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Latest Articles Include:

• Policy-ready science
  - Nat Genet 43(8):721 (2011)
  Nature Genetics | Editorial Policy-ready science Journal name:Nature GeneticsVolume: 43,Page:721Year published:(2011)DOI:doi:10.1038/ng.901Published online27 July 2011 Although Nature Genetics generally urges authors to keep their claims within the research arena, basic research occasionally turns up results that are ready for immediate application. In these cases we aim to assign some peer referees familiar with the needs of policy makers and to provide accompanying commentary that puts the research into an appropriate societal perspective. View full text Additional data
• Transition zone proteins and cilia dynamics
  - Nat Genet 43(8):723-724 (2011)
  Article preview View full access options Nature Genetics | News and Views Transition zone proteins and cilia dynamics * Thomas Benzing1 * Bernhard Schermer1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:723–724Year published:(2011)DOI:doi:10.1038/ng.896Published online27 July 2011 New work has identified networks of protein interactions at the transition zones of cilia. These discoveries provide insights into the molecular pathogenesis of ciliopathies and illustrate the power of linking proteomics technologies with human genetics to uncover critical disease pathways. 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 * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Thomas Benzing and Bernhard Schermer are at the Department of Medicine, Centre for Molecular Medicine and the Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Thomas Benzing Author Details * Thomas Benzing Contact Thomas Benzing Search for this author in: * NPG journals * PubMed * Google Scholar * Bernhard Schermer Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Epigenetic variation and cellular Darwinism
  - Nat Genet 43(8):724-726 (2011)
  Article preview View full access options Nature Genetics | News and Views Epigenetic variation and cellular Darwinism * Jean-Pierre Issa1Journal name:Nature GeneticsVolume: 43,Pages:724–726Year published:(2011)DOI:doi:10.1038/ng.897Published online27 July 2011 The human genome contains large areas with hypervariable DNA methylation that are associated with deregulation of gene expression. This epigenetic variation may be necessary for differentiation, but it also provides a mechanism for Darwinian evolution at the cellular level that may underlie age-related diseases such as cancer. 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 * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Jean-Pierre Issa is in the Department of Leukemia at the University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Jean-Pierre Issa Author Details * Jean-Pierre Issa Contact Jean-Pierre Issa Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• The timing of mitochondrial DNA mutations in aging
  - Nat Genet 43(8):726-727 (2011)
  Article preview View full access options Nature Genetics | News and Views The timing of mitochondrial DNA mutations in aging * Konstantin Khrapko1Journal name:Nature GeneticsVolume: 43,Pages:726–727Year published:(2011)DOI:doi:10.1038/ng.895Published online27 July 2011 Somatic mutations in mitochondrial DNA build up in aging tissues and are thought to contribute to physiological aging. Surprisingly, it is not known if these mutations occur early or late in life. A new study looks at mechanisms of accelerated mitochondrial aging in HIV-infected individuals treated with nucleoside analog anti-retroviral drugs and offers support for an early origin of mitochondrial DNA mutations. 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 * 日本語要約 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Konstantin Khrapko is at Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Konstantin Khrapko Author Details * Konstantin Khrapko Contact Konstantin Khrapko Search for this author in: * NPG journals * PubMed * Google Scholar Additional data
• Research highlights
  - Nat Genet 43(8):728 (2011)
  Article preview View full access options Nature Genetics | Research Highlights Research highlights Journal name:Nature GeneticsVolume: 43,Page:728Year published:(2011)DOI:doi:10.1038/ng.899Published online27 July 2011 Stress-induced epigenetic changes Evidence suggests that environmental stresses can lead to epigenetic modifications of the genome that can be inherited, although the molecular mechanisms of this process are not known. Now, Shunsuke Ishii and colleagues report that stress-induced phosphorylation of the transcription factor dATF-2 in Drosophila can disrupt heterochromatin, which can be transmitted to the next generation (Cell145, 1049–1061, 2011). The authors analyzed flies carrying the marker gene white close to centromeric heterochromatin. Loss of Atf-2 led to derepression of white, suggesting Atf-2 is required for the generation and maintenance of heterochromatin. Immunostaining experiments showed that dATF-2 localizes to heterochromatin and that heat shock in early fly embryos disrupts this localization. The authors then mated adult females (that were exposed during embryogenesis) to unstressed males. The authors observed derepression of the white gene in progeny, indicating that the defective heterochr! omatin was transmitted. The inheritance of the heat-shock effect was tested over multiple generations, and, although continued heat-shock treatment sustained white derepression over multiple generations, it eventually returned to the normal state. The authors suggest that stress-induced epigenetic changes are mediated by regulation of chromatin structure. PC Self-recognition in social organisms The capacity to discriminate between self and nonself is a critical trait for most organisms. The social amoebae Dictyostelium discoideum can aggregate and form multicellular fruiting bodies in which 20–30% of cells form a stalk and die, whereas 70–80% of cells survive as spores. The altruistic behavior of stalk cells is not well understood, although D. discoideum shows kin discrimination as a defense against cheater cells that sporulate but do not contribute to the stalk. Gad Shauldsky and colleagues now report that allelic pairs of tgrB1 and tgrC1 are necessary and sufficient for self-recognition in D. discoideum (Science published online, doi:10.1126/science.1203903, 23 June 2011). Cells deficient in tgrB1 and tgrC1 arrest in the aggregate stage and show compromised spore production, suggesting that these genes are required for self-recognition. The authors cloned these genes from four other wild strains, recombined them into the tgrB1−tgrC1− cells and mixed them ! with the parental strain. Cells carrying the wild replacement genes autosegregated from the parental strain, and each population formed separate fruiting bodies and spores appropriately. These results suggest that a matching pair of alleles at these genes is sufficient to confer self-recognition. The authors propose that TgrB1 and TgrC1 are cell-surface proteins that mediate self-recognition by direct binding. PC Article preview Read the full article * Instant access to this article: US$32 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. Additional data
• De novo nonsense mutations in ASXL1 cause Bohring-Opitz syndrome
  - Nat Genet 43(8):729-731 (2011)
  Nature Genetics | Brief Communication De novo nonsense mutations in ASXL1 cause Bohring-Opitz syndrome * Alexander Hoischen1, 13 * Bregje W M van Bon1, 13 * Benjamín Rodríguez-Santiago1, 2, 3, 13 * Christian Gilissen1 * Lisenka E L M Vissers1 * Petra de Vries1 * Irene Janssen1 * Bart van Lier1 * Rob Hastings4 * Sarah F Smithson4 * Ruth Newbury-Ecob4 * Susanne Kjaergaard5 * Judith Goodship6 * Ruth McGowan7 * Deborah Bartholdi8 * Anita Rauch8 * Maarit Peippo9 * Jan M Cobben10 * Dagmar Wieczorek11 * Gabriele Gillessen-Kaesbach12 * Joris A Veltman1 * Han G Brunner1 * Bert B B A de Vries1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:729–731Year published:(2011)DOI:doi:10.1038/ng.868Received28 February 2011Accepted31 May 2011Published online26 June 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Bohring-Opitz syndrome is characterized by severe intellectual disability, distinctive facial features and multiple congenital malformations. We sequenced the exomes of three individuals with Bohring-Opitz syndrome and in each identified heterozygous de novo nonsense mutations in ASXL1, which is required for maintenance of both activation and silencing of Hox genes. In total, 7 out of 13 subjects with a Bohring-Opitz phenotype had de novo ASXL1 mutations, suggesting that the syndrome is genetically heterogeneous. View full text Accession codes * Accession codes * Author information * Supplementary information Referenced accessions GenBank * NM_015338 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Alexander Hoischen, * Bregje W M van Bon & * Benjamín Rodríguez-Santiago Affiliations * Department of Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Alexander Hoischen, * Bregje W M van Bon, * Benjamín Rodríguez-Santiago, * Christian Gilissen, * Lisenka E L M Vissers, * Petra de Vries, * Irene Janssen, * Bart van Lier, * Joris A Veltman, * Han G Brunner & * Bert B B A de Vries * Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain. * Benjamín Rodríguez-Santiago * Centro de Investigación Biomédica en Red (CIBER) de Enfermedades Raras, CIBERER, Barcelona, Spain. * Benjamín Rodríguez-Santiago * Department of Clinical Genetics, St Michael's Hospital, Bristol, UK. * Rob Hastings, * Sarah F Smithson & * Ruth Newbury-Ecob * Department of Clinical Genetics, University Hospital of Copenhagen, Rigshospitalet, Denmark. * Susanne Kjaergaard * Institute of Human Genetics, Newcastle University, Newcastle Upon Tyne, UK. * Judith Goodship * Department of Medical Genetics, Yorkhill Hospitals, Yorkhill, Glasgow, UK. * Ruth McGowan * Institute of Medical Genetics, University of Zurich, Schwerzenbach, Switzerland. * Deborah Bartholdi & * Anita Rauch * Department of Medical Genetics, The Family Federation of Finland, Helsinki, Finland. * Maarit Peippo * Department of Pediatric Genetics, Emma Kinderziekenhuis Academisch Medisch Centrum (AMC), Amsterdam, The Netherlands. * Jan M Cobben * Institut für Humangenetik, Universitätsklinikum Essen, Essen, Germany. * Dagmar Wieczorek * Institut für Humangenetik Lübeck, Universität zu Lübeck, Lübeck, Germany. * Gabriele Gillessen-Kaesbach Contributions A.H., B.W.M.v.B., B.R.-S., H.G.B., B.B.B.A.d.V. and J.A.V. conceived the project and planned the experiments. B.W.M.v.B., H.G.B. and B.B.B.A.d.V. performed review of phenotypes and sample collection. H.G.B., R.H., S.F.S., R.N.-E., S.K., J.G., R.M., D.B., A.R., M.P., J.M.C., D.W. and G.G.-K. clinically characterized the Bohring-Opitz syndrome cases and collected blood samples. A.H., L.E.L.M.V., P.d.V., I.J. and B.v.L. performed next-generation sequencing experiments. P.d.V. and B.v.L. performed validation experiments. C.G., B.R.