2) do so in a concordant fashion (meaning variants that prolong PR duration also prolong QRS duration). The notable exception is a region on chromosome 12, where variants in the TBX5 locus have a concordant effect, whereas those in nearby TBX3 have a discordant effect. By contrast, although QRS (ventricular depolarization) and QT (ventricular repolarization) are moderately positively correlated, the majority of loci (SCN5A, SCN10A, PRKCA and NOS1AP) that influence both phenotypes do so in a discordant fashion (meaning variants that prolong the QRS interval shorten the QT interval). The exception is the locus at ! PLN, where the variants have a concordant effect. * Figure 4: Expression and function of Scn10a in the mouse heart. () Neonatal ventricular myocytes from Cntn2-eGFP BAC transgenic mice were fluorescence-activated cell sorted and eGFP+ and eGFP− pools were analyzed by RT-PCR. Transcripts encoding eGFP, Cntn2 and Scn10a were highly enriched in the eGFP+ fraction. Quantitative RT-PCR demonstrated 25.7-fold enrichment of Scn10a Nav1.8. () Representative telemetric electrocardiographic recordings (lead II configuration) obtained 30 min after administration of vehicle alone (black tracing) or the Scn10a Nav1.8 antagonist A-803467 (green tracing). The two tracings are aligned at the onset of the QRS wave, and both PR interval and QRS interval prolongation were observed in drug-treated mice. () Representative intracardiac recordings showing HV intervals obtained before (Pre) and after (Post) administration of vehicle or A-803467. Significant HV prolongation was observed in drug-treated mice. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Nona Sotoodehnia, * Aaron Isaacs, * Paul I W de Bakker, * Marcus Dörr, * Christopher Newton-Cheh, * Ilja M Nolte, * Pim van der Harst, * Martina Müller, * Mark Eijgelsheim, * Alvaro Alonso, * Andrew A Hicks, * Sandosh Padmanabhan, * Caroline Hayward, * Albert Vernon Smith, * Ozren Polasek, * Steven Giovannone, * Jingyuan Fu, * Igor Rudan, * Harold Snieder, * James F Wilson, * Peter P Pramstaller, * David S Siscovick, * Thomas J Wang, * Vilmundur Gudnason, * Cornelia M van Duijn, * Stephan B Felix, * Glenn I Fishman, * Yalda Jamshidi, * Bruno H Ch Stricker, * Nilesh J Samani, * Stefan Kääb & * Dan E Arking Affiliations * Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, USA. * Nona Sotoodehnia * Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA. * Nona Sotoodehnia, * Kristin D Marciante, * Joshua C Bis, * Bruce M Psaty, * Susan R Heckbert & * David S Siscovick * Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center (MC), Rotterdam, The Netherlands. * Aaron Isaacs, * Mark P S Sie & * Cornelia M van Duijn * Centre for Medical Systems Biology, Leiden, The Netherlands. * Aaron Isaacs, * Ben A Oostra & * Cornelia M van Duijn * Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Paul I W de Bakker * Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. * Paul I W de Bakker * Department of Medical Genetics, University Medical Center, Utrecht, The Netherlands. * Paul I W de Bakker * Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands. * Paul I W de Bakker * Department of Internal Medicine B, Ernst Moritz Arndt University Greifswald, Greifswald, Germany. * Marcus Dörr & * Stephan B Felix * Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA. * Christopher Newton-Cheh * Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA. * Christopher Newton-Cheh & * Thomas J Wang * National Heart, Lung, and Blood Institute's (NHLBI) Framingham Heart Study, Framingham, Massachusetts, USA. * Christopher Newton-Cheh, * Jared W Magnani, * Ying A Wang, * L Adrienne Cupples, * Daniel Levy, * Christopher J O'Donnell & * Thomas J Wang * Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. * Ilja M Nolte, * Jingyuan Fu, * Folkert W Asselbergs, * Xiaowen Lu & * Harold Snieder * Department of Cardiology, University Medical Center Groningen, University of Groningen, The Netherlands. * Pim van der Harst, * Irene Mateo Leach, * Folkert W Asselbergs, * Rudolf A de Boer, * Wiek H van Gilst & * Dirk J van Veldhuisen * Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany. * Martina Müller & * H-Erich Wichmann * Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany. * Martina Müller, * Moritz F Sinner & * Stefan Kääb * Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. * Martina Müller, * Norman Klopp & * H-Erich Wichmann * Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands. * Mark Eijgelsheim, * Fernando Rivadeneira, * André G Uitterlinden, * Jacqueline C M Witteman, * Albert Hofman & * Bruno H Ch Stricker * Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA. * Alvaro Alonso & * Aaron R Folsom * Institute of Genetic Medicine, European Academy Bozen-Bolzano (EURAC), Bolzano, Italy, affiliated institute of the University of Lübeck, Germany. * Andrew A Hicks, * Arne Pfeufer, * Christian Fuchsberger, * Christine Schwienbacher, * Claudia Beu Volpato & * Peter P Pramstaller * Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, University Place, Glasgow, UK. * Sandosh Padmanabhan & * Anna F Dominiczak * Medical Research Council (MRC) Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK. * Caroline Hayward & * Alan F Wright * Icelandic Heart Association, Kopavogur, Iceland. * Albert Vernon Smith, * Thor Aspelund & * Vilmundur Gudnason * University of Iceland, Reykjavik, Iceland. * Albert Vernon Smith, * Thor Aspelund & * Vilmundur Gudnason * Andrija Stampar School of Public Health, Medical School, University of Zagreb, Zagreb, Croatia. * Ozren Polasek * Leon H. Charney Division of Cardiology, New York University School of Medicine, New York, New York, USA. * Steven Giovannone, * Jiaxiang Qu, * Fang-Yu Liu & * Glenn I Fishman * Department of Genetics, University Medical Center Groningen, University of Groningen, The Netherlands. * Jingyuan Fu, * Lude Franke, * Rudolf S N Fehrmann, * Gerard te Meerman & * Cisca Wijmenga * Section of Cardiovascular Medicine, Boston University School of Medicine, Boston, Massachusetts, USA. * Jared W Magnani * Institute of Human Genetics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. * Arne Pfeufer & * Thomas Meitinger * Institute of Human Genetics, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany. * Arne Pfeufer & * Thomas Meitinger * Center for Lung Biology, Department of Medicine, University of Washington, Seattle, Washington, USA. * Sina A Gharib * Interfaculty Institute for Genetics and Functional Genomics, Ernst Moritz Arndt University Greifswald, Greifswald, Germany. * Alexander Teumer & * Uwe Völker * Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA. * Man Li & * W H Linda Kao * Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. * Fernando Rivadeneira, * Karol Estrada, * André G Uitterlinden & * Bruno H Ch Stricker * Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA. * Anna Köttgen * Clinical Pharmacology and Barts and the London Genome Centre, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary University of London, London, UK. * Toby Johnson, * Patricia B Munroe & * Mark J Caulfield * Barts and the London National Institute of Health Research Cardiovascular Biomedical Research Unit, London, UK. * Toby Johnson, * Patricia B Munroe & * Mark J Caulfield * Department of Biostatistics, University of Washington, Seattle, Washington, USA. * Kenneth Rice * Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA. * Ying A Wang & * L Adrienne Cupples * Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland. * Sarah H Wild, * Harry Campbell, * Igor Rudan & * James F Wilson * Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands. * Folkert W Asselbergs * McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Aravinda Chakravarti & * Dan E Arking * Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands. * Jan A Kors, * Gé van Herpen & * Bruno H Ch Stricker * Institute of Clinical Chemistry and Laboratory Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, Germany. * Astrid Petersmann * Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA. * Tamara B Harris & * Lenore Launer * Epidemiological Cardiology Research Center (EPICARE), Wake Forest University School of Medicine, Winston Salem, North Carolina, USA. * Elsayed Z Soliman * Department of Epidemiology, University of Washington, Seattle, Washington, USA. * Bruce M Psaty, * Susan R Heckbert & * David S Siscovick * Department of Health Services, University of Washington, Seattle, Washington, USA. * Bruce M Psaty * Group Health Research Institute, Group Health Cooperative, Seattle, Washington, USA. * Bruce M Psaty & * Susan R Heckbert * Department of Clinical Genetics, Erasmus MC, Rotterdam, The Netherlands. * Ben A Oostra * Institute for Biological and Medical Imaging, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. * Siegfried Perz * Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam, The Netherlands. * André G Uitterlinden, * Jacqueline C M Witteman, * Albert Hofman, * Cornelia M van Duijn & * Bruno H Ch Stricker * Institute for Community Medicine, Ernst Moritz Arndt University Greifswald, Greifswald, Germany. * Henry Völzke * Department of Twin Research and Genetic Epidemiology Unit, St. Thomas' Campus, King's College London, St. Thomas' Hospital, London, UK. * Timothy D Spector, * Harold Snieder & * Yalda Jamshidi * Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA. * Eric Boerwinkle * Institute for Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USA. * Eric Boerwinkle * Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. * Jerome I Rotter * National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA. * Daniel Levy & * Christopher J O'Donnell * Klinikum Grosshadern, Munich, Germany. * H-Erich Wichmann * Department of Pharmacology, Center for Pharmacology and Experimental Therapeutics, Ernst Moritz Arndt University Greifswald, Greifswald, Germany. * Heyo K Kroemer * Department of Experimental and Diagnostic Medicine, University of Ferrara, Ferrara, Italy. * Christine Schwienbacher * University of Dundee, Ninewells Hospital and Medical School, Dundee, UK. * John M Connell * Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. * Lude Franke * Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. * Harry J M Groen * Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. * Rinse K Weersma * Department of Neurology, Rudolf Magnus Institute, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands. * Leonard H van den Berg * Department of Medical Genetics and Rudolf Magnus Institute, University Medical Center Utrecht, Utrecht, The Netherlands. * Roel A Ophoff * Center for Neurobehavioral Genetics, University of California, Los Angeles, California, USA. * Roel A Ophoff * Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. * Gerjan Navis * Centre for Global Health, Medical School, University of Split, Split, Croatia. * Igor Rudan * Gen-info Ltd. Zagreb, Croatia. * Igor Rudan * Department of Neurology, General Central Hospital, Bolzano, Italy. * Peter P Pramstaller * Department of Neurology, University of Lübeck, Lübeck, Germany. * Peter P Pramstaller * Division of Clinical Developmental Sciences, St. George's University of London, London, UK. * Yalda Jamshidi * Inspectorate of Health Care, The Hague, The Netherlands. * Bruno H Ch Stricker * Department of Cardiovascular Sciences, University of Leicester, Leicester, UK. * Nilesh J Samani * Leicester NIHR Biomedical Research Unit in Cardiovascular Disease Glenfield Hospital, Leicester, UK. * Nilesh J Samani Contributions N.S., A.A., D.E.A., P.I.W.d.B., E.B., H.C., A.C., C.M.v.D., M.E., S.B.F., G.I.F., A.R.F., J.F., V.G., P.v.d.H., S.R.H., A.A.H., A.H., A.I., S.K., H.K.K., C.N.-C., B.A.O., A. Pfeufer, P.P.P., B.M.P., J.I.R., I.R., H.S., E.Z.S., B.H.C.S., A.G.U., A.V.S., U.V., H.V., T.J.W., J.F.W., A.F.W., N.J.S., Y.J. A.A., D.E.A., L.H.v.d.B., R.A.d.B., E.B., M.J.C., A.C., J.M.C., A.F.D., M.D., C.M.v.D., R.S.N.F., A.R.F., L.F., S.G., H.J.M.G., T.B.H., P.v.d.H., C.H., G.v.H., A.I., W.H.L.K., N.K., J.A.K., A.K., L.L., M.L., F.-Y.L., I.M.L., G.t.M., P.B.M., G.N., C.N.-C., B.A.O., R.A.O., S. Perz, A. Pfeufer, A. Petersmann, O.P., B.M.P., J.Q., F.R., J.I.R., I.R., N.J.S., C.S., M.P.S.S., M.F.S., E.Z.S., B.H.C.S., A.T., A.G.U., D.J.v.V., C.B.V., R.K.W., C.W., J.F.W., J.C.M.W., D.L., T.D.S. A.A., D.E.A., T.A., P.I.W.d.B., N.S., E.B., A.C., L.A.C., M.E., K.E., G.I.F., A.R.F., L.F., J.F., C.F., S.A.G., W.H.v.G., S.G., V.G., P.v.d.H., C.H., S.R.H., A.I., T.J., W.H.L.K., X.L., K.D.M., I.M.L., M.M., I.M.N., S. Padmanabhan, A. Pfeufer, O.P., B.M.P., K.R., H.S., A.T., A.V.S., S.H.W., Y.A.W., N.J.S. N.S., A.A., D.E.A, F.W.A., P.I.W.d.B., M.D., C.M.v.D,. M.E., G.I.F., J.F., S.A.G., V.G., C.H., A.I., Y.J., S.K., J.W.M., I.M.N., O.P., N.J.S., H.S., C.N.-C., P.v.d.H. A.A., D.E.A., T.A., F.W.A., J.C.B., R.A.d.B., E.B., H.C., M.J.C., A.C., J.M.C., L.A.C., A.F.D., M.D., C.M.v.D., M.E., K.E., S.B.F., G.I.F., A.R.F., J.F., W.H.v.G., V.G., T.B.H., P.v.d.H., C.H., S.R.H., G.v.H., A.A.H., A.H., A.I., Y.J., T.J., S.K., W.H.L.K., N.K., J.A.K., A.K., H.K.K., L.L., D.L., M.L., J.W.M., I.M.L., T.M., M.M., P.B.M., G.N., C.N.-C., I.M.N., C.J.O., B.A.O., S. Padmanabhan, S. Perz, A. Pfeufer, A. Petersmann, O.P., B.M.P., F.R., J.I.R., I.R., M.P.S.S., M.F.S., D.S.S., H.S., B.H.C.S., E.Z.S., A.T., A.G.U., D.J.v.V., U.V., H.V., T.J.W., H.-E.W., A.V.S., S.H.W., J.F.W., J.C.M.W., A.F.W. L.H.v.d.B., E.B., H.C., M.J.C., A.C., J.M.C., A.F.D., C.M.v.D., S.B.F., G.I.F., W.H.v.G., H.J.M.G., V.G., P.v.d.H., A.H., Y.J., S.K., H.K.K., L.L., P.B.M., G.N., C.N.-C., C.J.O., B.A.O., R.A.O., P.P.P., B.M.P., J.I.R., I.R., N.J.S., N.S., T.D.S., A.G.U., D.J.v.V., U.V., H.V., T.J.W., R.K.W., H.-E.W., C.W., J.F.W., A.F.W., D.L. Competing financial interests A.C. is a paid member of the Scientific Advisory Board of Affymetrix, a role that is managed by the Committee on Conflict of Interest of the Johns Hopkins University School of Medicine. Corresponding authors Correspondence to: * Nona Sotoodehnia (nsotoo@u.washington.edu) or * Stefan Kääb (skaab@med.lmu.de) or * Dan E Arking (arking@jhmi.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–4, Supplementary Tables 1–5 and Supplementary Note Additional data - Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies
- Nat Genet 42(12):1077-1085 (2010)
Nature Genetics | Article Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies * Cathy E Elks1, 106 Search for this author in: * NPG journals * PubMed * Google Scholar * John R B Perry2, 106 Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Sulem3, 106 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel I Chasman4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Nora Franceschini6 Search for this author in: * NPG journals * PubMed * Google Scholar * Chunyan He7, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Kathryn L Lunetta9, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Jenny A Visser11 Search for this author in: * NPG journals * PubMed * Google Scholar * Enda M Byrne12, 13 Search for this author in: * NPG journals * PubMed * Google Scholar * Diana L Cousminer14 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel F Gudbjartsson3 Search for this author in: * NPG journals * PubMed * Google Scholar * Tõnu Esko15, 16, 17 Search for this author in: * NPG journals * PubMed * Google Scholar * Bjarke Feenstra18 Search for this author in: * NPG journals * PubMed * Google Scholar * Jouke-Jan Hottenga19 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel L Koller20 Search for this author in: * NPG journals * PubMed * Google Scholar * Zoltán Kutalik21, 22 Search for this author in: * NPG journals * PubMed * Google Scholar * Peng Lin23 Search for this author in: * NPG journals * PubMed * Google Scholar * Massimo Mangino24 Search for this author in: * NPG journals * PubMed * Google Scholar * Mara Marongiu25 Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick F McArdle26 Search for this author in: * NPG journals * PubMed * Google Scholar * Albert V Smith27, 28 Search for this author in: * NPG journals * PubMed * Google Scholar * Lisette Stolk11, 29 Search for this author in: * NPG journals * PubMed * Google Scholar * Sophie H van Wingerden30 Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Hua Zhao1 Search for this author in: * NPG journals * PubMed * Google Scholar * Eva Albrecht31 Search for this author in: * NPG journals * PubMed * Google Scholar * Tanguy Corre32 Search for this author in: * NPG journals * PubMed * Google Scholar * Erik Ingelsson33 Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Hayward34 Search for this author in: * NPG journals * PubMed * Google Scholar * Patrik K E Magnusson33 Search for this author in: * NPG journals * PubMed * Google Scholar * Erin N Smith35, 36, 37 Search for this author in: * NPG journals * PubMed * Google Scholar * Shelia Ulivi38 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicole M Warrington39 Search for this author in: * NPG journals * PubMed * Google Scholar * Lina Zgaga40 Search for this author in: * NPG journals * PubMed * Google Scholar * Helen Alavere15 Search for this author in: * NPG journals * PubMed * Google Scholar * Najaf Amin30 Search for this author in: * NPG journals * PubMed * Google Scholar * Thor Aspelund27, 28 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefania Bandinelli41 Search for this author in: * NPG journals * PubMed * Google Scholar * Inês Barroso42 Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald S Berenson43 Search for this author in: * NPG journals * PubMed * Google Scholar * Sven Bergmann21, 22 Search for this author in: * NPG journals * PubMed * Google Scholar * Hannah Blackburn42 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Boerwinkle44 Search for this author in: * NPG journals * PubMed * Google Scholar * Julie E Buring4, 5, 45 Search for this author in: * NPG journals * PubMed * Google Scholar * Fabio Busonero25 Search for this author in: * NPG journals * PubMed * Google Scholar * Harry Campbell40 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Chanock46 Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Chen43 Search for this author in: * NPG journals * PubMed * Google Scholar * Marilyn C Cornelis47 Search for this author in: * NPG journals * PubMed * Google Scholar * David Couper48 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrea D Coviello49 Search for this author in: * NPG journals * PubMed * Google Scholar * Pio d'Adamo38 Search for this author in: * NPG journals * PubMed * Google Scholar * Ulf de Faire50 Search for this author in: * NPG journals * PubMed * Google Scholar * Eco J C de Geus19 Search for this author in: * NPG journals * PubMed * Google Scholar * Panos Deloukas42 Search for this author in: * NPG journals * PubMed * Google Scholar * Angela Döring31 Search for this author in: * NPG journals * PubMed * Google Scholar * George Davey Smith51 Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas F Easton52, 53 Search for this author in: * NPG journals * PubMed * Google Scholar * Gudny Eiriksdottir27 Search for this author in: * NPG journals * PubMed * Google Scholar * Valur Emilsson54 Search for this author in: * NPG journals * PubMed * Google Scholar * Johan Eriksson55, 56, 57, 58 Search for this author in: * NPG journals * PubMed * Google Scholar * Luigi Ferrucci59 Search for this author in: * NPG journals * PubMed * Google Scholar * Aaron R Folsom60 Search for this author in: * NPG journals * PubMed * Google Scholar * Tatiana Foroud20 Search for this author in: * NPG journals * PubMed * Google Scholar * Melissa Garcia61 Search for this author in: * NPG journals * PubMed * Google Scholar * Paolo Gasparini38 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Geller18 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Gieger31 Search for this author in: * NPG journals * PubMed * Google Scholar * The GIANT Consortium62 Search for this author in: * NPG journals * PubMed * Google Scholar * Vilmundur Gudnason27, 28 Search for this author in: * NPG journals * PubMed * Google Scholar * Per Hall33 Search for this author in: * NPG journals * PubMed * Google Scholar * Susan E Hankinson45, 63 Search for this author in: * NPG journals * PubMed * Google Scholar * Liana Ferreli25 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew C Heath64 Search for this author in: * NPG journals * PubMed * Google Scholar * Dena G Hernandez65 Search for this author in: * NPG journals * PubMed * Google Scholar * Albert Hofman29, 66 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank B Hu45, 47, 63 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Illig31 Search for this author in: * NPG journals * PubMed * Google Scholar * Marjo-Riitta Järvelin67 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew D Johnson9, 68 Search for this author in: * NPG journals * PubMed * Google Scholar * David Karasik69 Search for this author in: * NPG journals * PubMed * Google Scholar * Kay-Tee Khaw70 Search for this author in: * NPG journals * PubMed * Google Scholar * Douglas P Kiel69 Search for this author in: * NPG journals * PubMed * Google Scholar * Tuomas O Kilpeläinen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ivana Kolcic71 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Kraft45, 47, 63 Search for this author in: * NPG journals * PubMed * Google Scholar * Lenore J Launer61 Search for this author in: * NPG journals * PubMed * Google Scholar * Joop S E Laven72 Search for this author in: * NPG journals * PubMed * Google Scholar * Shengxu Li1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jianjun Liu73 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Levy9, 68, 74 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas G Martin75 Search for this author in: * NPG journals * PubMed * Google Scholar * Wendy L McArdle76 Search for this author in: * NPG journals * PubMed * Google Scholar * Mads Melbye18 Search for this author in: * NPG journals * PubMed * Google Scholar * Vincent Mooser77 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey C Murray78 Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah S Murray35, 36, 37 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Nalls79 Search for this author in: * NPG journals * PubMed * Google Scholar * Pau Navarro34 Search for this author in: * NPG journals * PubMed * Google Scholar * Mari Nelis15, 16, 17 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew R Ness80 Search for this author in: * NPG journals * PubMed * Google Scholar * Kate Northstone76 Search for this author in: * NPG journals * PubMed * Google Scholar * Ben A Oostra30, 81 Search for this author in: * NPG journals * PubMed * Google Scholar * Munro Peacock82 Search for this author in: * NPG journals * PubMed * Google Scholar * Lyle J Palmer39 Search for this author in: * NPG journals * PubMed * Google Scholar * Aarno Palotie14, 42, 83 Search for this author in: * NPG journals * PubMed * Google Scholar * Guillaume Paré4, 5, 84 Search for this author in: * NPG journals * PubMed * Google Scholar * Alex N Parker85 Search for this author in: * NPG journals * PubMed * Google Scholar * Nancy L Pedersen33 Search for this author in: * NPG journals * PubMed * Google Scholar * Leena Peltonen14, 42, 55, 83, 105 Search for this author in: * NPG journals * PubMed * Google Scholar * Craig E Pennell86 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Pharoah52, 53 Search for this author in: * NPG journals * PubMed * Google Scholar * Ozren Polasek71, 87 Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew S Plump88 Search for this author in: * NPG journals * PubMed * Google Scholar * Anneli Pouta55 Search for this author in: * NPG journals * PubMed * Google Scholar * Eleonora Porcu25 Search for this author in: * NPG journals * PubMed * Google Scholar * Thorunn Rafnar3 Search for this author in: * NPG journals * PubMed * Google Scholar * John P Rice23 Search for this author in: * NPG journals * PubMed * Google Scholar * Susan M Ring76 Search for this author in: * NPG journals * PubMed * Google Scholar * Fernando Rivadeneira11, 29, 66 Search for this author in: * NPG journals * PubMed * Google Scholar * Igor Rudan40, 89 Search for this author in: * NPG journals * PubMed * Google Scholar * Cinzia Sala32 Search for this author in: * NPG journals * PubMed * Google Scholar * Veikko Salomaa55 Search for this author in: * NPG journals * PubMed * Google Scholar * Serena Sanna25 Search for this author in: * NPG journals * PubMed * Google Scholar * David Schlessinger90 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas J Schork35, 36, 37 Search for this author in: * NPG journals * PubMed * Google Scholar * Angelo Scuteri25, 91 Search for this author in: * NPG journals * PubMed * Google Scholar * Ayellet V Segrè83, 92 Search for this author in: * NPG journals * PubMed * Google Scholar * Alan R Shuldiner26, 93 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicole Soranzo24, 42 Search for this author in: * NPG journals * PubMed * Google Scholar * Ulla Sovio67 Search for this author in: * NPG journals * PubMed * Google Scholar * Sathanur R Srinivasan43 Search for this author in: * NPG journals * PubMed * Google Scholar * David P Strachan94 Search for this author in: * NPG journals * PubMed * Google Scholar * Mar-Liis Tammesoo15 Search for this author in: * NPG journals * PubMed * Google Scholar * Emmi Tikkanen14, 55 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Toniolo32 Search for this author in: * NPG journals * PubMed * Google Scholar * Kim Tsui85 Search for this author in: * NPG journals * PubMed * Google Scholar * Laufey Tryggvadottir95 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathon Tyrer52, 53 Search for this author in: * NPG journals * PubMed * Google Scholar * Manuela Uda25 Search for this author in: * NPG journals * PubMed * Google Scholar * Rob M van Dam47, 96, 97 Search for this author in: * NPG journals * PubMed * Google Scholar * Joyce B J van Meurs11, 29 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Vollenweider98 Search for this author in: * NPG journals * PubMed * Google Scholar * Gerard Waeber98 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas J Wareham1 Search for this author in: * NPG journals * PubMed * Google Scholar * Dawn M Waterworth77 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael N Weedon2 Search for this author in: * NPG journals * PubMed * Google Scholar * H Erich Wichmann31, 99, 100 Search for this author in: * NPG journals * PubMed * Google Scholar * Gonneke Willemsen19 Search for this author in: * NPG journals * PubMed * Google Scholar * James F Wilson40 Search for this author in: * NPG journals * PubMed * Google Scholar * Alan F Wright34 Search for this author in: * NPG journals * PubMed * Google Scholar * Lauren Young85 Search for this author in: * NPG journals * PubMed * Google Scholar * Guangju Zhai24 Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Vivian Zhuang10 Search for this author in: * NPG journals * PubMed * Google Scholar * Laura J Bierut23 Search for this author in: * NPG journals * PubMed * Google Scholar * Dorret I Boomsma19 Search for this author in: * NPG journals * PubMed * Google Scholar * Heather A Boyd18 Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Crisponi25 Search for this author in: * NPG journals * PubMed * Google Scholar * Ellen W Demerath60 Search for this author in: * NPG journals * PubMed * Google Scholar * Cornelia M van Duijn29, 30 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Econs20, 82 Search for this author in: * NPG journals * PubMed * Google Scholar * Tamara B Harris61 Search for this author in: * NPG journals * PubMed * Google Scholar * David J Hunter45, 46, 47, 63 Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth J F Loos1 Search for this author in: * NPG journals * PubMed * Google Scholar * Andres Metspalu15, 16, 17 Search for this author in: * NPG journals * PubMed * Google Scholar * Grant W Montgomery101 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul M Ridker4, 5, 45, 102 Search for this author in: * NPG journals * PubMed * Google Scholar * Tim D Spector24 Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth A Streeten26 Search for this author in: * NPG journals * PubMed * Google Scholar * Kari Stefansson3, 103 Search for this author in: * NPG journals * PubMed * Google Scholar * Unnur Thorsteinsdottir3, 103 Search for this author in: * NPG journals * PubMed * Google Scholar * André G Uitterlinden11, 29, 66 Search for this author in: * NPG journals * PubMed * Google Scholar * Elisabeth Widen14 Search for this author in: * NPG journals * PubMed * Google Scholar * Joanne M Murabito9, 49, 107murabito@bu.edu Search for this author in: * NPG journals * PubMed * Google Scholar * Ken K Ong1, 104, 107ken.ong@mrc-epid.cam.ac.uk Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Murray2, 107anna.murray@pms.ac.uk Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 42,Pages:1077–1085Year published:(2010)DOI:doi:10.1038/ng.714Received10 May 2010Accepted19 October 2010Published online21 November 2010 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 To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P = 5.4 × 10−60) and 9q31.2 (P = 2.2 × 10−33), we identified 30 new menarche loci (all P < 5 × 10−8) and found suggestive evidence for a further 10 loci (P < 1.9 × 10−6). The new loci included four previously associated with body mass index (in or near FTO, SEC16B, TRA2B and TMEM18), three in or near other genes implicated in energy homeostasis (BSX, CRTC1 and MCHR2) and three in or near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and gene-set enrichment pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing. View full text Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Cathy E Elks, * John R B Perry & * Patrick Sulem Affiliations * Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK. * Cathy E Elks, * Jing Hua Zhao, * Tuomas O Kilpeläinen, * Shengxu Li, * Nicholas J Wareham, * Ruth J F Loos & * Ken K Ong * Genetics of Complex Traits, Peninsula Medical School, University of Exeter, UK. * John R B Perry, * Michael N Weedon & * Anna Murray * deCODE Genetics, Reykjavik, Iceland. * Patrick Sulem, * Daniel F Gudbjartsson, * Thorunn Rafnar, * Kari Stefansson & * Unnur Thorsteinsdottir * Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Daniel I Chasman, * Julie E Buring, * Guillaume Paré & * Paul M Ridker * Harvard Medical School, Boston, Massachusetts, USA. * Daniel I Chasman, * Julie E Buring, * Guillaume Paré & * Paul M Ridker * Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * Nora Franceschini * Department of Public Health, Indiana University School of Medicine, Indiana, USA. * Chunyan He * Melvin and Bren Simon Cancer Center, Indiana University, Indiana, USA. * Chunyan He * The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA. * Kathryn L Lunetta, * Andrew D Johnson, * Daniel Levy & * Joanne M Murabito * Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA. * Kathryn L Lunetta & * Wei Vivian Zhuang * Department of Internal Medicine, Erasmus Medical Center (MC), Rotterdam, The Netherlands. * Jenny A Visser, * Lisette Stolk, * Fernando Rivadeneira, * Joyce B J van Meurs & * André G Uitterlinden * Queensland Statistical Genetics, Queensland Institute of Medical Research, Brisbane, Australia. * Enda M Byrne * The University of Queensland, Brisbane, Australia. * Enda M Byrne * Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland. * Diana L Cousminer, * Aarno Palotie, * Leena Peltonen, * Emmi Tikkanen & * Elisabeth Widen * Estonian Genome Center, University of Tartu, Tartu, Estonia. * Tõnu Esko, * Helen Alavere, * Mari Nelis, * Mar-Liis Tammesoo & * Andres Metspalu * Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. * Tõnu Esko, * Mari Nelis & * Andres Metspalu * Genotyping Core Facility, Estonian Biocenter, Tartu, Estonia. * Tõnu Esko, * Mari Nelis & * Andres Metspalu * Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark. * Bjarke Feenstra, * Frank Geller, * Mads Melbye & * Heather A Boyd * Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands. * Jouke-Jan Hottenga, * Eco J C de Geus, * Gonneke Willemsen & * Dorret I Boomsma * Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA. * Daniel L Koller, * Tatiana Foroud & * Michael J Econs * Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland. * Zoltán Kutalik & * Sven Bergmann * Swiss Institute of Bioinformatics, Lausanne, Switzerland. * Zoltán Kutalik & * Sven Bergmann * Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. * Peng Lin, * John P Rice & * Laura J Bierut * Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. * Massimo Mangino, * Nicole Soranzo, * Guangju Zhai & * Tim D Spector * Istituto di Neurogenetica e Neurofarmacologia, Consiglio Nazionale delle Ricerche, Cagliari, Italy. * Mara Marongiu, * Fabio Busonero, * Liana Ferreli, * Eleonora Porcu, * Serena Sanna, * Angelo Scuteri, * Manuela Uda & * Laura Crisponi * Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Patrick F McArdle, * Alan R Shuldiner & * Elizabeth A Streeten * Icelandic Heart Association, Kopavogur, Iceland. * Albert V Smith, * Thor Aspelund, * Gudny Eiriksdottir & * Vilmundur Gudnason * University of Iceland, Reykjavik, Iceland. * Albert V Smith, * Thor Aspelund & * Vilmundur Gudnason * Netherlands Consortium of Healthy Aging, Rotterdam, The Netherlands. * Lisette Stolk, * Albert Hofman, * Fernando Rivadeneira, * Joyce B J van Meurs, * Cornelia M van Duijn & * André G Uitterlinden * Genetic-Epidemiology Unit, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands. * Sophie H van Wingerden, * Najaf Amin, * Ben A Oostra & * Cornelia M van Duijn * Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. * Eva Albrecht, * Angela Döring, * Christian Gieger, * Thomas Illig & * H Erich Wichmann * Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy. * Tanguy Corre, * Cinzia Sala & * Daniela Toniolo * Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. * Erik Ingelsson, * Patrik K E Magnusson, * Per Hall & * Nancy L Pedersen * MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, UK. * Caroline Hayward, * Pau Navarro & * Alan F Wright * Scripps Genomic Medicine, La Jolla, California, USA. * Erin N Smith, * Sarah S Murray & * Nicholas J Schork * The Scripps Translational Science Institute, La Jolla, California, USA. * Erin N Smith, * Sarah S Murray & * Nicholas J Schork * The Scripps Research Institute, La Jolla, California, USA. * Erin N