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• Full spectrum genetics
  - Nat Genet 44(1):1 (2012)
  Nature Genetics | Editorial Full spectrum genetics Journal name:Nature GeneticsVolume: 44,Page:1Year published:(2012)DOI:doi:10.1038/ng.1057Published online27 December 2011 Every instance of a variant in the human genome causing or correlated with a trait deserves to be databased and analyzed. As a consequence of rapidly evolving technology and strategies, more of the mutational spectrum of human disease is now accessible to research. Advised by our referees' progressively higher standards, we continue to select the most informative and useful results. View full text Additional data
• Rare and functional SIAE variants are not associated with autoimmune disease risk in up to 66,924 individuals of European ancestry
  - Nat Genet 44(1):3-5 (2012)
  Nature Genetics | Correspondence Rare and functional SIAE variants are not associated with autoimmune disease risk in up to 66,924 individuals of European ancestry * Karen A Hunt1 * Deborah J Smyth2 * Tobias Balschun3 * Maria Ban4 * Vanisha Mistry1 * Tariq Ahmad5 * Vidya Anand6 * Jeffrey C Barrett7 * Leena Bhaw-Rosun8 * Nicholas A Bockett1 * Oliver J Brand9 * Elisabeth Brouwer10 * Patrick Concannon11 * Jason D Cooper2 * Kerith-Rae M Dias8 * Cleo C van Diemen12 * Patrick C Dubois1 * Sarah Edkins7 * Regina Fölster-Holst13 * Karin Fransen12 * David N Glass14 * Graham A R Heap1 * Sylvia Hofmann3 * Tom W J Huizinga15 * Sarah Hunt7 * Cordelia Langford7 * James Lee16 * John Mansfield17 * Maria Giovanna Marrosu18 * Christopher G Mathew19 * Charles A Mein8 * Joachim Müller-Quernheim20 * Sarah Nutland2 * Suna Onengut-Gumuscu11 * Willem Ouwehand7, 21 * Kerra Pearce22 * Natalie J Prescott19 * Marcel D Posthumus10 * Simon Potter7 * Giulio Rosati23 * Jennifer Sambrook21 * Jack Satsangi24 * Stefan Schreiber3 * Corina Shtir2 * Matthew J Simmonds9 * Marc Sudman14 * Susan D Thompson14 * Rene Toes15 * Gosia Trynka12 * Timothy J Vyse6 * Neil M Walker2 * Stephan Weidinger13, 25 * Alexandra Zhernakova12, 15, 26, 27 * Magdalena Zoledziewska28 * Type 1 Diabetes Genetics Consortium 29 * UK Inflammatory Bowel Disease (IBD) Genetics Consortium 29 * Wellcome Trust Case Control Consortium 29 * Rinse K Weersma30 * Stephen C L Gough9 * Stephen Sawcer4 * Cisca Wijmenga12 * Miles Parkes16 * Francesco Cucca28, 31 * Andre Franke3 * Panos Deloukas7 * Stephen S Rich11 * John A Todd2 * David A van Heel1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:3–5Year published:(2012)DOI:doi:10.1038/ng.1037Published online27 December 2011 To the Editor: Recently, rare loss-of-function genetic variants in the SIAE gene, which encodes sialic acid acetylesterase, were reported to predispose to multiple autoimmune diseases1. In a pooled analysis of samples from ten autoimmune diseases, Surolia et al. identified 12 distinct nonsynonymous SIAE risk variant genotypes present in 24 of 923 affected persons (2.60%) versus 2 of 648 controls (0.31%; P = 0.0002; odds ratio of 8.6) that the authors considered to be "functionally defective SIAE alleles" because they result in defects in either esterase activity or secretion1. These nonsynonymous markers comprised one common allele frequency variant (rs78778622; encoding a p.Met89Val substitution in SIAE) and 11 rare allele frequency variants. Homozygosity for the SIAE variant (rs78778622, GG) causing the p.Met89Val alteration that resulted in a secretion-defective mutant was reported in 8 of 923 affected individuals (0.87%) but in none of the 648 control subjects1. To date, in contras! t to the numerous genome-wide association studies for common variants, there have been only a few studies reporting rare variants of large effect predisposing individuals to clinically typical autoimmune disease phenotypes, despite much recent enthusiasm for exome sequencing in these genetically complex conditions. View full text Author information * 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 Affiliations * Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. * Karen A Hunt, * Vanisha Mistry, * Nicholas A Bockett, * Patrick C Dubois, * Graham A R Heap & * David A van Heel * Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. * Deborah J Smyth, * Jason D Cooper, * Sarah Nutland, * Corina Shtir, * Neil M Walker & * John A Todd * Institute of Clinical Molecular Biology, Christian-Albrechts-Universität zu Kiel, Kiel, Germany. * Tobias Balschun, * Sylvia Hofmann, * Stefan Schreiber & * Andre Franke * Department of Clinical Neurosciences, Addenbrookes Hospital, University of Cambridge, Cambridge, UK. * Maria Ban & * Stephen Sawcer * Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK. * Tariq Ahmad * Division of Genetics and Molecular Medicine, King's College London, London, UK. * Vidya Anand & * Timothy J Vyse * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Jeffrey C Barrett, * Sarah Edkins, * Sarah Hunt, * Cordelia Langford, * Willem Ouwehand, * Simon Potter & * Panos Deloukas * Genome Centre, Barts and the London School of Medicine and Dentistry, John Vane Science Centre, London, UK. * Leena Bhaw-Rosun, * Kerith-Rae M Dias & * Charles A Mein * Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford and Oxford National Institute for Health Research (NIHR) Biomedical Centre, Oxford, UK. * Oliver J Brand, * Matthew J Simmonds & * Stephen C L Gough * Department of Rheumatology and Clinical Immunology, University Medical Centre Groningen and University of Groningen, Groningen, The Netherlands. * Elisabeth Brouwer & * Marcel D Posthumus * Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA. * Patrick Concannon, * Suna Onengut-Gumuscu & * Stephen S Rich * Genetics Department, University Medical Centre Groningen and University of Groningen, Groningen, The Netherlands. * Cleo C van Diemen, * Karin Fransen, * Gosia Trynka, * Alexandra Zhernakova & * Cisca Wijmenga * Department of Dermatology, University Clinic Schleswig-Holstein, Campus Kiel, Kiel, Germany. * Regina Fölster-Holst & * Stephan Weidinger * Division of Rheumatology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. * David N Glass, * Marc Sudman & * Susan D Thompson * Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands. * Tom W J Huizinga, * Rene Toes & * Alexandra Zhernakova * IBD Genetics Research Group, Addenbrooke's Hospital, Cambridge, UK. * James Lee & * Miles Parkes * Department of Gastroenterology & Hepatology, University of Newcastle upon Tyne, Royal Victoria Infirmary, Newcastle upon Tyne, UK. * John Mansfield * Centro Sclerosi Multipla, Dipartimento di Scienze Neurologiche e Cardiovascolari, Università di Cagliari, Cagliari, Italy. * Maria Giovanna Marrosu * Department of Medical and Molecular Genetics, King's College London School of Medicine, Guy's Hospital, London, UK. * Christopher G Mathew & * Natalie J Prescott * Department of Pneumology, University Medical Center, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. * Joachim Müller-Quernheim * Department of Haematology, University of Cambridge & National Health Service (NHS) Blood and Transplant, Cambridge, UK. * Willem Ouwehand & * Jennifer Sambrook * University College London Genomics, Institute of Child Health, University College London, London, UK. * Kerra Pearce * Istituto di Neurologia Clinica, Università di Sassari, Sassari, Italy. * Giulio Rosati * Gastrointestinal Unit, Division of Medical Sciences, School of Molecular and Clinical Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK. * Jack Satsangi * Department of Dermatology and Allergy, Technical University Munich, Munich, Germany. * Stephan Weidinger * Complex Genetics Section, Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands. * Alexandra Zhernakova * Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Alexandra Zhernakova * Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy. * Magdalena Zoledziewska & * Francesco Cucca * A full list of consortium members is provided in the Supplementary Note. * Type 1 Diabetes Genetics Consortium , * UK Inflammatory Bowel Disease (IBD) Genetics Consortium & * Wellcome Trust Case Control Consortium * Department of Gastroenterology and Hepatology, University Medical Centre Groningen and University of Groningen, Groningen, The Netherlands. * Rinse K Weersma * Istituto di Neurogenetica e Neurofarmacologia, Consiglio Natzionale delle Richerche (CNR), Monserrato, Italy. * Francesco Cucca Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David A van Heel Author Details * Karen A Hunt Search for this author in: * NPG journals * PubMed * Google Scholar * Deborah J Smyth Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias Balschun Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Ban Search for this author in: * NPG journals * PubMed * Google Scholar * Vanisha Mistry Search for this author in: * NPG journals * PubMed * Google Scholar * Tariq Ahmad Search for this author in: * NPG journals * PubMed * Google Scholar * Vidya Anand Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey C Barrett Search for this author in: * NPG journals * PubMed * Google Scholar * Leena Bhaw-Rosun Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas A Bockett Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver J Brand Search for this author in: * NPG journals * PubMed * Google Scholar * Elisabeth Brouwer Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Concannon Search for this author in: * NPG journals * PubMed * Google Scholar * Jason D Cooper Search for this author in: * NPG journals * PubMed * Google Scholar * Kerith-Rae M Dias Search for this author in: * NPG journals * PubMed * Google Scholar * Cleo C van Diemen Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick C Dubois Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah Edkins Search for this author in: * NPG journals * PubMed * Google Scholar * Regina Fölster-Holst Search for this author in: * NPG journals * PubMed * Google Scholar * Karin Fransen Search for this author in: * NPG journals * PubMed * Google Scholar * David N Glass Search for this author in: * NPG journals * PubMed * Google Scholar * Graham A R Heap Search for this author in: * NPG journals * PubMed * Google Scholar * Sylvia Hofmann Search for this author 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journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (188K) Supplementary Note and Supplementary Tables 1 and 2. Additional data
• Improved imputation of common and uncommon SNPs with a new reference set
  - Nat Genet 44(1):6-7 (2012)
  Nature Genetics | Correspondence Improved imputation of common and uncommon SNPs with a new reference set * Zhaoming Wang1, 2 * Kevin B Jacobs1, 2 * Meredith Yeager1, 2 * Amy Hutchinson1, 2 * Joshua Sampson2 * Nilanjan Chatterjee2 * Demetrius Albanes2 * Sonja I Berndt2 * Charles C Chung2 * W Ryan Diver3 * Susan M Gapstur3 * Lauren R Teras3 * Christopher A Haiman4 * Brian E Henderson4 * Daniel Stram4 * Xiang Deng1, 2 * Ann W Hsing2 * Jarmo Virtamo5 * Michael A Eberle6 * Jennifer L Stone6 * Mark P Purdue2 * Phil Taylor2 * Margaret Tucker2 * Stephen J Chanock2 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:6–7Year published:(2012)DOI:doi:10.1038/ng.1044Published online27 December 2011 Statistical imputation of genotype data is an important statistical technique that uses patterns of linkage disequilibrium observed in a reference set of haplotypes to computationally predict genetic variants in silico1. Currently, the most popular reference sets are the publicly available International HapMap2 and 1000 Genomes data sets3. Although these resources are valuable for imputing a sizeable fraction of common SNPs, they may not be optimal for imputing data for the next generation of genome-wide association studies (GWAS) and SNP arrays, which explore a fraction of uncommon variants. We have built a new resource for the imputation of SNPs for existing and future GWAS, known as the Division of Cancer Epidemiology and Genetics (DCEG) Reference Set. The data set has genotypes for cancer-free individuals, including 728 of European ancestry from three large prospectively sampled studies4, 5, 6, 98 African-American individuals from the Prostate, Lung, Colon and Ovary Cancer Screening Trial (PLCO), 74 Chinese individuals from a clinical trial in Shanxi, China (SHNX)7 and 349 individuals from the HapMap Project (Table 1). The final harmonized data set includes 2.8 million autosomal polymorphic SNPs for 1,249 individuals after rigorous quality control metrics were applied (see Supplementary Methods and Supplementary Tables 1 and 2). View full text Author information * 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 Affiliations * Core Genotyping Facility, SAIC-Frederick, National Cancer Institute (NCI)-Frederick, Frederick, Maryland, USA. * Zhaoming Wang, * Kevin B Jacobs, * Meredith Yeager, * Amy Hutchinson & * Xiang Deng * Division of Cancer Epidemiology and Genetics, NCI, US National Institutes of Health (NIH), Bethesda, Maryland, USA. * Zhaoming Wang, * Kevin B Jacobs, * Meredith Yeager, * Amy Hutchinson, * Joshua Sampson, * Nilanjan Chatterjee, * Demetrius Albanes, * Sonja I Berndt, * Charles C Chung, * Xiang Deng, * Ann W Hsing, * Mark P Purdue, * Phil Taylor, * Margaret Tucker & * Stephen J Chanock * Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA. * W Ryan Diver, * Susan M Gapstur & * Lauren R Teras * Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California, USA. * Christopher A Haiman, * Brian E Henderson & * Daniel Stram * Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland. * Jarmo Virtamo * Illumina, San Diego, California, USA. * Michael A Eberle & * Jennifer L Stone Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Stephen J Chanock Author Details * Zhaoming Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin B Jacobs Search for this author in: * NPG journals * PubMed * Google Scholar * Meredith Yeager Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Hutchinson Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Sampson Search for this author in: * NPG journals * PubMed * Google Scholar * Nilanjan Chatterjee Search for this author in: * NPG journals * PubMed * Google Scholar * Demetrius Albanes Search for this author in: * NPG journals * PubMed * Google Scholar * Sonja I Berndt Search for this author in: * NPG journals * PubMed * Google Scholar * Charles C Chung Search for this author in: * NPG journals * PubMed * Google Scholar * W Ryan Diver Search for this author in: * NPG journals * PubMed * Google Scholar * Susan M Gapstur Search for this author in: * NPG journals * PubMed * Google Scholar * Lauren R Teras Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher A Haiman Search for this author in: * NPG journals * PubMed * Google Scholar * Brian E Henderson Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Stram Search for this author in: * NPG journals * PubMed * Google Scholar * Xiang Deng Search for this author in: * NPG journals * PubMed * Google Scholar * Ann W Hsing Search for this author in: * NPG journals * PubMed * Google Scholar * Jarmo Virtamo Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Eberle Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer L Stone Search for this author in: * NPG journals * PubMed * Google Scholar * Mark P Purdue Search for this author in: * NPG journals * PubMed * Google Scholar * Phil Taylor Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret Tucker Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Chanock Contact Stephen J Chanock Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (779K) Supplementary Methods, Supplementary Figures 1–3 and Supplementary Tables 1 and 2. Additional data
• Spliceosome mutations in hematopoietic malignancies
  - Nat Genet 44(1):9-10 (2012)
  Article preview View full access options Nature Genetics | News and Views Spliceosome mutations in hematopoietic malignancies * Christopher N Hahn1 * Hamish S Scott1 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:9–10Year published:(2012)DOI:doi:10.1038/ng.1045Published online27 December 2011 Recent studies, including two in this issue, report heterozygous missense mutations in the U2AF1 and SF3B1 genes that encode spliceosome subunits. U2AF1 is frequently mutated in myeloid hematopoietic malignancies, especially in myelodysplastic syndrome (MDS), and SF3B1 is frequently mutated in both MDS and chronic lymphocytic leukemia (CLL). Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Christopher N. Hahn and Hamish S. Scott are in the Department of Molecular Pathology at the Centre for Cancer Biology at SA Pathology in Adelaide, Australia. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Christopher N Hahn or * Hamish S Scott Author Details * Christopher N Hahn Contact Christopher N Hahn Search for this author in: * NPG journals * PubMed * Google Scholar * Hamish S Scott Contact Hamish S Scott Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Following evolution of bacterial antibiotic resistance in real time
  - Nat Genet 44(1):11-13 (2012)
  Article preview View full access options Nature Genetics | News and Views Following evolution of bacterial antibiotic resistance in real time * Adam Z Rosenthal1 * Michael B Elowitz1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:11–13Year published:(2012)DOI:doi:10.1038/ng.1048Published online27 December 2011 A new study reports the development of the 'morbidostat', a device that allows for continuous culture of bacteria under a constant drug selection pressure using computer feedback control of antibiotic concentration. This device, together with bacterial whole-genome sequencing, allowed the authors to follow the evolution of resistance-conferring mutations in Escherichia coli populations in real time, providing support for deterministic evolution of resistance in some situations. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Adam Z. Rosenthal and Michael B. Elowitz are at the Howard Hughes Medical Institute, Division of Biology, California Institute of Technology, Pasadena, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michael B Elowitz Author Details * Adam Z Rosenthal Search for this author in: * NPG journals * PubMed * Google Scholar * Michael B Elowitz Contact Michael B Elowitz Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Dnmt3a silences hematopoietic stem cell self-renewal
  - Nat Genet 44(1):13-14 (2012)
  Article preview View full access options Nature Genetics | News and Views Dnmt3a silences hematopoietic stem cell self-renewal * Jennifer J Trowbridge1 * Stuart H Orkin1 * Affiliations * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:13–14Year published:(2012)DOI:doi:10.1038/ng.1043Published online27 December 2011 DNA methylation is an epigenetic mark stably directing gene expression throughout development. A new study uncovers a role for the DNA methyltransferase Dnmt3a in silencing self-renewal genes in hematopoietic stem cells (HSCs) to permit efficient hematopoietic differentiation. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Genetics for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Jennifer J. Trowbridge and Stuart H. Orkin are at the Department of Pediatric Oncology, Dana-Farber Cancer Institute and Division of Hematology/Oncology, Children's Hospital Boston, Boston, Massachusetts, USA. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jennifer J Trowbridge or * Stuart H Orkin Author Details * Jennifer J Trowbridge Contact Jennifer J Trowbridge Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart H Orkin Contact Stuart H Orkin Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma
  - Nat Genet 44(1):17-19 (2012)