-S. and A.H. analyzed and interpreted the data. All authors contributed to the final manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Han G Brunner Author Details * Alexander Hoischen Search for this author in: * NPG journals * PubMed * Google Scholar * Bregje W M van Bon Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamín Rodríguez-Santiago Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Gilissen Search for this author in: * NPG journals * PubMed * Google Scholar * Lisenka E L M Vissers Search for this author in: * NPG journals * PubMed * Google Scholar * Petra de Vries Search for this author in: * NPG journals * PubMed * Google Scholar * Irene Janssen Search for this author in: * NPG journals * PubMed * Google Scholar * Bart van Lier Search for this author in: * NPG journals * PubMed * Google Scholar * Rob Hastings Search for this author in: * NPG journals * PubMed * Google Scholar * Sarah F Smithson Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth Newbury-Ecob Search for this author in: * NPG journals * PubMed * Google Scholar * Susanne Kjaergaard Search for this author in: * NPG journals * PubMed * Google Scholar * Judith Goodship Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth McGowan Search for this author in: * NPG journals * PubMed * Google Scholar * Deborah Bartholdi Search for this author in: * NPG journals * PubMed * Google Scholar * Anita Rauch Search for this author in: * NPG journals * PubMed * Google Scholar * Maarit Peippo Search for this author in: * NPG journals * PubMed * Google Scholar * Jan M Cobben Search for this author in: * NPG journals * PubMed * Google Scholar * Dagmar Wieczorek Search for this author in: * NPG journals * PubMed * Google Scholar * Gabriele Gillessen-Kaesbach Search for this author in: * NPG journals * PubMed * Google Scholar * Joris A Veltman Search for this author in: * NPG journals * PubMed * Google Scholar * Han G Brunner Contact Han G Brunner Search for this author in: * NPG journals * PubMed * Google Scholar * Bert B B A de Vries Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Tables 1–3, Supplementary Figures 1 and 2 and Supplementary Note. Additional data
• NBEAL2 is mutated in gray platelet syndrome and is required for biogenesis of platelet α-granules
  - Nat Genet 43(8):732-734 (2011)
  Nature Genetics | Brief Communication NBEAL2 is mutated in gray platelet syndrome and is required for biogenesis of platelet α-granules * Meral Gunay-Aygun1, 2, 13 * Tzipora C Falik-Zaccai3, 4, 13 * Thierry Vilboux1 * Yifat Zivony-Elboum3 * Fatma Gumruk5 * Mualla Cetin5 * Morad Khayat3 * Cornelius F Boerkoel1 * Nehama Kfir3 * Yan Huang1 * Dawn Maynard1 * Heidi Dorward1 * Katherine Berger1 * Robert Kleta1 * Yair Anikster6, 7 * Mutlu Arat8 * Andrew S Freiberg9 * Beate E Kehrel10 * Kerstin Jurk10 * Pedro Cruz11 * Jim C Mullikin11 * James G White12 * Marjan Huizing1 * William A Gahl1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:732–734Year published:(2011)DOI:doi:10.1038/ng.883Received11 February 2011Accepted15 June 2011Published online17 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Gray platelet syndrome (GPS) is an autosomal recessive bleeding disorder that is characterized by large platelets that lack α-granules. Here we show that mutations in NBEAL2 (neurobeachin-like 2), which encodes a BEACH/ARM/WD40 domain protein, cause GPS and that megakaryocytes and platelets from individuals with GPS express a unique combination of NBEAL2 transcripts. Proteomic analysis of sucrose-gradient subcellular fractions of platelets indicated that NBEAL2 localizes to the dense tubular system (endoplasmic reticulum) in platelets. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Meral Gunay-Aygun & * Tzipora C Falik-Zaccai Affiliations * Section on Human Biochemical Genetics, Medical Genetics Branch, National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA. * Meral Gunay-Aygun, * Thierry Vilboux, * Cornelius F Boerkoel, * Yan Huang, * Dawn Maynard, * Heidi Dorward, * Katherine Berger, * Robert Kleta, * Marjan Huizing & * William A Gahl * Office of Rare Diseases Research, Office of the Director, US National Institutes of Health, Bethesda, Maryland, USA. * Meral Gunay-Aygun & * William A Gahl * Institute of Human Genetics, Western Galilee Hospital, Naharia, Israel. * Tzipora C Falik-Zaccai, * Yifat Zivony-Elboum, * Morad Khayat & * Nehama Kfir * Ruth and Bruce Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, Haifa, Israel. * Tzipora C Falik-Zaccai * Pediatric Hematology Unit, Hacettepe University Children's Hospital, Ankara, Turkey. * Fatma Gumruk & * Mualla Cetin * Metabolic Disease Unit, Edmond and Lily Safra Children′s Hospital, Sheba Medical Center, Tel Aviv, Israel. * Yair Anikster * Sackler Medical School, Tel Aviv, Israel. * Yair Anikster * Department of Hematology, Ankara University Faculty of Medicine, Ankara, Turkey. * Mutlu Arat * Division of Pediatric Hematology/Oncology, Penn State Hershey Children's Hospital, Hershey, Pennsylvania. * Andrew S Freiberg * Department of Anaesthesiology and Intensive Care, Experimental and Clinical Haemostasis, University Hospital Münster, Münster, Germany. * Beate E Kehrel & * Kerstin Jurk * US National Institutes of Health (NIH) Intramural Sequencing Center, NIH, Bethesda, Maryland, USA. * Pedro Cruz & * Jim C Mullikin * Department of Laboratory Medicine, University of Minnesota, Minneapolis, Minnesota, USA. * James G White Contributions M.G.-A. is the principal investigator of clinical trials NCT00069680 (Genetic Analysis of Gray Platelet Syndrome) and NCT00086476 (Investigations of Megakaryocytes from Patients with Abnormal Platelet Vesicles); M.G.-A. wrote the manuscript and cultured megakaryocytes; M.G.-A., W.A.G. and T.C.F.-Z. designed and supervised research; M.G.-A., T.C.F.-Z. and T.V. analyzed clinical and molecular data; T.C.F.-Z., is the principal investigator of the Israeli protocol 'Clinical and Genetic Analysis of Gray Platelet Syndrome'; W.A.G. is the principal investigator of clinical trial NCT00369421 (Diagnosis and Treatment of Patients With Inborn Errors of Metabolism) and accountable investigator of clinical trial NCT00069680 (Genetic Analysis of Gray Platelet Syndrome); M.G.-A., T.V., T.C.F.-Z., J.C.M., C.F.B. and M.H. supervised DNA sequencing; M.G.-A., T.V., Y.Z.-E., F.G., M.C., M.K., C.F.B., N.K., Y.H., K.B., R.K. and P.C., performed DNA sequencing; M.G.-A., T.C.F.-Z., F.G., M.C., M.K.! , N.K., R.K., Y.A., M.A., A.S.F., B.E.K., K.J. and J.G.W. recruited patients and provided clinical data; J.G.W. performed electron microscopy; D.M. performed proteomic analysis; H.D. cultured fibroblasts; T.C.F.-Z., T.V., Y.Z.-E., F.G., M.C., M.K., C.F.B., N.K., Y.H., D.M., H.D., K.B., R.K., Y.A., M.A., A.S.F., B.E.K., K.J., P.C., J.C.M., J.G.W., M.H. and W.A.G. participated in preparing the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Meral Gunay-Aygun Author Details * Meral Gunay-Aygun Contact Meral Gunay-Aygun Search for this author in: * NPG journals * PubMed * Google Scholar * Tzipora C Falik-Zaccai Search for this author in: * NPG journals * PubMed * Google Scholar * Thierry Vilboux Search for this author in: * NPG journals * PubMed * Google Scholar * Yifat Zivony-Elboum Search for this author in: * NPG journals * PubMed * Google Scholar * Fatma Gumruk Search for this author in: * NPG journals * PubMed * Google Scholar * Mualla Cetin Search for this author in: * NPG journals * PubMed * Google Scholar * Morad Khayat Search for this author in: * NPG journals * PubMed * Google Scholar * Cornelius F Boerkoel Search for this author in: * NPG journals * PubMed * Google Scholar * Nehama Kfir Search for this author in: * NPG journals * PubMed * Google Scholar * Yan Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Dawn Maynard Search for this author in: * NPG journals * PubMed * Google Scholar * Heidi Dorward Search for this author in: * NPG journals * PubMed * Google Scholar * Katherine Berger Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Kleta Search for this author in: * NPG journals * PubMed * Google Scholar * Yair Anikster Search for this author in: * NPG journals * PubMed * Google Scholar * Mutlu Arat Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew S Freiberg Search for this author in: * NPG journals * PubMed * Google Scholar * Beate E Kehrel Search for this author in: * NPG journals * PubMed * Google Scholar * Kerstin Jurk Search for this author in: * NPG journals * PubMed * Google Scholar * Pedro Cruz Search for this author in: * NPG journals * PubMed * Google Scholar * Jim C Mullikin Search for this author in: * NPG journals * PubMed * Google Scholar * James G White Search for this author in: * NPG journals * PubMed * Google Scholar * Marjan Huizing Search for this author in: * NPG journals * PubMed * Google Scholar * William A Gahl Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–4, Supplementary Table 1 and Supplementary Methods Additional data
• Exome sequencing identifies NBEAL2 as the causative gene for gray platelet syndrome
  - Nat Genet 43(8):735-737 (2011)
  Nature Genetics | Brief Communication Exome sequencing identifies NBEAL2 as the causative gene for gray platelet syndrome * Cornelis A Albers1, 2, 11 * Ana Cvejic1, 2, 11 * Rémi Favier3, 4 * Evelien E Bouwmans2 * Marie-Christine Alessi5 * Paul Bertone6 * Gregory Jordan6 * Ross N W Kettleborough1 * Graham Kiddle2 * Myrto Kostadima6 * Randy J Read7 * Botond Sipos6 * Suthesh Sivapalaratnam8 * Peter A Smethurst2 * Jonathan Stephens2 * Katrin Voss2 * Alan Nurden9 * Augusto Rendon2, 10 * Paquita Nurden9, 11 * Willem H Ouwehand1, 2, 11 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:735–737Year published:(2011)DOI:doi:10.1038/ng.885Received23 February 2011Accepted15 June 2011Published online17 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Gray platelet syndrome (GPS) is a predominantly recessive platelet disorder that is characterized by mild thrombocytopenia with large platelets and a paucity of α-granules; these abnormalities cause mostly moderate but in rare cases severe bleeding. We sequenced the exomes of four unrelated individuals and identified NBEAL2 as the causative gene; it has no previously known function but is a member of a gene family that is involved in granule development. Silencing of nbeal2 in zebrafish abrogated thrombocyte formation. View full text Accession codes * Accession codes * Author information * Supplementary information Referenced accessions ArrayExpress * E-MTAB-633 * E-MTAB-710 * E-MTAB-712 Entrez Nucleotide * NM_015175 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Cornelis A Albers, * Ana Cvejic, * Paquita Nurden & * Willem H Ouwehand Affiliations * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Cornelis A Albers, * Ana Cvejic, * Ross N W Kettleborough & * Willem H Ouwehand * Department of Haematology, University of Cambridge & NHS Blood and Transplant, Cambridge, UK. * Cornelis A Albers, * Ana Cvejic, * Evelien E Bouwmans, * Graham Kiddle, * Peter A Smethurst, * Jonathan Stephens, * Katrin Voss, * Augusto Rendon & * Willem H Ouwehand * Département d'Hématologie, Assistance-Publique Hôpitaux de Paris, Centre de Référence des Pathologies Plaquettaires, Hôpital Armand Trousseau, Paris, France. * Rémi Favier * Institut National de la Santé et de la Recherche Médicale U1009, Villejuif, France. * Rémi Favier * Institut National de la Santé et de la Recherche Médicale U626, Faculté de Médecine, Marseille, France. * Marie-Christine Alessi * European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * Paul Bertone, * Gregory Jordan, * Myrto Kostadima & * Botond Sipos * Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. * Randy J Read * Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands. * Suthesh Sivapalaratnam * Laboratoire d'Hématologie, Centre de Référence des Pathologies Plaquettaires, Hopital Xavier Arnozan, Pessac, France. * Alan Nurden & * Paquita Nurden * Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, UK. * Augusto Rendon Contributions P.N. and W.H.O. conceived and designed the study. C.A.A. analyzed the sequencing data and performed statistical analysis. A.C. performed the zebrafish experiments. E.E.B., R.N.W.K. and J.S. performed the capillary sequencing. M.K. and P.B. analyzed the RNA sequencing and expression data. R.J.R. performed the protein structural modeling. P.A.S. generated the RNA sequencing and expression data. A.R. performed the microarray gene-expression analysis. G.J. and B.S. performed the evolutionary analyses. K.V. performed the megakaryocyte culture experiments. G.K. supervised the sample collection and exome sequencing. S.S. generated the whole-genome expression data for platelets. R.F., M.