Smith, * Sarah S Murray & * Nicholas J Schork * Medical Genetics, Department of Reproductive Sciences and Development, University of Trieste, Trieste, Italy. * Shelia Ulivi, * Pio d'Adamo & * Paolo Gasparini * Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Crawley, Australia. * Nicole M Warrington & * Lyle J Palmer * Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, Scotland. * Lina Zgaga, * Harry Campbell, * Igor Rudan & * James F Wilson * Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy. * Stefania Bandinelli * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Inês Barroso, * Hannah Blackburn, * Panos Deloukas, * Aarno Palotie, * Leena Peltonen & * Nicole Soranzo * Tulane University, New Orleans, Louisiana, USA. * Gerald S Berenson, * Wei Chen & * Sathanur R Srinivasan * Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, USA. * Eric Boerwinkle * Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Julie E Buring, * Susan E Hankinson, * Frank B Hu, * Peter Kraft, * David J Hunter & * Paul M Ridker * Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA. * Stephen J Chanock & * David J Hunter * Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA. * Marilyn C Cornelis, * Frank B Hu, * Peter Kraft, * Rob M van Dam & * David J Hunter * Collaborative Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. * David Couper * Sections of General Internal Medicine, Preventive Medicine and Endocrinology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA. * Andrea D Coviello & * Joanne M Murabito * Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. * Ulf de Faire * MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Bristol, UK. * George Davey Smith * Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. * Douglas F Easton, * Paul Pharoah & * Jonathon Tyrer * Department of Oncology, Strangeways Research Laboratories, University of Cambridge, Cambridge, UK. * Douglas F Easton, * Paul Pharoah & * Jonathon Tyrer * Molecular Programming and Research Informatics (MPRI), Merck and Co., Inc., Rahway, New Jersey, USA. * Valur Emilsson * National Institute for Health and Welfare, Helsinki, Finland. * Johan Eriksson, * Leena Peltonen, * Anneli Pouta, * Veikko Salomaa & * Emmi Tikkanen * Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland. * Johan Eriksson * Helsinki University Central Hospital, Unit of General Practice, Helsinki, Finland. * Johan Eriksson * Folkhalsan Research Centre, Helsinki, Finland. * Johan Eriksson * Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, USA. * Luigi Ferrucci * Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA. * Aaron R Folsom & * Ellen W Demerath * Laboratory of Epidemiology, Demography and Biometry, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA. * Melissa Garcia, * Lenore J Launer & * Tamara B Harris * A full list of members is provided in the Supplementary Note. * The GIANT Consortium * Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA. * Susan E Hankinson, * Frank B Hu, * Peter Kraft & * David J Hunter * Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. * Andrew C Heath * Laboratory of Neurogenetics, National Institute of Aging, Bethesda, Maryland, USA. * Dena G Hernandez * Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands. * Albert Hofman, * Fernando Rivadeneira & * André G Uitterlinden * Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. * Marjo-Riitta Järvelin & * Ulla Sovio * National Heart, Lung, and Blood Institute (NHLBI) Center for Population Studies, Bethesda, Maryland, USA. * Andrew D Johnson & * Daniel Levy * Hebrew Senior Life Institute for Aging Research and Harvard Medical School, Boston, Massachusetts, USA. * David Karasik & * Douglas P Kiel * Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK. * Kay-Tee Khaw * Medical School, University of Zagreb, Zagreb, Croatia. * Ivana Kolcic & * Ozren Polasek * Department of Obstetrics and Gynaecology, Erasmus, MC, Rotterdam, The Netherlands. * Joop S E Laven * Human Genetics, Genome Institute of Singapore, Singapore. * Jianjun Liu * Division of Cardiology, Boston University School of Medicine, Boston, Massachusetts, USA. * Daniel Levy * Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia. * Nicholas G Martin * Avon Longitudinal Study of Parents and Children (ALSPAC), Department of Social Medicine, University of Bristol, Bristol, UK. * Wendy L McArdle, * Kate Northstone & * Susan M Ring * Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania, USA. * Vincent Mooser & * Dawn M Waterworth * Department of Pediatrics, University of Iowa, Iowa City, Iowa, USA. * Jeffrey C Murray * Laboratory of Neurogenetics, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA. * Michael A Nalls * Department of Oral and Dental Science, University of Bristol, Bristol, UK. * Andrew R Ness * Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands. * Ben A Oostra * Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA. * Munro Peacock & * Michael J Econs * Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. * Aarno Palotie, * Leena Peltonen & * Ayellet V Segrè * Genetic and Molecular Epidemiology Laboratory, McMaster University, Hamilton, Ontario, Canada. * Guillaume Paré * Amgen, Cambridge, Massachusetts, USA. * Alex N Parker, * Kim Tsui & * Lauren Young * School of Women's and Infants' Health, The University of Western Australia, Crawley, Australia. * Craig E Pennell * Gen-Info Ltd., Zagreb, Croatia. * Ozren Polasek * Cardiovascular Disease, Merck Research Laboratory, Rahway, New Jersey, USA. * Andrew S Plump * Croatian Centre for Global Health, University of Split Medical School, Split, Croatia. * Igor Rudan * Gerontology Research Center, National Institute on Aging, Baltimore, Maryland, USA. * David Schlessinger * Unità Operativa Geriatria-Istituto Nazionale Ricovero e Cura per Anziani (INRCA), IRCCS, Rome, Italy. * Angelo Scuteri * Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA. * Ayellet V Segrè * Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland, USA. * Alan R Shuldiner * Division of Community Health Sciences, St. George's, University of London, London, UK. * David P Strachan * Icelandic Cancer Registry, Reykjavik, Iceland. * Laufey Tryggvadottir * Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. * Rob M van Dam * Department of Medicine, National University of Singapore, Singapore. * Rob M van Dam * Department of Internal Medicine, BH-10 Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland. * Peter Vollenweider & * Gerard Waeber * Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany. * H Erich Wichmann * Klinikum Grosshadern, Munich, Germany. * H Erich Wichmann * Molecular Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia. * Grant W Montgomery * Division of Cardiology, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Paul M Ridker * Faculty of Medicine, University of Iceland, Reykjavik, Iceland. * Kari Stefansson & * Unnur Thorsteinsdottir * Department of Paediatrics, University of Cambridge, Cambridge, UK. * Ken K Ong * Deceased. * Leena Peltonen * These senior authors jointly supervised this work. * Joanne M Murabito, * Ken K Ong & * Anna Murray Contributions D.I.B., H.A.B., D.I.C., D.L.C., L.C., E.W.D., M.J.E., C.E.E., T.E., B.F., N.F., F.G., D.F.G., C. He, T.B.H., D.J.H., D.L.K., K.K.L., M. Mangino, M. Marongiu, J.M.M., A. Metspalu, A. Murray, K.K.O., J.R.B.P., L.S., E.A.S., P.S., U.T., A.G.U., C.M.v.D., S.H.v.W., J.A.V., E.W., G.Z. Competing financial interests P.S., D.F.G., K.S. and U.T. are employed by deCODE Genetics. V.E. and A. Plump are employed by Merck & Co. V.M. and D.M.W. are employed by GlaxoSmithKline. A. Parker, L.Y. and K.T. are employed by Amgen. I.B. and spouse own stock in Incyte Ltd and GlaxoSmithKline. Corresponding authors Correspondence to: * Anna Murray (anna.murray@pms.ac.uk) or * Ken K Ong (ken.ong@mrc-epid.cam.ac.uk) or * Joanne M Murabito (murabito@bu.edu) Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 12 (2M) Age at menarche associations for 8,770 SNPs in 16 candidate genes and their surrounding regions (+/-300kb). PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–8, Supplementary Tables 1–11 and 13–17 and Supplementary Note. Additional data - Overexpression of Fto leads to increased food intake and results in obesity
- Nat Genet 42(12):1086-1092 (2010)
Nature Genetics | Article Overexpression of Fto leads to increased food intake and results in obesity * Chris Church1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lee Moir1 Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona McMurray1 Search for this author in: * NPG journals * PubMed * Google Scholar * Christophe Girard2 Search for this author in: * NPG journals * PubMed * Google Scholar * Gareth T Banks1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lydia Teboul1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sara Wells1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jens C Brüning3 Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick M Nolan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Frances M Ashcroft2 Search for this author in: * NPG journals * PubMed * Google Scholar * Roger D Cox1r.cox@har.mrc.ac.uk Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1086–1092Year published:(2010)DOI:doi:10.1038/ng.713Received16 February 2010Accepted19 October 2010Published online14 November 2010 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 Genome-wide association studies have identified SNPs within FTO, the human fat mass and obesity–associated gene, that are strongly associated with obesity. Individuals homozygous for the at-risk rs9939609 A allele weigh, on average, ~3 kg more than individuals with the low-risk T allele. Mice that lack FTO function and/or Fto expression display increased energy expenditure and a lean phenotype. We show here that ubiquitous overexpression of Fto leads to a dose-dependent increase in body and fat mass, irrespective of whether mice are fed a standard or a high-fat diet. Our results suggest that increased body mass results primarily from increased food intake. Mice with increased Fto expression on a high-fat diet develop glucose intolerance. This study provides the first direct evidence that increased Fto expression causes obesity in mice. View full text Figures at a glance * Figure 1: Generation of a mouse model overexpressing Fto. () Relative Fto expression in the indicated tissues of FTO-2 (n = 10), FTO-3 (n = 10) and FTO-4 (n = 10) mice. Ab, abdominal; BAT, brown adipose tissue; Epi, epigonadal; Hypo, hypothalamus; Panc, pancreas, Sub, subcutaneous; WAT, white adipose tissue. Data are expressed as mean ± s.e.m. **P < 0.01; ***P < 0.0001; ns, nonsignificant. () Representative protein blots of FTO and actin (loading control) from skeletal muscle and liver from FTO-2, FTO-3 and FTO-4 mice. () RT-PCR of Egfp using brain cDNA prepared from FTO-2, FTO-3 and FTO-4 mice. Hprt1, encoding hypoxanthine-guanine phosphoribosyltransferase, is included as a control. * Figure 2: Fto dose-dependent increases in body weight are observed in male and female mice on standard (SD) and high-fat (HFD) diets. () Females on SD. FTO-2 (wild type, n = 16), FTO-3 (n = 28, P = 0.0003) and FTO-4 (n = 16, P < 0.0001). () Females on HFD. FTO-2 (n = 15), FTO-3 (n = 14, P = 0.04) and FTO-4 (n = 15, P = 0.002). () Males on SD. FTO-2 (n = 16), FTO-3 (n = 31, P = 0.001) and FTO-4 (n = 16, P = 0.0001). () Males on HFD. FTO-2 (n = 18), FTO-3 (n = 15, P = 0.01) and FTO-4 (n = 16, P < 0.0001). Data are expressed as mean ± s.e.m. Statistical analysis was performed using a repeated measures analysis of variance. All P values are against FTO-2. * Figure 3: Body composition varies with Fto copy number. () Body weight of 20-week-old male and female mice on a standard (SD) and high-fat diet (HFD). Males on SD: FTO-2 (n = 16), FTO-3 (n = 31) and FTO-4 (n = 15). Males on HFD: FTO-2 (n = 18), FTO-3 (n = 15) and FTO-4 (n = 16). Females on SD: FTO-2 (n = 16), FTO-3 (n = 28) and FTO-4 (n = 16). Females on HFD: FTO-2 (n = 15), FTO-3 (n = 14) and FTO-4 (n = 15). () Total fat mass measured by dual-energy X-ray absorptiometry (DEXA) scanning in male and female mice on SD and HFD. Males on SD: FTO-2 (n = 16), FTO-3 (n = 30) and FTO-4 (n = 15). Males on HFD: FTO-2 (n = 17), FTO-3 (n = 15) and FTO-4 (n = 16). Females on SD: FTO-2 (n = 16), FTO-3 (n = 28) and FTO-4 (n = 16). Females on HFD: FTO-2 (n = 16), FTO-3 (n = 14) and FTO-4 (n = 15). (,) Weights of epigonadal WAT () and abdominal WAT () in mice overexpressing Fto. Males on SD: FTO-2 (n = 16), FTO-3 (n = 21) and FTO-4 (n = 15). Males on HFD: FTO-2 (n = 15), FTO-3 (n = 14) and FTO-4 (n = 14). Females on SD: FTO-2 (n = 15), FTO-3 (n =! 28) and FTO-4 (n = 15). Females on HFD: FTO-2 (n = 12), FTO-3 (n = 14) and FTO-4 (n = 14). () Epigonadal adipocyte area is increased in female mice on both SD and HFD and in males on a HFD (n = 5 in each case). () Lean body mass in male and female mice on SD and HFD. Same mouse numbers as in . Data in – are expressed as mean ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.0001. * Figure 4: Effects of Fto on energy intake and plasma leptin. () Food intake over 24 h normalized to body weight (BW), measured in 10-week-old and 19-week-old mice. Males on SD: FTO-2 (n = 16), FTO-3 (n = 31) and FTO-4 (n = 16). Males on HFD: FTO-2 (n = 18), FTO-3 (n = 15) and FTO-4 (n = 16). Females on SD: FTO-2 (n = 16), FTO-3 (n = 28) and FTO-4 (n = 16). Females on HFD: FTO-2 (n = 15), FTO-3 (n = 14) and FTO-4 (n = 15). () Plasma leptin levels at 8 weeks of age adjusted for BW following an overnight 16-h fast. Males on SD: FTO-2 (n = 14), FTO-3 (n = 25) and FTO-4 (n = 14). Males on HFD: FTO-2 (n = 12), FTO-3 (n = 12) and FTO-4 (n = 12). Females on SD: FTO-2 (n = 16), FTO-3 (n = 25) and FTO-4 (n = 14). Females on HFD: FTO-2 (n = 13), FTO-3 (n = 10) and FTO-4 (n = 10). () Relative Lep (Leptin) gene expression in 20-week-old female epigonadal (Epi), abdominal (Ab) and subcutaneous (sub) WAT. FTO-2 (n = 10), FTO-3 (n = 10) and FTO-4 (n = 10). () Relative gene expression of hypothalamic neuropeptides. FTO-2 (n = 10), FTO-3 (n = 10) and F! TO-4 (n = 10) mice. Data in – are expressed as mean ± s.e.m. *P < 0.05; **P < 0.01. * Figure 5: Effects of Fto on energy expenditure and physical activity. (–) Heat production over a 22-h period during the light and dark phases for 18-week-old male () and female () mice on a standard (SD) or high-fat diet (HFD). Males on SD: FTO-2 (n = 15), FTO-3 (n = 25) and FTO-4 (n = 16). Males on HFD: FTO-2 (n = 12), FTO-3 (n = 12) and FTO-4 (n = 15). Females on SD: FTO-2 (n = 16), FTO-3 (n = 22) and FTO-4 (n = 15). Females on HFD: FTO-2 (n = 12), FTO-3 (n = 13) and FTO-4 (n = 14). () Physical activity was measured as the number of rotations of an activity wheel in a 7-d period, following a 3-d entrainment period. Males and females combined: FTO-2 (n = 7), FTO-3 (n = 7) and FTO-4 (n = 6). Data in – are expressed as mean ± s.e.m. *P < 0.05. * Figure 6: Glucose homeostasis and Fto overexpression. () Area under the curve (AUC) during a 120-min IPGTT in 12-week-old mice. Males on SD: FTO-2 (n = 15), FTO-3 (n = 25) and FTO-4 (n = 16). Males on HFD: FTO-2 (n = 12), FTO-3 (n = 13) and FTO-4 (n = 12). Females on SD: FTO-2 (n = 16), FTO-3 (n = 22) and FTO-4 (n = 15). Females on HFD: FTO-2 (n = 12), FTO-3 (n = 12) and FTO-4 (n = 13). () AUC analysis for glucose during a 30-min IPGTT in 16-week-old mice. Males on SD: FTO-2 (n = 15), FTO-3 (n = 23) and FTO-4 (n = 15). Males on HFD: FTO-2 (n = 12), FTO-3 (n = 12) and FTO-4 (n = 14). Females on SD: FTO-2 (n = 16), FTO-3 (n = 22) and FTO-4 (n = 15). Females on HFD: FTO-2 (n = 12), FTO-3 (n = 11) and FTO-4 (n = 15). () Adiponectin levels at 20 weeks of age following a 6-h light-phase fast. Males on SD: FTO-2 (n = 12), FTO-3 (n = 22) and FTO-4 (n = 10). Males on HFD: FTO-2 (n = 12), FTO-3 (n = 10) and FTO-4 (n = 10). Females on SD: FTO-2 (n = 11), FTO-3 (n = 22) and FTO-4 (n = 9). Females on HFD: FTO-2 (n = 10), FTO-3 (n = 9) and F! TO-4 (n = 10). Data in – are expressed as mean ± s.e.m. *P < 0.05; ***P < 0.0001. Author information * Abstract * Author information * Supplementary information Affiliations * MRC Harwell, Metabolism and Inflammation, Harwell Science and Innovation Campus, Harwell, UK. * Chris Church, * Lee Moir, * Fiona McMurray, * Gareth T Banks, * Lydia Teboul, * Sara Wells, * Patrick M Nolan & * Roger D Cox * Henry Wellcome Centre for Gene Function, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK. * Christophe Girard & * Frances M Ashcroft * Center of Molecular Medicine Cologne (CMMC) and Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany. * Jens C Brüning Contributions C.C., R.D.C. and F.M.A. planned the project and wrote the manuscript. C.C., L.M. and F.M. carried out the whole-animal experiments. P.M.N., S.W. and G.T.B. carried out the behavioral and circadian studies. J.C.B. and C.G. provided overexpression vector design, construction and methods. L.T. and C.C. carried out the transgenic work. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Roger D Cox (r.cox@har.mrc.ac.uk) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–9, Supplementary Tables 1–3 and Supplementary Note Additional data - Targets and dynamics of promoter DNA methylation during early mouse development