  Nature Genetics | Brief Communication Frequent mutations of genes encoding ubiquitin-mediated proteolysis pathway components in clear cell renal cell carcinoma * Guangwu Guo1, 10 * Yaoting Gui2, 10 * Shengjie Gao1, 10 * Aifa Tang2, 3, 10 * Xueda Hu1, 10 * Yi Huang2, 3, 10 * Wenlong Jia1 * Zesong Li2, 3 * Minghui He1 * Liang Sun2 * Pengfei Song1 * Xiaojuan Sun3 * Xiaokun Zhao4 * Sangming Yang1 * Chaozhao Liang5 * Shengqing Wan1 * Fangjian Zhou6 * Chao Chen1 * Jialou Zhu1, 7 * Xianxin Li2 * Minghan Jian1 * Liang Zhou2 * Rui Ye1 * Peide Huang1 * Jing Chen2 * Tao Jiang1 * Xiao Liu1 * Yong Wang2 * Jing Zou1 * Zhimao Jiang2 * Renhua Wu1 * Song Wu2 * Fan Fan1 * Zhongfu Zhang2 * Lin Liu1 * Ruilin Yang2 * Xingwang Liu1 * Haibo Wu1 * Weihua Yin2 * Xia Zhao1 * Yuchen Liu2 * Huanhuan Peng1 * Binghua Jiang2 * Qingxin Feng2 * Cailing Li2 * Jun Xie2 * Jingxiao Lu2 * Karsten Kristiansen1, 8 * Yingrui Li1 * Xiuqing Zhang1 * Songgang Li1 * Jian Wang1 * Huanming Yang1 * Zhiming Cai2, 3 * Jun Wang1, 8, 9 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:17–19Year published:(2012)DOI:doi:10.1038/ng.1014Received03 June 2011Accepted28 October 2011Published online04 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We sequenced whole exomes of ten clear cell renal cell carcinomas (ccRCCs) and performed a screen of ~1,100 genes in 88 additional ccRCCs, from which we discovered 12 previously unidentified genes mutated at elevated frequencies in ccRCC. Notably, we detected frequent mutations in the ubiquitin-mediated proteolysis pathway (UMPP), and alterations in the UMPP were significantly associated with overexpression of HIF1α and HIF2α in the tumors (P = 0.01 and 0.04, respectively). Our findings highlight the potential contribution of UMPP to ccRCC tumorigenesis through the activation of the hypoxia regulatory network. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Guangwu Guo, * Yaoting Gui, * Shengjie Gao, * Aifa Tang, * Xueda Hu & * Yi Huang Affiliations * Shenzhen Key Laboratory of Transomics Biotechnologies, BGI-Shenzhen, Shenzhen, China. * Guangwu Guo, * Shengjie Gao, * Xueda Hu, * Wenlong Jia, * Minghui He, * Pengfei Song, * Sangming Yang, * Shengqing Wan, * Chao Chen, * Jialou Zhu, * Minghan Jian, * Rui Ye, * Peide Huang, * Tao Jiang, * Xiao Liu, * Jing Zou, * Renhua Wu, * Fan Fan, * Lin Liu, * Xingwang Liu, * Haibo Wu, * Xia Zhao, * Huanhuan Peng, * Karsten Kristiansen, * Yingrui Li, * Xiuqing Zhang, * Songgang Li, * Jian Wang, * Huanming Yang & * Jun Wang * Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Institute of Urology, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen, China. * Yaoting Gui, * Aifa Tang, * Yi Huang, * Zesong Li, * Liang Sun, * Xianxin Li, * Liang Zhou, * Jing Chen, * Yong Wang, * Zhimao Jiang, * Song Wu, * Zhongfu Zhang, * Ruilin Yang, * Weihua Yin, * Yuchen Liu, * Binghua Jiang, * Qingxin Feng, * Cailing Li, * Jun Xie, * Jingxiao Lu & * Zhiming Cai * Shenzhen Second People′s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China. * Aifa Tang, * Yi Huang, * Zesong Li, * Xiaojuan Sun & * Zhiming Cai * Department of Urology, The Second Xiangya Hospital of Central-Southern University, Changsha, China. * Xiaokun Zhao * Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. * Chaozhao Liang * Department of Urology, Sun Yat-Sen University Cancer Center, Guangzhou, China. * Fangjian Zhou * College of Life Science, Wuhan University, Wuhan, China. * Jialou Zhu * Department of Biology, University of Copenhagen, Copenhagen, Denmark. * Karsten Kristiansen & * Jun Wang * The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark. * Jun Wang Contributions Jun Wang, Z.C., Jian Wang, H.Y., S.L. and Y. Li managed the project. Xiaokun Zhao, C. Liang, F.Z., Z.L., X. Li, L.Z., J.C., Y.W., Z.J., S. Wu, Z.Z., R. Yang, W.Y., Y. Liu, B.J., J.L. and Q.F. prepared the samples. X. Zhang, X.H., Xiao Liu, R.W., L.L., Xia Zhao, H.P. and K.K. performed the sequencing. G.G., Y.G., S.G., A.T., Y.H., W.J., M.H., S. Wan, C.C., M.J., T.J. and R. Ye performed the bioinformatic analysis. S.Y., P.S., P.H., J. Zou, F.F., Xingwang Liu and H.W. performed the validation of somatic mutations. L.S. and C. Li performed the immunohistochemistry analysis. Z.L., J.X. and J. Zhu performed the methylation analysis. G.G., Y.G., Y. Li, A.T. and Y.H. wrote the manuscript, and Y.H., Y.G., G.G., Y. Li and X.S. revised the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jun Wang or * Zhiming Cai or * Huanming Yang Author Details * Guangwu Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Yaoting Gui Search for this author in: * NPG journals * PubMed * Google Scholar * Shengjie Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Aifa Tang Search for this author in: * NPG journals * PubMed * Google Scholar * Xueda Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Yi Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Wenlong Jia Search for this author in: * NPG journals * PubMed * Google Scholar * Zesong Li Search for this author in: * NPG journals * PubMed * Google Scholar * Minghui He Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Pengfei Song Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaojuan Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaokun Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Sangming Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Chaozhao Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Shengqing Wan Search for this author in: * NPG journals * PubMed * Google Scholar * Fangjian Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Chao Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Jialou Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Xianxin Li Search for this author in: * NPG journals * PubMed * Google Scholar * Minghan Jian Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Rui Ye Search for this author in: * NPG journals * PubMed * Google Scholar * Peide Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Tao Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Xiao Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Zou Search for this author in: * NPG journals * PubMed * Google Scholar * Zhimao Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Renhua Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Song Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Fan Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Zhongfu Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Ruilin Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Xingwang Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Haibo Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Weihua Yin Search for this author in: * NPG journals * PubMed * Google Scholar * Xia Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Yuchen Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Huanhuan Peng Search for this author in: * NPG journals * PubMed * Google Scholar * Binghua Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Qingxin Feng Search for this author in: * NPG journals * PubMed * Google Scholar * Cailing Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Jingxiao Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Karsten Kristiansen Search for this author in: * NPG journals * PubMed * Google Scholar * Yingrui Li Search for this author in: * NPG journals * PubMed * Google Scholar * Xiuqing Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Songgang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jian Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Huanming Yang Contact Huanming Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Zhiming Cai Contact Zhiming Cai Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Wang Contact Jun Wang Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (958K) Supplementary Methods, Supplementary Figures 1–4 and Supplementary Tables 1, 2 and 6–9. 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• GATA6 haploinsufficiency causes pancreatic agenesis in humans
  - Nat Genet 44(1):20-22 (2012)
  Nature Genetics | Brief Communication GATA6 haploinsufficiency causes pancreatic agenesis in humans * Hana Lango Allen1, 6 * Sarah E Flanagan1, 6 * Charles Shaw-Smith1, 6 * Elisa De Franco1, 6 * Ildem Akerman2, 3, 4 * Richard Caswell1 * the International Pancreatic Agenesis Consortium * Jorge Ferrer2, 3, 4 * Andrew T Hattersley1 * Sian Ellard1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:20–22Year published:(2012)DOI:doi:10.1038/ng.1035Received03 August 2011Accepted15 November 2011Published online11 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Understanding the regulation of pancreatic development is key for efforts to develop new regenerative therapeutic approaches for diabetes. Rare mutations in PDX1 and PTF1A can cause pancreatic agenesis, however, most instances of this disorder are of unknown origin. We report de novo heterozygous inactivating mutations in GATA6 in 15/27 (56%) individuals with pancreatic agenesis. These findings define the most common cause of human pancreatic agenesis and establish a key role for the transcription factor GATA6 in human pancreatic development. View full text Figures at a glance * Figure 1: GATA6 mutations causing pancreatic agenesis. () Genomic and protein positions of the 14 GATA6 mutations. () Electrophoretic mobility shift assay showing that mutations abolish the binding to a predicted GATA6 binding sequence in the pancreatic HNF4A proximal promoter. We used nuclear extracts from cells transfected with a control vector or vectors expressing GATA6 wild-type (WT) or mutant (Mut) proteins (described at the level of the protein changes shown in Fig. 1a). Only wild-type GATA6 formed a retardation complex (arrow) that disappeared after preincubation with unlabeled wild-type but not mutated double-stranded oligonucleotide probes (competitor) and with GATA6 antiserum. Identical results were observed with in vitro translated wild-type and mutant GATA6 proteins using the TFF2 GATA6 binding site (data not shown). () Mutated GATA6 does not activate the GATA6-responsive WNT2 promoter-luciferase gene in HeLa cells. *Statistically significant difference in activity as compared to wild type (P < 0.0001). () Protein b! lot showing comparable expression of wild-type and mutant GATA6 proteins. * Figure 2: Clinical characteristics of the pancreatic agenesis cohort. In addition to pancreatic agenesis, GATA6 mutations cause several other phenotypes. The precise malformations seen in each subject are listed in Supplementary Table 4. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hana Lango Allen, * Sarah E Flanagan, * Charles Shaw-Smith & * Elisa De Franco Affiliations * Institute of Biomedical and Clinical Science, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter, UK. * Hana Lango Allen, * Sarah E Flanagan, * Charles Shaw-Smith, * Elisa De Franco, * Richard Caswell, * Andrew T Hattersley & * Sian Ellard * Genomic Programming of Beta-cells Laboratory, Institut d'Investigacions August Pi i Sunyer (IDIBAPS), Barcelona, Spain. * Ildem Akerman & * Jorge Ferrer * Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain. * Ildem Akerman & * Jorge Ferrer * Department of Endocrinology and Nutrition, Hospital Clínic de Barcelona, Barcelona, Spain. * Ildem Akerman & * Jorge Ferrer Consortia * the International Pancreatic Agenesis Consortium Contributions S.E., S.E.F., J.F. and A.T.H. designed the study. R.C. performed the exome sequencing and the structural modeling. H.L.A. did the bioinformatic analyses. E.D.F. and S.E.F. did the Sanger sequencing analysis and the interpretation of the resulting data. C.S.-S. and A.T.H. analyzed the clinical data. I.A. and J.F. performed the functional studies. H.L.A., C.S.-S., J.F., A.T.H. and S.E. prepared the draft manuscript. All authors contributed to the discussion of the results and the manuscript preparation. A full list of members is provided in the Supplementary Note. the International Pancreatic Agenesis Consortium Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Andrew T Hattersley Author Details * Hana Lango Allen Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah E Flanagan Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Shaw-Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Elisa De Franco Search for this author in: * NPG journals * PubMed * Google Scholar * Ildem Akerman Search for this author in: * NPG journals * PubMed * Google Scholar * Richard Caswell Search for this author in: * NPG journals * PubMed * Google Scholar * the International Pancreatic Agenesis Consortium * Jorge Ferrer Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew T Hattersley Contact Andrew T Hattersley Search for this author in: * NPG journals * PubMed * Google Scholar * Sian Ellard Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Note, Supplementary Methods, Supplementary Figures 1–3 and Supplementary Tables 1–4. Additional data
• Dnmt3a is essential for hematopoietic stem cell differentiation
  - Nat Genet 44(1):23-31 (2012)
  Nature Genetics | Article Dnmt3a is essential for hematopoietic stem cell differentiation * Grant A Challen1, 2, 3 * Deqiang Sun4, 5, 15 * Mira Jeong1, 2, 6, 15 * Min Luo6, 15 * Jaroslav Jelinek7, 15 * Jonathan S Berg8, 9, 15 * Christoph Bock10, 11 * Aparna Vasanthakumar12 * Hongcang Gu7 * Yuanxin Xi4, 5 * Shoudan Liang13 * Yue Lu7 * Gretchen J Darlington6 * Alexander Meissner10, 11 * Jean-Pierre J Issa7 * Lucy A Godley12 * Wei Li4, 5 * Margaret A Goodell1, 2, 14 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:23–31Year published:(2012)DOI:doi:10.1038/ng.1009Received06 July 2011Accepted25 October 2011Published online04 December 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Loss of the de novo DNA methyltransferases Dnmt3a and Dnmt3b in embryonic stem cells obstructs differentiation; however, the role of these enzymes in somatic stem cells is largely unknown. Using conditional ablation, we show that Dnmt3a loss progressively impairs hematopoietic stem cell (HSC) differentiation over serial transplantation, while simultaneously expanding HSC numbers in the bone marrow. Dnmt3a-null HSCs show both increased and decreased methylation at distinct loci, including substantial CpG island hypermethylation. Dnmt3a-null HSCs upregulate HSC multipotency genes and downregulate differentiation factors, and their progeny exhibit global hypomethylation and incomplete repression of HSC-specific genes. These data establish Dnmt3a as a critical participant in the epigenetic silencing of HSC regulatory genes, thereby enabling efficient differentiation. View full text Figures at a glance * Figure 1: Dnmt3a is highly expressed in HSCs and its ablation has profound functional effects. () Real-time PCR analysis of Dnmt3a mRNA in LT-HSCs, short-term HSCs (ST-HSCs) and representative committed progenitors and differentiated cells. MPPs, multi-potential progenitors; CLPs, common lymphoid progenitors; CMPs, common myeloid progenitors; MEPs, megakaryocyte-erythroid progenitors; GMPs, granulocyte-macrophage progenitors (see Online Methods for purification schemes). Mean ± s.e.m. values are shown for three biological replicates. () Contribution of control (Dnmt3afl/fl;Mx1-Cre−) and Dnmt3a-null (Dnmt3afl/fl;Mx1-Cre+) HSCs to recipient mouse peripheral blood in secondary competitive transplants, measured at monthly intervals. Mean ± s.e.m. values are shown. () Lineage differentiation in secondary recipients of transplanted control and Dnmt3a-null HSCs. Shown are percentages of donor-derived (CD45.2+) myeloid cells (Gr1+ or Mac1+), B cells (B220+) and T cells (CD4+ or CD8+) in peripheral blood analyzed 16 weeks after transplantation. Differences that are signifi! cant between control and Dnmt3a-null HSCs are indicated. Mean ± s.e.m. values are shown (N = 15–37 mice). () Hoechst staining and flow cytometry analysis of the bone marrow of secondary recipient mice. Top, the boxed region shows the percentage of side population (SP) cells from mice transplanted with HSCs of the indicated genotypes. Bottom, SP cells were further gated using c-Kit+, lineage− and Sca-1+ (KLS) markers to reveal the proportion of test (CD45.2+) versus competitor (CD45.1+) HSCs. () Alternative HSC phenotype schemes for test cells gated first by KLS show similar expansion of the Dnmt3a-null HSC compartment. () Quantification of total HSC frequency in the bone marrow of secondary recipient mice by three phenotypic definitions. Hatched area indicates the proportion of CD45.2+ test cells. SLAM is CD150+, CD48−, KLS gating. Mean ± s.e.m. values are shown. () Analysis of progenitor frequencies in secondary recipient mice. Hatched area indicates the contributi! on of CD45.2+ test cells. Mean ± s.e.m. values are shown. **P! < 0.001; ***P < 0.001. * Figure 2: Cellular kinetics of Dnmt3a-null HSCs. () HSC gating scheme for the analysis of proliferation and apoptosis in secondary recipient mice transplanted with control or Dnmt3a-null HSCs. () Ki67 staining shows a significant reduction in the proliferative index of Dnmt3a-null HSCs relative to control HSCs; *P < 0.05. () Annexin V staining shows no difference in the apoptotic rate between control and Dnmt3a-null HSCs. Bars in and indicate the mean values for each genotype. * Figure 3: Dnmt3a-null HSCs show inhibition of long-term differentiation in serial competitive transplantation of HSCs. () The proportion of peripheral blood generated from the test cells in recipient mice 16 weeks after transplantation. () Quantification of donor-derived HSCs in the bone marrow of recipient mice 18 weeks after transplantation, defined as CD45.2+, SPKLS cells. Data are representative of at least three individual transplantation experiments for each stage of serial transfer (N = 15–37 mice per group). Mean ± s.e.m. values are shown. () Flow cytometry data of quaternary recipient mice transplanted with control or Dnmt3a-null HSCs showing virtually all continuously amplified HSCs in bone marrow were derived from Dnmt3a-null HSCs (CD45.2+). () Differentiation and self-renewal quotients, calculated at the end of each round of transplantation with Dnmt3a-null and control HSCs. ***P < 0.001. * Figure 4: Dnmt3a loss in HSCs results in both hyper- and hypo-methylation. () HPLC-MS analysis of global 5mc levels as a proportion of the total cytosine in purified HSCs from secondary recipient mice (N = 2). () RRBS analysis of tertiary recipient mice transplanted with control or Dnmt3a-null HSCs. Plots show the degree of differential methylation (between Dnmt3a-null and control HSCs) and its relationship to local CpG density (blue). Left, all hypomethylated (red, CpGs ≤33% methylated) and hypermethylated (green, CpGs ≥33% methylated) DMCs in Dnmt3a-null HSCs. Right, DMCs located within CGIs. () Independent bisulfite sequencing analysis of selected hypermethylated CGIs in Dnmt3a-null HSCs. () Bisulfite sequencing analysis of selected hypomethylated genes in Dnmt3a-null HSCs. Schematic diagrams for each gene are shown in and (not to scale). Exons are represented by vertical rectangles. White horizontal bars indicate CGIs, and black horizontal bars show the tested region. Open and filled circles represent unmethylated and methylated CpGs, respe! ctively. Differences in methylation between control and Dnmt3a-null cells that were statistically significant are indicated; *P < 0.05, ***P < 0.001. * Figure 5: Dnmt3a loss in HSCs leads to higher expression of HSC multipotency genes. () Relative expression levels of select multipotency, HSC fingerprint18 and differentiation genes measured by real-time PCR analysis. Mean ± s.e.m. values are from three replicates in Dnmt3a-null HSCs normalized to the expression levels in control HSCs (dashed line). () Bisulfite sequencing analysis of multipotency and HSC fingerprint genes in control and Dnmt3a-null HSCs. Open horizontal bars indicate CGIs, and black horizontal bars show the tested region. Open and filled circles represent unmethylated and methylated CpGs, respectively. Differences in methylation between control and Dnmt3a-null HSCs that were statistically significant are indicated. () H3K4me3 ChIP analysis of DMRs in control and Dnmt3a-null HSCs. Mean ± s.e.m. values are shown (N = 3 replicate experiments). () Dnmt3a ChIP analysis of DMRs in wild-type hematopoietic progenitors (KLS cells, N = 2 replicate experiments) reveals Dnmt3a binding to CGIs in Runx1 and Gata3 but not in Nr4a2. Mean ± s.e.m. value! s are shown. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 6: Dnmt3a is required to suppress the stem cell program in HSCs to permit differentiation. () HPLC-MS analysis of global 5mc levels as a proportion of the total cytosine in B cells from secondary recipient mice. Mean ± s.e.m. values are shown (N = 7 mice). () DREAM analysis of B cells in secondary recipient mice. SmaI sites with at least 20 sequence tags in control B cells are plotted showing the methylation ratio between the genotypes. The red triangle indicates sites of hypomethylation in Dnmt3a-null B cells in 1.4% of all CpGs (FDR = 0.07%). () Bisulfite sequencing across the Vasn and Runx1 CGIs in control (top) and Dnmt3a-null (bottom) B cells. Differences in methylation between control and Dnmt3a-null cells that were statistically significant are indicated. () Cognate gene expression for cells analyzed in . Diamonds indicate control cells, and squares indicate Dntm3a-null cells. HSCs are represented by filled symbols and B cells by open symbols. Bars indicate the average gene expression for each cell population. Differences in expression between control and ! Dnmt3a-null cells that were statistically significant are indicated. () H3K4me3 ChIP analysis for Runx1 and Vasn in control and Dnmt3a-null B cells. Mean ± s.e.m. values are shown (N = 4 replicate experiments). () Expression of Dnmt3a-responsive genes at day 0 (d0) and day 6 (d6) after 5-FU exposure measured by real-time PCR. Expression levels are relative to normalized expression of that gene in d0 control HSCs. Mean ± s.e.m. values are shown, and statistically significant differences in expression at the two time points are indicated. () Proportion of methylated CpGs as detected by bisulfite sequencing across CGIs. Mean ± s.e.m. values are shown for three biological replicates, and statistically significant differences in methylation at the two time points are indicated. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 7: Exogenous Dnmt3a partially restores function and methylation patterns. () Analysis of HSC (CD150+, CD48−, KLS gated) frequency 18-weeks after transplantation. Sca-1+ cells from secondary recipient mice transplanted with Dnmt3a-null HSCs were transduced with either MSCV-Dnmt3a or control (MSCV-GFP) retroviruses and transplanted into tertiary recipients. Also shown are HSC frequencies in tertiary recipients transplanted with non-transduced control or Dnmt3a-null HSCs. Mean ± s.e.m. values are shown (N = 7–12 mice), and statistically significant differences are indicated. () Colony-forming capacity of Dnmt3a-null HSCs transduced with MSCV-GFP or MSCV-Dnmt3a. Also shown are colony formation from non-transduced control and Dnmt3a-null HSCs after the third serial transplantation. Mean ± s.e.m. values are shown (N = 4 replicate plates), and statistically significant differences are indicated. () Bisulfite sequencing in B cells across Vasn and Runx1 CGIs after forced exogenous Dnmt3a expression in Dnmt3a-null HSCs. Statistically significant diffe! rences in methylation are indicated. *P < 0.05, **P < 0.01, ***P < 0.001. * Figure 8: Model for Dnmt3a action in HSCs. HSC-specific genes are mostly unmethylated and expressed in normal HSCs (left). Upon receiving a signal to differentiate, Dnmt3a methylates and silences these regions to permit lineage commitment. This is associated with a loss of H3K4me3 and gene repression in B cells. Dnmt3a-null HSCs (right) cannot silence HSC genes, so upon receiving a stimulus to differentiate, HSC-specific genes remain expressed due to a lack of methylation and elevated H3K4me3. Upon cell division, the HSC self-renewal pathway remains active in Dnmt3a-null HSCs, resulting in their accumulation in the bone marrow. Of the few Dnmt3a-null HSCs that do differentiate, their progeny show incomplete methylation and partial repression of HSC genes. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE27322 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Deqiang Sun, * Mira Jeong, * Min Luo, * Jaroslav Jelinek & * Jonathan S Berg Affiliations * Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, Texas, USA. * Grant A Challen, * Mira Jeong & * Margaret A Goodell * Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, Texas, USA. * Grant A Challen, * Mira Jeong & * Margaret A Goodell * Department of Pathology, Baylor College of Medicine, Houston, Texas, USA. * Grant A Challen * Division of Biostatistics, Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas, USA. * Deqiang Sun, * Yuanxin Xi & * Wei Li * Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA. * Deqiang Sun, * Yuanxin Xi & * Wei Li * Huffington Center for Aging, Baylor College of Medicine, Houston, Texas, USA. * Mira Jeong, * Min Luo & * Gretchen J Darlington * Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Jaroslav Jelinek, * Hongcang Gu, * Yue Lu & * Jean-Pierre J Issa * Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA. * Jonathan S Berg * Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. * Jonathan S Berg * Broad Institute, Harvard University, Cambridge, Massachusetts, USA. * Christoph Bock & * Alexander Meissner * Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA. * Christoph Bock & * Alexander Meissner * Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, Illinois, USA. * Aparna Vasanthakumar & * Lucy A Godley * Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. * Shoudan Liang * Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA. * Margaret A Goodell Contributions G.A.C. designed and performed experiments, analyzed data and wrote the manuscript. Experiments were also designed by J.S.B., J.-P.J.I., L.A.G., H.G., C.B., W.L. and M.A.G. and were performed by M.J., M.L., A.V. and J.J. Data were additionally analyzed and interpreted by M.J., D.S., M.L., C.B., A.V., J.J., S.L., Y.L., A.M., J.-P.J.I., L.A.G., W.L. and M.A.G. D.S., C.B., Y.X., S.L. and Y.L. developed critical software. The manuscript was written or edited by G.J.D., W.L., L.A.G., J.-P.J.I., J.S.B., C.B. and M.A.G. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Margaret A Goodell or * Wei Li Author Details * Grant A Challen Search for this author in: * NPG journals * PubMed * Google Scholar * Deqiang Sun Search for this author in: * NPG journals * PubMed * Google Scholar * Mira Jeong Search for this author in: * NPG journals * PubMed * Google Scholar * Min Luo Search for this author in: * NPG journals * PubMed * Google Scholar * Jaroslav Jelinek Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan S Berg Search for this author in: * NPG journals * PubMed * Google Scholar * Christoph Bock Search for this author in: * NPG journals * PubMed * Google Scholar * Aparna Vasanthakumar Search for this author in: * NPG journals * PubMed * Google Scholar * Hongcang Gu Search for this author in: * NPG journals * PubMed * Google Scholar * Yuanxin Xi Search for this author in: * NPG journals * PubMed * Google Scholar * Shoudan Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Yue Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Gretchen J Darlington Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Meissner Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Pierre J Issa Search for this author in: * NPG journals * PubMed * Google Scholar * Lucy A Godley Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Li Contact Wei Li Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret A Goodell Contact Margaret A Goodell Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–12 and Supplementary Tables 1–5 and 9. Excel files * Supplementary Table 6 (512K) Annotation of differentially methylated regions (DMRs) * Supplementary Table 7 (31M) Microarray transcriptional profiling comparison of secondarily-transplanted control and Dnmt3a-KO HSCs * Supplementary Table 8 (158K) DREAM sequencing of secondary transplant control and Dnmt3a-KO B-cells Additional data
• Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm
  - Nat Genet 44(1):32-39 (2012)
  Nature Genetics | Article Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm * Xuehui Huang1, 2, 5 * Yan Zhao1, 2, 5 * Xinghua Wei3, 5 * Canyang Li1 * Ahong Wang1 * Qiang Zhao1 * Wenjun Li1 * Yunli Guo1 * Liuwei Deng1 * Chuanrang Zhu1 * Danlin Fan1 * Yiqi Lu1 * Qijun Weng1 * Kunyan Liu1 * Taoying Zhou1 * Yufeng Jing1 * Lizhen Si1 * Guojun Dong1, 3 * Tao Huang1 * Tingting Lu1 * Qi Feng1 * Qian Qian3 * Jiayang Li4 * Bin Han1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:32–39Year published:(2012)DOI:doi:10.1038/ng.1018Received03 June 2011Accepted02 November 2011Published online04 December 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg A high-density haplotype map recently enabled a genome-wide association study (GWAS) in a population of indica subspecies of Chinese rice landraces. Here we extend this methodology to a larger and more diverse sample of 950 worldwide rice varieties, including the Oryza sativa indica and Oryza sativa japonica subspecies, to perform an additional GWAS. We identified a total of 32 new loci associated with flowering time and with ten grain-related traits, indicating that the larger sample increased the power to detect trait-associated variants using GWAS. To characterize various alleles and complex genetic variation, we developed an analytical framework for haplotype-based de novo assembly of the low-coverage sequencing data in rice. We identified candidate genes for 18 associated loci through detailed annotation. This study shows that the integrated approach of sequence-based GWAS and functional genome annotation has the potential to match complex traits to their causal polymor! phisms in rice. View full text Figures at a glance * Figure 1: Genetic structure and population differentiation in 950 rice accessions. () Neighbor-joining tree of 950 rice accessions constructed from a simple matching distance of 4.1 million SNPs. The five divergent groups, indica (Ind), aus (Aus), temperate japonica (TeJ), tropical japonica (TrJ) and intermediate (Int), are colored in red, purple, blue, cyan and black, respectively. The scale bar indicates the simple matching distance. () The distributions of the pairwise population-differentiation statistic (Fst) across the rice genomes between indica and temperate japonica (in black), between temperate japonica and tropical japonica (in blue) and between indica and aus (in red). * Figure 2: Causal variant detection in six genes previously identified by GWAS in the indica population. The top of each panel shows the genomic location of the known gene and its gene structure. Exons and introns are depicted as rectangles and lines, respectively. Coding regions and untranslated regions are shown in black and gray, respectively. The points indicate the orientation of the genes. The red triangles indicate the location of causal variants detected in the gene. The bottom of each panel shows the contigs of two alleles (with major allele shown as a brown line and the minor allele shown as a green line) from local assembly, where the genotypes in the causal variant site are indicated. () Waxy is responsible for amylase content. () ALK is responsible for starch gelatinization temperature. () Rc is responsible for pericarp color. () OsC1 is responsible for apiculus color. () GS3 is responsible for grain length. () qSW5 is responsible for grain width. The dark red triangle indicates the location of 1.2-kb deletion in the reference genome. The indica cv. 93-11, which ha! s sequences from whole-genome shotgun and belongs to the haplotype group with the minor allele in qSW5, was used as a template to compare to assembly results. * Figure 3: Illustration of haplotype-based local assembly. () For each gene, genotypes for 55 SNP sites around the gene were retrieved from the genotype dataset. We calculated the simple matching distances for the 55 SNP sites and performed hierarchical cluster analysis by using the single linkage algorithm, which generated several haplotype groups (Hap1 through Hap4). The haplotype groups with a frequency of < 0.02 were excluded. () For each haplotype group, paired-end reads (shown as thick lines, with dashed lines connecting the read pairs) that were uniquely mapped onto the local region were collected together. In the case that only one of the paired-end reads was aligned, we picked up both of them. The sequence assembly was performed for each haplotype group separately, generating one (no gaps) or several contigs. () The contigs of different haplotype groups were aligned with the reference genome for sequence variation detection. The sequence variants detected, including SNPs and indels, were then used to predict the potential e! ffects on the gene. * Figure 4: Genome-wide association study of heading date in the indica population, the japonica population and the full population using the compressed MLM. For the significant loci identified, known loci are shown in purple and newly discovered loci are shown in green. Of these loci, those in Hd3a, Hd1 and Ghd7 had their causal variants detected through the haplotype-based assembly method, and there were no variants detected in the coding regions of RCN1 and OsGI (Supplementary Note). () Manhattan plots for heading date in indica population. The −log10P values from a genome-wide scan are plotted against the position on each of the 12 chromosomes. The horizontal dashed line indicates the genome-wide significance threshold (P = 5 × 10−8). () Quantile-quantile plot for heading date in the indica population. The horizontal axis shows −log10 transformed expected P values, and the vertical axis indicates −log10 transformed observed P values. () Manhattan plots for heading date in the japonica population and the genome-wide significance threshold (P = 2 × 10−7, shown as a dashed line). () Quantile-quantile plot for heading! date in japonica population. () Manhattan plots for heading date in the full population and the genome-wide significance threshold (P = 1 × 10−9, shown as a dashed line). () Quantile-quantile plot for heading date in the full population. Clear association signals around known genes that did not meet the genome-wide significance threshold are shown in orange. * Figure 5: Regions of the genome showing association signals and the expression profiles of candidate genes. () An associated locus for starch gelatinization temperature. The top of the panel shows the region on each side of the peak SNP. −log10 transformed P values from the compressed MLM are plotted on the vertical axis. The bottom of the panel shows a narrow region, with the candidate genes indicated by dark gray. () The expression pattern of a candidate gene (OsRAL6) in the amylase inhibitor gene cluster from public microarray data. DAP, days after pollination. Error bars, s.d. of three replicates. () Functional variants detected through de novo local assembly. The triangles indicate the location of causal variants detected in the gene. The colored lines show the contigs of two alleles (with the major allele shown as a brown line and the minor allele shown as a green line) from local assembly, where the genotypes of four non-synonymous SNPs are indicated. () An associated locus for hull color. () Expression pattern of the candidate gene (OsFBX310). () The ratio of read depths! (the ratio of the number of mapped reads from 41 indica rice lines with the minor allele to those from 41 indica lines with the major allele) are plotted against the local genomic region, and the positions of the candidate gene and the potential deletion are indicated. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Xuehui Huang, * Yan Zhao & * Xinghua Wei Affiliations * National Center for Gene Research, National Center for Plant Gene Research (Shanghai), Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. * Xuehui Huang, * Yan Zhao, * Canyang Li, * Ahong Wang, * Qiang Zhao, * Wenjun Li, * Yunli Guo, * Liuwei Deng, * Chuanrang Zhu, * Danlin Fan, * Yiqi Lu, * Qijun Weng, * Kunyan Liu, * Taoying Zhou, * Yufeng Jing, * Lizhen Si, * Guojun Dong, * Tao Huang, * Tingting Lu, * Qi Feng & * Bin Han * Chinese Academy of Sciences Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China. * Xuehui Huang, * Yan Zhao & * Bin Han * State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, China. * Xinghua Wei, * Guojun Dong & * Qian Qian * National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China. * Jiayang Li Contributions B.H. conceived of the project and its components. J.L. and B.H. contributed to the original concept of the project. W.L., Y.G., L.D., D.F., Y.L., Q.W. and Q.F. performed the genome sequencing. X.H., Q.Z., Y.Z., C.Z., K.L., L.S., T.H. and T.L. performed the genome data analyses. Y.Z., C.Z., Q.Z. and X.H. improved the imputation program for the data analyses. X.H., Q.Z. and Y.Z. developed an analytical framework for de novo assembly of the low-coverage sequencing data. X.W., C.L., A.W., T.Z., Y.J., G.D. and Q.Q. collected samples and performed the phenotyping. Y.Z. and X.H. performed the GWAS and statistical analyses. X.H. and B.H. analyzed all of the data together and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bin Han Author Details * Xuehui Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Xinghua Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Canyang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Ahong Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Qiang Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Wenjun Li Search for this author in: * NPG journals * PubMed * Google Scholar * Yunli Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Liuwei Deng Search for this author in: * NPG journals * PubMed * Google Scholar * Chuanrang Zhu Search for this author in: * NPG journals * PubMed * Google Scholar * Danlin Fan Search for this author in: * NPG journals * PubMed * Google Scholar * Yiqi Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Qijun Weng Search for this author in: * NPG journals * PubMed * Google Scholar * Kunyan Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Taoying Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Yufeng Jing Search for this author in: * NPG journals * PubMed * Google Scholar * Lizhen Si Search for this author in: * NPG journals * PubMed * Google Scholar * Guojun Dong Search for this author in: * NPG journals * PubMed * Google Scholar * Tao Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Tingting Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Qi Feng Search for this author in: * NPG journals * PubMed * Google Scholar * Qian Qian Search for this author in: * NPG journals * PubMed * Google Scholar * Jiayang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Bin Han Contact Bin Han Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (14M) Supplementary Note, Supplementary Tables 4, 6, 7 and 9–12 and Supplementary Figures 1–31. Excel files * Supplementary Table 1 (188K) The list of 950 accessions sampled in the collection. * Supplementary Table 2 (492K) The levels of sequence diversity (π) in each group across the rice genome. * Supplementary Table 3 (393K) The levels of pariwise population differentiation (Fst) across the rice genome. * Supplementary Table 5 (66K) The list of SNP sites with population-special alleles. * Supplementary Table 8 (811K) The detailed list of all the large-effect variations in rice genome. * Supplementary Table 13 (164K) The detailed list of the microarrays used in the study and their related descriptions. * Supplementary Table 14 (37K) The genotype dataset of indica accessions on the causal polymorphic sites of Hd3a. * Supplementary Table 15 (57K) The genotype dataset of indica accessions on the causal polymorphic sites of OsFBX310. * Supplementary Table 16 (29K) The genotype dataset of japonica accessions on the causal polymorphic sites of OsRAL6. Additional data
• Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains
  - Nat Genet 44(1):40-46 (2012)
  Nature Genetics | Letter Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina–associated domains * Benjamin P Berman1 * Daniel J Weisenberger1 * Joseph F Aman1 * Toshinori Hinoue1 * Zachary Ramjan1 * Yaping Liu1 * Houtan Noushmehr1 * Christopher P E Lange2, 3 * Cornelis M van Dijk4 * Rob A E M Tollenaar3 * David Van Den Berg1 * Peter W Laird1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:40–46Year published:(2012)DOI:doi:10.1038/ng.969Received02 March 2011Accepted13 September 2011Published online27 November 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Extensive changes in DNA methylation are common in cancer and may contribute to oncogenesis through transcriptional silencing of tumor-suppressor genes1. Genome-scale studies have yielded important insights into these changes2, 3, 4, 5 but have focused on CpG islands or gene promoters. We used whole-genome bisulfite sequencing (bisulfite-seq) to comprehensively profile a primary human colorectal tumor and adjacent normal colon tissue at single-basepair resolution. Regions of focal hypermethylation in the tumor were located primarily at CpG islands and were concentrated within regions of long-range (>100 kb) hypomethylation. These hypomethylated domains covered nearly half of the genome and coincided with late replication and attachment to the nuclear lamina in human cell lines. We confirmed the confluence of hypermethylation and hypomethylation within these domains in 25 diverse colorectal tumors and matched adjacent tissue. We propose that widespread DNA methylation changes! in cancer are linked to silencing programs orchestrated by the three-dimensional organization of chromatin within the nucleus. View full text Figures at a glance * Figure 1: Bisulfite-seq of a colon tumor and adjacent normal mucosa. Individual sequencing reads and summary methylation levels are shown within a 10-kb region around the STK33 gene promoter for the normal adjacent colon tissue (top) and matched colon tumor (bottom). Reads are shown without respect to strand orientation and are colored to indicate the percentage of CpG dinucleotides methylated within the read (reads with no CpGs are indicated in yellow). The percent methylation tracks summarize the percentage of reads methylated for each CpG dinucleotide (black dots) as well as the average methylation within sliding windows of five CpGs (solid brown graph). The methylation difference track at the bottom shows the average methylation difference between tumor and normal tissue within sliding windows of five CpGs, with red indicating tumor hypermethylation and green indicating tumor hypomethylation. * Figure 2: Three distinct methylation classes at focal elements. () Density plot of the average DNA methylation within all windows of five adjacent CpG dinucleotides on chromosome 4. Distinct subsets of methylation-prone (MP) and methylation-resistant (MR) windows are visible as high-density clusters, whereas the methylation-loss (ML) region is low density. () Comparison of each methylation class to ENCODE protein-DNA binding (ChIP-seq) data9 and other genomic features (for the full version, see Supplementary Fig. 6). We determined genomic enrichment by dividing the proportion of overlapping elements within each methylation class by the proportion of overlapping elements within size-matched, randomly generated genomic locations (shown as fold changes). All transcription factors are shown in a boxplot (left), and selected genomic features are shown as individual bars (right). * Figure 3: Focal methylation classes correspond to distinct epigenomic and sequence signatures. () UCSC Genome Browser plots of two downregulated (MGMT and MAF) and two upregulated (B3GNTL1 and TACSTD2) genes reveal that elements of the methylation-prone (MP), methylation-resistant (MR) and methylation-loss (ML) classes often coincide with a combination of promoter or enhancer histone modifications (H3K4 methylation), DNase I hypersensitivity (HS) and transcription-factor binding. In the enhancer and promoter tracks, each color represents an individual ENCODE cell line, and all cell lines are combined in the DNase HS and transcription factor tracks. () Significant results from HOMER19 sequence motif searches within each of the three methylation classes (for the full results, see the Supplementary Figs. 13–15). Because methylation-prone and methylation-resistant elements most often corresponded to CGI TSS, alignments for these two classes are relative to the oriented TSS, whereas those for the methylation-loss class (right) show alignments relative to the center of th! e unoriented methylation-loss element. Matches to known motifs from the HOMER database are shown below the de novo motif they match (Nrf1 and AP-1). * Figure 4: Hypermethylated CGIs fall within long, tumor-specific PMDs. () Density plot of average DNA methylation within all 20-kb windows on chromosome 4 showing a distinct subset of windows representing PMDs in the tumor but not normal colon tissue. () We identified PMDs for four cell types by searching for 100-kb partially methylated windows (see text), and we compared the percentage of the genome contained within PMDs between the tumor and normal colon tissue along with two other cell types7. () The average methylation change is shown as a function of distance from CGI promoters for all promoters that were unmethylated in the normal colon (with mean methylation <0.2). We divided promoters into methylation-prone (MP; with mean tumor methylation >0.3) and methylation-resistant (MR; with mean tumor methylation <0.2), and the plots are oriented to show the transcribed region toward the right side. () UCSC Genome Browser plot of a representative 10-Mb region on chromosome 3q showing substantial overlap between colon tumor and IMR-90 PMDs, Lamin-! B1 marks and focal hypermethylation (methylation-resistant elements are visible as red spikes in the methylation change track). Lamin-B1 and ENCODE enhancer and promoter tracks are from the UCSC annotation database. * Figure 5: Properties of PMD boundaries. () UCSC Genome Browser plot of a 13-Mb region with several PMD boundaries specific to either the colon tumor or IMR-90 fibroblasts7. Tumor-specific PMD regions are annotated, showing that the two epithelial tumor suppressors NRG1 and SFRP1 fall within these regions. () A higher resolution view of the highlighted area surrounding SFRP1 showing that the gene promoter is hypermethylated in the tumor and defines a cell-type–specific PMD boundary in IMR-90 cells. (,) Average genomic density of a number of annotation features is plotted for 10-kb bins relative to colon tumor () and IMR-90 () PMD boundaries. Plots are oriented with regions outside the PMD to the left of the midpoint and regions inside the PMD to the right of the midpoint, as shown in the diagrams below each plot. We normalized the genomic density by dividing the value within each bin by the average density within bins lying outside of PMDs. For complete boundary plots, see Supplementary Figure 11. * Figure 6: Tumor-specific hypermethylation and hypomethylation are correlated and are strongly enriched within PMDs in a diverse set of 25 colon tumors. () Infinium HumanMethylation27k array values (β values) for five representative tumors, each compared to adjacent normal colon mucosa from the same individual. The tumor sequenced using bisulfite-seq (from individual 14838) is shown alongside one tumor of each methylation subtype from ref. 6, and colored points indicate probes identified as one of four methylation classes: methylation prone (MP, red), methylation resistant (MR, cyan), partial methylation loss (PML, green) and constitutively methylated (CM, purple). Probes not clearly falling into one of these categories are shown in orange. () The mean hypermethylation of methylation-prone probes (tumor β minus normal β) and the mean hypomethylation of methylation-loss probes (normal β minus tumor β) show a strong linear correlation (Pearson r = 0.80) across all samples. Colored lines indicate the best robust linear regression fit for each methylation subtype. () For each tumor-normal comparison, the fraction of microar! ray features falling within different genomic regions (H3K27me3, bisulfite-seq PMDs, and so on) is shown, with features separated by methylation class (methylation resistant, methylation prone, methylation loss and constitutively methylated). Shapes indicate tumor subtype as in panel , with the bisulfite-seq data colored solid black. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE25070 * GSE25062 * GSE18199 Author information * Accession codes * Author information * Supplementary information Affiliations * University of Southern California Epigenome Center, University of Southern California, Keck School of Medicine, Los Angeles, California, USA. * Benjamin P Berman, * Daniel J Weisenberger, * Joseph F Aman, * Toshinori Hinoue, * Zachary Ramjan, * Yaping Liu, * Houtan Noushmehr, * David Van Den Berg & * Peter W Laird * Department of Surgery, Groene Hart Hospital, Gouda, The Netherlands. * Christopher P E Lange * Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands. * Christopher P E Lange & * Rob A E M Tollenaar * Department of Pathology, Groene Hart Hospital, Gouda, The Netherlands. * Cornelis M van Dijk Contributions The project was conceived and the experiments were designed by P.W.L., D.J.W., B.P.B., D.V.D.B. and T.H. The Bisulfite-seq library construction and Genome Analyzer sequencing were performed by D.J.W., J.F.A. and D.V.D.B. The Infinium genotyping and data analysis was performed by B.P.B., motif analysis by H.N. and pipeline automation by B.P.B. and Z.R. Bisulfite-seq data processing and analysis were performed by B.P.B., Z.R. and H.N. Validation samples were collected and analyzed by C.P.E.L., C.M.v.D., R.A.E.M.T., B.P.B., D.J.W. and T.H. The manuscript was prepared by B.P.B. and P.W.L., and the study was supervised by P.W.L. Competing financial interests P.W.L. is scientific advisory board member and consultant for Epigenomics, AG, which has a commercial interest in DNA methylation biomarkers. The work described in this manuscript was not supported by nor will it benefit Epigenomics, AG. Corresponding author Correspondence to: * Peter W Laird Author Details * Benjamin P Berman Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J Weisenberger Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph F Aman Search for this author in: * NPG journals * PubMed * Google Scholar * Toshinori Hinoue Search for this author in: * NPG journals * PubMed * Google Scholar * Zachary Ramjan Search for this author in: * NPG journals * PubMed * Google Scholar * Yaping Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Houtan Noushmehr Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher P E Lange Search for this author in: * NPG journals * PubMed * Google Scholar * Cornelis M van Dijk Search for this author in: * NPG journals * PubMed * Google Scholar * Rob A E M Tollenaar Search for this author in: * NPG journals * PubMed * Google Scholar * David Van Den Berg Search for this author in: * NPG journals * PubMed * Google Scholar * Peter W Laird Contact Peter W Laird Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (18M) Supplementary Note, Supplementary Figures 1–16 Excel files * Supplementary Tables 1 and 2 (94K) Bisulfite-seq summary statistics and Bisulfite-seq detailed statistics by chromosome Additional data
• Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia
  - Nat Genet 44(1):47-52 (2012)
  Nature Genetics | Letter Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia * Víctor Quesada1 * Laura Conde2 * Neus Villamor2 * Gonzalo R Ordóñez1 * Pedro Jares2 * Laia Bassaganyas3 * Andrew J Ramsay1 * Sílvia Beà2 * Magda Pinyol4 * Alejandra Martínez-Trillos5 * Mónica López-Guerra2 * Dolors Colomer2 * Alba Navarro2 * Tycho Baumann5 * Marta Aymerich2 * María Rozman2 * Julio Delgado5 * Eva Giné5 * Jesús M Hernández6 * Marcos González-Díaz6 * Diana A Puente1 * Gloria Velasco1 * José M P Freije1 * José M C Tubío3 * Romina Royo7 * Josep L Gelpí7 * Modesto Orozco7 * David G Pisano8 * Jorge Zamora8 * Miguel Vázquez8 * Alfonso Valencia8 * Heinz Himmelbauer9 * Mónica Bayés10 * Simon Heath10 * Marta Gut10 * Ivo Gut10 * Xavier Estivill3 * Armando López-Guillermo5 * Xose S Puente1 * Elías Campo2, 11 * Carlos López-Otín1, 11 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:47–52Year published:(2012)DOI:doi:10.1038/ng.1032Received12 July 2011Accepted10 November 2011Published online11 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Here we perform whole-exome sequencing of samples from 105 individuals with chronic lymphocytic leukemia (CLL)1, 2, the most frequent leukemia in adults in Western countries. We found 1,246 somatic mutations potentially affecting gene function and identified 78 genes with predicted functional alterations in more than one tumor sample. Among these genes, SF3B1, encoding a subunit of the spliceosomal U2 small nuclear ribonucleoprotein (snRNP), is somatically mutated in 9.7% of affected individuals. Further analysis in 279 individuals with CLL showed that SF3B1 mutations were associated with faster disease progression and poor overall survival. This work provides the first comprehensive catalog of somatic mutations in CLL with relevant clinical correlates and defines a large set of new genes that may drive the development of this common form of leukemia. The results reinforce the idea that targeting several well-known genetic pathways, including mRNA splicing, could be useful i! n the treatment of CLL and other malignancies. View full text Figures at a glance * Figure 1: Somatic mutation profiles of 105 CLL exomes. () Chromosomal distribution and location of protein-coding mutations (dots) and insertions and deletions (indels; Xs) identified by exome sequencing of 60 CLL samples with IGHV region mutations (blue) and 45 without IGHV region mutations (red). RM-CLL genes are highlighted with vertical bars and summarized for each individual with orange dots. () Box plot representation comparing the frequency of coding, nonsynonymous somatic mutations in CLL samples with and without IGHV region mutations. Error bars, range of the data set (*P = 0.038). () Frequency of substitutions in the 105 CLL samples for the six possible mutation classes. Error bars, s.d. (***P = 0.0017). * Figure 2: Structural impact of SF3B1 alterations. () Protein sequence alignments of the SF3B1 C-terminal domain around the altered residues (arrows) in evolutionarily diverse species. () Schematic representation of the human SF3B1 protein with the primary structural domains highlighted. The locations of the different somatic alterations determined to be encoded in CLL samples (top) and the frequencies of each alteration (bottom) are shown. () Molecular model of the C-terminal portion of the human SF3B1 protein and detailed view of the altered amino acids identified in CLL cases. * Figure 3: Novel alternative splicing of FOXP1 in CLL cases. () An expanded view of the protein interval subject to truncation as a result of alternative splicing is shown. The alternative splicing event that generates the novel transcript encoding this protein, FOXP1w, is shown as a red line, and the primers used for RT-PCR amplification of FOXP1w and full-length FOXP1 (control) as arrows. Q-rich, glutamine-rich region; C2H2-Zf, Cys2His2 zinc finger. () Quantitative RT-PCR analysis of truncated FOXP1w levels in CLL samples with and without SF3B1 somatic mutations. Error bars, s.d. * Figure 4: Clinical analysis of SF3B1 in CLL. () Distribution of disease stage (Binet), IGHV region mutational status and ZAP-70 expression in individuals with (MUT) or without (WT) mutations in SF3B1 (***P = 0.004, *P = 0.03). () Actuarial probability of disease progression of CLL cases with mutated or wild-type SF3B1 (P = 0.0001). () Actuarial probability of overall survival of CLL cases with mutated or wild-type SF3B1 (P = 0.002). Author information * Author information * Supplementary information Affiliations * Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología, Universidad de Oviedo, Oviedo, Spain. * Víctor Quesada, * Gonzalo R Ordóñez, * Andrew J Ramsay, * Diana A Puente, * Gloria Velasco, * José M P Freije, * Xose S Puente & * Carlos López-Otín * Unidad de Hematopatología, Servicio de Anatomía Patológica, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. * Laura Conde, * Neus Villamor, * Pedro Jares, * Sílvia Beà, * Mónica López-Guerra, * Dolors Colomer, * Alba Navarro, * Marta Aymerich, * María Rozman & * Elías Campo * Genes and Disease Programme, Center for Genomic Regulation, Pompeu Fabra University (CRG-UPF), Barcelona, Spain. * Laia Bassaganyas, * José M C Tubío & * Xavier Estivill * Unidad de Genómica, IDIBAPS, Barcelona, Spain. * Magda Pinyol * Servicio de Hematología, Hospital Clínic, Universidad de Barcelona, Barcelona, Spain. * Alejandra Martínez-Trillos, * Tycho Baumann, * Julio Delgado, * Eva Giné & * Armando López-Guillermo * Servicio de Hematología, Hospital Universitario, Centro de Investigación del Cáncer, Universidad de Salamanca, Salamanca, Spain. * Jesús M Hernández & * Marcos González-Díaz * Programa Conjunto de Biología Computacional, Barcelona Supercomputing Center (BSC), Institut de Reçerca Biomèdica (IRB), Spanish National Bioinformatics Institute, Universitat de Barcelona, Barcelona, Spain. * Romina Royo, * Josep L Gelpí & * Modesto Orozco * Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Spanish National Bioinformatics Institute, Madrid, Spain. * David G Pisano, * Jorge Zamora, * Miguel Vázquez & * Alfonso Valencia * Ultrasequencing Unit, CRG-UPF, Barcelona, Spain. * Heinz Himmelbauer * Centro Nacional de Análisis Genómico, Parc Científic de Barcelona, Barcelona, Spain. * Mónica Bayés, * Simon Heath, * Marta Gut & * Ivo Gut * These authors jointly directed this work. * Elías Campo & * Carlos López-Otín Contributions V.Q., G.R.O., A.J.R., G.V., J.M.P.F. and X.S.P. developed the bioinformatic algorithms and performed the analysis of sequence data. L.C., P.J., M.P., M.L.-G., D.C. and A.N. were responsible for downstream validation analysis and functional studies. L.B., S.B. and J.M.C.T. studied structural variants. D.A.P., H.H., M.B., S.H. and M.G. were responsible for generating libraries, performing exome capture and running sequencers. M.A. prepared and supervised the bioethics requirements. N.V., A.M.-T., T.B., J.D., E.G., A.L.-G. and E.C. performed clinical and biological studies. M.R., M.G.-D., N.V. and J.M.H. reviewed the pathologic data and confirmed the diagnosis. R.R., J.L.G., M.O., D.G.P., J.Z., M.V. and A.V. were in charge of bioinformatics data management. I.G. coordinated the sequencing efforts and performed primary data analysis. V.Q., X.S.P., X.E., A. L.-G., E.C. and C.L.-O. directed the research and wrote the manuscript, which all authors have approved. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Carlos López-Otín or * Elías Campo Author Details * Víctor Quesada Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Conde Search for this author in: * NPG journals * PubMed * Google Scholar * Neus Villamor Search for this author in: * NPG journals * PubMed * Google Scholar * Gonzalo R Ordóñez Search for this author in: * NPG journals * PubMed * Google Scholar * Pedro Jares Search for this author in: * NPG journals * PubMed * Google Scholar * Laia Bassaganyas Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew J Ramsay Search for this author in: * NPG journals * PubMed * Google Scholar * Sílvia Beà Search for this author in: * NPG journals * PubMed * Google Scholar * Magda Pinyol Search for this author in: * NPG journals * PubMed * Google Scholar * Alejandra Martínez-Trillos Search for this author in: * NPG journals * PubMed * Google Scholar * Mónica López-Guerra Search for this author in: * NPG journals * PubMed * Google Scholar * Dolors Colomer Search for this author in: * NPG journals * PubMed * Google Scholar * Alba Navarro Search for this author in: * NPG journals * PubMed * Google Scholar * Tycho Baumann Search for this author in: * NPG journals * PubMed * Google Scholar * Marta Aymerich Search for this author in: * NPG journals * PubMed * Google Scholar * María Rozman Search for this author in: * NPG journals * PubMed * Google Scholar * Julio Delgado Search for this author in: * NPG journals * PubMed * Google Scholar * Eva Giné Search for this author in: * NPG journals * PubMed * Google Scholar * Jesús M Hernández Search for this author in: * NPG journals * PubMed * Google Scholar * Marcos González-Díaz Search for this author in: * NPG journals * PubMed * Google Scholar * Diana A Puente Search for this author in: * NPG journals * PubMed * Google Scholar * Gloria Velasco Search for this author in: * NPG journals * PubMed * Google Scholar * José M P Freije Search for this author in: * NPG journals * PubMed * Google Scholar * José M C Tubío Search for this author in: * NPG journals * PubMed * Google Scholar * Romina Royo Search for this author in: * NPG journals * PubMed * Google Scholar * Josep L Gelpí Search for this author in: * NPG journals * PubMed * Google Scholar * Modesto Orozco Search for this author in: * NPG journals * PubMed * Google Scholar * David G Pisano Search for this author in: * NPG journals * PubMed * Google Scholar * Jorge Zamora Search for this author in: * NPG journals * PubMed * Google Scholar * Miguel Vázquez Search for this author in: * NPG journals * PubMed * Google Scholar * Alfonso Valencia Search for this author in: * NPG journals * PubMed * Google Scholar * Heinz Himmelbauer Search for this author in: * NPG journals * PubMed * Google Scholar * Mónica Bayés Search for this author in: * NPG journals * PubMed * Google Scholar * Simon Heath Search for this author in: * NPG journals * PubMed * Google Scholar * Marta Gut Search for this author in: * NPG journals * PubMed * Google Scholar * Ivo Gut Search for this author in: * NPG journals * PubMed * Google Scholar * Xavier Estivill Search for this author in: * NPG journals * PubMed * Google Scholar * Armando López-Guillermo Search for this author in: * NPG journals * PubMed * Google Scholar * Xose S Puente Search for this author in: * NPG journals * PubMed * Google Scholar * Elías Campo Contact Elías Campo Search for this author in: * NPG journals * PubMed * Google Scholar * Carlos López-Otín Contact Carlos López-Otín Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (651K) Supplementary Note, Supplementary Figures 1–3 and Supplementary Tables 1–3 and 5–16 Excel files * Supplementary Table 4 (201K) Somatic mutations in CLL patients Additional data
• Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes
  - Nat Genet 44(1):53-57 (2012)
  Nature Genetics | Letter Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes * Timothy A Graubert1, 2, 3, 9 * Dong Shen4, 9 * Li Ding4, 5, 9 * Theresa Okeyo-Owuor1 * Cara L Lunn1 * Jin Shao1 * Kilannin Krysiak1 * Christopher C Harris4 * Daniel C Koboldt4 * David E Larson4 * Michael D McLellan4 * David J Dooling4 * Rachel M Abbott4 * Robert S Fulton4 * Heather Schmidt4 * Joelle Kalicki-Veizer4 * Michelle O'Laughlin4 * Marcus Grillot1 * Jack Baty6 * Sharon Heath1 * John L Frater3 * Talat Nasim7, 8 * Daniel C Link1, 2 * Michael H Tomasson1, 2 * Peter Westervelt1, 2 * John F DiPersio1, 2 * Elaine R Mardis2, 4, 5 * Timothy J Ley1, 2, 4, 5 * Richard K Wilson2, 4, 5 * Matthew J Walter1, 2, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:53–57Year published:(2012)DOI:doi:10.1038/ng.1031Received03 August 2011Accepted09 November 2011Published online11 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Myelodysplastic syndromes (MDS) are hematopoietic stem cell disorders that often progress to chemotherapy-resistant secondary acute myeloid leukemia (sAML). We used whole-genome sequencing to perform an unbiased comprehensive screen to discover the somatic mutations in a sample from an individual with sAML and genotyped the loci containing these mutations in the matched MDS sample. Here we show that a missense mutation affecting the serine at codon 34 (Ser34) in U2AF1 was recurrently present in 13 out of 150 (8.7%) subjects with de novo MDS, and we found suggestive evidence of an increased risk of progression to sAML associated with this mutation. U2AF1 is a U2 auxiliary factor protein that recognizes the AG splice acceptor dinucleotide at the 3′ end of introns, and the alterations in U2AF1 are located in highly conserved zinc fingers of this protein1, 2. Mutant U2AF1 promotes enhanced splicing and exon skipping in reporter assays in vitro. This previously unidentified, re! current mutation in U2AF1 implicates altered pre-mRNA splicing as a potential mechanism for MDS pathogenesis. View full text Figures at a glance * Figure 1: U2AF1 mutations found in individuals with MDS. () Missense mutations were detected in codons 34 and 157 of U2AF1. The ZnF1 (zinc finger 1), UHM (U2AF homology motif), ZnF2 (zinc finger 2) and RS (arginine-serine rich) domains are shown. The amino acid sequence of the ZnF1 domain is highly conserved (gray shaded area). The zinc coordinating and mutated residues are shown in blue (asterisks) and red (arrow), respectively. () Deep sequencing of U2AF1 using DNA collected from paired normal, MDS or sAML samples. Mutant allele frequencies represent the proportion of sequencing reads supporting the mutant allele compared to the total reads. Total read counts are shown below as gray bars (with a mean of 5,651 reads per sample). The mutation was present in the majority of cells (mutant allele frequency of 31.4–48.2%) in all of the samples. Two subjects with MDS had a second bone marrow sample harvested and analyzed (MDS (2nd)) () Deep sequencing of cDNA from MDS or sAML samples. The mutant allele is expressed in all the samples! tested. * Figure 2: Impact of U2AF1 mutations on clinical outcome. (,) Overall () and disease-free () survival are not affected by U2AF1 genotype. () The probability of sAML progression is increased in individuals with MDS with U2AF1 mutations (P = 0.03). * Figure 3: U2AF1 p.Ser34Phe alteration induces splicing alterations in 293T cells. () In the absence of the Tra2α splicing enhancer or the hnRNPG splicing inhibitor, transient coexpression of mutated U2AF1 increases splicing of the pTN24 construct, resulting in an increase in the ratio of luciferase expression relative to β-galactosidase expression, compared to expression of wild-type U2AF1 (P < 0.001). Tra2α (positive control) and hnRNPG (negative control) cause increased or decreased splicing efficiency, respectively. A protein blot of U2AF1 using extracts from the same cells used for the luciferase assays is shown below each combination of plasmids. () Expression of the Ser34Phe mutant results in an increase in the splicing of the pTN24 construct and an increase in the luciferase expression compared to the control plasmid (P < 0.001). This result is independent of endogenous U2AF1 expression. A protein blot of U2AF1 using lysates from the same cells is shown below each condition. () The GH1 minigene was transiently transfected into 293T cells with a ! control plasmid, wild-type U2AF1 cDNA or mutant p.Ser34Phe U2AF1 cDNA in the presence of a control siRNA (siControl) or siRNA targeting endogenous U2AF1 (siU2AF1). RT-PCR using the indicated primers resulted in a fully spliced 505-bp amplicon or a 386-bp amplicon that skips the middle exon, which is shaded in black (exon skipping). A representative PCR gel image is shown, and the ratio of the lower band (amplicon b, representing exon skipping) relative to the normally spliced upper band (amplicon a) is shown above each condition. Expression of the Ser34Phe mutant results in an increase in exon skipping compared to expression of control or wild-type U2AF1 (P < 0.02). WT, wild type; Mut, mutant; T7, T7 primer. Error bars, s.d. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE30195 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Timothy A Graubert, * Dong Shen & * Li Ding Affiliations * Department of Internal Medicine, Division of Oncology, Washington University, St. Louis, Missouri, USA. * Timothy A Graubert, * Theresa Okeyo-Owuor, * Cara L Lunn, * Jin Shao, * Kilannin Krysiak, * Marcus Grillot, * Sharon Heath, * Daniel C Link, * Michael H Tomasson, * Peter Westervelt, * John F DiPersio, * Timothy J Ley & * Matthew J Walter * Siteman Cancer Center, Washington University, St. Louis, Missouri, USA. * Timothy A Graubert, * Daniel C Link, * Michael H Tomasson, * Peter Westervelt, * John F DiPersio, * Elaine R Mardis, * Timothy J Ley, * Richard K Wilson & * Matthew J Walter * Department of Pathology and Immunology, Washington University, St. Louis, Missouri, USA. * Timothy A Graubert & * John L Frater * The Genome Institute, Washington University, St. Louis, Missouri, USA. * Dong Shen, * Li Ding, * Christopher C Harris, * Daniel C Koboldt, * David E Larson, * Michael D McLellan, * David J Dooling, * Rachel M Abbott, * Robert S Fulton, * Heather Schmidt, * Joelle Kalicki-Veizer, * Michelle O'Laughlin, * Elaine R Mardis, * Timothy J Ley & * Richard K Wilson * Department of Genetics, Washington University, St. Louis, Missouri, USA. * Li Ding, * Elaine R Mardis, * Timothy J Ley, * Richard K Wilson & * Matthew J Walter * Division of Biostatistics, Washington University, St. Louis, Missouri, USA. * Jack Baty * Department of Medical and Molecular Genetics, King's College, Guy's Hospital, London, UK. * Talat Nasim * National Institute for Health Research (NIHR), Biomedical Research Centre, Guy's and St. Thomas' National Health Service (NHS) Foundation Trust and King's College London, London, UK. * Talat Nasim Contributions The project leaders were T.A.G., D.S., L.D. and M.J.W. Study design and project conception were performed by T.A.G., L.D., D.C.L., M.H.T., P.W., J.F.D., E.R.M., T.J.L., R.K.W. and M.J.W. Sequence and data analysis were performed by D.S., L.D., C.C.H., D.C.K., D.E.L., M.D.M., D.J.D., R.M.A., R.S.F., H.S., J.K.-V. and M.O. In vitro splicing assays, PCR or gene expression analyses were performed by T.O.-O., C.L.L., J.S., K.K. and T.N. Clinical data management, specimen acquisition or statistical analyses were performed by M.G., S.H. and J.B. Hematopathology was performed by J.L.F. Manuscript preparation was performed by T.A.G., D.S., L.D., D.C.L., J.F.D., E.R.M., T.J.L., R.K.W. and M.J.W. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthew J Walter Author Details * Timothy A Graubert Search for this author in: * NPG journals * PubMed * Google Scholar * Dong Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Li Ding Search for this author in: * NPG journals * PubMed * Google Scholar * Theresa Okeyo-Owuor Search for this author in: * NPG journals * PubMed * Google Scholar * Cara L Lunn Search for this author in: * NPG journals * PubMed * Google Scholar * Jin Shao Search for this author in: * NPG journals * PubMed * Google Scholar * Kilannin Krysiak Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher C Harris Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel C Koboldt Search for this author in: * NPG journals * PubMed * Google Scholar * David E Larson Search for this author in: * NPG journals * PubMed * Google Scholar * Michael D McLellan Search for this author in: * NPG journals * PubMed * Google Scholar * David J Dooling Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel M Abbott Search for this author in: * NPG journals * PubMed * Google Scholar * Robert S Fulton Search for this author in: * NPG journals * PubMed * Google Scholar * Heather Schmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Joelle Kalicki-Veizer Search for this author in: * NPG journals * PubMed * Google Scholar * Michelle O'Laughlin Search for this author in: * NPG journals * PubMed * Google Scholar * Marcus Grillot Search for this author in: * NPG journals * PubMed * Google Scholar * Jack Baty Search for this author in: * NPG journals * PubMed * Google Scholar * Sharon Heath Search for this author in: * NPG journals * PubMed * Google Scholar * John L Frater Search for this author in: * NPG journals * PubMed * Google Scholar * Talat Nasim Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel C Link Search for this author in: * NPG journals * PubMed * Google Scholar * Michael H Tomasson Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Westervelt Search for this author in: * NPG journals * PubMed * Google Scholar * John F DiPersio Search for this author in: * NPG journals * PubMed * Google Scholar * Elaine R Mardis Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy J Ley Search for this author in: * NPG journals * PubMed * Google Scholar * Richard K Wilson Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew J Walter Contact Matthew J Walter Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (9M) Supplementary Note, Supplementary Figures 1–4 and Supplementary Tables 1–9 Additional data
• Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk
  - Nat Genet 44(1):58-61 (2012)
  Nature Genetics | Letter Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk * Peter Broderick1, 15 * Daniel Chubb1, 15 * David C Johnson2, 15 * Niels Weinhold3, 15 * Asta Försti4 * Amy Lloyd1 * Bianca Olver1 * Yussanne P Ma1 * Sara E Dobbins1 * Brian A Walker2 * Faith E Davies2 * Walter A Gregory5 * J Anthony Child5 * Fiona M Ross6 * Graham H Jackson7 * Kai Neben3 * Anna Jauch8 * Per Hoffmann9 * Thomas W Mühleisen9 * Markus M Nöthen9, 10 * Susanne Moebus11 * Ian P Tomlinson12 * Hartmut Goldschmidt3, 13 * Kari Hemminki4, 14, 16 * Gareth J Morgan2, 16 * Richard S Houlston1, 2, 16 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:58–61Year published:(2012)DOI:doi:10.1038/ng.993Received08 September 2011Accepted03 October 2011Published online27 November 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To identify risk variants for multiple myeloma, we conducted a genome-wide association study of 1,675 individuals with multiple myeloma and 5,903 control subjects. We identified risk loci for multiple myeloma at 3p22.1 (rs1052501 in ULK4; odds ratio (OR) = 1.32; P = 7.47 × 10−9) and 7p15.3 (rs4487645, OR = 1.38; P = 3.33 × 10−15). In addition, we observed a promising association at 2p23.3 (rs6746082, OR = 1.29; P = 1.22 × 10−7). Our study identifies new genomic regions associated with multiple myeloma risk that may lead to new etiological insights. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Peter Broderick, * Daniel Chubb, * David C Johnson & * Niels Weinhold Affiliations * Molecular and Population Genetics, Division of Genetics and Epidemiology, Institute of Cancer Research, Surrey, UK. * Peter Broderick, * Daniel Chubb, * Amy Lloyd, * Bianca Olver, * Yussanne P Ma, * Sara E Dobbins & * Richard S Houlston * Haemato-Oncology Research Unit, Division of Molecular Pathology, Institute of Cancer Research, Surrey, UK. * David C Johnson, * Brian A Walker, * Faith E Davies, * Gareth J Morgan & * Richard S Houlston * Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany. * Niels Weinhold, * Kai Neben & * Hartmut Goldschmidt * German Cancer Research Center, Heidelberg, Germany. * Asta Försti & * Kari Hemminki * Clinical Trials Research Unit, University of Leeds, Leeds, UK. * Walter A Gregory & * J Anthony Child * Cytogenetics Group, Wessex Regional Cytogenetic Laboratory, Salisbury, UK. * Fiona M Ross * Royal Victoria Infirmary, Newcastle upon Tyne, UK. * Graham H Jackson * Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany. * Anna Jauch * Institute of Human Genetics, University of Bonn, Bonn, Germany. * Per Hoffmann, * Thomas W Mühleisen & * Markus M Nöthen * German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. * Markus M Nöthen * Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University of Duisburg–Essen, Essen, Germany. * Susanne Moebus * Molecular and Population Genetics, Wellcome Trust Centre for Human Genetics, Oxford, UK. * Ian P Tomlinson * National Centre of Tumour Diseases, Heidelberg, Germany. * Hartmut Goldschmidt * Center for Primary Health Care Research, Lund University, Malmo, Sweden. * Kari Hemminki * These authors jointly directed this work. * Kari Hemminki, * Gareth J Morgan & * Richard S Houlston Contributions R.S.H. designed the study. R.S.H. and G.J.M. obtained financial support in the UK, and K.H. and H.G. obtained funding in Germany. D.C. performed the main statistical and bioinformatic analyses, and Y.P.M. and S.E.D. performed additional related analyses. P.B. coordinated laboratory studies. A.L. and B.O. performed genotyping of the UK samples. P.H., T.W.M. and M.M.N. performed and coordinated genotyping of the German controls; K.H. and A.F. coordinated genotyping of the German cases. D.C.J. managed and prepared the Myeloma-VII and Myeloma-IX case study DNA samples. H.G., K.N. and N.W. coordinated and managed the German DNA samples. G.J.M., F.E.D., W.A.G., G.H.J. and J.A.C. ascertained and collected case study samples from the UK Myeloma-VII and Myeloma-IX studies. S.M. obtained and managed the HNR samples. I.P.T. acquired colorectal cancer control samples. B.A.W. performed expression analyses on the UK samples. F.M.R. performed FISH analyses on the UK samples, and A.J. perfo! rmed these analyses on the German samples. R.S.H. drafted the manuscript, and all authors contributed to the final version. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard S Houlston Author Details * Peter Broderick Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel Chubb Search for this author in: * NPG journals * PubMed * Google Scholar * David C Johnson Search for this author in: * NPG journals * PubMed * Google Scholar * Niels Weinhold Search for this author in: * NPG journals * PubMed * Google Scholar * Asta Försti Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Lloyd Search for this author in: * NPG journals * PubMed * Google Scholar * Bianca Olver Search for this author in: * NPG journals * PubMed * Google Scholar * Yussanne P Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Sara E Dobbins Search for this author in: * NPG journals * PubMed * Google Scholar * Brian A Walker Search for this author in: * NPG journals * PubMed * Google Scholar * Faith E Davies Search for this author in: * NPG journals * PubMed * Google Scholar * Walter A Gregory Search for this author in: * NPG journals * PubMed * Google Scholar * J Anthony Child Search for this author in: * NPG journals * PubMed * Google Scholar * Fiona M Ross Search for this author in: * NPG journals * PubMed * Google Scholar * Graham H Jackson Search for this author in: * NPG journals * PubMed * Google Scholar * Kai Neben Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Jauch Search for this author in: * NPG journals * PubMed * Google Scholar * Per Hoffmann Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas W Mühleisen Search for this author in: * NPG journals * PubMed * Google Scholar * Markus M Nöthen Search for this author in: * NPG journals * PubMed * Google Scholar * Susanne Moebus Search for this author in: * NPG journals * PubMed * Google Scholar * Ian P Tomlinson Search for this author in: * NPG journals * PubMed * Google Scholar * Hartmut Goldschmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Kari Hemminki Search for this author in: * NPG journals * PubMed * Google Scholar * Gareth J Morgan Search for this author in: * NPG journals * PubMed * Google Scholar * Richard S Houlston Contact Richard S Houlston Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Tables 1–3 and Supplementary Figures 1–4. Additional data
• Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations
  - Nat Genet 44(1):62-66 (2012)
  Nature Genetics | Letter Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations * Chen Wu1, 22 * Xiaoping Miao2, 22 * Liming Huang1 * Xu Che3 * Guoliang Jiang4 * Dianke Yu1 * Xianghong Yang5 * Guangwen Cao6 * Zhibin Hu7 * Yongjian Zhou8 * Chaohui Zuo9 * Chunyou Wang10 * Xianghong Zhang11 * Yifeng Zhou12 * Xianjun Yu13 * Wanjin Dai5 * Zhaoshen Li14 * Hongbing Shen7 * Luming Liu15 * Yanling Chen16 * Sheng Zhang17 * Xiaoqi Wang18 * Kan Zhai1 * Jiang Chang1 * Yu Liu1 * Menghong Sun19 * Wei Cao5 * Jun Gao14 * Ying Ma5 * Xiongwei Zheng20 * Siu Tim Cheung18 * Yongfeng Jia21 * Jian Xu1 * Wen Tan1 * Ping Zhao3 * Tangchun Wu2 * Chengfeng Wang3, 23 * Dongxin Lin1, 23 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:62–66Year published:(2012)DOI:doi:10.1038/ng.1020Received06 April 2011Accepted03 November 2011Published online11 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Pancreatic cancer has the lowest survival rate among human cancers, and there are no effective markers for its screening and early diagnosis. To identify genetic susceptibility markers for this cancer, we carried out a genome-wide association study on 981 individuals with pancreatic cancer (cases) and 1,991 cancer-free controls of Chinese descent using 666,141 autosomal SNPs. Promising associations were replicated in an additional 2,603 pancreatic cancer cases and 2,877 controls recruited from 25 hospitals in 16 provinces or cities in China. We identified five new susceptibility loci at chromosomes 21q21.3, 5p13.1, 21q22.3, 22q13.32 and 10q26.11 (P = 2.24 × 10−13 to P = 4.18 × 10−10) in addition to 13q22.1 previously reported in populations of European ancestry. These results advance our understanding of the development of pancreatic cancer and highlight potential targets for the prevention or treatment of this cancer. View full text Figures at a glance * Figure 1: Manhattan plot of genome-wide P values of association. Association was assessed using an additive model in logistic regression analysis with adjustment for age, sex and the top three principal components of population stratification analysis. The −log10P values of 666,141 SNPs in 981 cases and 1,991 controls (y axis) are shown relative to their chromosomal positions (x axis). The 33 loci with P < 1 × 10−6 are above the blue horizontal line, and the smallest P value is 2.10 × 10−10. * Figure 2: Regional plots of association results and recombination rates within six significantly associated susceptibility loci . (–) –log10P values of SNPs relative to their positions at chromosomes 21q21.3 (), 5p13.1 (), 21q22.3 (), 13q22.1 (), 22q13.32 () and 10q26.11 () . Estimated recombination rates (cM/Mb) from HapMap Project (NCBI Build 36) are light blue lines, and the genomic locations of genes within the regions of interest on the NCBI Build 36 human assembly were annotated from the UCSC Genome Browser (arrows). For each locus, both genotyped (diamond) and imputed (circle) SNPs are shown, and the top SNP is labeled by rs ID. SNPs in red, orange, yellow and white have r2 of ≥0.8, ≥0.5, ≥0.2 and <0.2 with the top SNP, respectively. Blue diamonds are SNPs that showed association in the combined genome-wide association and replication samples. * Figure 3: Odds ratio for pancreatic cancer versus number of risk genotypes. Bars indicate 95% confidence intervals. Dotted line, null value (OR = 1.0). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Chen Wu & * Xiaoping Miao Affiliations * State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Chen Wu, * Liming Huang, * Dianke Yu, * Kan Zhai, * Jiang Chang, * Yu Liu, * Jian Xu, * Wen Tan & * Dongxin Lin * Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China. * Xiaoping Miao & * Tangchun Wu * Department of Abdominal Surgery, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Xu Che, * Ping Zhao & * Chengfeng Wang * Department of Radiation Oncology, Cancer Hospital, Fudan University, Shanghai, China. * Guoliang Jiang * Department of Pathology, Shengjing Hospital, China Medical University, Shenyang, China. * Xianghong Yang, * Wanjin Dai, * Wei Cao & * Ying Ma * Department of Epidemiology, Second Military Medical University, Shanghai, China. * Guangwen Cao * Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, China. * Zhibin Hu & * Hongbing Shen * Department of Gastrointestinal Surgery, Union Hospital of Fujian Medical University, Fuzhou, China. * Yongjian Zhou * Department of Gastroduodenal and Pancreatic Surgery, Hunan Province Tumor Hospital, Changsha, China. * Chaohui Zuo * Union Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China. * Chunyou Wang * Department of Experimental Pathology, Hebei Medical University, Shijiazhuang, China. * Xianghong Zhang * Laboratory of Cancer Molecular Genetics, Medical College of Soochow University, Suzhou, China. * Yifeng Zhou * Department of Pancreas and Hepatobiliary Surgery, Cancer Hospital, Fudan University, Shanghai, China. * Xianjun Yu * Department of Gastroenterology, First Affiliated Hospital, Second Military Medical University, Shanghai, China. * Zhaoshen Li & * Jun Gao * Department of Integrative Oncology, Cancer Hospital, Fudan University, Shanghai, China. * Luming Liu * Department of Hepatobiliary and Pancreatic Surgery, Union Hospital of Fujian Medical University, Fuzhou, China. * Yanling Chen * Department of Pathology, First Affiliated Hospital of Fujian Medical University, Fuzhou, China. * Sheng Zhang * Department of Surgery, The University of Hong Kong, Hong Kong, China. * Xiaoqi Wang & * Siu Tim Cheung * Department of Pathology, Cancer Hospital, Fudan University, Shanghai, China. * Menghong Sun * Department of Pathology, Fujian Provincial Cancer Hospital, Fuzhou, China. * Xiongwei Zheng * Department of Pathology, Affiliated Hospital, Inner Mongolia School of Medicine, Huhhot, Inner Mongolia. * Yongfeng Jia * These authors jointly directed this work. * Chengfeng Wang & * Dongxin Lin Contributions D.L. conceived, designed and oversaw the study, obtained financial support, interpreted the results and wrote parts of and synthesized the paper. C. Wu managed the project, oversaw laboratory and statistical analyses and drafted the initial manuscript. Chengfeng Wang designed the study and oversaw pancreatic cancer patient recruitment. X.M. designed the study and carried out statistical analyses, subject recruitment and sample preparation of Zhejiang samples. L.H., K.Z., J.C. and J.X. prepared samples and did TaqMan genotyping. X.C., D.Y., Y.L., W.T. and P.Z. recruited subjects from Beijing, Shandong, Sichuan and Chongqing. Various authors recruited subjects and samples from Shanghai (G.J., G.C., X.Y., Z.L., L.L., M.S. and J.G.), Liaoning (X.Y., W.D., W.C. and Y.M.), Jiangsu (Z.H., H.S. and Yifeng Zhou), Fujian (Yongjian Zhou, Y.C., S.Z. and X. Zheng), Hunan (C.Z.), Hubei (Chunyou Wang and T.W.), Hebei (X. Zhang and Y.J.) and Hong Kong (X.W. and S.T.C.). Competing financial interests The authors declare no competing financial interests. 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• Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians
  - Nat Genet 44(1):67-72 (2012)
  Nature Genetics | Letter Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians * Yoon Shin Cho1, 46 * Chien-Hsiun Chen2, 3, 46 * Cheng Hu4, 46 * Jirong Long5, 46 * Rick Twee Hee Ong6, 46 * Xueling Sim7, 46 * Fumihiko Takeuchi8, 46 * Ying Wu9, 46 * Min Jin Go1, 46 * Toshimasa Yamauchi10, 46 * Yi-Cheng Chang11, 46 * Soo Heon Kwak12, 46 * Ronald C W Ma13, 46 * Ken Yamamoto14, 46 * Linda S Adair15 * Tin Aung16, 17 * Qiuyin Cai5 * Li-Ching Chang2 * Yuan-Tsong Chen2 * Yutang Gao18 * Frank B Hu19 * Hyung-Lae Kim1, 20 * Sangsoo Kim21 * Young Jin Kim1 * Jeannette Jen-Mai Lee22 * Nanette R Lee23 * Yun Li9, 24 * Jian Jun Liu25 * Wei Lu26 * Jiro Nakamura27 * Eitaro Nakashima27, 28 * Daniel Peng-Keat Ng22 * Wan Ting Tay16 * Fuu-Jen Tsai3 * Tien Yin Wong16, 17, 29 * Mitsuhiro Yokota30 * Wei Zheng5 * Rong Zhang4 * Congrong Wang4 * Wing Yee So13 * Keizo Ohnaka31 * Hiroshi Ikegami32 * Kazuo Hara10 * Young Min Cho12 * Nam H Cho33 * Tien-Jyun Chang11 * Yuqian Bao4 * Åsa K Hedman34 * Andrew P Morris34 * Mark I McCarthy34, 35 * DIAGRAM Consortium * MuTHER Consortium * Ryoichi Takayanagi37, 47 * Kyong Soo Park12, 38, 47 * Weiping Jia4, 47 * Lee-Ming Chuang11, 39, 47 * Juliana C N Chan13, 47 * Shiro Maeda39, 47 * Takashi Kadowaki10, 47 * Jong-Young Lee1, 47 * Jer-Yuarn Wu2, 3, 47 * Yik Ying Teo6, 7, 22, 25, 41, 47 * E Shyong Tai22, 42, 43, 47 * Xiao Ou Shu5, 47 * Karen L Mohlke9, 47 * Norihiro Kato8, 47 * Bok-Ghee Han1, 47 * Mark Seielstad25, 44, 45, 47 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:67–72Year published:(2012)DOI:doi:10.1038/ng.1019Received12 April 2011Accepted02 November 2011Published online11 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We conducted a three-stage genetic study to identify susceptibility loci for type 2 diabetes (T2D) in east Asian populations. We followed our stage 1 meta-analysis of eight T2D genome-wide association studies (6,952 cases with T2D and 11,865 controls) with a stage 2 in silico replication analysis (5,843 cases and 4,574 controls) and a stage 3 de novo replication analysis (12,284 cases and 13,172 controls). The combined analysis identified eight new T2D loci reaching genome-wide significance, which mapped in or near GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16, MAEA, GCC1-PAX4, PSMD6 and ZFAND3. GLIS3, which is involved in pancreatic beta cell development and insulin gene expression1, 2, is known for its association with fasting glucose levels3, 4. The evidence of an association with T2D for PEPD5 and HNF4A6, 7 has been shown in previous studies. KCNK16 may regulate glucose-dependent insulin secretion in the pancreas. These findings, derived from an east Asian population, provide ! new perspectives on the etiology of T2D. View full text Figures at a glance * Figure 1: Genome-wide Manhattan plot for the east Asian T2D stage 1 meta-analysis. Shown are the –log10P values using the trend test for SNPs distributed across the entire autosomal genome. The red dots at each locus indicate the signals with P < 10−6 detected in the genome-wide meta-analysis. A total of 1,934,619 SNPs that were present in at least five stage 1 studies were used to generate the plot. * Figure 2: Regional association plots for new T2D loci. – At the top, the positions of SNPs are shown, and in the middle, the regional association results from the genome-wide meta-analysis are shown. The trend test –log10P values are shown for SNPs distributed in a 0.8-Mb genomic region centered on the most strongly associated signal, which is depicted as a purple diamond for the stage 1 results and a red diamond for the combined stage 1, 2 and 3 results. At the bottom, the locations of known genes in the region are shown. The genetic information was from the Human Genome build hg18, and the LD structure was based on the 1000 Genomes Project JPT+CHB data (June 2010). Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Yoon Shin Cho, * Chien-Hsiun Chen, * Cheng Hu, * Jirong Long, * Rick Twee Hee Ong, * Xueling Sim, * Fumihiko Takeuchi, * Ying Wu, * Min Jin Go, * Toshimasa Yamauchi, * Yi-Cheng Chang, * Soo Heon Kwak, * Ronald C W Ma & * Ken Yamamoto Affiliations * Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex, Chungcheongbuk-do, Cheongwon-gun, Gangoe-myeon, Yeonje-ri, Korea. * Yoon Shin Cho, * Min Jin Go, * Hyung-Lae Kim, * Young Jin Kim, * Jong-Young Lee & * Bok-Ghee Han * Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan. * Chien-Hsiun Chen, * Li-Ching Chang, * Yuan-Tsong Chen & * Jer-Yuarn Wu * School of Chinese Medicine, China Medical University, Taichung, Taiwan. * Chien-Hsiun Chen, * Fuu-Jen Tsai & * Jer-Yuarn Wu * Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China. * Cheng Hu, * Rong Zhang, * Congrong Wang, * Yuqian Bao & * Weiping Jia * Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. * Jirong Long, * Qiuyin Cai, * Wei Zheng & * Xiao Ou Shu * Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore. * Rick Twee Hee Ong & * Yik Ying Teo * Centre for Molecular Epidemiology, National University of Singapore, Singapore, Singapore. * Xueling Sim & * Yik Ying Teo * Research Institute, National Center for Global Health and Medicine, Shinjuku-ku, Tokyo, Japan. * Fumihiko Takeuchi & * Norihiro Kato * Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA. * Ying Wu, * Yun Li & * Karen L Mohlke * Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. * Toshimasa Yamauchi, * Kazuo Hara & * Takashi Kadowaki * Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan. * Yi-Cheng Chang, * Tien-Jyun Chang & * Lee-Ming Chuang * Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea. * Soo Heon Kwak, * Young Min Cho & * Kyong Soo Park * Department of Medicine and Therapeutics, Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China. * Ronald C W Ma, * Wing Yee So & * Juliana C N Chan * Division of Genome Analysis, Research Center for Genetic Information, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan. * Ken Yamamoto * Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, USA. * Linda S Adair * Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore. * Tin Aung, * Wan Ting Tay & * Tien Yin Wong * Department of Ophthalmology, National University of Singapore, Singapore, Singapore. * Tin Aung & * Tien Yin Wong * Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China. * Yutang Gao * Department of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Frank B Hu * Department of Biochemistry, School of Medicine, Ewha Womans University, Seoul, Korea. * Hyung-Lae Kim * School of Systems Biomedical Science, Soongsil University, Dongjak-gu, Seoul, Korea. * Sangsoo Kim * Department of Epidemiology and Public Health, National University of Singapore, Singapore, Singapore. * Jeannette Jen-Mai Lee, * Daniel Peng-Keat Ng, * Yik Ying Teo & * E Shyong Tai * Office of Population Studies Foundation Inc., University of San Carlos, Cebu City, Philippines. * Nanette R Lee * Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA. * Yun Li * Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore. * Jian Jun Liu, * Yik Ying Teo & * Mark Seielstad * Shanghai Institute of Preventive Medicine, Shanghai, China. * Wei Lu * Division of Endocrinology and Diabetes, Department of Internal Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan. * Jiro Nakamura & * Eitaro Nakashima * Department of Diabetes and Endocrinology, Chubu Rosai Hospital, Nagoya, Japan. * Eitaro Nakashima * Centre for Eye Research Australia, University of Melbourne, East Melbourne, Victoria, Australia. * Tien Yin Wong * Department of Genome Science, Aichi-Gakuin University, School of Dentistry, Nagoya, Japan. * Mitsuhiro Yokota * Department of Geriatric Medicine, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan. * Keizo Ohnaka * Department of Endocrinology, Metabolism and Diabetes, Kinki University School of Medicine, Osaka-sayama, Osaka, Japan. * Hiroshi Ikegami * Department of Preventive Medicine, Ajou University School of Medicine, Suwon, Korea. * Nam H Cho * Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. * Åsa K Hedman, * Andrew P Morris & * Mark I McCarthy * Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, UK. * Mark I McCarthy * Department of Internal Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Higashi-ku, Fukuoka, Japan. * Ryoichi Takayanagi * World Class University program, Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology and College of Medicine, Seoul National University, Seoul, Korea. * Kyong Soo Park * Graduate Institute of Clinical Medicine, National Taiwan University School of Medicine, Taipei, Taiwan. * Lee-Ming Chuang & * Shiro Maeda * Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic Medicine, Yokohama, Japan. * Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore. * Yik Ying Teo * Department of Medicine, National University of Singapore, Singapore, Singapore. * E Shyong Tai * Duke-National University of Singapore Graduate Medical School, Singapore, Singapore. * E Shyong Tai * Institute for Human Genetics, University of California, San Francisco, California, USA. * Mark Seielstad * Blood Systems Research Institute, San Francisco, California, USA. * Mark Seielstad * These authors jointly directed this work. * Ryoichi Takayanagi, * Kyong Soo Park, * Weiping Jia, * Lee-Ming Chuang, * Juliana C N Chan, * Shiro Maeda, * Takashi Kadowaki, * Jong-Young Lee, * Jer-Yuarn Wu, * Yik Ying Teo, * E Shyong Tai, * Xiao Ou Shu, * Karen L Mohlke, * Norihiro Kato, * Bok-Ghee Han & * Mark Seielstad Consortia * DIAGRAM Consortium * MuTHER Consortium Contributions The study was supervised by E.S.T., B.