-C.A., A.N. and P.N. recruited and clinically characterized the cases. P.N. performed the electron microscopy experiments. C.A.A., A.C., P.N. and W.H.O. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Cornelis A Albers or * Willem H Ouwehand Author Details * Cornelis A Albers Contact Cornelis A Albers Search for this author in: * NPG journals * PubMed * Google Scholar * Ana Cvejic Search for this author in: * NPG journals * PubMed * Google Scholar * Rémi Favier Search for this author in: * NPG journals * PubMed * Google Scholar * Evelien E Bouwmans Search for this author in: * NPG journals * PubMed * Google Scholar * Marie-Christine Alessi Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Bertone Search for this author in: * NPG journals * PubMed * Google Scholar * Gregory Jordan Search for this author in: * NPG journals * PubMed * Google Scholar * Ross N W Kettleborough Search for this author in: * NPG journals * PubMed * Google Scholar * Graham Kiddle Search for this author in: * NPG journals * PubMed * Google Scholar * Myrto Kostadima Search for this author in: * NPG journals * PubMed * Google Scholar * Randy J Read Search for this author in: * NPG journals * PubMed * Google Scholar * Botond Sipos Search for this author in: * NPG journals * PubMed * Google Scholar * Suthesh Sivapalaratnam Search for this author in: * NPG journals * PubMed * Google Scholar * Peter A Smethurst Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Stephens Search for this author in: * NPG journals * PubMed * Google Scholar * Katrin Voss Search for this author in: * NPG journals * PubMed * Google Scholar * Alan Nurden Search for this author in: * NPG journals * PubMed * Google Scholar * Augusto Rendon Search for this author in: * NPG journals * PubMed * Google Scholar * Paquita Nurden Search for this author in: * NPG journals * PubMed * Google Scholar * Willem H Ouwehand Contact Willem H Ouwehand Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Methods, Supplementary Note, Supplementary Figures 1–15 and Supplementary Tables 1 and 2 Additional data
• Mutations in NBEAL2, encoding a BEACH protein, cause gray platelet syndrome
  - Nat Genet 43(8):738-740 (2011)
  Nature Genetics | Brief Communication Mutations in NBEAL2, encoding a BEACH protein, cause gray platelet syndrome * Walter HA Kahr1, 2, 3, 14 * Jesse Hinckley4 * Ling Li2 * Hansjörg Schwertz5 * Hilary Christensen2 * Jesse W Rowley6 * Fred G Pluthero2 * Denisa Urban2, 3 * Shay Fabbro4 * Brie Nixon4 * Rick Gadzinski7 * Mike Storck7 * Kai Wang8 * Gi-Yung Ryu9 * Shawn M Jobe10 * Brian C Schutte11 * Jack Moseley12 * Noeleen B Loughran13 * John Parkinson13 * Andrew S Weyrich6 * Jorge Di Paola4, 14 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:738–740Year published:(2011)DOI:doi:10.1038/ng.884Received18 May 2011Accepted15 June 2011Published online17 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Next-generation RNA sequence analysis of platelets from an individual with autosomal recessive gray platelet syndrome (GPS, MIM139090) detected abnormal transcript reads, including intron retention, mapping to NBEAL2 (encoding neurobeachin-like 2). Genomic DNA sequencing confirmed mutations in NBEAL2 as the genetic cause of GPS. NBEAL2 encodes a protein containing a BEACH domain that is predicted to be involved in vesicular trafficking and may be critical for the development of platelet α-granules. View full text Figures at a glance * Figure 1: Abnormal sequence reads in NBEAL2 transcripts observed in platelet RNA from an individual with GPS. Snapshots shown from Integrated Genome Browser (IGB) for NBEAL2 transcripts expressed in platelets isolated from (top) a healthy donor and (bottom) an individual with GPS. Bar heights represent relative numbers of 50–base pair reads spanning NBEAL2; Refseq gene annotation for NBEAL2 is shown at the bottom. The red box outlines a region where introns are abnormally retained in NBEAL2 transcripts from platelets from the individual with GPS. See Supplementary Methods for details. * Figure 2: Mutation analysis of NBEAL2. () Pedigrees for three GPS-affected families with mutation status shown beneath symbols for each individual. () Schematic of NBEAL2, which is composed of 54 exons encoding untranslated regions (yellow) and protein-coding sequences (blue; the BEACH domain is encoded by exons 37–45). Red arrows indicate the locations of mutations in the NBEAL2 genomic sequence found in individuals with GPS (chromatograms are shown in Supplementary Fig. 3). Accession codes * Accession codes * Author information * Supplementary information Referenced accessions GenBank * 23218 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Walter HA Kahr & * Jorge Di Paola Affiliations * Department of Paediatrics, University of Toronto, Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada. * Walter HA Kahr * Program in Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. * Walter HA Kahr, * Ling Li, * Hilary Christensen, * Fred G Pluthero & * Denisa Urban * Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada. * Walter HA Kahr & * Denisa Urban * Department of Pediatrics and Human Medical Genetics Program, University of Colorado–Denver, Aurora, Colorado, USA. * Jesse Hinckley, * Shay Fabbro, * Brie Nixon & * Jorge Di Paola * Department of Surgery, Division of Vascular Surgery and Program in Molecular Medicine, University of Utah, Salt Lake City, Utah, USA. * Hansjörg Schwertz * Department of Internal Medicine and Molecular Medicine Program, Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah, USA. * Jesse W Rowley & * Andrew S Weyrich * Functional Biosciences, Madison, Wisconsin, USA. * Rick Gadzinski & * Mike Storck * Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USA. * Kai Wang * Institute for Clinical and Translational Science at the University of Iowa, Iowa City, Iowa, USA. * Gi-Yung Ryu * Aflac Cancer Center and Blood Disorders Service, Children's Healthcare of Atlanta and Emory University, Atlanta, Georgia, USA. * Shawn M Jobe * Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, USA. * Brian C Schutte * Northern Oklahoma Resource Center, Enid, Oklahoma, USA. * Jack Moseley * Department of Biochemistry & Molecular and Medical Genetics, University of Toronto, Program in Molecular Structure & Function, The Hospital for Sick Children, Toronto, Ontario, Canada. * Noeleen B Loughran & * John Parkinson Contributions Designed experiments: W.H.A.K., A.S.W., J.H. and J.D.P. Performed research, analyzed data: W.H.A.K., A.S.W., J.D.P., J.H., L.L., H.S., H.C., J.W.R., F.G.P., D.U., S.F., B.N., R.G., M.S., K.W., G.-Y.R., S.M.J., B.C.S., J.M., N.B.L. and J.P. Wrote and edited manuscript: W.H.A.K., A.S.W., J.D.P., J.P., N.B.L., J.H., J.W.R. and F.G.P. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Walter HA Kahr or * Jorge Di Paola Author Details * Walter HA Kahr Contact Walter HA Kahr Search for this author in: * NPG journals * PubMed * Google Scholar * Jesse Hinckley Search for this author in: * NPG journals * PubMed * Google Scholar * Ling Li Search for this author in: * NPG journals * PubMed * Google Scholar * Hansjörg Schwertz Search for this author in: * NPG journals * PubMed * Google Scholar * Hilary Christensen Search for this author in: * NPG journals * PubMed * Google Scholar * Jesse W Rowley Search for this author in: * NPG journals * PubMed * Google Scholar * Fred G Pluthero Search for this author in: * NPG journals * PubMed * Google Scholar * Denisa Urban Search for this author in: * NPG journals * PubMed * Google Scholar * Shay Fabbro Search for this author in: * NPG journals * PubMed * Google Scholar * Brie Nixon Search for this author in: * NPG journals * PubMed * Google Scholar * Rick Gadzinski Search for this author in: * NPG journals * PubMed * Google Scholar * Mike Storck Search for this author in: * NPG journals * PubMed * Google Scholar * Kai Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Gi-Yung Ryu Search for this author in: * NPG journals * PubMed * Google Scholar * Shawn M Jobe Search for this author in: * NPG journals * PubMed * Google Scholar * Brian C Schutte Search for this author in: * NPG journals * PubMed * Google Scholar * Jack Moseley Search for this author in: * NPG journals * PubMed * Google Scholar * Noeleen B Loughran Search for this author in: * NPG journals * PubMed * Google Scholar * John Parkinson Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew S Weyrich Search for this author in: * NPG journals * PubMed * Google Scholar * Jorge Di Paola Contact Jorge Di Paola Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Methods, Supplementary Table 1 and Supplementary Figures 1–6 Additional data
• Analyses of X-linked and autosomal genetic variation in population-scale whole genome sequencing
  - Nat Genet 43(8):741-743 (2011)