- Nat Genet 42(12):1093-1100 (2010)
Nature Genetics | Article Targets and dynamics of promoter DNA methylation during early mouse development * Julie Borgel1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sylvain Guibert1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yufeng Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Hatsune Chiba2 Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Schübeler3 Search for this author in: * NPG journals * PubMed * Google Scholar * Hiroyuki Sasaki2 Search for this author in: * NPG journals * PubMed * Google Scholar * Thierry Forné1 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Weber1michael.weber@igmm.cnrs.fr Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1093–1100Year published:(2010)DOI:doi:10.1038/ng.708Received08 April 2010Accepted14 September 2010Published online07 November 2010 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 DNA methylation is extensively reprogrammed during the early phases of mammalian development, yet genomic targets of this process are largely unknown. We optimized methylated DNA immunoprecipitation for low numbers of cells and profiled DNA methylation during early development of the mouse embryonic lineage in vivo. We observed a major epigenetic switch during implantation at the transition from the blastocyst to the postimplantation epiblast. During this period, DNA methylation is primarily targeted to repress the germline expression program. DNA methylation in the epiblast is also targeted to promoters of lineage-specific genes such as hematopoietic genes, which are subsequently demethylated during terminal differentiation. De novo methylation during early embryogenesis is catalyzed by Dnmt3b, and absence of DNA methylation leads to ectopic gene activation in the embryo. Finally, we identify nonimprinted genes that inherit promoter DNA methylation from parental gametes, su! ggesting that escape of post-fertilization DNA methylation reprogramming is prevalent in the mouse genome. View full text Figures at a glance * Figure 1: Profiling of DNA methylation during early mouse embryogenesis. () Comparison of MeDIP-WGA on 150 ng of DNA and of pooled unamplified MeDIPs on 2 μg of the same DNA (Supplementary Fig. 1). The scatter plot compares average log2 ratios in −400 bp to +400 bp relative to all TSS. () We hybridized MeDIP samples from E3.5 blastocysts, E6.5 epiblasts (EPB) and total E9.5 embryos on NimbleGen HD2 arrays covering 11 kb of all mouse promoters. () The top graph shows the fraction of tiles with a methylated region as a function of the distance to the TSS (black arrow). For comparison, the average CpG count per kilobase along the tiles is shown (red dotted line, right axis). The bottom graph shows the fraction of tiles with a de novo methylation peak as a function of the distance to the TSS. () MeDIP profiles at the imprinted gene Plagl1 confirm the presence of a germline methylation mark47. The graphs show smoothed MeDIP over input ratios of individual oligonucleotides. Here and in all figures, the MeDIP profiles we obtained with unamplified poo! led MeDIPs at E9.5 are also shown for validation. The gene is shown below the graphs as a gray box, and the transcription start site is shown as a gray arrow. Red bars represent the position of the CpGs. () The Heatmap shows the dynamics of DNA methylation at 691 genes with a methylated promoter in E9.5 embryos. Group I genes are de novo methylated in early embryos, whereas group II genes are already hypermethylated in preimplantation blastocysts. * Figure 2: De novo CpG island methylation in epiblast cells. () De novo methylation of the pluripotency gene Tcl1 in epiblast (EPB) cells. Other examples of de novo methylated pluripotency genes are given in Supplementary Figure 4. The graphs show smoothed MeDIP over input ratios of individual oligonucleotides. Red bars represent the position of CpGs. () Examples of germline-specific genes de novo methylated during implantation in epiblast cells. More examples are given in Supplementary Figure 5. () Validation of promoter DNA methylation by COBRA. All five tested germline-specific genes are de novo methylated at E6.5 in the EPB and the extraembryonic ectoderm (EE). The promoter of Oct4, which has been shown to be methylated in extraembryonic lineages48 and partially de novo methylated in E9.5 embryos38, is used as a control. Here and in all figures, the number of TaqαI sites in the amplified fragment is indicated in parenthesis, and asterisks mark restriction fragments representing end products of the digestion. () Bisulfite sequenci! ng in the promoters of Dazl and Sycp1 confirms de novo methylation during implantation in epiblast cells. Other validations by bisulfite sequencing are shown in Supplementary Figure 7. Circles represent CpG dinucleotides either unmethylated (open) or methylated (closed). * Figure 3: Promoter DNA methylation at hematopoietic genes is erased during hematopoietic differentiation. () The hematopoietic genes Pou2af1, Tlr6 and Cytip gain promoter DNA methylation in EPB cells. The graphs show smoothed MeDIP over input ratios of individual oligonucleotides. Red bars represent the position of CpGs. () Validation by COBRA confirms that all three tested hematopoietic genes gain promoter DNA methylation during implantation and are hypermethylated at E6.5 in the EPB, the EE and in E9.5 embryos. All genes also show substantial promoter DNA methylation in hematopoietic stem cells (HSCs) isolated from E10.5 embryos. Subsequently in adults (Ad), Cytip and Tlr6 promoter methylation is lost in bone marrow HSCs, B cells and T cells but is maintained in other tissues such as liver. For the B-cell–specific gene Pou2af1, promoter methylation is specifically erased during differentiation of adult HSCs into B cells. * Figure 4: Inheritance of promoter DNA methylation from oocytes at nonimprinted genes. () Examples of hematopoietic genes (Fyb) and germline-specific genes (Piwil1 and Csnka2ip) with high levels of promoter DNA methylation throughout early development. More examples are given in Supplementary Figure 10. The graphs show smoothed MeDIP over input ratios of individual oligonucleotides. Red bars represent the position of CpGs. () Validation of promoter DNA methylation by COBRA. All tested genes show hypermethylation in EPB and EE in E6.5 embryos. E3.5 blastocysts and E2.5 morulas show a consistent pattern of mixed methylated and unmethylated alleles. () Bisulfite sequencing in the promoter of Piwil1 in gametes and early embryos. () Bisulfite sequencing in the Piwil1 promoter in BL6 × JF1 E3.5 blastocysts shows that only maternal alleles carry DNA methylation. Mat, maternal alleles; pat, paternal alleles. () Bisulfite sequencing in the promoter of Tssk2 in gametes and early embryos. () Bisulfite sequencing in the Tssk2 promoter in BL6 × JF1 E3.5 blastocysts shows! that only maternal alleles carry DNA methylation. Circles represent CpG dinucleotides either unmethylated (open) or methylated (closed). * Figure 5: Promoter DNA methylation mediated by Dnmt3b maintains gene repression in vivo. () DNA methylation in the promoter of the indicated germline-specific genes was analyzed by COBRA in wildtype (WT) and mutant E9.5 embryos heterozygous or homozygous for Dnmt3 deletions. Most tested genes showed severe reduction of promoter DNA methylation in Dnmt3b−/− embryos but were unaffected in Dnmt3a−/− embryos. Additional validations by bisulfite sequencing are shown in Supplementary Figure 12. () Promoter DNA methylation by COBRA in wildtype and Dnmt3 mutant E9.5 embryos at pluripotency genes (left), hematopoietic genes (middle) and eye genes (right). () Absence of promoter de novo methylation is associated with gene reactivation. Expression of indicated genes was measured by real-time qPCR in wildtype (WT), Dnmt3a−/− and Dnmt3b−/− E9.5 embryos. Values are arbitrary units after normalization to three housekeeping genes (Gapdh, Rpl13A and Actb). Error bars represent standard deviations from two or three independent experiments. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE22831 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Institute of Molecular Genetics, Centre National de la Recherche Scientifique (CNRS) UMR 5535, Université Montpellier 2, Université Montpellier 1, Montpellier, France. * Julie Borgel, * Sylvain Guibert, * Thierry Forné & * Michael Weber * Department of Molecular Genetics, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan. * Yufeng Li, * Hatsune Chiba & * Hiroyuki Sasaki * Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. * Dirk Schübeler Contributions J.B. performed all experiments and data analysis and contributed to the writing of the manuscript. S.G. developed R scripts and participated in data analysis. Y.L., H.C. and H.S. prepared samples from Dnmt mutant embryos. D.S. and T.F. participated in the study design and writing of the manuscript. M.W. designed and supervised the study, participated in data analysis and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michael Weber (michael.weber@igmm.cnrs.fr) Supplementary information * Abstract * Accession codes * Author information * Supplementary information Zip files * Supplementary Table 1 (72K) Genes with methylated promoters identified in early mouse embryos. PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–13 and Supplementary Table 2 Additional data - miR-212 and miR-132 are required for epithelial stromal interactions necessary for mouse mammary gland development
- Nat Genet 42(12):1101-1108 (2010)
Nature Genetics | Article miR-212 and miR-132 are required for epithelial stromal interactions necessary for mouse mammary gland development * Ahmet Ucar1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Vida Vafaizadeh2 Search for this author in: * NPG journals * PubMed * Google Scholar * Hubertus Jarry3 Search for this author in: * NPG journals * PubMed * Google Scholar * Jan Fiedler4 Search for this author in: * NPG journals * PubMed * Google Scholar * Petra A B Klemmt2 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Thum4 Search for this author in: * NPG journals * PubMed * Google Scholar * Bernd Groner2 Search for this author in: * NPG journals * PubMed * Google Scholar * Kamal Chowdhury1kchowdh@gwdg.de Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1101–1108Year published:(2010)DOI:doi:10.1038/ng.709Received12 April 2010Accepted13 October 2010Published online07 November 2010 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 MicroRNAs are small noncoding RNAs that carry out post-transcriptional regulation of the expression of their target genes. However, their roles in mammalian organogenesis are only beginning to be understood. Here we show that the microRNA-212/132 family (which comprises miR-212 and miR-132) is indispensable during the development of the mammary glands in mice, particulary for the regulation of the outgrowth of the epithelial ducts. Mammary transplantation experiments revealed that the function of the miR-212/132 family is required in the stroma but not in the epithelia. Both miR-212 and miR-132 are expressed exclusively in mammary stroma and directly target the matrix metalloproteinase MMP-9. In glands that lack miR-212 and miR-132, MMP-9 expression increases and accumulates around the ducts. This may interfere with collagen deposition and lead to hyperactivation of the tumor growth factor-β signaling pathway, thereby impairing ductal outgrowth. Our results identify the miR! -212/132 family as one of the main regulators of the epithelial-stromal interactions that are required for proper pubertal development of the mammary gland. View full text Figures at a glance * Figure 1: Genetic deletion of miR-212/132 in mice results in mammary gland defects associated with impaired nourishment of pups. () Pup growth curve analysis. Pups of homozygous mutant females had retarded growth. The differences in body weights of miR-212/132−/− mothers' pups were significant at postnatal day 2 (P < 0.01) and at all subsequent days (P < 0.001) compared with pups of wild-type mothers. No significant difference was observed between the growth curves for pups of wild-type or heterozygous mothers. Three litters per genotype were used for this analysis. Data are expressed as mean ± s.d.; n = 15 for each group. () Neutral-red staining of sections from lactating mammary gland samples. The dense epithelial staining (red) was seen throughout the whole section of the glands from wild-type and heterozygous mice. Epithelia within homozygous mutant glands were observed only in the proximal region close to the nipple. The boundaries of the section for the homozygous mutant sample are shown with dashed lines, as the non-epithelial fat pad does not show any staining. * Figure 2: miR-212/132−/− mammary glands have a defect in pubertal ductal outgrowth, but not in ductal side branching and lobuloalveolar differentiation. (–) Whole-mount analyses of inguinal (no. 4) mammary glands from wild-type and homozygous mutant littermates at different stages of mammary gland development. Virgin stages are pre-pubertal (3 weeks; ,), early pubertal (5 weeks; ,), late pubertal (10 weeks; ,), and 40-week-old adult (,). Lactating samples (,) were obtained from mice killed 1 day after giving birth. Proximal-to-distal orientations of all samples are shown as left-to-right. The grayscale insets are high-magnification pictures of corresponding samples for detailed visualization. In wild-type glands, TEBs were formed (arrow in ) and ductal outgrowth took place during puberty (,). No TEBs were observed at the distal ends of the ducts in mutant glands (arrowhead in ) and ductal outgrowth did not take place (,). Ductal side branching was observed in mutant glands (arrows in ) similar to wild-type glands (). Lobuloalveolar structures were formed in mutant glands during pregnancy and milk-producing alveoli were see! n during lactation () similar to wild-type glands (), but restricted to the proximal region where the rudimentary ductal tree was located. At least five mice were analyzed for each genotype at each stage. Scale bars represent 1 cm; for grayscale insets, scale bars represent 1 mm. * Figure 3: miR-212/132−/− mammary epithelia have normal ductal architecture. (–) Mammary gland sections from 6-week-old wild-type (,,) and homozygous mutant (,,) littermates were analyzed by hematoxylin-eosin (H&E) staining (,), and immunohistochemistry for E-cadherin (E-cadh; ,) and cytokeratin 14 (CK-14; ,). E-cadherin is a marker for luminal epithelial cells, whereas cytokeratin-14 is expressed only by myoepithelial cells within the ductal structures. Both wild-type and homozygous mutant mammary ducts have an inner single layer of luminal epithelial cells and outer layer of myoepithelial cells, as shown by E-cadherin and cytokeratin-14 staining, respectively. Fibroblasts are distinguishable within the periductal stroma for both genotypes. Scale bars represent 100 μm. * Figure 4: miR-212/132−/− mammary epithelia have normal capacity for ductal outgrowth. () The strategy of the mammary epithelial transplantation. The inguinal (no. 4) glands are shown together with rudimentary epithelial tree in the proximal region close to the nipple area. The locations of the cuts for clearing the fat pads are shown as dashed lines. The arrows show the origin of transplants and the location to which they were transplanted. (–) Whole-mount staining of the transplanted mammary glands analyzed 3 and 12 weeks after the operation (p.o.) and in very early lactation. Asterisks show the region where the transplants were placed during the operation. Homozygous mutant epithelia (−/−E) formed TEBs (arrow in ) and underwent proper ductal outgrowth within the wild-type fat pad stroma (+/+S; –). During pregnancy these ductal structures differentiated into lobuloalveolar structures (). By contrast, wild-type epithelia (+/+E) did not generate TEBs to invade the homozygous mutant fat pad stroma (−/−S; –). However, these underdeveloped ductal st! ructures, which were restricted to the region of transplantation, underwent lobuloalveolar differentiation during pregnancy (). and are low-magnification pictures of and , respectively. Scale bars represent 1 cm (,,,) and 2 mm (,,,). * Figure 5: The miR-212/132−/− stroma is not permissive for pubertal outgrowth of the epithelial ducts. () Whole-mount staining of transplanted whole mammary glands analyzed 5 and 10 weeks after the operation (p.o.). Outlines in , and – show locations of the grayscale insets images. Asterisks show the parts of nipple tissues that were also transplanted. Mammary glands from 5-week-old wild-type or heterozygous donor mice had the proper TEBs and ductal outgrowth after being transplanted into homozygous (,) or wild-type (,) recipient mice. By contrast, mammary glands of 5-week-old miR-212/132−/− donor mice showed no ductal outgrowth or TEB structures when they were transplanted into miR-212/132−/− () or wild-type () mice. Mammary glands of 3-week-old wild-type mice were also able to initiate ductal outgrowth in miR-212/132−/− recipient mice (), whereas, the glands of 3-week-old mutant