-G.H., N.K., Y.S.C., Y.Y.T., W.Z., Q.C., X.O.S., Y.-T.C., J.-Y.W., L.S.A., K.L.M., T.K., C.H., W.J., L.-M.C., Y.M.C., K.S.P., J.-Y.L. and J.C.N.C. The experiments were conceived of and designed by Y.S.C., E.S.T., N.K., D.P.-K.N., J.J.-M.L., M.S., T.Y.W., Y.Y.T., W.Z., F.B.H., X.O.S., C.-H.C., F.-J.T., Y.-T.C., J.-Y.W., L.S.A., K.L.M., S.M., C.H., L.-M.C., K.S.P., M.J.G., M.I.M. and R.C.W.M. The experiments were performed by J.L., M.S., J.J.L., J.-Y.W., S.M., R.Z., K.Y., Y.-C.C., T.-J.C., L.-M.C. and S.H.K. Statistical analyses was performed by M.J.G., X.S., Y.J.K., R.T.H.O., W.T.T., Y.Y.T., F.T., J.L., C.-H.C., L.-C.C., Y.W., Y.L., K.H., C.H., Y.-C.C., S.H.K., A.P.M. and R.C.W.M. The data were analyzed by M.J.G., X.S., Y.J.K., R.T.H.O., W.T.T., Y.Y.T., J.L., C.-H.C., L.-C.C., Y.W., N.R.L., Y.L., L.S.A., K.L.M., T.Y., C.H., Y.-C.C., S.H.K., Y.S.C., S.K., Å.K.H. and R.C.W.M. The reagents, materials and analysis tools were contributed by E! .S.T., B.-G.H., N.K., D.P.-K.N., J.J.-M.L., J.L., M.S., T.A., T.Y.W., E.N., M.Y., J.N., J.J.L., W.Z., Q.C., Y.G., W.L., F.B.H., X.O.S., F.-J.T., Y.-T.C., J.-Y.W., N.R.L., Y.L., K.O., H.I., R.T., C.W., Y.B., T.-J.C., L.-M.C., K.S.P., H.-L.K., N.H.C., J.-Y.L., W.Y.S. and J.C.N.C. The manuscript was written by Y.S.C., M.S. and E.S.T. All authors reviewed the manuscript. A list of full members is provided in the Supplementary Note. DIAGRAM Consortium MuTHER Consortium Competing financial interests The authors declare no competing financial interests. 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• A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis
  - Nat Genet 44(1):73-77 (2012)
  Nature Genetics | Letter A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis * Zhiming Lin1, 24 * Jin-Xin Bei2, 24 * Meixin Shen3 * Qiuxia Li1 * Zetao Liao1 * Yanli Zhang1 * Qing Lv1 * Qiujing Wei1 * Hui-Qi Low4 * Yun-Miao Guo2 * Shuangyan Cao1 * Mingcan Yang1 * Zaiying Hu1 * Manlong Xu1 * Xinwei Wang1 * Yanlin Wei1 * Li Li1 * Chao Li1 * Tianwang Li1 * Jianlin Huang1 * Yunfeng Pan1 * Ou Jin1 * Yuqiong Wu1 * Jing Wu1 * Zishi Guo1 * Peigen He5 * Shaoxian Hu5 * Husheng Wu6 * Hui Song6 * Feng Zhan7 * Shengyun Liu8 * Guanmin Gao8 * Zhangsuo Liu8 * Yinong Li9 * Changhong Xiao10 * Juan Li11 * Zhizhong Ye12 * Weizhen He12 * Dongzhou Liu13 * Lingxun Shen14 * Anbin Huang14 * Henglian Wu15 * Yi Tao16 * Xieping Pan17 * Buyun Yu1 * E Shyong Tai18, 19 * Yi-Xin Zeng2 * Ee Chee Ren20, 21 * Yan Shen22 * Jianjun Liu4, 23, 25 * Jieruo Gu1, 25 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:73–77Year published:(2012)DOI:doi:10.1038/ng.1005Received31 May 2011Accepted17 October 2011Published online04 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg To identify susceptibility loci for ankylosing spondylitis, we performed a two-stage genome-wide association study in Han Chinese. In the discovery stage, we analyzed 1,356,350 autosomal SNPs in 1,837 individuals with ankylosing spondylitis and 4,231 controls; in the validation stage, we analyzed 30 suggestive SNPs in an additional 2,100 affected individuals and 3,496 controls. We identified two new susceptibility loci between EDIL3 and HAPLN1 at 5q14.3 (rs4552569; P = 8.77 × 10−10) and within ANO6 at 12q12 (rs17095830; P = 1.63 × 10−8). We also confirmed previously reported associations in Europeans within the major histocompatibility complex (MHC) region (top SNP, rs13202464; P < 5 × 10−324) and at 2p15 (rs10865331; P = 1.98 × 10−8). We show that rs13202464 within the MHC region mainly represents the risk effect of HLA-B*27 variants (including HLA-B*2704, HLA-B*2705 and HLA-B*2715) in Chinese. The two newly discovered loci implicate genes related to bone format! ion and cartilage development, suggesting their potential involvement in the etiology of ankylosing spondylitis. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Zhiming Lin & * Jin-Xin Bei Affiliations * Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. * Zhiming Lin, * Qiuxia Li, * Zetao Liao, * Yanli Zhang, * Qing Lv, * Qiujing Wei, * Shuangyan Cao, * Mingcan Yang, * Zaiying Hu, * Manlong Xu, * Xinwei Wang, * Yanlin Wei, * Li Li, * Chao Li, * Tianwang Li, * Jianlin Huang, * Yunfeng Pan, * Ou Jin, * Yuqiong Wu, * Jing Wu, * Zishi Guo, * Buyun Yu & * Jieruo Gu * State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China. * Jin-Xin Bei, * Yun-Miao Guo & * Yi-Xin Zeng * Department of Microbiology, National University of Singapore, Singapore. * Meixin Shen * Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore. * Hui-Qi Low & * Jianjun Liu * Department of Immunology and Rheumatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Hubei, China. * Peigen He & * Shaoxian Hu * Department of Rheumatology & Immunology, Beijing Jishuitan Hospital, Beijing, China. * Husheng Wu & * Hui Song * Department of Rheumatology, Hainan Provincial People's Hospital, Hainan, China. * Feng Zhan * Department of Urology and Rheumatology, The First Affiliated Hospital of Zhengzhou University, Henan, China. * Shengyun Liu, * Guanmin Gao & * Zhangsuo Liu * Department of Rheumatology, Fuzhou General Hospital of Nanjing Military Command, Fuzhou, China. * Yinong Li * Rheumatology & Immunology Department, Hospital of Integrated Traditional Chinese Medicine & Western Medicine of Southern Medical University, Guangzhou, China. * Changhong Xiao * Department of Rheumatology, Nanfang Hospital, Southern Medical University, Guangzhou, China. * Juan Li * Xiangmihu Rheumatology Branch, Fourth People's Hospital of Shenzhen, Shenzhen Rheumatology Institute of Guangdong Medical College, Shenzhen, China. * Zhizhong Ye & * Weizhen He * Department of Rheumatology and Immunology, Jinan University Second Clinical Medical College, Shenzhen People's Hospital, Shenzhen, China. * Dongzhou Liu * Department of Immunology and Rheumatology, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology, Hubei, China. * Lingxun Shen & * Anbin Huang * Department of Urology, Dongguan People's Hospital, Guangdong, China. * Henglian Wu * Department of Rheumatology and Immunology, Second Affiliated Hospital of Guangzhou Medical College, Guangzhou, China. * Yi Tao * Department of Rheumatology and Immunology, The First People′s Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Jiangsu, China. * Xieping Pan * Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. * E Shyong Tai * Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. * E Shyong Tai * Singapore Immunology Network, A*STAR, Singapore. * Ee Chee Ren * Department of Microbiology, National University of Singapore, Singapore. * Ee Chee Ren * National Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Science and Peking Union Medical College, Tsinghua University, Beijing, China. * Yan Shen * The School of Life Sciences, Anhui Medical University, Hefei, China. * Jianjun Liu * These authors jointly directed this work. * Jianjun Liu & * Jieruo Gu Contributions J.G. and J. Liu conceived of the study and obtained financial support. J.G., J. Liu and J.-X.B. designed the study. Z. Lin, Z. Liao, Y.Z., Q. Lv, S.C., M.Y., Z.H., M.X., X.W., Y. Wei, L.L., C.L., T.L., J.H., Y.P., O.J., Y. Wu, J.W., Z.G., P.H., S.H., Husheng Wu, H.S., F.Z., S.L., G.G., Z. Liu, Y.L., C.X., J. Li, Z.Y., W.H., D.L., L.S., A.H., Henglian Wu, Y.T., X.P. and B.Y. coordinated recruitment and obtained biological samples. Z. Lin, Q. Li and Q.W. undertook sample preparation and storage. M.S. and E.C.R. were involved in HLA genotyping. E.S.T. and Y.-X.Z. provided genotypes. J.-X.B. conducted the statistical analyses with help from H.-Q.L., Z. Lin, Y.-M.G., Y.S. and J. Liu. J.-X.B. drafted the manuscript with contributions from J. Liu, Z. Lin, Y.Z., E.C.R. and J.G. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Jianjun Liu or * Jieruo Gu Author Details * Zhiming Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Jin-Xin Bei Search for this author in: * NPG journals * PubMed * Google Scholar * Meixin Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Qiuxia Li Search for this author in: * NPG journals * PubMed * Google Scholar * Zetao Liao Search for this author in: * NPG journals * PubMed * Google Scholar * Yanli Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Qing Lv Search for this author in: * NPG journals * PubMed * Google Scholar * Qiujing Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Hui-Qi Low Search for this author in: * NPG journals * PubMed * Google Scholar * Yun-Miao Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Shuangyan Cao Search for this author in: * NPG journals * PubMed * Google Scholar * Mingcan Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Zaiying Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Manlong Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Xinwei Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Yanlin Wei Search for this author in: * NPG journals * PubMed * Google Scholar * Li Li Search for this author in: * NPG journals * PubMed * Google Scholar * Chao Li Search for this author in: * NPG journals * PubMed * Google Scholar * Tianwang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Jianlin Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Yunfeng Pan Search for this author in: * NPG journals * PubMed * Google Scholar * Ou Jin Search for this author in: * NPG journals * PubMed * Google Scholar * Yuqiong Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Zishi Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Peigen He Search for this author in: * NPG journals * PubMed * Google Scholar * Shaoxian Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Husheng Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Hui Song Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Zhan Search for this author in: * NPG journals * PubMed * Google Scholar * Shengyun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Guanmin Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Zhangsuo Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Yinong Li Search for this author in: * NPG journals * PubMed * Google Scholar * Changhong Xiao Search for this author in: * NPG journals * PubMed * Google Scholar * Juan Li Search for this author in: * NPG journals * PubMed * Google Scholar * Zhizhong Ye Search for this author in: * NPG journals * PubMed * Google Scholar * Weizhen He Search for this author in: * NPG journals * PubMed * Google Scholar * Dongzhou Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Lingxun Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Anbin Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Henglian Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Yi Tao Search for this author in: * NPG journals * PubMed * Google Scholar * Xieping Pan Search for this author in: * NPG journals * PubMed * Google Scholar * Buyun Yu Search for this author in: * NPG journals * PubMed * Google Scholar * E Shyong Tai Search for this author in: * NPG journals * PubMed * Google Scholar * Yi-Xin Zeng Search for this author in: * NPG journals * PubMed * Google Scholar * Ee Chee Ren Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Jianjun Liu Contact Jianjun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Jieruo Gu Contact Jieruo Gu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Tables 1–9 and Supplementary Figures 1–5. 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• Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder
  - Nat Genet 44(1):78-84 (2012)
  Nature Genetics | Letter Genome-wide copy number variation study associates metabotropic glutamate receptor gene networks with attention deficit hyperactivity disorder * Josephine Elia1, 2, 46 * Joseph T Glessner3, 46 * Kai Wang3 * Nagahide Takahashi4 * Corina J Shtir5 * Dexter Hadley3 * Patrick M A Sleiman3 * Haitao Zhang3 * Cecilia E Kim3 * Reid Robison6 * Gholson J Lyon6 * James H Flory3 * Jonathan P Bradfield3 * Marcin Imielinski3 * Cuiping Hou3 * Edward C Frackelton3 * Rosetta M Chiavacci3 * Takeshi Sakurai4 * Cara Rabin7 * Frank A Middleton8 * Kelly A Thomas3 * Maria Garris3 * Frank Mentch3 * Christine M Freitag9 * Hans-Christoph Steinhausen10, 11, 12 * Alexandre A Todorov13 * Andreas Reif14 * Aribert Rothenberger15 * Barbara Franke16, 17 * Eric O Mick18 * Herbert Roeyers19 * Jan Buitelaar20 * Klaus-Peter Lesch14 * Tobias Banaschewski21 * Richard P Ebstein22 * Fernando Mulas23 * Robert D Oades24 * Joseph Sergeant25 * Edmund Sonuga-Barke26, 27, 28 * Tobias J Renner29 * Marcel Romanos29, 30 * Jasmin Romanos29 * Andreas Warnke29 * Susanne Walitza10, 29 * Jobst Meyer31 * Haukur Pálmason31 * Christiane Seitz32 * Sandra K Loo33 * Susan L Smalley33 * Joseph Biederman18 * Lindsey Kent34 * Philip Asherson10 * Richard J L Anney35 * J William Gaynor36 * Philip Shaw7 * Marcella Devoto37, 38, 39, 40 * Peter S White41, 42 * Struan F A Grant3, 37, 38 * Joseph D Buxbaum4 * Judith L Rapoport7 * Nigel M Williams43 * Stanley F Nelson5 * Stephen V Faraone8, 44 * Hakon Hakonarson3, 36, 37, 45 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:78–84Year published:(2012)DOI:doi:10.1038/ng.1013Received24 June 2011Accepted28 October 2011Published online04 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Attention deficit hyperactivity disorder (ADHD) is a common, heritable neuropsychiatric disorder of unknown etiology. We performed a whole-genome copy number variation (CNV) study on 1,013 cases with ADHD and 4,105 healthy children of European ancestry using 550,000 SNPs. We evaluated statistically significant findings in multiple independent cohorts, with a total of 2,493 cases with ADHD and 9,222 controls of European ancestry, using matched platforms. CNVs affecting metabotropic glutamate receptor genes were enriched across all cohorts (P = 2.1 × 10−9). We saw GRM5 (encoding glutamate receptor, metabotropic 5) deletions in ten cases and one control (P = 1.36 × 10−6). We saw GRM7 deletions in six cases, and we saw GRM8 deletions in eight cases and no controls. GRM1 was duplicated in eight cases. We experimentally validated the observed variants using quantitative RT-PCR. A gene network analysis showed that genes interacting with the genes in the GRM family are enriche! d for CNVs in ~10% of the cases (P = 4.38 × 10−10) after correction for occurrence in the controls. We identified rare recurrent CNVs affecting glutamatergic neurotransmission genes that were overrepresented in multiple ADHD cohorts. View full text Figures at a glance * Figure 1: A deletion directly affecting GRM5 that is exclusive to cases with ADHD and that was replicated in the IMAGE and PUWMa studies. Four hemizygous deletions in GRM5 in cases with ADHD from the CHOP study that were replicated by two deletions and three larger deletions found in the IMAGE study and one deletion found in the PUWMa study. The SNP coverage of the Illumina 550K, Perlegen 600K, Illumina 1M and Affymetrix 5.0 arrays is shown by vertical blue lines. M.Of.M.Cs., Massachusetts General Hospital offspring male case; W.Fa.M.Cn., Washington University father male control. * Figure 2: GRM receptor gene interaction networks affected in ADHD. () GRM receptor genes are shown as large diamond-shaped nodes, and other genes within two degrees of interaction with GRM genes are shown as smaller circular nodes. Nodes are colored to represent the enrichment of the CNVs: dark red represents deletions enriched in cases, light red represents deletions enriched in controls, dark turquoise represents duplications enriched in cases, light turquoise represents duplications enriched in controls, and gray represents diploids that are devoid of CNVs. Thick blue dashed lines highlight edges that are connected to at least one GRM gene, and thin gray lines represent all other gene interactions. Highly connected modules enriched for significant functional annotations are highlighted by blue shaded ellipses. Details on the gene-based CNV observations are included in Supplementary Table 16, and the respective gene functional clusters are listed in Supplementary Table 17. () A schematic overview showing the interaction of GRM receptors a! ffected in ADHD with modules of genes enriched for functional significance. GRM receptor genes are shown as diamonds colored either turquoise or red to represent duplications and deletions, respectively, that were enriched in cases. Boxes highlight the functional modules defined by the network of interacting genes () that are significantly enriched for Gene Ontology annotations. The functional modules describe significant functional annotations and are labeled with the cluster name and the number of component genes in parenthesis. Functional annotations that may be particularly pertinent to the underlying pathophysiology of ADHD are shown in bold. The edges of the network connect GRM receptor genes to functional modules: solid lines indicate membership of the GRM-interacting gene in the functional module, and dotted lines indicate a first-degree relationship between GRM receptor genes and at least one component gene of a functional module. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Josephine Elia & * Joseph T Glessner Affiliations * Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Josephine Elia * Department of Child and Adolescent Psychiatry, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Josephine Elia * Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Joseph T Glessner, * Kai Wang, * Dexter Hadley, * Patrick M A Sleiman, * Haitao Zhang, * Cecilia E Kim, * James H Flory, * Jonathan P Bradfield, * Marcin Imielinski, * Cuiping Hou, * Edward C Frackelton, * Rosetta M Chiavacci, * Kelly A Thomas, * Maria Garris, * Frank Mentch, * Struan F A Grant & * Hakon Hakonarson * Laboratory of Molecular Neuropsychiatry, Department of Psychiatry, Mount Sinai School of Medicine, New York, New York, USA. * Nagahide Takahashi, * Takeshi Sakurai & * Joseph D Buxbaum * Department of Human Genetics and Psychiatry, University of California Los Angeles, Los Angeles, California, USA. * Corina J Shtir & * Stanley F Nelson * Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA. * Reid Robison & * Gholson J Lyon * Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland, USA. * Cara Rabin, * Philip Shaw & * Judith L Rapoport * Department of Neuroscience and Physiology, State University of New York Upstate Medical University, Syracuse, New York, USA. * Frank A Middleton & * Stephen V Faraone * Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe University, Frankfurt am Main, Germany. * Christine M Freitag * Department of Child and Adolescent Psychiatry, University of Zurich, Zurich, Switzerland. * Hans-Christoph Steinhausen, * Susanne Walitza & * Philip Asherson * Aalborg Psychiatric Hospital, Aarhus University Hospital, Aarhus, Denmark. * Hans-Christoph Steinhausen * Institute of Psychology, University of Basel, Basel, Switzerland. * Hans-Christoph Steinhausen * Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA. * Alexandre A Todorov * Attention Deficit Hyperactivity Disorder Clinical Research Network, Unit of Molecular Psychiatry, Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany. * Andreas Reif & * Klaus-Peter Lesch * Child and Adolescent Psychiatry, University of Göttingen, Göttingen, Germany. * Aribert Rothenberger * Department of Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Barbara Franke * Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Barbara Franke * Department of Psychiatry, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA. * Eric O Mick & * Joseph Biederman * Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium. * Herbert Roeyers * Radboud University Nijmegen Medical Centre, Donders Institute for Brain Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, The Netherlands. * Jan Buitelaar * Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany. * Tobias Banaschewski * Department of Psychology, National University of Singapore, Queenstown, Singapore. * Richard P Ebstein * Department of Neuropaediatrics, La Fe University Hospital, Faculty of Medicine, Valencia, Spain. * Fernando Mulas * Clinic for Child and Adolescent Psychiatry, University of Duisburg-Essen, Essen, Germany. * Robert D Oades * Vrije Universiteit, De Boelelaan, Amsterdam, The Netherlands. * Joseph Sergeant * School of Psychology, Institute for Disorder on Impulse and Attention, University of Southampton, Highfield, Southampton, UK. * Edmund Sonuga-Barke * Department of Experimental Clinical and Health Psychology, Ghent University, Dunantlaan, Ghent, Belgium. * Edmund Sonuga-Barke * Child Study Center, New York University, New York, New York, USA. * Edmund Sonuga-Barke * Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany. * Tobias J Renner, * Marcel Romanos, * Jasmin Romanos, * Andreas Warnke & * Susanne Walitza * Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Munich, Munich, Germany. * Marcel Romanos * Institute of Psychobiology, Department of Neurobehavioral Genetics, University of Trier, Trier, Germany. * Jobst Meyer & * Haukur Pálmason * Department of Child and Adolescent Psychiatry, Saarland University, Homburg, Germany. * Christiane Seitz * Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, USA. * Sandra K Loo & * Susan L Smalley * Bute Medical School, St. Andrews, Scotland, UK. * Lindsey Kent * Department of Psychiatry, Trinity Centre for Health Sciences, St. James's Hospital, Dublin, Ireland. * Richard J L Anney * Division of Cardiothoracic Surgery, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * J William Gaynor & * Hakon Hakonarson * Division of Genetics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Marcella Devoto, * Struan F A Grant & * Hakon Hakonarson * Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Marcella Devoto & * Struan F A Grant * Dipartimento di Medicina Sperimentale, University La Sapienza, Rome, Italy. * Marcella Devoto * Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Marcella Devoto * Center for Biomedical Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Peter S White * Division of Oncology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Peter S White * Department of Psychological Medicine, School of Medicine, Cardiff University, Cardiff, Wales, UK. * Nigel M Williams * Department of Psychiatry, State University of New York Upstate Medical University, Syracuse, New York, USA. * Stephen V Faraone * Division of Pulmonary Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Hakon Hakonarson Contributions H.H. and J.E. designed the CHOP study and supervised the data analyses and interpretation. S.V.F., M.G., P.A. and J. Buitelaar designed the IMAGE and IMAGE II studies. S.V.F. designed the PUWMa study and coordinated the analyses for the IMAGE, IMAGE II and PUWMa studies. J.T.G. and K.W. conducted the statistical analyses. C.E.K. and E.C.F. directed the stage 1 genotyping. J.D.B. coordinated the validation analyses. N.T. performed the qRT-PCR validation of the CNVs. J.T.G. and H.H. drafted the manuscript. J.E. collected the CHOP samples. C.R., P.S. and J.L.R. collected the NIMH samples. C.M.F., H.