  Nature Genetics | Brief Communication Analyses of X-linked and autosomal genetic variation in population-scale whole genome sequencing * Srikanth Gottipati1, 3 * Leonardo Arbiza1, 3 * Adam Siepel1 * Andrew G Clark1, 2 * Alon Keinan1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:741–743Year published:(2011)DOI:doi:10.1038/ng.877Received03 March 2011Accepted07 June 2011Published online24 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The ratio of genetic diversity on chromosome X to that on the autosomes is sensitive to both natural selection and demography. On the basis of whole-genome sequences of 69 females, we report that whereas this ratio increases with genetic distance from genes across populations, it is lower in Europeans than in West Africans independent of proximity to genes. This relative reduction is most parsimoniously explained by differences in demographic history without the need to invoke natural selection. View full text Figures at a glance * Figure 1: Autosomal, X-linked and absolute X/A diversity increase with genetic distance from the nearest gene. () Nucleotide diversity normalized by genetic divergence from rhesus macaque for a partition of the genome by distance from the nearest gene (Supplementary Methods). There are different scales of the y axis for the two populations (CEU and YRI), which are proportional to autosomal normalized diversity. () X/A ratios corresponding to the estimates from (horizontal dashed line represents the expectation of three-quarters). In all panels, x-axis labels represent the boundaries between partitions, which were selected such that each partition encompassed an equal fraction of chromosome X (Supplementary Fig. 2). Error bars denote standard error estimated by a block bootstrap approach (Supplementary Methods). We obtained similar results when we used divergence from orangutan for normalization (Supplementary Fig. 3) and observed similar trends when we considered only levels of human nucleotide diversity, without any normalization by divergence (Supplementary Fig. 4). * Figure 2: Relative autosomal, X-linked and X/A diversity are not correlated with genetic distance from the nearest gene. (,) For a partition of the genome as in Figure 1, X-linked and autosomal nucleotide diversity in CEU divided by the corresponding in YRI () and X/A ratio in CEU divided by X/A ratio in YRI (). Estimates <1 in reflect the reduced diversity in non-Africans, most notably due to the out-of-Africa population bottleneck. Estimates <1 in indicate a reduction in X-linked diversity compared to autosomal diversity that is specific to non-Africans (the horizontal dashed line denotes the estimate based on pooled, genome-wide intergenic data). Error bars denote standard error estimated by a block bootstrap approach (Supplementary Methods). These results are independent of normalization by divergence, as normalizing diversity in both populations by the same divergence estimates would have canceled out in the CEU-to-YRI ratio. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Srikanth Gottipati & * Leonardo Arbiza Affiliations * Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA. * Srikanth Gottipati, * Leonardo Arbiza, * Adam Siepel, * Andrew G Clark & * Alon Keinan * Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA. * Andrew G Clark Contributions A.K. conceived of and designed the study. S.G. and L.A. performed the experiments. S.G., L.A. and A.K. analyzed the results and performed statistical analysis. A.S., A.G.C. and A.K. contributed analysis tools. A.K. wrote the paper, with review and contributions by all authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Alon Keinan Author Details * Srikanth Gottipati Search for this author in: * NPG journals * PubMed * Google Scholar * Leonardo Arbiza Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Siepel Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew G Clark Search for this author in: * NPG journals * PubMed * Google Scholar * Alon Keinan Contact Alon Keinan Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Methods, Supplementary Table 1, Suplementary Figures 1–4 and Supplementary Note Additional data
• Extensive genomic and transcriptional diversity identified through massively parallel DNA and RNA sequencing of eighteen Korean individuals
  - Nat Genet 43(8):745-752 (2011)
  Nature Genetics | Article Extensive genomic and transcriptional diversity identified through massively parallel DNA and RNA sequencing of eighteen Korean individuals * Young Seok Ju1, 2, 9 * Jong-Il Kim1, 3, 4, 5, 9 * Sheehyun Kim1, 2, 9 * Dongwan Hong1, 8 * Hansoo Park1, 6 * Jong-Yeon Shin1, 5 * Seungbok Lee1, 4 * Won-Chul Lee1, 4 * Sujung Kim5 * Saet-Byeol Yu5 * Sung-Soo Park5 * Seung-Hyun Seo5 * Ji-Young Yun5 * Hyun-Jin Kim1, 4 * Dong-Sung Lee1, 4 * Maryam Yavartanoo1, 4 * Hyunseok Peter Kang1 * Omer Gokcumen6 * Diddahally R Govindaraju6 * Jung Hee Jung2 * Hyonyong Chong2, 7 * Kap-Seok Yang2 * Hyungtae Kim2 * Charles Lee6 * Jeong-Sun Seo1, 2, 3, 4, 5, 7 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:745–752Year published:(2011)DOI:doi:10.1038/ng.872Received17 December 2010Accepted03 June 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Massively parallel sequencing technologies have identified a broad spectrum of human genome diversity. Here we deep sequenced and correlated 18 genomes and 17 transcriptomes of unrelated Korean individuals. This has allowed us to construct a genome-wide map of common and rare variants and also identify variants formed during DNA-RNA transcription. We identified 9.56 million genomic variants, 23.2% of which appear to be previously unidentified. From transcriptome sequencing, we discovered 4,414 transcripts not previously annotated. Finally, we revealed 1,809 sites of transcriptional base modification, where the transcriptional landscape is different from the corresponding genomic sequences, and 580 sites of allele-specific expression. Our findings suggest that a considerable number of unexplored genomic variants still remain to be identified in the human genome, and that the integrated analysis of genome and transcriptome sequencing is powerful for understanding the diversity! and functional aspects of human genomic variants. View full text Figures at a glance * Figure 1: New and rare SNPs in individual genomes. () The allele frequencies of autosomal SNPs from whole-genome sequence data. The number of singleton and new SNPs in each individual genome is also shown. () The number of new SNPs and non-synonymous SNPs as the number of personal genomes increased through the simulation study. * Figure 2: Linkage disequilibrium between new non-synonymous SNPs and known SNPs. () An example of a new non-synonymous SNP on IL23R genes (with an estimated allele frequency of ~25% in the Korean population) showed low LD (r2 < 0.1) with known SNPs nearby. () We did not identify significant linkage disequilibrium for more than 50% of the new non-synonymous SNPs detected. The criteria for strong LD was r2 ≥ 0.8. * Figure 3: Detection of large deletions. () A combined approach of read-depth, stretched-reads, split-reads analyses from high-throughput sequencing. When available, we used CGH array data for validating large deletions identified by high-throughput sequencing. () Comparison of large deletion breakpoints identified with those previously reported by BreakSeq33 and the 1000 Genomes Project34. () The proportion of the different mechanisms generating the CNVs identified. * Figure 4: Transcriptome analyses. () Schematic overview of our pipeline for transcriptome analysis. When aligning RNA short reads on reference genome sequences directly, the reads spanning different exons (junction-reads; red) cannot be mapped in situ. Instead, these junction-reads can be aligned on wrong places, such as corresponding pseudogenes, where sequences are similar but where no junction exists. When aligning reads on cDNA first, all the reads can be mapped in situ, hence providing more accurate results. () Length distribution of unknown transcripts identified from sequencing of 15 transcriptomes. () X-inactivation profiles and representative genes escaping the inactivation. Five genes escaping X inactivation, which had not been previously identified, are marked in red. * Figure 5: Comparison of genome and transcriptome sequences. () Relative contributions of each pattern of TBMs. () Genes showing allele-specific expression. We compared variant allele frequencies of genome and transcriptome sequence reads at SNP sites tested. Both the allele frequencies are balanced at the majority of the loci (black). Five hundred eighty genomic loci showing allele-specific expression are shown in red. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Young Seok Ju, * Jong-Il Kim & * Sheehyun Kim Affiliations * Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Korea. * Young Seok Ju, * Jong-Il Kim, * Sheehyun Kim, * Dongwan Hong, * Hansoo Park, * Jong-Yeon Shin, * Seungbok Lee, * Won-Chul Lee, * Hyun-Jin Kim, * Dong-Sung Lee, * Maryam Yavartanoo, * Hyunseok Peter Kang & * Jeong-Sun Seo * Macrogen Inc., Seoul, Korea. * Young Seok Ju, * Sheehyun Kim, * Jung Hee Jung, * Hyonyong Chong, * Kap-Seok Yang, * Hyungtae Kim & * Jeong-Sun Seo * Department of Biochemistry, Seoul National University College of Medicine, Seoul, Korea. * Jong-Il Kim & * Jeong-Sun Seo * Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea. * Jong-Il Kim, * Seungbok Lee, * Won-Chul Lee, * Hyun-Jin Kim, * Dong-Sung Lee, * Maryam Yavartanoo & * Jeong-Sun Seo * Psoma Therapeutics Inc., Seoul, Korea. * Jong-Il Kim, * Jong-Yeon Shin, * Sujung Kim, * Saet-Byeol Yu, * Sung-Soo Park, * Seung-Hyun Seo, * Ji-Young Yun & * Jeong-Sun Seo * Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Hansoo Park, * Omer Gokcumen, * Diddahally R Govindaraju & * Charles Lee * Axeq Technologies, Rockville, Maryland, USA. * Hyonyong Chong & * Jeong-Sun Seo * Present address: Division of Convergence Technology, Functional Genomics Branch, National Cancer Center, Goyang-si, Korea. * Dongwan Hong Contributions J.-S.S. and C.L. conceived of the project. J.-S.S. planned and managed the project. Y.S.J., J.-I.K., Sheehyun Kim, D.H., W.-C.L., Sujung Kim and S.-B.Y. analyzed sequencing data. D.H. and S.-S.P. developed the genome browser. J.-Y.S., S.-H.S., J.-Y.Y., H.C., K.-S.Y. and H.K. constructed libraries and executed sequencing. J.H.J. analyzed genotyping microarray experiments. H.P., S.L., H.-J.K., H.P.K. and O.G. assisted in the data analysis. Y.S.J., S.L., D.-S.L. and M.Y. performed validation analyses. J.-S.S., C.L., Y.S.J., J.-I.K., Sheehyun Kim, D.H., O.G. and D.R.G. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jeong-Sun Seo Author Details * Young Seok Ju Search for this author in: * NPG journals * PubMed * Google Scholar * Jong-Il Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Sheehyun Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Dongwan Hong Search for this author in: * NPG journals * PubMed * Google Scholar * Hansoo Park Search for this author in: * NPG journals * PubMed * Google Scholar * Jong-Yeon Shin Search for this author in: * NPG journals * PubMed * Google Scholar * Seungbok Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Won-Chul Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Sujung Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Saet-Byeol Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Sung-Soo Park Search for this author in: * NPG journals * PubMed * Google Scholar * Seung-Hyun Seo Search for this author in: * NPG journals * PubMed * Google Scholar * Ji-Young Yun Search for this author in: * NPG journals * PubMed * Google Scholar * Hyun-Jin Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Dong-Sung Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Maryam Yavartanoo Search for this author in: * NPG journals * PubMed * Google Scholar * Hyunseok Peter Kang Search for this author in: * NPG journals * PubMed * Google Scholar * Omer Gokcumen Search for this author in: * NPG journals * PubMed * Google Scholar * Diddahally R Govindaraju Search for this author in: * NPG journals * PubMed * Google Scholar * Jung Hee Jung Search for this author in: * NPG journals * PubMed * Google Scholar * Hyonyong Chong Search for this author in: * NPG journals * PubMed * Google Scholar * Kap-Seok Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Hyungtae Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Charles Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Jeong-Sun Seo Contact Jeong-Sun Seo Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 3 (41K) Primers for validations * Supplementary Table 6 (8M) Non-synonymous SNP list detected from 18 individuals * Supplementary Table 7 (713K) Funtional assessment of nsSNP of 18 individuals * Supplementary Table 8 (49K) Super nsSNP gene list * Supplementary Table 9 (217K) List of Korean common novel nsSNP LD * Supplementary Table 10 (1M) Total 5,496 large deletion list of 8 individuals * Supplementary Table 12 (86K) Breakpoints list of NA10851 * Supplementary Table 16 (12M) Expression map represented in RPKM value on all RefSeq genes * Supplementary Table 17 (799K) List of Korean common novel transcripts * Supplementary Table 19 (2M) 1,809 TBM sites * Supplementary Table 20 (459K) 580 Allele Specific Expression sites * Supplementary Table 21 (5M) Contig list generated by de novo assembly * Supplementary Table 22 (70K) Alignment result of de novo assemble contigs Text files * Supplementary Table 4 (71M) Indel list of 10 individuals extracted by whole genome sequencing PDF files * Supplementary Text and Figures (2M) Supplementary Note, Supplementary Figures 1–9 and Supplementary Tables 1, 2, 4, 5, 11, 13–15 and 18. Additional data
• Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile
  - Nat Genet 43(8):753-760 (2011)
  Nature Genetics | Article Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile * Tuomas O Kilpeläinen1 * M Carola Zillikens2, 3 * Alena Stančákova4 * Francis M Finucane1 * Janina S Ried5 * Claudia Langenberg1 * Weihua Zhang6 * Jacques S Beckmann7 * Jian'an Luan1 * Liesbeth Vandenput8 * Unnur Styrkarsdottir9 * Yanhua Zhou10 * Albert Vernon Smith11 * Jing-Hua Zhao1 * Najaf Amin12 * Sailaja Vedantam13, 14 * So-Youn Shin15 * Talin Haritunians16 * Mao Fu17 * Mary F Feitosa18 * Meena Kumari19 * Bjarni V Halldorsson9, 20 * Emmi Tikkanen21, 22 * Massimo Mangino23 * Caroline Hayward24 * Ci Song25 * Alice M Arnold26 * Yurii S Aulchenko12 * Ben A Oostra12 * Harry Campbell27 * L Adrienne Cupples10, 28 * Kathryn E Davis29 * Angela Döring5 * Gudny Eiriksdottir11 * Karol Estrada2, 3, 12 * José Manuel Fernández-Real30 * Melissa Garcia31 * Christian Gieger5 * Nicole L Glazer32, 33 * Candace Guiducci13 * Albert Hofman3, 12 * Steve E Humphries34 * Bo Isomaa35, 36 * Leonie C Jacobs2 * Antti Jula37 * David Karasik38 * Magnus K Karlsson39, 40 * Kay-Tee Khaw41 * Lauren J Kim31 * Mika Kivimäki42 * Norman Klopp5 * Brigitte Kühnel5 * Johanna Kuusisto4 * Yongmei Liu43 * Östen Ljunggren44 * Mattias Lorentzon8 * Robert N Luben41 * Barbara McKnight26, 32 * Dan Mellström8 * Braxton D Mitchell17 * Vincent Mooser45 * José Maria Moreno30 * Satu Männistö46 * Jeffery R O'Connell17 * Laura Pascoe47 * Leena Peltonen15, 21, 22, 75 * Belén Peral48 * Markus Perola21, 22 * Bruce M Psaty32, 49, 50, 51, 52 * Veikko Salomaa46 * David B Savage53 * Robert K Semple53 * Tatjana Skaric-Juric54 * Gunnar Sigurdsson55, 56 * Kijoung S Song45 * Timothy D Spector23 * Ann-Christine Syvänen57 * Philippa J Talmud34 * Gudmar Thorleifsson9 * Unnur Thorsteinsdottir9, 56 * André G Uitterlinden2, 3, 12 * Cornelia M van Duijn3, 12, 58 * Antonio Vidal-Puig53 * Sarah H Wild27 * Alan F Wright24 * Deborah J Clegg29 * Eric Schadt59, 60 * James F Wilson27 * Igor Rudan27, 61, 62 * Samuli Ripatti21, 22 * Ingrid B Borecki18 * Alan R Shuldiner17, 63 * Erik Ingelsson25, 64 * John-Olov Jansson65 * Robert C Kaplan66 * Vilmundur Gudnason11, 67 * Tamara B Harris31 * Leif Groop68 * Douglas P Kiel38 * Fernando Rivadeneira2, 3, 12 * Mark Walker47 * Inês Barroso15, 53 * Peter Vollenweider69 * Gérard Waeber69 * John C Chambers6 * Jaspal S Kooner70 * Nicole Soranzo15 * Joel N Hirschhorn13, 14, 71 * Kari Stefansson9, 56 * H-Erich Wichmann5, 72 * Claes Ohlsson8 * Stephen O'Rahilly53 * Nicholas J Wareham1 * Elizabeth K Speliotes13, 73 * Caroline S Fox74 * Markku Laakso4 * Ruth J F Loos1 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:753–760Year published:(2011)DOI:doi:10.1038/ng.866Received18 January 2011Accepted25 May 2011Published online26 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Genome-wide association studies have identified 32 loci influencing body mass index, but this measure does not distinguish lean from fat mass. To identify adiposity loci, we meta-analyzed associations between ~2.5 million SNPs and body fat percentage from 36,626 individuals and followed up the 14 most significant (P < 10−6) independent loci in 39,576 individuals. We confirmed a previously established adiposity locus in FTO (P = 3 × 10−26) and identified two new loci associated with body fat percentage, one near IRS1 (P = 4 × 10−11) and one near SPRY2 (P = 3 × 10−8). Both loci contain genes with potential links to adipocyte physiology. Notably, the body-fat–decreasing allele near IRS1 is associated with decreased IRS1 expression and with an impaired metabolic profile, including an increased visceral to subcutaneous fat ratio, insulin resistance, dyslipidemia, risk of diabetes and coronary artery disease and decreased adiponectin levels. Our findings provide new i! nsights into adiposity and insulin resistance. View full text Figures at a glance * Figure 1: Manhattan plot showing the significance of association with body fat percentage for SNPs in the stage 1 meta-analysis of all individuals (n = 36,626). SNPs are plotted on the x axis according to their position on each chromosome against association with body fat percentage on the y axis (shown as −log10P). The loci highlighted in blue are the 11 loci that reached an association P < 10−6 in the stage 1 meta-analysis of all individuals, Europeans, men or women and were taken forward for follow-up analyses but did not achieve genome-wide significance (P < 5 × 10−8) in the meta-analyses combining GWAS and follow-up data. The three loci colored in red are those that reached genome-wide significant association (P < 5 × 10−8) in the meta-analyses combining GWAS and follow-up data. * Figure 2: Regional plot of the loci near IRS1, near SPRY2 and in FTO that reached genome-wide significant evidence for association with body fat percentage. The plotted data for the locus near SPRY2 are from the meta-analysis of individuals of European descent only, and the data for the loci near IRS1 and in FTO are from the meta-analysis of all individuals. The rs2943650 (near IRS1), rs534870 (near SPRY2) and rs8050136 (FTO) SNPs that showed the strongest association with body fat percentage are indicated. For the locus near IRS1, rs2972146, rs2943641 and rs2943634, which have been associated with blood levels of HDL cholesterol and triglycerides9, risk of type 2 diabetes10 and risk of coronary artery disease11, respectively, in GWAS meta-analyses, are also indicated. The plot was generated using LocusZoom44 (see URLs). * Figure 3: Association of the body-fat-percentage–decreasing (T) allele of rs2943650 near IRS1 with blood lipids, insulin sensitivity traits, leptin and adiponectin. The error bars indicate 95% confidence intervals. All traits were inverse normally transformed to approximate normality (mean = 0, s.d. = 1) in men and women separately. All models were adjusted for age and age squared. The numeric values for the associations are presented in Supplementary Table 6. We found a significant difference between men and women for the levels of HDL cholesterol (P = 0.027), triglycerides (P = 0.025) and adiponectin (P = 0.040). InsAUC/GluAUC, insulin area under the curve (AUC) to glucose AUC ratio. Author information * Abstract * Author information * Supplementary information Affiliations * Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK. * Tuomas O Kilpeläinen, * Francis M Finucane, * Claudia Langenberg, * Jian'an Luan, * Jing-Hua Zhao, * Nicholas J Wareham & * Ruth J F Loos * Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. * M Carola Zillikens, * Karol Estrada, * Leonie C Jacobs, * André G Uitterlinden & * Fernando Rivadeneira * Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden, The Netherlands. * M Carola Zillikens, * Karol Estrada, * Albert Hofman, * André G Uitterlinden, * Cornelia M van Duijn & * Fernando Rivadeneira * Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland. * Alena Stančákova, * Johanna Kuusisto & * Markku Laakso * Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. * Janina S Ried, * Angela Döring, * Christian Gieger, * Norman Klopp, * Brigitte Kühnel & * H-Erich Wichmann * Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK. * Weihua Zhang & * John C Chambers * Department of Medical Genetics, Lausanne University Hospital, Lausanne, Switzerland. * Jacques S Beckmann * Centre for Bone and Arthritis Research, Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. * Liesbeth Vandenput, * Mattias Lorentzon, * Dan Mellström & * Claes Ohlsson * deCODE Genetics, Reykjavik, Iceland. * Unnur Styrkarsdottir, * Bjarni V Halldorsson, * Gudmar Thorleifsson, * Unnur Thorsteinsdottir & * Kari Stefansson * Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA. * Yanhua Zhou & * L Adrienne Cupples * Icelandic Heart Association, Heart Preventive Clinic and Research Institute, Kopavogur, Iceland. * Albert Vernon Smith, * Gudny Eiriksdottir & * Vilmundur Gudnason * Genetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands. * Najaf Amin, * Yurii S Aulchenko, * Ben A Oostra, * Karol Estrada, * Albert Hofman, * André G Uitterlinden, * Cornelia M van Duijn & * Fernando Rivadeneira * Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. * Sailaja Vedantam, * Candace Guiducci, * Joel N Hirschhorn & * Elizabeth K Speliotes * Divisions of Genetics and Endocrinology and Program in Genomics, Children's Hospital, Boston, Massachusetts, USA. * Sailaja Vedantam & * Joel N Hirschhorn * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * So-Youn Shin, * Leena Peltonen, * Inês Barroso & * Nicole Soranzo * Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. * Talin Haritunians * Division of Endocrinology, Diabetes & Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Mao Fu, * Braxton D Mitchell, * Jeffery R O'Connell & * Alan R Shuldiner * Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, USA. * Mary F Feitosa & * Ingrid B Borecki * Genetic Epidemiology Group, Department of Epidemiology, University College London, London, UK. * Meena Kumari * Reykjavik University, Reykjavik, Iceland. * Bjarni V Halldorsson * Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland. * Emmi Tikkanen, * Leena Peltonen, * Markus Perola & * Samuli