mice failed to form proper TEBs in wild-type mice (). () The strategy of the whole mammary gland transplantation. The inguinal (no. 4) glands are shown on both sides of mice in the low! er abdomen. Both right and left glands of donor mice were used for transplanting, each into a different recipient mouse (arrows). The location where the transplants were placed is shown in the upper abdomen in a perpendicular orientation to the endogenous inguinal glands. Arrows in and show TEBs on the distal tips of primary ducts. Scale bars represent 1 cm. * Figure 6: In the mammary gland, miR-212 and miR-132 are exclusively expressed in the stromal cells, but not in the epithelia. () Quantitative RT-PCR analyses were performed to detect the expression of miR-212 and miR-132 in 10-week-old whole mammary glands, cleared fat pads and stromal and epithelial organoid fractions from digested glands. Cleared fat pad samples, which are epithelium-free, have a three-fold higher expression of miR-212 and miR-132 than whole glands. Isolated stromal fractions from mammary glands have the highest expression of both miR-212 and miR-132, whereas isolated epithelial organoids express neither miR-212 nor miR-132. All expression levels were calculated as fold-changes compared to their levels in intact mammary glands from 10-week-old mice. () Quantitative RT-PCR analyses were performed for E-cadherin and vimentin to determine the presence of epithelial and fibroblast cells, respectively, in the same sample set as in . The absence of E-cadherin expression in cleared fat pad and isolated stromal fraction samples proved that these samples were epithelium-free. The absence ! of vimentin expression in isolated epithelial organoids also showed that there was no fibroblast contamination in their preparation. Data are expressed as mean ± s.d.; n = 3 for each sample set. * Figure 7: Loss of miR-212 and miR-132 causes high MMP-9 levels and hyperactivity of TGF-β pathway in mutant mammary glands. Immunohistochemical staining for MMP-9 (–) and phosphorylated Smad2 or 3 (p-Smad2/3; ) on mammary gland sections from 6-week-old wild-type mice (–, –) and homozygous mutant littermates (–, –). In wild-type mammary glands, high MMP-9 was seen only in the periductal stroma of TEBs. Arrows show the MMP-9–expressing fibroblasts in . The periductal stroma of growth-quiescent ducts in wild-type glands has low MMP-9 staining (arrowheads in and ). In the fat pad between ducts, there are a few scattered fibroblasts expressing high levels of MMP-9 (blue arrows in ). The periductal stroma in mutant glands shows high MMP-9 (arrowheads in and ), and both periductal stroma and fat pad contain many MMP-9–expressing fibroblasts (black and blue arrows, respectively, in ). In wild-type mammary glands, p-Smad2/3 is present in the epithelial cells of TEBs and fibroblasts around TEBs (). However, p-Smad2/3 is not detectable in the epithelia or stroma of growth-quiescent ducts of wi! ld-type glands (,). In mutant glands, most epithelial cells of all ducts are stained for p-Smad2/3 (). There were also several p-Smad2/3-positive fibroblasts in both periductal stroma and fat pads of mutant glands (blue arrowheads in –). Scale bars represent 50 μm for ,,, and 100 μm for other panels. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * HM627212 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Department of Molecular Cell Biology, Max Planck Institute of Biophysical Chemistry, Goettingen, Germany. * Ahmet Ucar & * Kamal Chowdhury * Georg-Speyer-Haus, Institute for Biomedical Research, Frankfurt am Main, Germany. * Vida Vafaizadeh, * Petra A B Klemmt & * Bernd Groner * Department of Endocrinology, University of Goettingen, Goettingen, Germany. * Hubertus Jarry * Institute for Molecular and Translational Therapeutic Strategies, Hannover Medical School, Germany. * Jan Fiedler & * Thomas Thum * Present address: Cellular Senescence Group, German Cancer Research Center (DKFZ), Heidelberg, Germany. * Ahmet Ucar Contributions A.U. and K.C. developed the concept of this study. A.U. performed all experiments except the mammary epithelial transplantation analyses, which were performed by V.V. and P.A.B.K. in the laboratory of B.G., serum hormone level analyses, which were done by H.J., and luciferase assays, which were done by J.F. in the laboratory of T.T. The manuscript was written by A.U. and K.C. Important suggestions were made by B.G., V.V. and T.T. which improved the quality of the manuscript. All authors contributed to the discussion of the data and commented on the final version of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Kamal Chowdhury (kchowdh@gwdg.de) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (9M) Supplementary Figures 1–10, Supplementary Table 1 and Supplementary Note Additional data - A de novo paradigm for mental retardation
- Nat Genet 42(12):1109-1112 (2010)
Nature Genetics | Letter A de novo paradigm for mental retardation * Lisenka E L M Vissers1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Joep de Ligt1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Gilissen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Irene Janssen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Marloes Steehouwer1 Search for this author in: * NPG journals * PubMed * Google Scholar * Petra de Vries1 Search for this author in: * NPG journals * PubMed * Google Scholar * Bart van Lier1 Search for this author in: * NPG journals * PubMed * Google Scholar * Peer Arts1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nienke Wieskamp1 Search for this author in: * NPG journals * PubMed * Google Scholar * Marisol del Rosario1 Search for this author in: * NPG journals * PubMed * Google Scholar * Bregje W M van Bon1 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Hoischen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Bert B A de Vries1 Search for this author in: * NPG journals * PubMed * Google Scholar * Han G Brunner1, 3h.brunner@antrg.umcn.nl Search for this author in: * NPG journals * PubMed * Google Scholar * Joris A Veltman1, 3j.veltman@antrg.umcn.nl Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 42,Pages:1109–1112Year published:(2010)DOI:doi:10.1038/ng.712Received11 August 2010Accepted18 October 2010Published online14 November 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The per-generation mutation rate in humans is high. De novo mutations may compensate for allele loss due to severely reduced fecundity in common neurodevelopmental and psychiatric diseases, explaining a major paradox in evolutionary genetic theory. Here we used a family based exome sequencing approach to test this de novo mutation hypothesis in ten individuals with unexplained mental retardation. We identified and validated unique non-synonymous de novo mutations in nine genes. Six of these, identified in six different individuals, are likely to be pathogenic based on gene function, evolutionary conservation and mutation impact. Our findings provide strong experimental support for a de novo paradigm for mental retardation. Together with de novo copy number variation, de novo point mutations of large effect could explain the majority of all mental retardation cases in the population. View full text Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * NM_001376 * NM_001007248 * NM_171998 * NM_003403 * NM_174897 * NM_014224 * NM_021008 * NM_015125 * NM_006772 * NM_001146702 * NM_021569.2 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Lisenka E L M Vissers & * Joep de Ligt Affiliations * Department of Human Genetics, Nijmegen Centre for Molecular Life Sciences and Institute for Genetic and Metabolic Disorders, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Lisenka E L M Vissers, * Joep de Ligt, * Christian Gilissen, * Irene Janssen, * Marloes Steehouwer, * Petra de Vries, * Bart van Lier, * Peer Arts, * Nienke Wieskamp, * Marisol del Rosario, * Bregje W M van Bon, * Alexander Hoischen, * Bert B A de Vries, * Han G Brunner & * Joris A Veltman * These authors jointly directed this work. * Han G Brunner & * Joris A Veltman Contributions J.A.V., L.E.L.M.V. and H.G.B. conceived the project and planned the experiments. B.B.A.d.V. and B.W.M.v.B. performed sample collection and reviewed phenotypes. L.E.L.M.V., A.H., I.J., M.S., P.d.V., B.v.L. and P.A. performed next-generation sequencing experiments using a custom pipeline set up by C.G. and A.H. J.d.L. and C.G. analyzed and interpreted the data with support from N.W. and M.d.R. L.E.L.M.V., P.d.V., I.J. and M.S. performed validation experiments. L.E.L.M.V., J.d.L. and J.A.V. prepared the first draft of the manuscript. All authors contributed to the final manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Han G Brunner (h.brunner@antrg.umcn.nl) or * Joris A Veltman (j.veltman@antrg.umcn.nl) Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Note, Supplementary Tables 1 and 2 and Supplementary Figures 1–5 Additional data - Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells
- Nat Genet 42(12):1113-1117 (2010)
Nature Genetics | Letter Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells * Sabine Loewer1, 2, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Moran N Cabili5, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Mitchell Guttman5, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Yuin-Han Loh1, 2, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Kelly Thomas5, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * In Hyun Park1, 2, 3, 4, 12 Search for this author in: * NPG journals * PubMed * Google Scholar * Manuel Garber5 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew Curran1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Tamer Onder1, 2, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Suneet Agarwal1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Philip D Manos1, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Sumon Datta1, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric S Lander5, 6, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Thorsten M Schlaeger1, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * George Q Daley1, 2, 3, 4, 8, 9george.daley@childrens.harvard.edu Search for this author in: * NPG journals * PubMed * Google Scholar * John L Rinn5, 10, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1113–1117Year published:(2010)DOI:doi:10.1038/ng.710Received25 May 2010Accepted18 September 2010Published online07 November 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The conversion of lineage-committed cells to induced pluripotent stem cells (iPSCs) by reprogramming is accompanied by a global remodeling of the epigenome1, 2, 3, 4, 5, resulting in altered patterns of gene expression2, 6, 7, 8, 9. Here we characterize the transcriptional reorganization of large intergenic non-coding RNAs (lincRNAs)10, 11 that occurs upon derivation of human iPSCs and identify numerous lincRNAs whose expression is linked to pluripotency. Among these, we defined ten lincRNAs whose expression was elevated in iPSCs compared with embryonic stem cells, suggesting that their activation may promote the emergence of iPSCs. Supporting this, our results indicate that these lincRNAs are direct targets of key pluripotency transcription factors. Using loss-of-function and gain-of-function approaches, we found that one such lincRNA (lincRNA-RoR) modulates reprogramming, thus providing a first demonstration for critical functions of lincRNAs in the derivation of pluripote! nt stem cells. View full text Figures at a glance * Figure 1: Direct reprogramming of fibroblasts converts both protein-coding genes and lincRNA expression to a pluripotent cell-specific profile. (,) Unsupervised hierarchical clustering of protein-coding gene expression () and lincRNA expression () segregates fibroblasts (red) from ESCs and fibroblast-derived iPSCs (blue). (,) Supervised hierarchical clustering analysis identified 6,865 protein-coding genes () and 237 lincRNAs () that are differentially expressed between ESCs and iPSCs, and fibroblasts (genes, greater than twofold, P < 0.05; lincRNAs, greater than twofold, FWER < 0.05). Expression values are represented in shades of red and blue relative to being above (red) or below (blue) the median expression value across all samples (log scale 2, from −3 to +3). hFib2 fibroblasts are represented as two replicates (hFib2 and hFib2a). () Examples of reprogrammed lincRNAs; left, lincRNA expressed in all fibroblasts is repressed in all pluripotent cells; right, a pluripotent cell-specific lincRNA that becomes activated during reprogramming. Expression values for each tiled probe (x axis) are displayed as normalized! hybridization intensity (y axis). () Correlation analysis of lincRNAs and neighboring genes. Density plot of multiple testing–corrected P values (x axis) for lincRNAs that are positively (blue) or negatively (red) correlated with their protein-coding gene neighbors. * Figure 2: Several lincRNAs show enriched expression in iPSCs compared with ESCs. () Heatmap of 28 and 52 lincRNAs that are more highly expressed in fibroblast-derived iPSCs (left) and CD34+-derived iPSCs (right), respectively, compared with ESCs (greater than twofold, FWER < 0.05). Expression values are represented in shades of red and blue relative to being above (red) or below (blue) the median expression value across all samples (log scale 2, from −3 to +3). () Above, the venn diagram shows ten lincRNAs that are commonly enriched in fibroblast- and CD34+-derived iPSCs. Below, qRT-PCR validation of the ten commonly enriched lincRNAs (named according to their 3′ protein-coding gene neighbor) across three human ESC lines (H1, H9, BG01), fibroblasts (MRC5, MSC, hFib2) and CD34+ cells, and their derivative iPSC lines. Expression values are represented relative to the RNA levels in H9 ESCs. * Figure 3: Transcriptional regulation of iPSC-enriched lincRNAs. () iPSC-enriched lincRNA loci are bound by pluripotency transcription factors. Above, lincRNA loci demarcated by domains enriched in histone H3K4me3-indicating RNA polymerase II promoters and H3K36me3-indicating regions of transcriptional elongation10, 29 in human ESCs (green and blue, respectively). Below, ChIP in hFib2-iPS5 cells followed by quantitative PCR analysis detects binding of OCT4, SOX2 and NANOG within lincRNA-SFMBT2, lincRNA-VLDLR and lincRNA-RoR regions close to lincRNA promoter regions (peaks of H3K4me). ChIP enrichment values are displayed normalized to a control region (chromosome 12, positions 7,839,777–7,839,966; hg18); anti-GFP ChIP was used as a negative control. Positions of ChIP-PCR fragments are indicated by black lines. () Changes in iPSC-enriched lincRNA levels upon siRNA-mediated knockdown of OCT4 in iPSC. Above, qRT-PCR of OCT4, NANOG and LMNA transcript levels upon depletion of OCT4. Below, qRT-PCR of iPSC-enriched lincRNA levels upon depletio! n of OCT4. Transcript levels are displayed relative to non-targeting control siRNAs (ctrl siRNA) (n = 3; error bars, ± s.e.m). () iPSC-enriched lincRNA expression during embryoid body differentiation. Above, qRT-PCR analysis monitoring transcript levels of pluripotency markers (OCT4 and NANOG) and the differentiation marker LMNA over a 10-day differentiation time-course. Below, qRT-PCR analysis of iPSC-enriched lincRNAs. RNA levels are depicted relative to undifferentiated cells on day 0 (n = 3; error bars, ± s.e.m). * Figure 4: LincRNA-RoR expression modulates reprogramming. () qRT-PCR verifies lincRNA-RoR knockdown with Linc-sh1 and Linc-sh2 in hFib2-iPS5 cells relative to a non-targeting shRNA control (n = 2, error bar, ± s.e.m). () Quantification of Tra-1-60+ iPSC colonies upon knockdown of lincRNA-RoR relative to the control (day 21; n = 4; error bar, ± s.e.m). () Quantification of cell numbers on days 6 and 7 of reprogramming in lincRNA-RoR shRNA samples relative to the control (n = 4; error bar, ± s.e.m). () Images showing quarters of Tra-1-60 stained reprogramming plates upon infection of a non-targeting control and two lincRNA-RoR targeting shRNAs. Arrowheads mark Tra-1-60+ iPSC colonies. () Structure of the lincRNA-RoR locus. Green and blue, demarcation of the H3K4me-H3K36me domain in ESCs. Red, structure of lincRNA-RoR RNA. The asterisk marks the position of OCT4-SOX2-NANOG binding (Fig. 3a). Right, RNA hybridization of lincRNA-RoR detects a 2.6-kb transcript in hFib2-iSP5 but not in dH1f (for full-length blot, see Supplementary Fig! . 12). () qRT-PCR verifies lincRNA-RoR overexpression from a retroviral vector (pBabe-lincRNA-RoR) compared with pBabe-puro and pBabe-puro-GFP vectors in dH1f relative to the levels in H9 ESCs and hFib2-iPS5 (n = 2; error bars, ± s.e.m). () Quantification of Tra-1-60+ iPSC colonies upon overexpression of lincRNA-RoR compared to pBabe and pBabe-GFP controls (n = 5; error bar, ± s.e.m.). () Quantification of cell numbers on days 6 and 7 in lincRNA-RoR–overexpressing cells and controls. Cell numbers are relative to the pBabe control (day 28 ± 2 days; n = 5; error bar, ± s.e.m.). () Image of quarter-plates of Tra-1-60 stained colonies (arrowheads) in pBabe, pBabe-GFP and pBabe-lincRNA-RoR infected samples. Statistical analysis was performed using a Student's t-test. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE24182 Author information * Accession codes * Author information * Supplementary information Affiliations * Stem Cell Transplantation Program, Division of Pediatric Hematology and Oncology, Manton Center for Orphan Disease Research, Children's Hospital Boston and Dana Farber Cancer Institute, Boston, Massachusetts, USA. * Sabine Loewer, * Yuin-Han Loh, * In Hyun Park, * Matthew Curran, * Tamer Onder, * Suneet Agarwal, * Philip D Manos, * Sumon Datta, * Thorsten M Schlaeger & * George Q Daley * Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA. * Sabine Loewer, * Yuin-Han Loh, * In Hyun Park, * Tamer Onder, * Suneet Agarwal & * George Q Daley * Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. * Sabine Loewer, * Yuin-Han Loh, * In Hyun Park, * Matthew Curran, * Tamer Onder, * Suneet Agarwal, * Philip D Manos, * Sumon Datta, * Thorsten M Schlaeger & * George Q Daley * Stem Cell Program, Children's Hospital Boston, Boston, Massachusetts, USA. * Sabine Loewer, * Yuin-Han Loh, * In Hyun Park, * Tamer Onder, * Philip D Manos, * Sumon Datta, * Thorsten M Schlaeger & * George Q Daley * The Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Moran N Cabili, * Mitchell Guttman, * Kelly Thomas, * Manuel Garber, * Eric S Lander & * John L Rinn * Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. * Moran N Cabili & * Eric S Lander * Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Mitchell Guttman & * Eric S Lander * Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Kelly Thomas & * George Q Daley * Howard Hughes Medical Institute, Chevy Chase, Maryland, USA. * George Q Daley * Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * John L Rinn * Department of Pathology, Beth Israel and Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA. * John L Rinn * Present address: Yale Stem Cell Center, Department of genetics, Yale School of Medicine, New Haven, Connecticut, USA. * In Hyun Park Contributions Co-direction of the project: G.Q.D. and J.L.R. Study concept and design: G.Q.D., J.L.R. and S.L. LincRNA array design: M. Guttman and J.L.R. iPSC generation and characterization: I.H.P., S.L., T.O., S.A. and P.D.M. LincRNA array hybridization, lincRNA and protein-coding gene expression analysis: M.N.C., K.T., M. Guttman, S.L. and M. Garber. Computational studies: M.N.C., M. Guttman and M. Garber. LincRNA transcriptional regulation: S.L. ChIP assays: Y.-H.L. LincRNA loss-of-function and gain-of-function studies: S.L., M.C. and S.D. T.M.S. and E.S.L. provided essential ideas and suggestions on the manuscript. Manuscript preparation: G.Q.D., J.L.R. and S.L. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * George Q Daley (george.daley@childrens.harvard.edu) Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (8M) Supplementary Figures 1–14 and Supplementary Tables 1–4 Additional data - Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci
- Nat Genet 42(12):1118-1125 (2010)
Nature Genetics | Letter Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci * Andre Franke1, 70 Search for this author in: * NPG journals * PubMed * Google Scholar * Dermot P B McGovern2, 3, 70 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey C Barrett4, 70 Search for this author in: * NPG journals * PubMed * Google Scholar * Kai Wang5 Search for this author in: * NPG journals * PubMed * Google Scholar * Graham L Radford-Smith6 Search for this author in: * NPG journals * PubMed * Google Scholar * Tariq Ahmad7 Search for this author in: * NPG journals * PubMed * Google Scholar * Charlie W Lees8 Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias Balschun9 Search for this author in: * NPG journals * PubMed * Google Scholar * James Lee10 Search for this author in: * NPG journals * PubMed * Google Scholar * Rebecca Roberts11 Search for this author in: * NPG journals * PubMed * Google 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* PubMed * Google Scholar * Theodore M Bayless22 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephan Brand23 Search for this author in: * NPG journals * PubMed * Google Scholar * Carsten Büning24 Search for this author in: * NPG journals * PubMed * Google Scholar * Albert Cohen25 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Frederick Colombel26 Search for this author in: * NPG journals * PubMed * Google Scholar * Mario Cottone27 Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Stronati28 Search for this author in: * NPG journals * PubMed * Google Scholar * Ted Denson29 Search for this author in: * NPG journals * PubMed * Google Scholar * Martine De Vos30 Search for this author in: * NPG journals * PubMed * Google Scholar * Renata D'Inca31 Search for this author in: * NPG journals * PubMed * Google Scholar * Marla Dubinsky32 Search for this author in: * NPG journals * PubMed * Google Scholar * Cathryn 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this author in: * NPG journals * PubMed * Google Scholar * Steven R Brant22 Search for this author in: * NPG journals * PubMed * Google Scholar * John D Rioux61 Search for this author in: * NPG journals * PubMed * Google Scholar * Mauro D'Amato62 Search for this author in: * NPG journals * PubMed * Google Scholar * Rinse K Weersma63 Search for this author in: * NPG journals * PubMed * Google Scholar * Subra Kugathasan64 Search for this author in: * NPG journals * PubMed * Google Scholar * Anne M Griffiths60 Search for this author in: * NPG journals * PubMed * Google Scholar * John C Mansfield65 Search for this author in: * NPG journals * PubMed * Google Scholar * Severine Vermeire52 Search for this author in: * NPG journals * PubMed * Google Scholar * Richard H Duerr50, 66 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark S Silverberg55 Search for this author in: * NPG journals * PubMed * Google Scholar * Jack Satsangi8 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Schreiber1, 67 Search for this author in: * NPG journals * PubMed * Google Scholar * Judy H Cho20, 68 Search for this author in: * NPG journals * PubMed * Google Scholar * Vito Annese16, 69 Search for this author in: * NPG journals * PubMed * Google Scholar * Hakon Hakonarson5, 21 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Daly15, 71 Search for this author in: * NPG journals * PubMed * Google Scholar * Miles Parkes10, 71miles.parkes@addenbrookes.nhs.uk Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1118–1125Year published:(2010)DOI:doi:10.1038/ng.717Received24 June 2010Accepted22 October 2010Published online21 November 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10−8). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease. View full text Figures at a glance * Figure 1: Gene relationships across implicated loci (GRAIL) pathway analysis. Links between genes at 23 of 71 Crohn's disease–associated loci which scored P < 0.01 using GRAIL. Specifically, of the 71 Crohn's disease–associated SNPs, 69 are in linkage disequilibrium intervals containing or within 50 kb of at least one gene. In total, there were 355 genes implicated by proximity to these 69 SNPs. Each observed association was scored with GRAIL, which takes each gene mapping within Crohn's disease–associated intervals and evaluates for each whether it is non-randomly linked to the other genes through word usage in PubMed abstracts. The 23 SNPs shown in the outer circle are significant at P < 0.01, indicating that the regions which they tag contain genes which are more significantly linked to genes in the other 68 regions than expected by chance at that level. The lines between genes represent individually significant connections that contribute to the positive signal, with the thickness of the lines being inversely proportional to the probability ! that a literature-based connection would be seen by chance. To accurately assess the statistical significance of this set of connections, we conducted simulations in which we selected 1,000 sets of 69 SNPs implicating in total 355 genes ± 18 genes (5%) (selecting the SNPs randomly and using rejection sampling, only taking lists that implicated the same number of genes). Each of those 1,000 sets was scored with GRAIL. The mean number of P < 0.01 hits in a simulated list was 0.91, with a range in the 1,000 sets from 0 to 11, suggesting that the likelihood of observing 23 hits with P < 0.01 is far less than 0.1%. * Figure 2: Cumulative fraction of genetic variance explained by 71 Crohn's disease risk loci. The loci are ordered from largest to smallest individual contribution. Black points were identified pre-GWAS, green points were identified in the first generation GWAS, blue points were identified in an earlier meta-analysis, and cyan points were identified in this analysis. The inset shows a logarithmic fit to these data extrapolated to an extreme scenario where 20,000 independent common alleles are associated with disease. Even in this situation, less than half of the genetic variance would be explained. This demonstrates that other types of effect (for example, low frequency and rare alleles with higher penetrance) must also exist. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Andre Franke, * Dermot P B McGovern & * Jeffrey C Barrett Affiliations * Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany. * Andre Franke, * David Ellinghaus & * Stefan Schreiber * Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. * Dermot P B McGovern & * Stephan R Targan * Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. * Dermot P B McGovern, * Talin Haritunians, * Jerome I Rotter & * Kent D Taylor * Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * Jeffrey C Barrett, * Carl A Anderson, * Suzanne Bumpstead & * Luke Jostins * Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Kai Wang & * Hakon Hakonarson * Inflammatory Bowel Disease Research Group, Queensland Institute of Medical Research, Brisbane, Australia. * Graham L Radford-Smith & * Lisa A Simms * Peninsula College of Medicine and Dentistry, Exeter, UK. * Tariq Ahmad * Gastrointestinal Unit, Molecular Medicine Centre, University of Edinburgh, Western General Hospital, Edinburgh, UK. * Charlie W Lees & * Jack Satsangi * PopGen Biobank, Christian-Albrechts University Kiel, Kiel, Germany. * Tobias Balschun * Inflammatory Bowel Disease Research Group, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK. * James Lee & * Miles Parkes * Department of Medicine, University of Otago, Christchurch, New Zealand. * Rebecca Roberts, * Murray Barclay & * Richard Gearry * Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, USA. * Joshua C Bis * Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands. * Eleonora M Festen & * Cisca Wijmenga * Department of Genetics, Faculty of Veterinary Medicine, University of Liège, Liège, Belgium. * Michel Georges * Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Todd Green, * Soumya Raychaudhuri & * Mark J Daly * Unit of Gastroenterology, Istituto di Ricovero e Cura a Carattere Scientifico-Casa Sollievo della Sofferenza (IRCCS-CSS) Hospital, San Giovanni Rotondo, Italy. * Anna Latiano & * Vito Annese * Department of Medical and Molecular Genetics, King's College London School of Medicine, Guy's Hospital, London, UK. * Christopher G Mathew & * Natalie J Prescott * Molecular Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia. * Grant W Montgomery & * David Whiteman * Department of Health Studies, University of Chicago, Chicago, Illinois, USA. * Philip Schumm * Section of Digestive Diseases, Department of Medicine, Yale University, New Haven, Connecticut, USA. * Yashoda Sharma, * Deborah D Proctor & * Judy H Cho * Department of Pediatrics, Center for Pediatric Inflammatory Bowel Disease, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Robert N Baldassano & * Hakon Hakonarson * Inflammatory Bowel Disease Center, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Theodore M Bayless & * Steven R Brant * Department of Medicine II, University Hospital Munich Grosshadern, Ludwig-Maximilians-University, Munich, Germany. * Stephan Brand & * Jürgen Glas * Department of Gastroenterology, Charité, Campus Mitte, Universitätsmedizin Berlin, Berlin, Germany. * Carsten Büning * Montreal Jewish General Hospital, Montréal, Québec, Canada. * Albert Cohen * Registre des Maladies Inflammatoires du Tube Digestif du Nord-Ouest de la France (EPIMAD), Université de Lille, Lille, France. * Jean-Frederick Colombel * Unit of Gastroenterology, Cervello Hospital, Palermo, Italy. * Mario Cottone * Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo economico sostenibile (ENEA), Department of Biology of Radiations and Human Health, Rome, Italy. * Laura Stronati * Pediatric Gastroenterology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. * Ted Denson * Department of Hepatology and Gastroenterology, Ghent University Hospital, Ghent, Belgium. * Martine De Vos & * Debby Laukens * Division of Gastroenterology, University Hospital Padua, Padua, Italy. * Renata D'Inca * Department of Pediatrics, Cedars Sinai Medical Center, Los Angeles, California, USA. * Marla Dubinsky * Torbay Hospital, Torbay, Devon, UK. * Cathryn Edwards * Department of Gastroenterology, Mater Health Services, Brisbane, Australia. * Tim Florin * Department of Gastroenterology, Erasmus Hospital, Free University of Brussels, Brussels, Belgium. * Denis Franchimont & * Andre Van Gossum * Department of Preventive Dentistry and Periodontology, Ludwig-Maximilians-University, Munich, Germany. * Jürgen Glas * Department of Human Genetics, Rheinisch-Westfälische Technische Hochschule Aachen, Germany. * Jürgen Glas * Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah, USA. * Stephen L Guthery * Department of Medicine, Örebro University Hospital, Örebro, Sweden. * Jonas Halfvarson * Department of Gastroenterology, Leiden University Medical Center, Leiden, The Netherlands. * Hein W Verspaget * Université Paris Diderot, Paris, France. * Jean-Pierre Hugot * Department of Gastroenterology, Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel. * Amir Karban * School of Medicine and Pharmacology, The University of Western Australia, Fremantle, Australia. * Ian Lawrance * GETAID group, Université Paris Diderot, Paris, France. * Marc Lemann * Pediatric Gastroenterology Unit, Wolfson Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. * Arie Levine * Division of Gastroenterology, Centre Hospitalier Universitaire, Université de Liège, Liège, Belgium. * Cecile Libioulle & * Edouard Louis * Department of Medicine, Ninewells Hospital and Medical School, Dundee, UK. * Craig Mowat & * Anne Phillips * Department of Medical Genetics, University of Manchester, Manchester, UK. * William Newman * Department of Gastroenterology, Hospital Clínic/Institut d'Investigació Biomèdica August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBER EHD), Barcelona, Spain. * Julián Panés & * Miquel Sans * Division of Gastroenterology, Hepatology and Nutrition, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Miguel Regueiro & * Richard H Duerr * Department of Paediatric Gastroenterology, Yorkhill Hospital, Glasgow, UK. * Richard Russell * Division of Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium. * Paul Rutgeerts & * Severine Vermeire * Department of Gastroenterology, Guy's and St Thomas' National Health Service Foundation Trust, St Thomas' Hospital, London, UK. * Jeremy Sanderson * Division of Gastroenterology, Inselspital, University of Bern, Bern, Switzerland. * Frank Seibold * Mount Sinai Hospital Inflammatory Bowel Disease Centre, University of Toronto, Toronto, Ontario, Canada. * A Hillary Steinhart & * Mark S Silverberg * Department of Gastroenterology, Academic Medical Center, Amsterdam, The Netherlands. * Pieter C F Stokkers * Department of Clinical Science Intervention and Technology, Karolinska Institutet, Stockholm, Sweden. * Leif Torkvist * Division of Clinical Pharmacology and Toxicology, University Hospital Zurich, Zurich, Switzerland. * Gerd Kullak-Ublick * Child Life and Health, University of Edinburgh, Edinburgh, UK. * David Wilson * The Hospital for Sick Children, University of Toronto, Ontario, Canada. * Thomas Walters & * Anne M Griffiths * Université de Montréal and the Montreal Heart Institute, Research Center, Montréal, Québec, Canada. * John D Rioux * Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden. * Mauro D'Amato * Department of Gastroenterology, University Medical Center Groningen, Groningen, The Netherlands. * Rinse K Weersma * Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA. * Subra Kugathasan * Institute of Human Genetics, Newcastle University, Newcastle upon Tyne, UK. * John C Mansfield * Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Richard H Duerr * Department for General Internal Medicine, Christian-Albrechts-University, Kiel, Germany. * Stefan Schreiber * Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA. * Judy H Cho * Unit of Gastroenterology, University Hospital Careggi, Florence, Italy. * Vito Annese * These authors jointly directed this project. * Mark J Daly & * Miles Parkes Contributions A.F., D.P.B.M., G.L.R.-S., T.A., J.L., R. Roberts, J.C. Bis, T.H., A. Latiano, C.G.M., N.J.P., J.I.R., P.S., Y.S., L.A.S., K.D.T., D. Whiteman, C.W., G.K.-U., J.D.R., M.D.'A., R.K.W., S.V., R.H.D., J. Satsangi, S.S., V.A., H.H. and M.P. were involved in establishing DNA collections and/or assembling phenotypic data. A.F., D.E., J.C. Barrett, K.W., T.G., S.R., C.A.A., L.J. and M.J.D. performed statistical analyses. D.P.B.M., G.L.R.-S., C.W.L., E.M.F., R.N.B., M.B., T.M.B., S. Brand, C.B., A.C., J.-F.C., M.C., L.S., T.D., M.D.V., R.D.'I., M.D., C.E., T.F., D.F., A.M.G., R.G., J.G., A.V.G., S.L.G., J.H., H.W.V., J.-P.H., A.K., D.L., I.L., M.L., A. Levine, C.L., E.L., C.M., W.N., J.P., A.P., D.D.P., M.R., P.R., R. Russell, J. Satsangi, M.S.S., M.S., F.S., A.H.S., P.C.F.S., S.R.T., L.T., T.W., S.R.B., R.K.W., S.K., A.M.G., J.C.M., S.V., D. Wilson, R.H.D., M.S., J. Sanderson, S.S., J.H.C., V.A. and M.P. recruited patients. A.F., D.P.B.M., T.B., S. Bumpstead, J.I.R., M.G. and G.W.M! . supervised laboratory work. A.F., D.P.B.M., J.C. Barrett, K.W., S. Brand, R.H.D., J. Satsangi, S.S., J.H.C., M.J.D. and M.P. contributed to writing the manuscript. All authors read and approved the final manuscript before submission. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Miles Parkes (miles.parkes@addenbrookes.nhs.uk) Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 3 (76K) Odds ratios (OR) and risk allele frequencies (RAF) for the 71 SNPs listed in Tables 1 and 2 * Supplementary Table 4 (172K) Raw allele counts and empirical variance for the 71 SNPs listed in Tables 1 and 2 * Supplementary Table 6 (68K) Positional candidate genes mapping within regions of confirmed association with Crohn's disease PDF files * Supplementary Text and Figures (2M) Supplementary Tables 1–6, Supplementary Figures 1–4 and Supplementary Note. Additional data - A genome-wide association study of Hodgkin's lymphoma identifies new susceptibility loci at 2p16.1 (REL), 8q24.21 and 10p14 (GATA3)