-C.S., A.A.T., A. Reif, A. Rothenberger, B.F., E.O.M., H.R., J. Buitelaar, K.-P.L., L.K., T.B., R.P.E., F.M., R.D.O., J.S., E.S.-B., T.J.R., M.R., J.R., A.W., S.W., J.M., H.P., C.S., S.K.L., S.L.S., J. Biederman, L.K., P.A. and R.J.L.A. collected data for the IMAGE, IMAGE II and PUWMa projects. J. Biederman, E.O.M., S.V.F., S.K.L., S.L.S. and A.A.T. collected samples for the PUWMa st! udy. F.A.M. genotyped the IMAGE II data. H.H. directed and D.H. and J.T.G. performed the gene interaction network and functional enrichment analyses. All authors contributed to the manuscript preparation. S.F.A.G. accessed the public domain data, assisted with the interpretation of the data and edited the manuscript. All other authors contributed samples and/or were involved with data mining and processing. Competing financial interests For the last 3 years, M.R. has been in the speakers' bureau for Janssen-Cilag. In previous years, he was on the speakers' bureau for MEDICE. 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• Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome
  - Nat Genet 44(1):85-88 (2012)
  Nature Genetics | Letter Mutations at a single codon in Mad homology 2 domain of SMAD4 cause Myhre syndrome * Carine Le Goff1 * Clémentine Mahaut1 * Avinash Abhyankar2 * Wilfried Le Goff3 * Valérie Serre1 * Alexandra Afenjar4 * Anne Destrée5 * Maja di Rocco6 * Delphine Héron7 * Sébastien Jacquemont8 * Sandrine Marlin9 * Marleen Simon10 * John Tolmie11 * Alain Verloes12 * Jean-Laurent Casanova2, 13 * Arnold Munnich1 * Valérie Cormier-Daire1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:85–88Year published:(2012)DOI:doi:10.1038/ng.1016Received04 August 2011Accepted31 October 2011Published online11 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Myhre syndrome (MIM 139210) is a developmental disorder characterized by short stature, short hands and feet, facial dysmorphism, muscular hypertrophy, deafness and cognitive delay. Using exome sequencing of individuals with Myhre syndrome, we identified SMAD4 as a candidate gene that contributes to this syndrome on the basis of its pivotal role in the bone morphogenetic pathway (BMP) and transforming growth factor (TGF)-β signaling. We identified three distinct heterozygous missense SMAD4 mutations affecting the codon for Ile500 in 11 individuals with Myhre syndrome. All three mutations are located in the region of SMAD4 encoding the Mad homology 2 (MH2) domain near the site of monoubiquitination at Lys519, and we found a defect in SMAD4 ubiquitination in fibroblasts from affected individuals. We also observed decreased expression of downstream TGF-β target genes, supporting the idea of impaired TGF-β–mediated transcriptional control in individuals with Myhre syndrome. View full text Figures at a glance * Figure 1: Clinical and radiological manifestations of individuals with Myhre syndrome. (–) Photographs of affected individuals at ages 4 years (case 8) (), 16 years (case 5) () and 8 years (case 1) (). Note the short palpebral fissures, maxillary hypoplasia, prognathism, muscular build and short extremities. (,) Hand X rays are shown for affected individuals at ages 4 years () and 14 years (). Note the generalized brachydactyly and delayed carpal ossification at age 4 years. () Spine X ray of a case at age 14 years. Note the large vertebrae with short and large pedicles. (,) Skull X rays of cases at age 10 years () and age 16 years (). Note the thickened calvarium. Informed consent was obtained from all individuals or the legal guardians of minors. * Figure 2: Functional consequences of SMAD4 mutations in fibroblasts from individuals with Myhre syndrome. () Characterization of wild-type and mutant SMAD4 protein expression. Increased levels of SMAD4 were seen for mutant SMAD4 (from cases 1 and 4) compared to wild-type SMAD4. () Ubiquitination of wild-type and mutant SMAD4. SMAD4 was immunoprecipitated from cell lysates, and protein blots were performed with an antibody that recognizes ubiquitinated proteins. Mutated SMAD4 (in case 1) was ubiquitinated to a lesser extent than wild-type SMAD4 proteins. * Figure 3: Levels of phosphorylated SMAD proteins in skin fibroblasts from individuals with Myhre syndrome and age- and passage-matched controls. Enhanced levels of phosphorylation of SMAD2/3 and of SMAD/5/8 were seen in cells from subjects with Myhre syndrome (cases 1 and 4) compared to controls. The levels of phosphorylated SMADs were normalized to total SMAD protein. * Figure 4: Cellular localization of phosphorylated SMAD proteins. Phosphorylated SMAD2/3 and phosphorylated SMAD1/5/8 localized to the nucleus in fibroblasts cultured from case 1. * Figure 5: Expression analysis of TGF-β– and BMP-driven target genes in fibroblasts from control and case subjects. (,) Quantification of the mRNA levels of COL1A1, CTGF and SERPINE1 (TGF-β signaling pathway) () and ID3 and SMAD6 (BMP signaling pathway) () was performed by quantitative RT-PCR in fibroblasts from controls (controls 1 and 2) and a case (case 1). mRNA levels were normalized to the expression of housekeeping genes (HSP90AA1 and NONO) and to 18S rRNA levels. Values are expressed as mean ± s.e.m. (N = 6; #P < 0.01 compared to control 2; *P < 0.05, **P < 0.01 compared to control 1.) Author information * Author information * Supplementary information Affiliations * Département de Génétique, Unité INSERM U781, Université Paris Descartes, Sorbonne Paris Cité, Hôpital Necker Enfants Malades, Paris, France. * Carine Le Goff, * Clémentine Mahaut, * Valérie Serre, * Arnold Munnich & * Valérie Cormier-Daire * St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA. * Avinash Abhyankar & * Jean-Laurent Casanova * INSERM, Unité Mixte de Recherche (UMR) S939, Dyslipidemia, Inflammation and Atherosclerosis in Metabolic Diseases, University of Pierre and Marie Curie (UPMC)–Université Paris VI, Paris, France. * Wilfried Le Goff * Service de Neuropédiatrie, Centre de Référence Anomalies du Développement, Hôpital Armand Trousseau, Paris, France. * Alexandra Afenjar * Institut de Génétique Humaine, Insitute de Pathologie et de Génétique (IPG), Charleroi, Belgium. * Anne Destrée * Unit of Rare Diseases, Department of Pediatrics, Gaslini Institute, Genoa, Italy. * Maja di Rocco * Unité de Génetique Clinique, Hôpital La Pité Salpétrière, Paris, France. * Delphine Héron * Service de Génétique Médicale, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland. * Sébastien Jacquemont * Unité de Génétique Clinique, Hôpital Armand Trousseau, Paris, France. * Sandrine Marlin * Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, The Netherlands. * Marleen Simon * Department of Medical Genetics, Ferguson Smith Centre, Yorkhill Hospital, Glasgow, UK. * John Tolmie * Département de Génétique, INSERM U676, Hôpital Robert Debré, Paris, France. * Alain Verloes * Laboratory of Human Genetics of Infectious Diseases, University Paris Descartes and INSERM U980, Necker Medical School, Paris, France. * Jean-Laurent Casanova Contributions C.L.G. designed the experiments, analyzed the exome sequencing data, performed protein blot analysis and wrote the manuscript. C.M. performed Sanger sequencing analysis. A. Abhyankar and J.-L.C. performed exome capture. W.L.G. performed quantitative RT-PCR analysis. V.S. performed three-dimensional structure analysis. A. Afenjar, A.D., M.d.R., D.H., S.J., S.M., M.S., J.T. and A.V. provided clinical data. A.M. wrote the manuscript. V.C.-D. provided clinical data, analyzed the exome sequencing data, oversaw all aspects of the research and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Valérie Cormier-Daire Author Details * Carine Le Goff Search for this author in: * NPG journals * PubMed * Google Scholar * Clémentine Mahaut Search for this author in: * NPG journals * PubMed * Google Scholar * Avinash Abhyankar Search for this author in: * NPG journals * PubMed * Google Scholar * Wilfried Le Goff Search for this author in: * NPG journals * PubMed * Google Scholar * Valérie Serre Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandra Afenjar Search for this author in: * NPG journals * PubMed * Google Scholar * Anne Destrée Search for this author in: * NPG journals * PubMed * Google Scholar * Maja di Rocco Search for this author in: * NPG journals * PubMed * Google Scholar * Delphine Héron Search for this author in: * NPG journals * PubMed * Google Scholar * Sébastien Jacquemont Search for this author in: * NPG journals * PubMed * Google Scholar * Sandrine Marlin Search for this author in: * NPG journals * PubMed * Google Scholar * Marleen Simon Search for this author in: * NPG journals * PubMed * Google Scholar * John Tolmie Search for this author in: * NPG journals * PubMed * Google Scholar * Alain Verloes Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Laurent Casanova Search for this author in: * NPG journals * PubMed * Google Scholar * Arnold Munnich Search for this author in: * NPG journals * PubMed * Google Scholar * Valérie Cormier-Daire Contact Valérie Cormier-Daire Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (315K) Supplementary Figures 1 and 2 and Supplementary Tables 1–4. Additional data
• Large-scale discovery of enhancers from human heart tissue
  - Nat Genet 44(1):89-93 (2012)
  Nature Genetics | Letter Large-scale discovery of enhancers from human heart tissue * Dalit May1 * Matthew J Blow1, 2 * Tommy Kaplan3, 4 * David J McCulley5 * Brian C Jensen6, 8 * Jennifer A Akiyama1 * Amy Holt1 * Ingrid Plajzer-Frick1 * Malak Shoukry1 * Crystal Wright2 * Veena Afzal1 * Paul C Simpson5, 7 * Edward M Rubin1, 2 * Brian L Black5 * James Bristow1, 2 * Len A Pennacchio1, 2 * Axel Visel1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:89–93Year published:(2012)DOI:doi:10.1038/ng.1006Received13 April 2011Accepted20 October 2011Published online04 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Development and function of the human heart depend on the dynamic control of tissue-specific gene expression by distant-acting transcriptional enhancers. To generate an accurate genome-wide map of human heart enhancers, we used an epigenomic enhancer discovery approach and identified ~6,200 candidate enhancer sequences directly from fetal and adult human heart tissue. Consistent with their predicted function, these elements were markedly enriched near genes implicated in heart development, function and disease. To further validate their in vivo enhancer activity, we tested 65 of these human sequences in a transgenic mouse enhancer assay and observed that 43 (66%) drove reproducible reporter gene expression in the heart. These results support the discovery of a genome-wide set of noncoding sequences highly enriched in human heart enhancers that is likely to facilitate downstream studies of the role of enhancers in development and pathological conditions of the heart. View full text Figures at a glance * Figure 1: ChIP-Seq identification of candidate enhancer regions from human fetal and adult heart. Human fetal heart was obtained at gestational week 16, and adult heart tissue was obtained from the septum of an adult failing heart. () Overview of strategy and results of ChIP-Seq analysis. In total, 5,047 regions from fetal heart and 2,233 from adult heart were significantly enriched in p300/CBP-binding sites and were considered as candidate human heart enhancers (distal: ≥2.5 kb from the nearest transcript start site; proximal or promoter associated: <2.5 kb from the nearest TSS). () Overlap of candidate enhancers identified in fetal and adult heart tissues. () ChIP-Seq profiles of p300/CBP in the genomic region of the tested hs1763 element (thin black bar). Thick black bars indicate two regions significantly enriched for p300/CBP binding in introns of the INPP5A gene. The thin black line represents a read depth of 10; maximum read depth shown is 50. * Figure 2: Human p300/CBP candidate enhancers are enriched near genes expressed in human heart. (,) Frequency of human fetal heart candidate enhancers (red) compared to matched random regions (black) near genes that are overexpressed () or underexpressed () in fetal heart relative to other human tissues (see Online Methods). Error bars indicate 95% confidence intervals. * Figure 3: In vivo testing of predicted human heart enhancer activities in transgenic mice. () In vivo enhancer activity of the 65 tested elements. () Proportion of reproducible enhancers by extent of sequence constraint (+, phastCons > 350; −, phastCons ≤ 350). () Proportion of reproducible enhancers by binding conservation to the mouse (+, p300/CBP binding significant or subsignificant but above background; −, p300/CBP binding not above background or in non-alignable peaks). Pairwise comparison for each subcategory was calculated with two-tailed Fisher's exact test; P > 0.05 in all cases. * Figure 4: In vivo activity of human cardiac enhancers in embryonic and 4-week-old transgenic mice. (–) From left to right: whole-mount stained E11.5 embryo, close-up and histological section of heart at E11.5, whole-mount stained heart at P28 and longitudinal section of heart at P28. All specimens were stained for LacZ enhancer reporter activity (dark blue). Element ID, reproducibility in E11.5 embryos and flanking genes are indicated. LV, left ventricle; RV, right ventricle; LA, left atrium; RA, right atrium; OFT, outflow tract; PA, pulmonary artery; Ao, aorta. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE1789 * GSE32587 Author information * Accession codes * Author information * Supplementary information Affiliations * Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA. * Dalit May, * Matthew J Blow, * Jennifer A Akiyama, * Amy Holt, * Ingrid Plajzer-Frick, * Malak Shoukry, * Veena Afzal, * Edward M Rubin, * James Bristow, * Len A Pennacchio & * Axel Visel * United States Department of Energy Joint Genome Institute, Walnut Creek, California, USA. * Matthew J Blow, * Crystal Wright, * Edward M Rubin, * James Bristow, * Len A Pennacchio & * Axel Visel * Department of Molecular and Cell Biology, California Institute of Quantitative Biosciences, University of California, Berkeley, Berkeley, California, USA. * Tommy Kaplan * School of Computer Science and Engineering, The Hebrew University, Jerusalem, Israel. * Tommy Kaplan * Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA. * David J McCulley, * Paul C Simpson & * Brian L Black * Division of Cardiology, University of California, San Francisco, San Francisco, California, USA. * Brian C Jensen * Cardiology Division, Virginia Medical Center, San Francisco, California, USA. * Paul C Simpson * Current address: Division of Cardiology, University of North Carolina, Chapel Hill, North Carolina, USA. * Brian C Jensen Contributions D.M., E.M.R., J.B., L.A.P. and A.V. conceived of and designed the experiments. D.M., M.J.B., T.K., D.J.M., B.C.J., J.A.A., A.H., I.P.-F., M.S., C.W. and V.A. performed experiments and data analysis. P.C.S. and B.L.B. provided reagents and materials and performed data analysis. All authors contributed to the writing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Len A Pennacchio or * Axel Visel Author Details * Dalit May Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew J Blow Search for this author in: * NPG journals * PubMed * Google Scholar * Tommy Kaplan Search for this author in: * NPG journals * PubMed * Google Scholar * David J McCulley Search for this author in: * NPG journals * PubMed * Google Scholar * Brian C Jensen Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer A Akiyama Search for this author in: * NPG journals * PubMed * Google Scholar * Amy Holt Search for this author in: * NPG journals * PubMed * Google Scholar * Ingrid Plajzer-Frick Search for this author in: * NPG journals * PubMed * Google Scholar * Malak Shoukry Search for this author in: * NPG journals * PubMed * Google Scholar * Crystal Wright Search for this author in: * NPG journals * PubMed * Google Scholar * Veena Afzal Search for this author in: * NPG journals * PubMed * Google Scholar * Paul C Simpson Search for this author in: * NPG journals * PubMed * Google Scholar * Edward M Rubin Search for this author in: * NPG journals * PubMed * Google Scholar * Brian L Black Search for this author in: * NPG journals * PubMed * Google Scholar * James Bristow Search for this author in: * NPG journals * PubMed * Google Scholar * Len A Pennacchio Contact Len A Pennacchio Search for this author in: * NPG journals * PubMed * Google Scholar * Axel Visel Contact Axel Visel Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Note, Supplementary Figures 1–12 and Supplementary Tables 1–12. Additional data
• A chromatin-modifying function of JNK during stem cell differentiation
  - Nat Genet 44(1):94-100 (2012)