Ripatti * Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland. * Emmi Tikkanen, * Leena Peltonen, * Markus Perola & * Samuli Ripatti * King's College London, London, UK. * Massimo Mangino & * Timothy D Spector * MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK. * Caroline Hayward & * Alan F Wright * Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. * Ci Song & * Erik Ingelsson * Department of Biostatistics, University of Washington, Seattle, Washington, USA. * Alice M Arnold & * Barbara McKnight * Centre for Population Health Sciences, The University of Edinburgh Medical School, Edinburgh, UK. * Harry Campbell, * Sarah H Wild, * James F Wilson & * Igor Rudan * Framingham Heart Study, Framingham, Massachusetts, USA. * L Adrienne Cupples * Touchstone Diabetes Center, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * Kathryn E Davis & * Deborah J Clegg * Department of Diabetes, Endocrinology and Nutrition, Institut d'Investigació Biomédica de Girona, Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN) (CB06/03/0010), Girona, Spain. * José Manuel Fernández-Real & * José Maria Moreno * Intramural Research Program, National Institute on Aging, US National Institutes of Health, Bethesda, Maryland, USA. * Melissa Garcia, * Lauren J Kim & * Tamara B Harris * Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA. * Nicole L Glazer, * Barbara McKnight & * Bruce M Psaty * Department of Medicine, University of Washington, Seattle, Washington, USA. * Nicole L Glazer * Centre for Cardiovascular Genetics, Department of Medicine, University College London, London, UK. * Steve E Humphries & * Philippa J Talmud * Folkhälsan Research Centre, Helsinki, Finland. * Bo Isomaa * Department of Social Services and Health Care, Jakobstad, Finland. * Bo Isomaa * Population Studies Unit, National Institute for Health and Welfare, Helsinki, Finland. * Antti Jula * Institute for Aging Research, Hebrew SeniorLife and Harvard Medical School, Boston, Massachusetts, USA. * David Karasik & * Douglas P Kiel * Department of Clinical Sciences, Lund University, Malmö, Sweden. * Magnus K Karlsson * Department of Orthopaedics, Malmö University Hospital, Malmö, Sweden. * Magnus K Karlsson * Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK. * Kay-Tee Khaw & * Robert N Luben * Department of Epidemiology and Public Health, University College London, London, UK. * Mika Kivimäki * Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA. * Yongmei Liu * Department of Medical Sciences, Uppsala University, Uppsala, Sweden. * Östen Ljunggren * Genetic, Research & Development, GlaxoSmithKline, King of Prussia, Philadelphia, USA. * Vincent Mooser & * Kijoung S Song * Chronic Disease Epidemiology and Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland. * Satu Männistö & * Veikko Salomaa * Institute of Cell & Molecular Biosciences, Newcastle University, Newcastle, UK. * Laura Pascoe & * Mark Walker * Instituto de Investigaciones Biomédicas, Alberto Sols, Consejo Superior de Investigaciones Científicas (CSIC) & Universidad Autónoma de Madrid, Madrid, Spain. * Belén Peral * Department of Medicine, University of Washington, Seattle, Washington, USA. * Bruce M Psaty * Department of Epidemiology, University of Washington, Seattle, Washington, USA. * Bruce M Psaty * Department of Health Services, University of Washington, Seattle, Washington, USA. * Bruce M Psaty * Group Health Research Institute, Group Health Cooperative, Seattle, Washington, USA. * Bruce M Psaty * University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK. * David B Savage, * Robert K Semple, * Antonio Vidal-Puig, * Inês Barroso & * Stephen O'Rahilly * Institute for Anthropological Research, Zagreb, Croatia. * Tatjana Skaric-Juric * Department of Endocrinology and Metabolism, University Hospital, Reykjavik, Iceland. * Gunnar Sigurdsson * Faculty of Medicine, University of Iceland, Reykjavik, Iceland. * Gunnar Sigurdsson, * Unnur Thorsteinsdottir & * Kari Stefansson * Department of Medical Sciences, Molecular Medicine, Science for Life Laboratory, Uppsala University, Uppsala, Sweden. * Ann-Christine Syvänen * National Genetics Institute (NGI), Centre for Medical Systems Biology (CMSB), Leiden, The Netherlands. * Cornelia M van Duijn * Pacific Biosciences, Menlo Park, California, USA. * Eric Schadt * Sage Bionetworks, Seattle, Washington, USA. * Eric Schadt * Croatian Centre for Global Health, University of Split Medical School, Split, Croatia. * Igor Rudan * Gen Info Ltd, Zagreb, Croatia. * Igor Rudan * Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland, USA. * Alan R Shuldiner * Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden. * Erik Ingelsson * Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. * John-Olov Jansson * Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York, USA. * Robert C Kaplan * University of Iceland, Reykjavik, Iceland. * Vilmundur Gudnason * Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden. * Leif Groop * Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland. * Peter Vollenweider & * Gérard Waeber * National Heart and Lung Institute, Imperial College London, Hammersmith Hospital, London, UK. * Jaspal S Kooner * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Joel N Hirschhorn * Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität and Klinikum Großhadern, Munich, Germany. * H-Erich Wichmann * Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA. * Elizabeth K Speliotes * National Heart, Lung, and Blood Institute and Harvard Medical School, Boston, Massachusetts, USA. * Caroline S Fox * Deceased. * Leena Peltonen Contributions A full list of author contributions appears in the Supplementary Note. Competing financial interests I.B. and spouse own stock in Incyte Ltd and GlaxoSmithKline. K.S., G.T., U.T. and U.S. are employed by deCODE Genetics. V.M. is a full-time employee of GlaxoSmithKline. G.W. and P.V. received funding from GlaxoSmithKline to build the CoLaus Study. 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Additional data
• Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility
  - Nat Genet 43(8):761-767 (2011)
  Nature Genetics | Article Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility * The Australo-Anglo-American Spondyloarthritis Consortium (TASC)45 * the Wellcome Trust Case Control Consortium 2 (WTCCC2)45 * David M Evans1, 43 * Chris C A Spencer2, 43 * Jennifer J Pointon3 * Zhan Su2 * David Harvey3 * Grazyna Kochan4 * Udo Opperman4 * Alexander Dilthey1 * Matti Pirinen1 * Millicent A Stone5 * Louise Appleton3 * Loukas Moutsianis6 * Stephen Leslie7 * Tom Wordsworth3 * Tony J Kenna8 * Tugce Karaderi3 * Gethin P Thomas8 * Michael M Ward9 * Michael H Weisman10 * Claire Farrar3 * Linda A Bradbury8 * Patrick Danoy8 * Robert D Inman11 * Walter Maksymowych12 * Dafna Gladman11 * Proton Rahman13 * Spondyloarthritis Research Consortium of Canada (SPARCC) * Ann Morgan14 * Helena Marzo-Ortega14 * Paul Bowness3 * Karl Gaffney15 * J S Hill Gaston16 * Malcolm Smith17 * Jacome Bruges-Armas18, 19 * Ana-Rita Couto18 * Rosa Sorrentino20 * Fabiana Paladini20 * Manuel A Ferreira21 * Huji Xu22 * Yu Liu22 * Lei Jiang22 * Carlos Lopez-Larrea23 * Roberto Díaz-Peña23 * Antonio López-Vázquez23 * Tetyana Zayats1 * Gavin Band2 * Céline Bellenguez2 * Hannah Blackburn23 * Jenefer M Blackwell25, 26 * Elvira Bramon24 * Suzannah J Bumpstead24 * Juan P Casas27 * Aiden Corvin28 * Nicholas Craddock29 * Panos Deloukas24 * Serge Dronov24 * Audrey Duncanson30 * Sarah Edkins24 * Colin Freeman2 * Matthew Gillman24 * Emma Gray24 * Rhian Gwilliam24 * Naomi Hammond24 * Sarah E Hunt24 * Janusz Jankowski31 * Alagurevathi Jayakumar24 * Cordelia Langford24 * Jennifer Liddle24 * Hugh S Markus32 * Christopher G Mathew33 * Owen T McCann24 * Mark I McCarthy34 * Colin N A Palmer35 * Leena Peltonen24 * Robert Plomin36 * Simon C Potter24 * Anna Rautanen24 * Radhi Ravindrarajah24 * Michelle Ricketts24 * Nilesh Samani37 * Stephen J Sawcer38 * Amy Strange2 * Richard C Trembath33 * Ananth C Viswanathan39, 40 * Matthew Waller24 * Paul Weston24 * Pamela Whittaker24 * Sara Widaa24 * Nicholas W Wood41 * Gilean McVean2 * John D Reveille42 * B Paul Wordsworth3 * Matthew A Brown8, 44 * Peter Donnelly2, 44 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:761–767Year published:(2011)DOI:doi:10.1038/ng.873Received21 December 2011Accepted03 June 2011Published online10 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Ankylosing spondylitis is a common form of inflammatory arthritis predominantly affecting the spine and pelvis that occurs in approximately 5 out of 1,000 adults of European descent. Here we report the identification of three variants in the RUNX3, LTBR-TNFRSF1A and IL12B regions convincingly associated with ankylosing spondylitis (P < 5 × 10−8 in the combined discovery and replication datasets) and a further four loci at PTGER4, TBKBP1, ANTXR2 and CARD9 that show strong association across all our datasets (P < 5 × 10−6 overall, with support in each of the three datasets studied). We also show that polymorphisms of ERAP1, which encodes an endoplasmic reticulum aminopeptidase involved in peptide trimming before HLA class I presentation, only affect ankylosing spondylitis risk in HLA-B27–positive individuals. These findings provide strong evidence that HLA-B27 operates in ankylosing spondylitis through a mechanism involving aberrant processing of antigenic peptides. View full text Figures at a glance * Figure 1: Association findings for the ERAP1 SNP rs30187 stratified by the HLA-B27 tag SNP rs4349859. rs4349859 allele A tags HLA-B27. Error bars, 95% confidence intervals. Note that the ERAP1 risk allele T only increases risk in individuals carrying at least one copy of the HLA-B27 risk allele tag. The odds ratios for the genotype combinations were calculated using logistic regression in the R software package including covariates for ancestry (where appropriate). Genotypes with the low risk CC/GG genotype were set as the baseline, and the other genotype combinations were coded according to a series of dichotomous indicator variables. Odds ratios were derived by exponentiating the relevant coefficient from the logistic regression. * Figure 2: The mean rate of trimming of N-terminal tryptophan (mol, substrate/mol, enzyme/sec) from 10-mer peptide WRVYEKCALK by wild-type ERAP1 and variants associated with ankylosing spondylitis (rs30187 (p.Lys528Arg), rs17482078 (p.Arg725Gln) and rs10050860 (p.Asp575Asn)). WT, wild type. Circles represent results for wild-type genotype samples, squares represent results for rs30187, upright triangles represent results for rs17482078 and inverted triangles refer to results for rs10050860. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * David M Evans & * Chris C A Spencer Affiliations * Medical Research Council (MRC) Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, Bristol, UK. * David M Evans, * Alexander Dilthey, * Matti Pirinen & * Tetyana Zayats * Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. * Chris C A Spencer, * Zhan Su, * Gavin Band, * Céline Bellenguez, * Colin Freeman, * Amy Strange, * Gilean McVean & * Peter Donnelly * National Institute for Health Research Musculoskeletal Biomedical Research Unit, Nuffield Orthopaedic Centre, Headington, Oxford, UK. * Jennifer J Pointon, * David Harvey, * Louise Appleton, * Tom Wordsworth, * Tugce Karaderi, * Claire Farrar, * Paul Bowness & * B Paul Wordsworth * Structural Genomics Consortium, University of Oxford, Oxford, UK. * Grazyna Kochan & * Udo Opperman * University of Bath, Bath, UK. * Millicent A Stone * Department of Statistics, University of Oxford, Oxford, UK. * Loukas Moutsianis * Department of Clinical Pharmacology, University of Oxford, Oxford, UK. * Stephen Leslie * University of Queensland Diamantina Institute, Princess Alexandra Hospital, Brisbane, Australia. * Tony J Kenna, * Gethin P Thomas, * Linda A Bradbury, * Patrick Danoy & * Matthew A Brown * National Institute of Arthritis and Musculoskeletal and Skin Diseases, US National Institutes of Health (NIH), Bethesda, Maryland, USA. * Michael M Ward * Department of Medicine/Rheumatology, Cedars-Sinai Medical Centre, Los Angeles, California, USA. * Michael H Weisman * University of Toronto, Toronto, Ontario, Canada. * Robert D Inman & * Dafna Gladman * Department of Medicine, University of Alberta, Edmonton, Alberta, Canada. * Walter Maksymowych * Memorial University, St. John's, Newfoundland, Canada. * Proton Rahman * National Institute for Health Research (NIHR)–Leeds Musculoskeletal Biomedical Research Unit, University of Leeds, Leeds, UK. * Ann Morgan & * Helena Marzo-Ortega * Department of Rheumatology, Norfolk & Norwich University Hospital, Norfolk, UK. * Karl Gaffney * Department of Medicine, University of Cambridge, Addenbrookes Hospital, Cambridge, UK. * J S Hill Gaston * Repatriation General Hospital, Adelaide, Australia. * Malcolm Smith * Serviço Especializado De Epidemiologia E Biologia Molecular, Hospital de Santo Espírito, Angra do Heroísmo, Terceira, The Azores, Portugal. * Jacome Bruges-Armas & * Ana-Rita Couto * Genetics and Arthritis Research Group, Institute for Molecular and Cell Biology (IBMC), University of Porto, Porto, Portugal. * Jacome Bruges-Armas * Department of Biology and Biotechnology 'Charles Darwin', Sapienza University of Rome, Rome, Italy. * Rosa Sorrentino & * Fabiana Paladini * Queensland Institute of Medical Research, Brisbane, Australia. * Manuel A Ferreira * Department of Rheumatology and Immunology, Shanghai Changzheng Hospital, The Second Military Medical University Hospital, Shanghai, China. * Huji Xu, * Yu Liu & * Lei Jiang * Department of Immunology, Hospital Universitario Central de Asturias, Oviedo, Spain. * Carlos Lopez-Larrea, * Roberto Díaz-Peña, * Antonio López-Vázquez & * Hannah Blackburn * Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. * Elvira Bramon, * Suzannah J Bumpstead, * Panos Deloukas, * Serge Dronov, * Sarah Edkins, * Matthew Gillman, * Emma Gray, * Rhian Gwilliam, * Naomi Hammond, * Sarah E Hunt, * Alagurevathi Jayakumar, * Cordelia Langford, * Jennifer Liddle, * Owen T McCann, * Leena Peltonen, * Simon C Potter, * Anna Rautanen, * Radhi Ravindrarajah, * Michelle Ricketts, * Matthew Waller, * Paul Weston, * Pamela Whittaker & * Sara Widaa * Telethon Institute for Child Health Research, Centre for Child Health Research, University of Western Australia, Perth, Australia. * Jenefer M Blackwell * Genetics and Infection Laboratory, Cambridge Institute of Medical Research, Addenbrooke's Hospital, Cambridge, UK. * Jenefer M Blackwell * Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK. * Juan P Casas * Neuropsychiatric Genetics Research Group, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland. * Aiden Corvin * Department of Psychological Medicine, School of Medicine, Cardiff University, Cardiff, Wales. * Nicholas Craddock * Molecular and Physiological Sciences, The Wellcome Trust, London, UK. * Audrey Duncanson * Centre for Gastroenterology, Bart's and the London School of Medicine and Dentistry, London, UK. * Janusz Jankowski * Clinical Neurosciences, St. George's University of London, London, UK. * Hugh S Markus * Division of Genetics and Molecular Medicine, King's College London, London, UK. * Christopher G Mathew & * Richard C Trembath * Oxford Centre for Diabetes, Endocrinology and Metabolism, Churchill Hospital, Oxford, UK. * Mark I McCarthy * Biomedical Research Centre, Ninewells Hospital and Medical School, Dundee, UK. * Colin N A Palmer * Social, Genetic and Developmental Psychiatry Centre, King's College London Institute of Psychiatry, Denmark Hill, London, UK. * Robert Plomin * Department of Cardiovascular Science, University of Leicester, Glenfield General Hospital, Leicester, UK. * Nilesh Samani * University of Cambridge, Department of Clinical Neurosciences, Addenbrooke's Hospital, Cambridge, UK. * Stephen J Sawcer * Glaucoma Research Unit, Moorfields Eye Hospital NHS Foundation Trust, London, UK. * Ananth C Viswanathan * Department of Genetics, University College London Institute of Ophthalmology, London, UK. * Ananth C Viswanathan * Department of Molecular Neuroscience, Institute of Neurology, Queen Square, London, UK. * Nicholas W Wood * Rheumatology and Clinical Immunogenetics, University of Texas Health Science Center at Houston, Houston, Texas, USA. * John D Reveille * These authors jointly directed this work. * Matthew A Brown & * Peter Donnelly * A full list of members is provided in the Supplementary Note. * Peter Donnelly Consortia * The Australo-Anglo-American Spondyloarthritis Consortium (TASC) * the Wellcome Trust Case Control Consortium 2 (WTCCC2) * Spondyloarthritis Research Consortium of Canada (SPARCC) Contributions M.A.B., L.A.B., C. Farrar, J.D.R., J.J.P., B.P.W., D.G., W.M. and P.R. oversaw cohort collection for the discovery and replication datasets. The WTCCC2 DNA, genotyping, data quality control and informatics group (S.J.B., S.D., S.E., E.G., C.L. and L.P.) executed GWAS sample handling, genotyping and quality control. The WTCCC2 data and analysis group (A.S., C.C.A.S., G.B., C.B., C. Freeman and P. Donnelly), D.M.E. and M.A.B. performed statistical analyses. M.A.B., D.M.E., C.C.A.S. and P. Donnelly contributed to writing the manuscript. The WTCCC2 management committee (J.M.B., E.B., M.A.B., J.P.C., A.C., P. Deloukas, P. Donnelly (chairperson), A. Duncanson, J.J., J.L., H.S.M., C.G.M., C.N.A.P., L.P., R.P., A.R., S.J.S., R.C.T., A.C.V. and N.W.W.) monitored the execution of the GWAS. D.H., G.K. and U.O. performed analyses of recombinant ERAP1 function. T.J.K. and G.P.T. performed gene expression, cell count and ERAP1 sheddase functional studies. Other authors contributed various! ly to sample collection and all other aspects of the study. All authors reviewed the final manuscript. David M Evans1,43, Chris C A Spencer2,43, Jennifer J Pointon3, Zhan Su2, David Harvey3, Grazyna Kochan4, Udo Opperman4, Alexander Dilthey1, Matti Pirinen1, Millicent A Stone5, Louise Appleton3, Loukas Moutsianis6, Stephen Leslie7, Tom Wordsworth3, Tony J Kenna8, Tugce Karaderi3, Gethin P Thomas8, Michael M Ward9, Michael H Weisman10, Claire Farrar3, Linda A Bradbury8, Patrick Danoy8, Robert D Inman11, Walter Maksymowych12, Dafna Gladman11, Proton Rahman13, Spondyloarthritis Research Consortium of Canada (SPARCC), Ann Morgan14, Helena Marzo-Ortega14, Paul Bowness3, Karl Gaffney15, J S Hill Gaston16, Malcolm Smith17, Jacome Bruges-Armas18,19, Ana-Rita Couto18, Rosa Sorrentino20, Fabiana Paladini20, Manuel A Ferreira21, Huji Xu22, Yu Liu22, Lei Jiang22, Carlos Lopez-Larrea23, Roberto Díaz-Peña23, Antonio López-Vázquez23, Tetyana Zayats1, Gavin Band2, Céline Bellenguez2, Hannah Blackburn23, Jenefer M Blackwell25,26, Elvira Bramon24, Suzannah J Bumpstead24, Juan P Casas27, A! iden Corvin28, Nicholas Craddock29, Panos Deloukas24, Serge Dronov24, Audrey Duncanson30, Sarah Edkins24, Colin Freeman2, Matthew Gillman24, Emma Gray24, Rhian Gwilliam24, Naomi Hammond24, Sarah E Hunt24, Janusz Jankowski31, Alagurevathi Jayakumar24, Cordelia Langford24, Jennifer Liddle24, Hugh S Markus32, Christopher G Mathew33, Owen T McCann24, Mark I McCarthy34, Colin N A Palmer35, Leena Peltonen24, Robert Plomin36, Simon C Potter24, Anna Rautanen24, Radhi Ravindrarajah24, Michelle Ricketts24, Nilesh Samani37, Stephen J Sawcer38, Amy Strange2, Richard C Trembath33, Ananth C Viswanathan39,40, Matthew Waller24, Paul Weston24, Pamela Whittaker24, Sara Widaa24, Nicholas W Wood41, Gilean McVean2, John D Reveille42, B Paul Wordsworth3, Matthew A Brown8,44 & Peter Donnelly2,44 Competing financial interests The University of Queensland has applied for patents relating to material presented in this manuscript in Australia, Europe and the United States. Corresponding authors Correspondence to: * Peter Donnelly or * Matthew A Brown Author Details * The Australo-Anglo-American Spondyloarthritis Consortium (TASC) * the Wellcome Trust Case Control Consortium 2 (WTCCC2) * David M Evans Search for this author in: * NPG journals * PubMed * Google Scholar * Chris C A Spencer Search for this author in: * NPG journals * PubMed * Google Scholar * Jennifer J Pointon Search for this author in: * NPG journals * PubMed * Google Scholar * Zhan Su Search for this author in: * NPG journals * PubMed * Google Scholar * David Harvey Search for this author in: * NPG journals * PubMed * Google Scholar * Grazyna Kochan Search for this author in: * NPG journals * PubMed * Google Scholar * Udo Opperman Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Dilthey Search for this author in: * NPG journals * PubMed * Google Scholar * Matti Pirinen Search for this author in: * NPG journals * PubMed * Google Scholar * Millicent A Stone 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• Increased methylation variation in epigenetic domains across cancer types
  - Nat Genet 43(8):768-775 (2011)