- Nat Genet 42(12):1126-1130 (2010)
Nature Genetics | Letter A genome-wide association study of Hodgkin's lymphoma identifies new susceptibility loci at 2p16.1 (REL), 8q24.21 and 10p14 (GATA3) * Victor Enciso-Mora1, 29 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Broderick1, 29 Search for this author in: * NPG journals * PubMed * Google Scholar * Yussanne Ma1, 29 Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth F Jarrett2 Search for this author in: * NPG journals * PubMed * Google Scholar * Henrik Hjalgrim3 Search for this author in: * NPG journals * PubMed * Google Scholar * Kari Hemminki4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Anke van den Berg6 Search for this author in: * NPG journals * PubMed * Google Scholar * Bianca Olver1 Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Lloyd1 Search for this author in: * NPG journals * PubMed * Google Scholar * Sara E Dobbins1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tracy Lightfoot7 Search for this author in: * NPG journals * PubMed * Google Scholar * Flora E van Leeuwen8 Search for this author in: * NPG journals * PubMed * Google Scholar * Asta Försti4 Search for this author in: * NPG journals * PubMed * Google Scholar * Arjan Diepstra5 Search for this author in: * NPG journals * PubMed * Google Scholar * Annegien Broeks9 Search for this author in: * NPG journals * PubMed * Google Scholar * Jayaram Vijayakrishnan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lesley Shield2 Search for this author in: * NPG journals * PubMed * Google Scholar * Annette Lake2 Search for this author in: * NPG journals * PubMed * Google Scholar * Dorothy Montgomery2 Search for this author in: * NPG journals * PubMed * Google Scholar * Eve Roman7 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Engert10 Search for this author in: * NPG journals * PubMed * Google Scholar * Elke Pogge von Strandmann10 Search for this author in: * NPG journals * PubMed * Google Scholar * Katrin S Reiners10 Search for this author in: * NPG journals * PubMed * Google Scholar * Ilja M Nolte11 Search for this author in: * NPG journals * PubMed * Google Scholar * Karin E Smedby12 Search for this author in: * NPG journals * PubMed * Google Scholar * Hans-Olov Adami13, 14 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicola S Russell15 Search for this author in: * NPG journals * PubMed * Google Scholar * Bengt Glimelius16, 17 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen Hamilton-Dutoit18 Search for this author in: * NPG journals * PubMed * Google Scholar * Marieke de Bruin8 Search for this author in: * NPG journals * PubMed * Google Scholar * Lars P Ryder19 Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Molin20 Search for this author in: * NPG journals * PubMed * Google Scholar * Karina Meden Sorensen21 Search for this author in: * NPG journals * PubMed * Google Scholar * Ellen T Chang22, 23 Search for this author in: * NPG journals * PubMed * Google Scholar * Malcolm Taylor24 Search for this author in: * NPG journals * PubMed * Google Scholar * Rosie Cooke25 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Hofstra26 Search for this author in: * NPG journals * PubMed * Google Scholar * Helga Westers26 Search for this author in: * NPG journals * PubMed * Google Scholar * Tom van Wezel27 Search for this author in: * NPG journals * PubMed * Google Scholar * Ronald van Eijk27 Search for this author in: * NPG journals * PubMed * Google Scholar * Alan Ashworth28 Search for this author in: * NPG journals * PubMed * Google Scholar * Klaus Rostgaard3 Search for this author in: * NPG journals * PubMed * Google Scholar * Mads Melbye3 Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony J Swerdlow22 Search for this author in: * NPG journals * PubMed * Google Scholar * Richard S Houlston1richard.houlston@icr.ac.uk Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1126–1130Year published:(2010)DOI:doi:10.1038/ng.696Received30 June 2010Accepted30 September 2010Published online31 October 2010 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 susceptibility loci for classical Hodgkin's lymphoma (cHL), we conducted a genome-wide association study of 589 individuals with cHL (cases) and 5,199 controls with validation in four independent samples totaling 2,057 cases and 3,416 controls. We identified three new susceptibility loci at 2p16.1 (rs1432295, REL, odds ratio (OR) = 1.22, combined P = 1.91 × 10−8), 8q24.21 (rs2019960, PVT1, OR = 1.33, combined P = 1.26 × 10−13) and 10p14 (rs501764, GATA3, OR = 1.25, combined P = 7.05 × 10−8). Furthermore, we confirmed the role of the major histocompatibility complex in disease etiology by revealing a strong human leukocyte antigen (HLA) association (rs6903608, OR = 1.70, combined P = 2.84 × 10−50). These data provide new insight into the pathogenesis of cHL. View full text Figures at a glance * Figure 1: Genome-wide association results from the discovery phase. Shown are the genome-wide P values obtained using the Cochran-Armitage trend test from 504,374 autosomal SNPs in 589 cHL cases and 5,199 controls. P values (–log10P, y axis) are plotted against their respective chromosomal positions (x axis). Each chromosome is depicted in a different color. The points with P < 10−10 were truncated; the smallest P value obtained was 8.12 × 10−21. * Figure 2: Regional plots of association results and recombination rates for the 2p16.1, 8q24.21 and 10p14 susceptibility loci. (–) Association results of both genotyped (triangles) and imputed (circles) SNPs in the GWAS samples and recombination rates within the three loci: () 2p16.1, () 8q24.21 and () 10p14. For each plot, −log10P values (y axis) of the SNPs are shown according to their chromosomal positions (x axis). The top genotyped SNP in the combined analysis is labeled by its rsID. The color intensity of each symbol reflects the extent of LD with the top genotyped SNP: red or blue (r2 > 0.8) through to white (r2 < 0.2). Genetic recombination rates (cM/Mb), estimated using HapMap CEU samples, are shown with a light blue line. Physical positions are based on NCBI build 36 of the human genome. Also shown are the relative positions of genes and transcripts mapping to each region of association. Genes and microRNAs have been redrawn to show the relative positions; therefore, maps are not to physical scale. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Victor Enciso-Mora, * Peter Broderick & * Yussanne Ma Affiliations * Section of Cancer Genetics, Institute of Cancer Research, Sutton, UK. * Victor Enciso-Mora, * Peter Broderick, * Yussanne Ma, * Bianca Olver, * Amy Lloyd, * Sara E Dobbins, * Jayaram Vijayakrishnan & * Richard S Houlston * Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK. * Ruth F Jarrett, * Lesley Shield, * Annette Lake & * Dorothy Montgomery * Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark. * Henrik Hjalgrim, * Klaus Rostgaard & * Mads Melbye * Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. * Kari Hemminki & * Asta Försti * Center for Primary Health Care Research, Clinical Research Center, Lund University, Malmö, Sweden. * Kari Hemminki & * Arjan Diepstra * Department of Pathology and Medical Biology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands. * Anke van den Berg * Epidemiology and Genetics Unit, Department of Health Sciences, University of York, York, UK. * Tracy Lightfoot & * Eve Roman * Department of Epidemiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. * Flora E van Leeuwen & * Marieke de Bruin * Department of Experimental Therapy, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. * Annegien Broeks * University Hospital of Cologne, Department of Internal Medicine, Cologne, Germany. * Andreas Engert, * Elke Pogge von Strandmann & * Katrin S Reiners * Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands. * Ilja M Nolte * Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, Stockholm, Sweden. * Karin E Smedby * Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. * Hans-Olov Adami * Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Hans-Olov Adami * Department of Radiotherapy, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. * Nicola S Russell * Department of Pathology and Oncology, Karolinska Institutet, Stockholm, Sweden. * Bengt Glimelius * Department of Oncology, Radiology and Clinical Immunology, Uppsala University, Uppsala, Sweden. * Bengt Glimelius * Institute of Pathology, Aarhus University Hospital, Aarhus, Denmark. * Stephen Hamilton-Dutoit * Department of Clinical Immunology, University Hospital of Copenhagen, Rigshospitalet, Copenhagen, Denmark. * Lars P Ryder * Department of Oncology, Radiology and Clinical Immunology, Uppsala University, Uppsala, Sweden. * Daniel Molin * Department of Clinical Biochemistry, Statens Serum Institut, Copenhagen, Denmark. * Karina Meden Sorensen * Cancer Prevention Institute of California, Fremont, California, USA. * Ellen T Chang & * Anthony J Swerdlow * Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA. * Ellen T Chang * Cancer Immunogenetics Group, School of Cancer and Enabling Sciences, University of Manchester, Research Floor, St. Mary's Hospital, Manchester, UK. * Malcolm Taylor * Section of Epidemiology, Institute of Cancer Research, Sutton, UK. * Rosie Cooke * Department of Genetics University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands. * Robert Hofstra & * Helga Westers * Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands. * Tom van Wezel & * Ronald van Eijk * The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK. * Alan Ashworth Contributions R.S.H. designed the study and obtained financial support. R.S.H. drafted the manuscript with contributions from P.B., V.E.-M., Y.M. and S.E.D. Y.M. and V.E.-M. performed statistical and bioinformatic analyses. P.B. performed sample coordination and laboratory analyses. B.O., Amy Lloyd and J.V. performed genotyping. A.J.S., A.A. and R.C. provided samples and data from a study conducted at the Institute of Cancer Research. E.R. initiated ELCCS. T.L., M.T. and E.R. managed and prepared Epidemiology and Genetics Lymphoma Case-Control Study samples. R.F.J. designed and conducted studies contributing to the UK replication series, and R.F.J., L.S., Annette Lake and Dorothy Montgomery prepared samples and collated data. F.E.v.L. designed the Dutch NKI study and obtained financial support. N.S.R. and M.d.B. were involved in identification and inclusion of Dutch cases, study design, review board approval and clinical implementation. A.B. coordinated collection and preparation of the N! KI samples. A.F., K.H., A.E., E.P.v.S. and K.S.R. provided samples and data from German cases and controls. A.D., I.M.N. and A.v.d.B. collected samples and data from cHL cases ascertained through Groningen University. R.H., H.W., T.v.W. and R.v.E. performed ascertainment and collection of control samples from The Netherlands. H.H., M.M., K.R., L.P.R., K.E.S., H.-O.A., B.G., Daniel Molin, S.H.-D., K.M.S. and E.T.C. provided samples and data from the SCALE study in Denmark and Sweden. S.H.-D. analyzed samples and provided data from Danish cHL cases. All authors contributed to the final paper. R.F.J. and H.H. contributed equally to the paper and should be considered to have equal positional status in the author list. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard S Houlston (richard.houlston@icr.ac.uk) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (10M) Supplementary Figures 1–5, Supplementary Tables 1–8 and Supplementary Note Additional data - Exome sequencing identifies ACAD9 mutations as a cause of complex I deficiency
- Nat Genet 42(12):1131-1134 (2010)
Nature Genetics | Letter Exome sequencing identifies ACAD9 mutations as a cause of complex I deficiency * Tobias B Haack1, 2, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Katharina Danhauser1, 2, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Birgit Haberberger1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Hoser3 Search for this author in: * NPG journals * PubMed * Google Scholar * Valentina Strecker4 Search for this author in: * NPG journals * PubMed * Google Scholar * Detlef Boehm5 Search for this author in: * NPG journals * PubMed * Google Scholar * Graziella Uziel6 Search for this author in: * NPG journals * PubMed * Google Scholar * Eleonora Lamantea7 Search for this author in: * NPG journals * PubMed * Google Scholar * Federica Invernizzi7 Search for this author in: * NPG journals * PubMed * Google Scholar * Joanna Poulton8 Search for this author in: * NPG journals * PubMed * Google Scholar * Boris Rolinski9 Search for this author in: * NPG journals * PubMed * Google Scholar * Arcangela Iuso1 Search for this author in: * NPG journals * PubMed * Google Scholar * Saskia Biskup5 Search for this author in: * NPG journals * PubMed * Google Scholar * Thorsten Schmidt3 Search for this author in: * NPG journals * PubMed * Google Scholar * Hans-Werner Mewes3, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Ilka Wittig4 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Meitinger1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Massimo Zeviani7zeviani@istituto-besta.it Search for this author in: * NPG journals * PubMed * Google Scholar * Holger Prokisch1, 2prokisch@helmholtz-muenchen.de Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 42,Pages:1131–1134Year published:(2010)DOI:doi:10.1038/ng.706Received26 July 2010Accepted08 October 2010Published online07 November 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg An isolated defect of respiratory chain complex I activity is a frequent biochemical abnormality in mitochondrial disorders. Despite intensive investigation in recent years, in most instances, the molecular basis underpinning complex I defects remains unknown. We report whole-exome sequencing of a single individual with severe, isolated complex I deficiency. This analysis, followed by filtering with a prioritization of mitochondrial proteins, led us to identify compound heterozygous mutations in ACAD9, which encodes a poorly understood member of the mitochondrial acyl-CoA dehydrogenase protein family. We demonstrated the pathogenic role of the ACAD9 variants by the correction of the complex I defect on expression of the wildtype ACAD9 protein in fibroblasts derived from affected individuals. ACAD9 screening of 120 additional complex I–defective index cases led us to identify two additional unrelated cases and a total of five pathogenic ACAD9 alleles. View full text Figures at a glance * Figure 1: ACAD9 gene structure and conservation of affected amino acid residues of identified mutations (shown in red). * Figure 2: Cellular complementation experiment. We transduced case () and control () fibroblast cell lines with ACAD9 overexpressing construct and we determined respiratory chain complex I, IV (RCCI and IV) and citrate synthase (CS) activities. The data are based on two independent transduction experiments for each cell line. Activities were determined at three different time points and expressed in percent of lowest control value ± standard deviation (s.d.). *P < 0.05, **P < 0.01. g NCP, gram non-collagen protein. * Figure 3: Complex I assembly in fibroblasts. Two-dimensional blue native–SDS-PAGE separation and quantification of fluorescent-labeled mitochondrial complexes from 10 mg patient (P), patient transduced with wildtype ACAD9 (P-T) and control fibroblasts (C) are shown. Densitometric quantitation of supercomplex (S) fluorescence intensity was normalized to complex V (C, n = 5; P, n = 3; P-T, n = 2). Error bars indicate ± s.d. Gels from one typical experiment are shown as pseudocolors. Assignment of complexes: VM, monomeric complex V or ATP synthase; III, complex III or cytochrome c reductase; IV, complex IV or cytochrome c oxidase; S, supercomplexes composed of respiratory chain complexes I, III and IV. Arrows indicate the subunits that were chosen for quantification. * Figure 4: Effect of riboflavin treatment in fibroblasts. We treated case I:A, I:B and control fibroblast cell lines with riboflavin (5.3 μM for 72 h) and we determined respiratory chain complex I, IV (RCCI and IV) and citrate synthase activities. Measurements were performed in three independent experiments. Activities are expressed in percent of lowest control value ± s.d. *P < 0.05, **P < 0.01. g NCP, gram non-collagen protein. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Tobias B Haack & * Katharina Danhauser Affiliations * Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. * Tobias B Haack, * Katharina Danhauser, * Birgit Haberberger, * Arcangela Iuso, * Thomas Meitinger & * Holger Prokisch * Institute of Human Genetics, Technische Universität München, Munich, Germany. * Tobias B Haack, * Katharina Danhauser, * Birgit Haberberger, * Thomas Meitinger & * Holger Prokisch * Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Germany Research Center for Environmental Health, Neuherberg, Germany. * Jonathan Hoser, * Thorsten Schmidt & * Hans-Werner Mewes * Molecular Bioenergetics, Medical School, Goethe-Universität Frankfurt, Frankfurt am Main, Germany. * Valentina Strecker & * Ilka Wittig * CeGaT GmbH, Tübingen, Germany. * Detlef Boehm & * Saskia Biskup * Unit of Child Neurology, Neurological Institute 'Carlo Besta'-Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS) Foundation, Milan, Italy. * Graziella Uziel * Unit of Molecular Neurogenetics, Neurological Institute 'Carlo Besta'-IRCCS Foundation, Milan, Italy. * Eleonora Lamantea, * Federica Invernizzi & * Massimo Zeviani * Nuffield Department of Obstetrics and Gynaecology, University of Oxford, The Women's Centre, John Radcliffe Hospital, Oxford, UK. * Joanna Poulton * Städtisches Klinikum München GmbH, Department Klinische Chemie, Munich, Germany. * Boris Rolinski * Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, Freising-Weihenstephan, Technische Universität München, Munich, Germany. * Hans-Werner Mewes Contributions Project planning: T.M., M.Z., H.P. Experimental design: H.P. Review of phenotypes and sample collection: G.U., E.L., F.I., J.P. and B.R. Mutation screening: T.B.H., D.B. and S.B. Data analysis: T.B.H., J.H., T.S., H.-W.M. and H.P. Cell biology experiments: K.D., B.H., V.S., I.W. and A.I. Manuscript writing: T.B.H., T.M., M.Z. and H.P. Critical revision of the manuscript: all authors. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Holger Prokisch (prokisch@helmholtz-muenchen.de) or * Massimo Zeviani (zeviani@istituto-besta.it) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (112K) Supplementary Tables 1–4 and Supplementary Note Additional data - Natural variation at Strubbelig Receptor Kinase 3 drives immune-triggered incompatibilities between Arabidopsis thaliana accessions
- Nat Genet 42(12):1135-1139 (2010)
Nature Genetics | Letter Natural variation at Strubbelig Receptor Kinase 3 drives immune-triggered incompatibilities between Arabidopsis thaliana accessions * Rubén Alcázar1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ana V García2 Search for this author in: * NPG journals * PubMed * Google Scholar * Ilkka Kronholm1 Search for this author in: * NPG journals * PubMed * Google Scholar * Juliette de Meaux1 Search for this author in: * NPG journals * PubMed * Google Scholar * Maarten Koornneef1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jane E Parker2 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthieu Reymond1reymond@mpiz-koeln.mpg.de Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 42,Pages:1135–1139Year published:(2010)DOI:doi:10.1038/ng.704Received30 March 2010Accepted07 October 2010Published online31 October 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Accumulation of genetic incompatibilities within species can lead to reproductive isolation and, potentially, speciation. In this study, we show that allelic variation at SRF3 (Strubbelig Receptor Family 3), encoding a receptor-like kinase, conditions the occurrence of incompatibility between Arabidopsis thaliana accessions. The geographical distribution of SRF3 alleles reveals that allelic forms causing epistatic incompatibility with a Landsberg erecta allele at the RPP1 resistance locus are present in A. thaliana accessions in central Asia. Incompatible SRF3 alleles condition for an enhanced early immune response to pathogens as compared to the resistance-dampening effect of compatible SRF3 forms in isogenic backgrounds. Variation in disease susceptibility suggests a basis for the molecular patterns of a recent selective sweep detected at the SRF3 locus in central Asian populations. View full text Figures at a glance * Figure 1: SRF3 allelic forms and world-wide distribution of incompatible alleles. () Schematic representation of compatible (Ler and Col) and incompatible (Kas-2 and Kond) SRF3 allele polymorphisms. Amino acid changes in incompatible alleles are indicated. TM, transmembrane domain; JM, juxtamembrane. () Geographical distribution of incompatible SRF3 alleles in Eurasia. The origins of the 603 accessions used in this study are spotted on the map and listed in Supplementary Table 3. The different SRF3 alleles are distinguished by color: Kas-2 (yellow), Kond (red), others (blue). * Figure 2: Genetic structure and SRF3 allelic diversity. () Estimated population structure of accessions used for crosses. Each accession is represented by a horizontal bar partitioned into three-colored segments depicting each individual's estimated membership fractions in three clusters (north Europe, south Europe and central Asia). () Neighbor-joining tree showing diversity at the SRF3 locus in A. thaliana accessions. The origin of accessions studied is indicated by colored spots (blue, north Europe; red, south Europe; yellow, central Asia). * Figure 3: Immune responses in compatible and incompatible lines. () Growth of Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) and Pst DC3000 hrcC bacteria in compatible and incompatible genotypes, as indicated. Kas-2SRF3Ler and Kas-2 plants (left panel) were grown and infected at 20 °C to suppress spontaneous cell death of Kas-2 accession at low temperature (14 °C)6. All other genotypes (right panel) were grown at 14 °C and infected at 20 °C. Bacterial counting was performed at 3 h (day 0) and 3 days post-inoculation. Results are averaged from two independent experiments with four replicates each. Values with different letters (a,b,c,d for Pst DC3000; a',b',c' for Pst DC3000 hrcC) are significantly different at P < 0.01 in a Student-Newman-Keuls test. Error bars, standard deviation. () Immunocomplex-based assays of MPK3, MPK4 and MPK6 activities in compatible and incompatible lines at indicated time points after flg22 elicitation. Myelin basic protein was used as an artificial substrate for MPK phosphorylation. Equal loading of bl! ots is shown by the MPK4 signal after probing with anti-MPK4 antibody and ponceau S staining of Rubisco large subunit. * Figure 4: Normalized Fay and Wu's H statistic across the SRF3 genomic region in central Asian (red) and north European (blue) accessions. Negative values at the SRF3 locus in central Asia (Hn = −2.42, P = 0.0254) are consistent with an excess of derived high-frequency mutations, a molecular pattern that commonly accompanies selective sweeps. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions ArrayExpress * E-MEXP-2569 Entrez Nucleotide * GU570412 * GU570413 * GU571158 * HM538833 * HM539307 Author information * Accession codes * Author information * Supplementary information Affiliations * Department of Plant Breeding and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany. * Rubén Alcázar, * Ilkka Kronholm, * Juliette de Meaux, * Maarten Koornneef & * Matthieu Reymond * Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany. Present addresses: Institut Jean-Pierre Bourgin, UMR1318 INRA-AgroParisTech, Institut National de la Recherche Agronomique (INRA) Centre de Versailles-Grignon, Route de St-Cyr (RD10), Versailles Cedex, France (M.R.) and Unité de Recherche en Génomique Végétale, INRA-Centre National de la Recherche Scientifique-Université Evry Val d'Essonne (CNRS-UEVE), Evry CEdex, France (A.V.G.). * Ana V García & * Jane E Parker Contributions R.A., M.K., J.E.P. and M.R. conceived the study. R.A. performed most of the experimental work with contributions from A.V.G. in the pathogen infection assays. I.K. and J.d.M. performed the computer analysis and interpretation of Fay and Wu's Hn statistics. M.K. provided accessions and European F2 populations. J.E.P. provided materials for immune analyses. M.R. performed all statistical analyses. All authors analyzed the data. R.A., J.E.P and M.R. wrote the paper with contributions from all authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthieu Reymond (reymond@mpiz-koeln.mpg.de) Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–11 and Supplementary Tables 1–6 Additional data - Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity
- Nat Genet 42(12):1140-1143 (2010)
Nature Genetics | Letter Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity * Giovanna Morelli1, 16 Search for this author in: * NPG journals * PubMed * Google Scholar * Yajun Song2, 3, 16 Search for this author in: * NPG journals * PubMed * Google Scholar * Camila J Mazzoni1, 3, 16 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Eppinger4, 16 Search for this author in: * NPG journals * PubMed * Google Scholar * Philippe Roumagnac1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * David M Wagner6 Search for this author in: * NPG journals * PubMed * Google Scholar * Mirjam Feldkamp1 Search for this author in: * NPG journals * PubMed * Google Scholar * Barica Kusecek1 Search for this author in: * NPG journals * PubMed * Google Scholar * Amy J Vogler6 Search for this author in: * NPG journals * PubMed * Google Scholar * Yanjun Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Yujun Cui2 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas R Thomson7 Search for this author in: * NPG journals * PubMed * Google Scholar * Thibaut Jombart8 Search for this author in: * NPG journals * PubMed * Google Scholar * Raphael Leblois9 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Lichtner10 Search for this author in: * NPG journals * PubMed * Google Scholar * Lila Rahalison11 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeannine M Petersen12 Search for this author in: * NPG journals * PubMed * Google Scholar * Francois Balloux8 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Keim6, 13 Search for this author in: * NPG journals * PubMed * Google Scholar * Thierry Wirth1, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacques Ravel4 Search for this author in: * NPG journals * PubMed * Google Scholar * Ruifu Yang2ruifuyang@gmail.com Search for this author in: * NPG journals * PubMed * Google Scholar * Elisabeth Carniel14elisabeth.carniel@pasteur.fr Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Achtman1, 3, 15m.achtman@ucc.ie Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 42,Pages:1140–1143Year published:(2010)DOI:doi:10.1038/ng.705Received11 January 2010Accepted08 October 2010Published online31 October 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Plague is a pandemic human invasive disease caused by the bacterial agent Yersinia pestis. We here report a comparison of 17 whole genomes of Y. pestis isolates from global sources. We also screened a global collection of 286 Y. pestis isolates for 933 SNPs using Sequenom MassArray SNP typing. We conducted phylogenetic analyses on this sequence variation dataset, assigned isolates to populations based on maximum parsimony and, from these results, made inferences regarding historical transmission routes. Our phylogenetic analysis suggests that Y. pestis evolved in or near China and spread through multiple radiations to Europe, South America, Africa and Southeast Asia, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the United States reflect one radiation, and 82 isolates from Madagascar represent a second radiation. Subsequent local microevolution of Y. pestis is marked by sequential, geographically specific SNPs. View full text Figures at a glance * Figure 1: Genomic maximum parsimony tree and divergence dates based on 1,364 non-repetitive, non-homoplastic SNPs from 3,349 coding sequences in 16 Y. pestis genomes (excluding FV-1). Black text, names of genomic sequences (Supplementary Table 1); colored text, branch and population names; gray, ranges of maximal and minimal dates of divergence for individual branches calculated22 with strict mutation rates of 2.9 × 10−9 and 2.3 × 10−8 per site per year (Supplementary Table 2). Comparable results were obtained using intergenic SNPs or a variable clock rate (Supplementary Table 2b). ya, years ago. * Figure 2: Fully parsimonious minimal spanning tree of 933 SNPs for 282 isolates of Y. pestis colored by location. Large, bold text, branches 1, 2 and 0; smaller letters, populations (for example, 1.ORI3); lower case letters, nodes (for example, 1.ORI3.a). Strain designations near terminal nodes, genomic sequences. Roman numbers, hypothetical nodes. Gray text on lines between nodes, numbers of SNPs, except that one or two SNPs are indicated by thick and thin black lines, respectively. Six additional isolates in 0.PE1 and 0.PE2b (blue dashes) were tested only for selected, informative SNPs. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions ArrayExpress * E-MTAB-213 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. Present addresses: Max-Planck-Institut für molekulare Genetik, Berlin, Germany (G.M. and B.K.), Berlin Center for Genomics in Biodiversity Research, Berlin, Germany (C.J.M.) and Max-Delbrück-Centrum für molekulare Medizin (MDC) Berlin-Buch, Berlin, Germany (M.F.). * Giovanna Morelli, * Yajun Song, * Camila J Mazzoni & * Mark Eppinger Affiliations * Max-Planck-Institut für Infektionsbiologie, Department of Molecular Biology, Berlin, Germany. * Giovanna Morelli, * Camila J Mazzoni, * Philippe Roumagnac, * Mirjam Feldkamp, * Barica Kusecek, * Thierry Wirth & * Mark Achtman * State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China. * Yajun Song, * Yanjun Li, * Yujun Cui & * Ruifu Yang * Environmental Research Institute, University College Cork, Cork, Ireland. * Yajun Song, * Camila J Mazzoni & * Mark Achtman * Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Mark Eppinger & * Jacques Ravel * Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Mixte Research Unit Biology and Genetics of Plant/Pathogen Interactions (UMR BGPI), Montpellier, France. * Philippe Roumagnac * Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA. * David M Wagner, * Amy J Vogler & * Paul Keim * The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * Nicholas R Thomson * Medical Research Council (MRC) Centre for Outbreak Analysis and Modeling, Imperial College Faculty of Medicine, London, UK. * Thibaut Jombart & * Francois Balloux * Muséum National d′Histoire Naturelle–Ecole Pratique des Hautes Etudes, Department of Systematics and Evolution UMR-CNRS 7205, Paris, France. * Raphael Leblois & * Thierry Wirth * Institute of Human Genetics, German Research Center for Environmental Health, Neuherberg, Germany. * Peter Lichtner * Unité Peste, Institut Pasteur de Madagascar, Madagascar. * Lila Rahalison * Division of Vector-Borne Infectious Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado, USA. * Jeannine M Petersen * Pathogen Genomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA. * Paul Keim * Institut Pasteur, Yersinia Research Unit, Paris, France. * Elisabeth Carniel * Department of Microbiology, University College Cork, Cork, Ireland. * Mark Achtman Contributions M.A., T.W., D.M.W., P.R., J.R., R.Y. and P.K. designed the study. L.R., J.M.P., R.Y. and E.C. contributed Y. pestis DNA and demographic information. G.M., Y.S., M.E., P.R., M.F., B.K., A.J.V., Y.L., Y.C., P.L. and N.R.T. performed sequencing, SNP discovery, MassArray and SNP testing. G.M., Y.S., C.J.M., M.E., P.R., D.M.W. and P.L. performed bioinformatic analyses of the data. C.J.M., T.J., R.L., F.B. and T.W. performed population genetic analyses. M.A., C.J.M., M.E., P.R., D.M.W., T.J., F.B., P.K., T.W., J.R., R.Y. and E.C. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Mark Achtman (m.achtman@ucc.ie) or * Elisabeth Carniel (elisabeth.carniel@pasteur.fr) or * Ruifu Yang (ruifuyang@gmail.com) Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–5, Supplementary Tables 1 and 2 and Supplementary Note Additional data
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