  Nature Genetics | Letter A chromatin-modifying function of JNK during stem cell differentiation * Vijay K Tiwari1 * Michael B Stadler1, 2 * Christiane Wirbelauer1 * Renato Paro3, 4 * Dirk Schübeler1, 4 * Christian Beisel3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 44,Pages:94–100Year published:(2012)DOI:doi:10.1038/ng.1036Received12 July 2011Accepted15 November 2011Published online18 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Signaling mediates cellular responses to extracellular stimuli. The c-Jun NH2-terminal kinase (JNK) pathway exemplifies one subgroup of the mitogen-activated protein (MAP) kinases, which, besides having established functions in stress response, also contribute to development by an unknown mechanism1, 2, 3, 4. We show by genome-wide location analysis that JNK binds to a large set of active promoters during the differentiation of stem cells into neurons. JNK-bound promoters are enriched with binding motifs for the transcription factor NF-Y but not for AP-1. NF-Y occupies these predicted sites, and overexpression of dominant-negative NF-YA reduces the JNK presence on chromatin. We find that histone H3 Ser10 (H3S10) is a substrate for JNK, and JNK-bound promoters are enriched for H3S10 phosphorylation. Inhibition of JNK signaling in post-mitotic neurons reduces phosphorylation at H3S10 and the expression of target genes. These results establish MAP kinase binding and function on! chromatin at a novel class of target genes during stem cell differentiation. View full text Figures at a glance * Figure 1: JNK is upregulated during stem cell differentiation and directly binds promoters. () Analysis of Mapk8, Mapk9 and Mapk10 transcript levels by real-time PCR during neuronal differentiation of stem cells, revealing that Mapk8 and Mapk10 levels are low in ES cells but are upregulated upon differentiation. mRNA levels are plotted on the y axis and are normalized to Gapdh levels. Error bars represent s.e.m. () Protein blot detection of JNK1/3 in protein extracts isolated from ES, NP and TN cells showing protein upregulation during differentiation. Lamin B1 serves as loading control. () Protein blot analysis of samples in using an antibody specific to phosphorylated JNK (p-JNK), revealing the presence of the active form in TNs. () Immunofluorescence with DAPI staining of the nucleus (blue) and an antibody specific to JNK1/3 (red). An overlay of DAPI and JNK staining shows that a substantial fraction of JNK localizes to the nucleus. Scale bar, 80 μm. () Genome browser view of enrichment for JNK binding at the G3bp1 locus in the three stages of ES cell different! iation as determined by ChIP. Tag densities are normalized to the total number of reads in each sample. () Peaks of JNK binding are enriched at promoters but not in exons, introns or intergenic regions in all three stages of ES cell differentiation. Enrichments are calculated relative to the genomic size of each type of region. () Averaged JNK coverage around TSSs in neurons normalized to the input chromatin control sample and grouped into ten classes of decreasing JNK1/3 ChIP-Seq signal. * Figure 2: JNK target promoters are active and show increased JNK binding during terminal differentiation. () ChIP-qPCR validation of the enrichment of JNK binding at various identified targets and non-target controls using JNK1/3 ChIP samples derived from all three stages of ES cell differentiation. Average enrichments from separate assays are plotted on the y axis as the ratio of precipitated DNA relative to the total input DNA and are further normalized to a control region. Error bars show s.e.m. () ChIP-qPCR validation of select JNK targets in JNK1/3 ChIP samples derived from adult mouse brain plotted as in , showing the in vivo targeting of chromatin by JNK. () Genes were classified as JNK positive (enrichment at TSS ≥ 0.7, solid lines) or negative (enrichment at TSS < 0.7, dotted lines) on the basis of JNK levels at the TSS of each gene. The distribution of signals in TN cells for each group of genes is shown for RNA Pol II binding, mRNA levels and H3K4me2 and H3K27me3 chromatin modifications. * Figure 3: NF-Y mediates JNK recruitment to chromatin. () Sequence logos of motifs identified in JNK target genes. () Performance (adjusted r2) of different linear models in predicting JNK binding at promoters in TNs as a function of chromatin features (RNA Pol II binding and H3K27me3 and H3K4me2 marks), sequence features (number of NF-Y–, SP-1– or AP-1–like motifs), mRNA levels, or a combination of all (full) or two (RNA Pol II binding and a given motif) of these features. RNA Pol II + NF-Y–like motif almost achieve the performance of the full model, explaining 40% of the observed variance in JNK binding (left). Models including the NF-YA binding data derived from ChIP-Seq (right) explain more than 60% of the variance. () Genome browser screenshot comparing JNK and NF-YA binding at the G3bp1 locus showing similar binding dynamics during neuronal differentiation. () NF-YA enrichment as determined using ChIP-qPCR for various JNK targets in ES, NP and TN stages. NF-Y occupies all tested JNK targets and shows similar bindin! g dynamics during differentiation. Error bars show s.e.m. () Comparison of enrichment in JNK and NF-YA binding at promoters in TN cells. Promoters with proximal JNK peaks (within 1 kb of the TSS) are shown in red. Dotted lines indicate the cut-off values used in this study to define promoters positive for JNK and NF-YA binding (0.7 and 0.5, respectively). () Dominant-negative NF-YA reduces JNK binding to chromatin. HEK293 cells were stably transfected with constructs encoding wild-type (WT) or dominant-negative (DN) NF-YA, and NF-YA expression was induced by the addition of tetracycline (Tet) to cells. Subsequently, JNK binding was determined by ChIP-qPCR using primers specific for JNK target genes. Error bars represent s.e.m. * Figure 4: Inhibition of JNK signaling blocks differentiation and reduces H3S10 phosphorylation. () Exposure of cells to the JNK inhibitor SP600125 (JNKi) during the transition from neuronal progenitors to neurons abrogates neurogenesis. NPs were treated with either DMSO (control) or with SP600125 for 2 h after plating, and light-field microscopy images were taken 24 h later. Scale bar, 70 μm. () TNs were exposed to either DMSO or SP600125, and the levels of phosphorylated JNK were detected by protein blotting. The levels of total JNK1/3 and Lamin B1 (loading control) are also shown. Exposure to SP600125 leads to a substantial reduction in the levels of phosphorylated JNK. () Total histones were isolated from TNs exposed to either DMSO or SP600125, and levels of phosphorylated H3S10 (p-H3S10), H3T3 (p-H3T3), H3T11 (p-H3T3) and H3S28 (p-H3S28), as well as acetylated H3 (H3Ac), H3K4me2, H3K9me3 and total histone H4 were detected by protein blotting. Total histone H3 levels are also shown as a loading control. Inhibition of JNK signaling leads to a substantial reduction i! n the levels of phosphorylated H3S10. () JNK phosphorylates H3S10 in vitro. Recombinant active JNK was incubated with ATP and recombinant H3.1 and kinase reactions were performed, followed by protein blotting with the indicated antibodies. () Phosphorylated H3S10 is preferentially enriched at JNK target genes. ChIP-qPCR for phosphorylated H3S10 presence at various JNK target and non-target control genes using phosphorylated H3S10 ChIP samples derived from the TN cells. Error bars represent s.e.m. * Figure 5: Blocking JNK kinase activity downregulates target gene expression. () Effect of JNK inhibition on the transcriptome. Genes were divided into ten equally sized bins according to expression levels, median-centered and SP600125-induced expression changes were compared between JNK target and non-target genes. Bins with significant differences in expression between the two gene classes are indicated by asterisks (P value < 0.001, two-sided Wilcoxon rank-sum test with continuity correction). These data show preferential downregulation of JNK targets with moderate expression levels. Dotted lines show the minimum and maximum range of data. () Analysis performed as in for NF-YA target genes illustrating the same preferential downregulation by the inhibition of JNK kinase activity. () Real-time PCR analysis of transcript levels of two JNK targets (Mcm10 and Traf2) in TNs exposed to either DMSO or SP600125. Relative mRNA levels were determined by normalization to Gapdh expression, and average data from independent assays are plotted on the y axis. Err! or bars show s.e.m. () Correlation between expression changes caused by the inhibition of JNK kinase activity (x axis) and changes in JNK binding at the TSS of relevant genes (y axis). The red line corresponds to a local smoothed line fitted to the values for individual genes (loess fit with smoothing parameter α = 0.15). Changes in expression and JNK binding are significantly correlated (Spearman's rank correlation ρ = 0.255, P < 2.2 × 10−16). Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE25533 Author information * Accession codes * Author information * Supplementary information Affiliations * Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland. * Vijay K Tiwari, * Michael B Stadler, * Christiane Wirbelauer & * Dirk Schübeler * Swiss Institute of Bioinformatics, Basel, Switzerland. * Michael B Stadler * Department of Biosystems Science and Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, Basel, Switzerland. * Renato Paro & * Christian Beisel * Faculty of Science, University of Basel, Basel, Switzerland. * Renato Paro & * Dirk Schübeler Contributions V.K.T. initiated and designed the study, performed experiments, analyzed data and wrote the manuscript. M.B.S. designed and performed the computational analysis and wrote the manuscript. C.W. performed experiments and analyzed data. R.P. provided input during the study and comments on the manuscript. C.B. initiated the study, performed experiments, analyzed data and wrote the manuscript. D.S. designed the study, analyzed data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Dirk Schübeler or * Michael B Stadler Author Details * Vijay K Tiwari Search for this author in: * NPG journals * PubMed * Google Scholar * Michael B Stadler Contact Michael B Stadler Search for this author in: * NPG journals * PubMed * Google Scholar * Christiane Wirbelauer Search for this author in: * NPG journals * PubMed * Google Scholar * Renato Paro Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk Schübeler Contact Dirk Schübeler Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Beisel Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–10. Additional data
• Evolutionary paths to antibiotic resistance under dynamically sustained drug selection
  - Nat Genet 44(1):101-105 (2012)
  Nature Genetics | Letter Evolutionary paths to antibiotic resistance under dynamically sustained drug selection * Erdal Toprak1, 6 * Adrian Veres2, 6 * Jean-Baptiste Michel1, 3 * Remy Chait1 * Daniel L Hartl4 * Roy Kishony1, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:101–105Year published:(2012)DOI:doi:10.1038/ng.1034Received09 May 2011Accepted15 November 2011Published online18 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Antibiotic resistance can evolve through the sequential accumulation of multiple mutations1. To study such gradual evolution, we developed a selection device, the 'morbidostat', that continuously monitors bacterial growth and dynamically regulates drug concentrations, such that the evolving population is constantly challenged2, 3, 4, 5. We analyzed the evolution of resistance in Escherichia coli under selection with single drugs, including chloramphenicol, doxycycline and trimethoprim. Over a period of ~20 days, resistance levels increased dramatically, with parallel populations showing similar phenotypic trajectories. Whole-genome sequencing of the evolved strains identified mutations both specific to resistance to a particular drug and shared in resistance to multiple drugs. Chloramphenicol and doxycycline resistance evolved smoothly through diverse combinations of mutations in genes involved in translation, transcription and transport3. In contrast, trimethoprim resistanc! e evolved in a stepwise manner1, 6, through mutations restricted to the gene encoding the enzyme dihydrofolate reductase (DHFR)7, 8. Sequencing of DHFR over the time course of the experiment showed that parallel populations evolved similar mutations and acquired them in a similar order9. View full text Figures at a glance * Figure 1: The morbidostat is a continuous-culture device that automatically tunes drug concentration to maintain constant growth inhibition. () The assay runs in cycles of growth periods (Δt = 11 min) and adds dilutions with either fresh medium (green) or drug solution (magenta). The population is diluted with antibiotic solution when the OD exceeds ODTHR (0.15) and the net growth over the complete cycle is positive (ΔOD > 0). () Representative bacterial growth in the morbidostat. OD is recorded at 1 Hz (plotted at 0.1 Hz, gray dots). The growth rate (r) within a growth period is calculated by fitting the exponential growth function (black lines). Magenta and green markers indicate dilutions with drug solution and fresh medium, respectively. Inset, parameters calculated at each growth cycle are shown. () Representative bacterial growth and inhibition in the morbidostat for an extended time period. For clarity, only final ODs within growth cycles are plotted. The grey rectangle delimits data shown in . Magenta circles indicate the cycles after the addition of drug solution. * Figure 2: Parallel populations attain high levels of drug resistance in similar adaptive trajectories. () Sample measurements of OD versus time (circles) and fitted growth rates (exponential fit; color represents normalized growth rate r/r0) of the ancestral strain in different trimethoprim concentrations. () Normalized growth rates of bacterial populations obtained from daily samples (x axis) of the evolving populations in a range of fixed drug concentrations (y axis). Day 0 corresponds to the ancestral strain before evolution. IC50 values are represented with black circles (r/r0 = 0.5). (–) Resistance levels over time for parallel populations evolving under inhibition by trimethoprim (), chloramphenicol () and doxycycline (). Resistance increases by ~1,680, 870 and 10 fold, respectively. Trimethoprim resistance increases in a stepwise fashion. The resistance data for each of the 15 populations are derived from high-throughput phenotyping as shown in (the TMP-1 population in (black circles) is the one represented in ). * Figure 3: Unique and common genetic changes identified by whole-genome sequencing. () SNPs identified by Illumina and Sanger sequencing. The horizontal arrow blocks and rectangles represent the coding and noncoding regions of genes, respectively. SNPs found in the 15 evolved populations are shown by different symbols, with colors indicating the drug applied during evolution (magenta, chloramphenicol; green, doxycycline; blue, trimethoprim). Note that SNPs found in multiple populations are shown with vertically stacked symbols appended to the genes. SNPs are localized to genes that fall into three major functional groups: (i) transcription and translation, (ii) folic acid biosynthesis and (iii) membrane transport. Arrow thickness reflects the frequency of mutations occurring within each functional group when the bacterial populations were challenged with the specified drugs. pDHFR, DHFR promoter; pcmr, cmr promoter. () Resistance levels (of Illumina-sequenced clones) to chloramphenicol, doxycycline and trimethoprim. Black dashed lines indicate minimum inhib! itory concentration (MIC) for the ancestral strain. Panels with colored background show MIC values for the evolved strains for the drugs to which they evolved resistance. Strains evolved in the presence of chloramphenicol exhibit elevated doxycycline resistance and vice versa, whereas evolution in the presence of trimethoprim inhibition led to little or no cross-resistance for either doxycycline or chloramphenicol. * Figure 4: Semi-ordered acquisition of trimethoprim resistance mutations. () Structure of E. coli DHFR enzyme (PDB 1RX2) bound to its substrate, dihydrofolate (black, arrow), with mutated residues shown in color. () IC50 values (gray lines) and time-resolved alterations in DHFR for each of the five replicate (TMP-1–TMP-5). For each day, alterations found in four randomly sampled clones are represented in a pie chart, with color indicating a specific alteration and shape of the chart indicating whether the alteration was a promoter mutation or amino acid substitution. The quadrants of the pie chart indicate the presence (filled) or absence (empty) of this alteration in each of the four sequenced clones (the correspondence between clones and quadrants is conserved across all mutations to indicate whether mutations are found in the same or different clones). Colors of the pie charts correspond to the colors of the mutated sites shown in . Inset, additional colonies (TMP-4) were sequenced from bacteria isolated at days 8–10 to verify the disappear! ance of the W30C alteration. () Reproducibility of the order of fixation of mutations compared for the five parallel populations in the observed data (arrow) and when the order of mutations is randomly permuted (histogram bar). Only 0.2% of randomly permuted trajectories are equally or more reproducible than the trajectories observed in . Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Sequence Read Archive * SRA046097 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Erdal Toprak & * Adrian Veres Affiliations * Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. * Erdal Toprak, * Jean-Baptiste Michel, * Remy Chait & * Roy Kishony * Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA. * Adrian Veres * Program for Evolutionary Dynamics, Harvard University, Cambridge, Massachusetts, USA. * Jean-Baptiste Michel * Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA. * Daniel L Hartl * School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA. * Roy Kishony Contributions E.T., A.V., R.C., D.L.H. and R.K. designed the project. E.T. and A.V. performed the experiments and E.T., A.V., J.-B.M. and R.K. analyzed the data. All authors contributed to preparing the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Roy Kishony Author Details * Erdal Toprak Search for this author in: * NPG journals * PubMed * Google Scholar * Adrian Veres Search for this author in: * NPG journals * PubMed * Google Scholar * Jean-Baptiste Michel Search for this author in: * NPG journals * PubMed * Google Scholar * Remy Chait Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel L Hartl Search for this author in: * NPG journals * PubMed * Google Scholar * Roy Kishony Contact Roy Kishony Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–4, Supplementary Tables 1–3 and Supplementary Note. Additional data
• Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes
  - Nat Genet 44(1):106-110 (2012)
  Nature Genetics | Letter Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes * Iñaki Comas1, 8 * Sonia Borrell2, 3 * Andreas Roetzer4 * Graham Rose1 * Bijaya Malla2, 3 * Midori Kato-Maeda5 * James Galagan6, 7 * Stefan Niemann4 * Sebastien Gagneux2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 44,Pages:106–110Year published:(2012)DOI:doi:10.1038/ng.1038Received10 June 2011Accepted16 November 2011Published online18 December 2011 Article tools * Full text * Print * Email * pdf options * download pdf * view interactive pdfpowered by ReadCube * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Epidemics of drug-resistant bacteria emerge worldwide, even as resistant strains frequently have reduced fitness compared to their drug-susceptible counterparts1. Data from model systems suggest that the fitness cost of antimicrobial resistance can be reduced by compensatory mutations2; however, there is limited evidence that compensatory evolution has any significant role in the success of drug-resistant bacteria in human populations3, 4, 5, 6. Here we describe a set of compensatory mutations in the RNA polymerase genes of rifampicin-resistant M. tuberculosis, the etiologic agent of human tuberculosis (TB). M. tuberculosis strains harboring these compensatory mutations showed a high competitive fitness in vitro. Moreover, these mutations were associated with high fitness in vivo, as determined by examining their relative clinical frequency across patient populations. Of note, in countries with the world's highest incidence of multidrug-resistant (MDR) TB7, more than 30% of ! MDR clinical isolates had this form of mutation. Our findings support a role for compensatory evolution in the global epidemics of MDR TB8. View full text Figures at a glance * Figure 1: Putative compensatory mutations in rpoA and rpoC of M. tuberculosis. (,) Mutations identified after genome sequencing of experimentally evolved strains (circle) or paired clinical isolates (triangles) are indicated above the gene diagrams of rpoA () and rpoC (). Mutations identified by screening a global and a high-burden collection of MDR strains are indicated by stars below the gene diagrams. Colors indicate the respective strain lineage (blue, lineage 2; red, lineage 4; brown, lineage 5; pink, lineage 1). Some of these mutations occurred in multiple lineages or affect the same codon position. * Figure 2: Putative compensatory mutations in rpoA and rpoC fall in regions encoding the interface of the RNA polymerase subunits. Amino acid substitutions identified in rifampicin-resistant experimentally evolved isolates and paired clinical isolates were mapped onto the structure of the E. coli RNA polymerase. The alterations are localized to residues of RpoA (light blue) and RpoC (orange) that are predicted to have roles in RNA polymerase subunit interaction. Residue numbers are indicated according to M. tuberculosis coordinates. RpoA (α subunit), blue; RpoB (β subunit), red; RpoC (β′ subunit), yellow; RpoD (σ subunit), green. * Figure 3: Experimental and clinical relevance of putative compensatory mutations. () Experimental competitive fitness of ten clinical isolates that acquired rifampicin resistance over the course of treatment compared to their susceptible counterparts. The amino acid changes encoded by HCMs are indicated in the pair in which they were identified. Bar colors indicate strain lineage (blue, lineage 2; red, lineage 4). () Difference in relative fitness between ten rifampicin-resistant paired clinical isolates compared to laboratory-generated mutants carrying the same rifampicin resistance–conferring mutation and with the same genetic background as defined by strain lineage. Data are shown for clinical strains with or without an HCM. Horizontal lines indicate median fitness differences. () Time in months between the isolation of the first and the second strain of each clinical pair. Horizontal lines indicate the median time intervals. () Percentage of MDR strains with putative compensatory mutations in rpoA or rpoC. Gray bars, the percentage of strains carryi! ng HCMs; black bars, strains carrying any putative compensatory mutation. Data for a global collection of strains and for regions of Abkhazia/Georgia, Uzbekistan and Kazakhstan with high MDR TB burden are shown. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Sequence Read Archive * SRP001097 Author information * Accession codes * Author information * Supplementary information Affiliations * Division of Mycobacterial Research, Medical Research Council, National Institute for Medical Research, London, UK. * Iñaki Comas & * Graham Rose * Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland. * Sonia Borrell, * Bijaya Malla & * Sebastien Gagneux * University of Basel, Basel, Switzerland. * Sonia Borrell, * Bijaya Malla & * Sebastien Gagneux * Molecular Mycobacteriology, Research Centre Borstel, Borstel, Germany. * Andreas Roetzer & * Stefan Niemann * Department of Medicine, San Francisco General Hospital, University of California, San Francisco, San Francisco, California, USA. * Midori Kato-Maeda * The Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA. * James Galagan * Department of Microbiology, Boston University, Boston, Massachusetts, USA. * James Galagan * Current address: Genomics and Health Unit, Centre for Public Health Research, Valencia, Spain. * Iñaki Comas Contributions I.C., S.B. and S.G. planned the experiments. I.C., S.B., A.R., B.M., G.R., M.K.-M., J.G. and S.G. performed the experiments. I.C., S.B., A.R., G.R., S.N. and S.G. analyzed the data. I.C., S.B. and S.G. wrote the manuscript. All authors critically reviewed the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Sebastien Gagneux Author Details * Iñaki Comas Search for this author in: * NPG journals * PubMed * Google Scholar * Sonia Borrell Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Roetzer Search for this author in: * NPG journals * PubMed * Google Scholar * Graham Rose Search for this author in: * NPG journals * PubMed * Google Scholar * Bijaya Malla Search for this author in: * NPG journals * PubMed * Google Scholar * Midori Kato-Maeda Search for this author in: * NPG journals * PubMed * Google Scholar * James Galagan Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Niemann Search for this author in: * NPG journals * PubMed * Google Scholar * Sebastien Gagneux Contact Sebastien Gagneux Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (295K) Supplementary Tables 1–8. 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