  Nature Genetics | Article Increased methylation variation in epigenetic domains across cancer types * Kasper Daniel Hansen1, 2, 10 * Winston Timp2, 3, 4, 10 * Héctor Corrada Bravo2, 5, 10 * Sarven Sabunciyan2, 6, 10 * Benjamin Langmead1, 2, 10 * Oliver G McDonald2, 7 * Bo Wen2, 3 * Hao Wu8 * Yun Liu2, 3 * Dinh Diep9 * Eirikur Briem2, 3 * Kun Zhang9 * Rafael A Irizarry1, 2 * Andrew P Feinberg2, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume: 43,Pages:768–775Year published:(2011)DOI:doi:10.1038/ng.865Received10 January 2011Accepted25 May 2011Published online26 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Tumor heterogeneity is a major barrier to effective cancer diagnosis and treatment. We recently identified cancer-specific differentially DNA-methylated regions (cDMRs) in colon cancer, which also distinguish normal tissue types from each other, suggesting that these cDMRs might be generalized across cancer types. Here we show stochastic methylation variation of the same cDMRs, distinguishing cancer from normal tissue, in colon, lung, breast, thyroid and Wilms' tumors, with intermediate variation in adenomas. Whole-genome bisulfite sequencing shows these variable cDMRs are related to loss of sharply delimited methylation boundaries at CpG islands. Furthermore, we find hypomethylation of discrete blocks encompassing half the genome, with extreme gene expression variability. Genes associated with the cDMRs and large blocks are involved in mitosis and matrix remodeling, respectively. We suggest a model for cancer involving loss of epigenetic stability of well-defined genomic do! mains that underlies increased methylation variability in cancer that may contribute to tumor heterogeneity. View full text Figures at a glance * Figure 1: Increased methylation variance of common CpG sites across human cancer types. (–) Methylation levels measured at 384 CpG sites using a custom Illumina array show an increase in across-sample variability in colon (), lung (), breast (), thyroid () and kidney () (Wilms' tumor) cancers. Each panel shows the across-sample standard deviation of the methylation level for each CpG in normal and matched cancer samples. The solid line is the identity line; CpGs above this line have greater variability in cancer. The dashed line indicates the threshold at which differences in methylation variance become significant (F test at a 99% level). In all five tissue types, the vast majority of CpGs are above the solid line, indicating that variability is larger in cancer samples than in normal samples. Colors indicate the location of each CpG with respect to canonical annotated CpG islands. () Using the CpGs that showed the largest increase in variability, we performed hierarchical clustering on the normal samples. The heatmap of the methylation values for these CpGs! clearly distinguishes the tissue types, indicating that these sites of increased methylation heterogeneity in cancer are tissue-specific DMRs. * Figure 2: Large hypomethylated genomic blocks in human colon cancer. (,) Shown in and are smoothed methylation values from bisulfite sequencing data for cancer samples (red) and normal samples (blue) in two genomic regions. The hypomethylated blocks are shown with pink shading. Gray bars indicate the location of PMDs, LOCKs, LADs, CpG islands and gene exons. Note that the blocks coincide with the PMDs, LOCKS and LADs in but not in . Also one can see small hypermethylated blocks at the right edge, which account for 3% of the blocks. () The distribution of high-frequency smoothed methylation values for the normal samples (blue) versus the cancer samples (red) shows global hypomethylation of cancer compared to normal. () The distribution of methylation values in the blocks (solid lines) and outside the blocks (dashed lines) for normal samples (blue) and cancer samples (red). Note that although the normal and cancer distributions are similar outside the blocks, within the blocks, methylation values for cancer show a general shift. () Distribution! of methylation differences between cancer and normal samples stratified by inclusion in repetitive DNA and blocks. Inside the blocks, the average difference was ~20% in both in repeat and non-repeat areas. Outside the blocks, the average difference was ~0% in repeat and non-repeat areas, indicating that blocks rather than repeats account for the observed differences in DNA methylation. The boxes show the 25% quantile, the median and the 75% quantile, and each whisker has a length of 1.5 times the interquartile range. * Figure 3: Loss of methylation stability at small DMRs. Methylation estimates plotted against genomic location for normal samples (blue) and cancer samples (red). The small DMR locations are shaded pink. Gray bars indicate the location of CpG islands and gene exons. Tick marks along the bottom axis indicate the location of CpGs. (–) Pictured are examples of a methylation boundary shift outward (), a methylation boundary shift inward (), a loss of methylation boundary () and a novel hypomethylation DMR (). * Figure 4: Adenomas show intermediate methylation variability. () Multidimensional scaling of pairwise distances derived from methylation levels assayed on a custom Illumina array. Note that cancer samples (red) are largely far from the tight cluster of normal samples (blue), whereas adenoma samples (black) have a range of distances: some are as close as other normal samples, others are as far as cancer samples and many are at intermediate distances. () Multidimensional scaling of pairwise distances derived from average methylation values in blocks identified with bisulfite sequencing. Matching sequenced adenoma samples (labeled 1 and 2) appear in the same locations relative to the cluster of normal samples in both and . () Methylation values for normal (blue), cancer (red) and two adenoma samples (black). Adenoma 1, which appeared closer to normal samples in the multidimensional scaling analysis (), follows a similar methylation pattern to the normal samples. However, in some regions (shaded with pink), we observed differences between ! adenoma 1 and the normal samples. Adenoma 2 shows a similar pattern to cancers. * Figure 5: High variability of gene expression associated with blocks. () An example of hypervariably expressed genes contained within a block; note that MMP7, MMP10 and MMP3 are highlighted in red. Methylation values for cancer samples (red) and normal samples (blue) with hypomethylated block locations highlighted (pink shading) are plotted against genomic location. Gray bars are as in Figure 2. () Standardized log expression values for 26 hypervariable genes in cancer located within hypomethylated block regions (normal samples in blue and cancer samples in red). Standardization was performed using the gene expression barcode. Genes with standardized expression values below 2.54 or the 99.5th percentile of a normal distribution (horizontal dashed line) were determined to be silenced by the barcode method26. Vertical dashed lines separate the values for the different genes. Note there is consistent expression silencing in normal samples compared to hypervariable expression in cancer samples. A similar plot drawn from an alternative GEO dataset ! is shown in Supplementary Figure 18. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Kasper Daniel Hansen, * Winston Timp, * Héctor Corrada Bravo, * Sarven Sabunciyan & * Benjamin Langmead Affiliations * Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. * Kasper Daniel Hansen, * Benjamin Langmead & * Rafael A Irizarry * Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Kasper Daniel Hansen, * Winston Timp, * Héctor Corrada Bravo, * Sarven Sabunciyan, * Benjamin Langmead, * Oliver G McDonald, * Bo Wen, * Yun Liu, * Eirikur Briem, * Rafael A Irizarry & * Andrew P Feinberg * Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Winston Timp, * Bo Wen, * Yun Liu, * Eirikur Briem & * Andrew P Feinberg * Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA. * Winston Timp * Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, Maryland, USA. * Héctor Corrada Bravo * Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Sarven Sabunciyan * Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Oliver G McDonald * Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA. * Hao Wu * Department of Bioengineering, Institute for Genomic Medicine and Institute of Engineering in Medicine, University of California at San Diego, San Diego, California, USA. * Dinh Diep & * Kun Zhang Contributions K.D.H. and R.A.I. wrote the DMR finder and smoothing algorithms. W.T. performed and analyzed the arrays with H.C.B., who wrote new software for this purpose. S.S. made the libraries and performed validation. B.L. wrote new methylation sequence alignment software. O.G.M. performed the histopathologic analysis. B.W. and H.W. performed LOCK experiments. Y.L. performed copy number experiments. D.D. and K.Z. performed bisulfite capture. E.B. performed the sequencing. R.A.I. and A.P.F. conceived and led the experiments and wrote the paper with the predominant assistance of K.D.H., W.T., H.C.B. and B.L. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Rafael A Irizarry or * Andrew P Feinberg Author Details * Kasper Daniel Hansen Search for this author in: * NPG journals * PubMed * Google Scholar * Winston Timp Search for this author in: * NPG journals * PubMed * Google Scholar * Héctor Corrada Bravo Search for this author in: * NPG journals * PubMed * Google Scholar * Sarven Sabunciyan Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin Langmead Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver G McDonald Search for this author in: * NPG journals * PubMed * Google Scholar * Bo Wen Search for this author in: * NPG journals * PubMed * Google Scholar * Hao Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Yun Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Dinh Diep Search for this author in: * NPG journals * PubMed * Google Scholar * Eirikur Briem Search for this author in: * NPG journals * PubMed * Google Scholar * Kun Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Rafael A Irizarry Contact Rafael A Irizarry Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew P Feinberg Contact Andrew P Feinberg Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 3 (3M) List of block locations * Supplementary Table 7 (2M) List of small DMRs * Supplementary Table 11 (264K) List of genes showing statistically significant over-expression in cancer compared to normal samples and are within 2,000 bp from an outward methylation boundary shift. * Supplementary Table 12 (172K) Genes with higher gene expression variability in cancer compared to normal. * Supplementary Table 20 (9M) As Supplementary Table 3, but for sample-specific blocks. * Supplementary Table 21 (5M) As Supplementary Table 7, but for sample-specific small DMRs. * Supplementary Table 22 (36K) Primers used for bisulfite pyrosequencing * Supplementary Table 23 (88K) List of microarrays used to identify tissue-specific genes PDF files * Supplementary Text and Figures (15M) Supplementary Note, Supplementary Tables 1, 2, 4–6, 8–10 and 13–19 and Supplementary Figures 1–24. Additional data
• A transition zone complex regulates mammalian ciliogenesis and ciliary membrane composition
  - Nat Genet 43(8):776-784 (2011)
  Nature Genetics | Article A transition zone complex regulates mammalian ciliogenesis and ciliary membrane composition * Francesc R Garcia-Gonzalo1, 2, 11 * Kevin C Corbit1, 2, 11 * María Salomé Sirerol-Piquer3 * Gokul Ramaswami4, 5 * Edgar A Otto4, 5 * Thomas R Noriega1 * Allen D Seol1, 2 * Jon F Robinson6, 7 * Christopher L Bennett6, 7 * Dragana J Josifova8 * José Manuel García-Verdugo3, 9 * Nicholas Katsanis6, 7 * Friedhelm Hildebrandt4, 5, 10 * Jeremy F Reiter1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:776–784Year published:(2011)DOI:doi:10.1038/ng.891Received01 April 2011Accepted01 June 2011Published online03 July 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Mutations affecting ciliary components cause ciliopathies. As described here, we investigated Tectonic1 (Tctn1), a regulator of mouse Hedgehog signaling, and found that it is essential for ciliogenesis in some, but not all, tissues. Cell types that do not require Tctn1 for ciliogenesis require it to localize select membrane-associated proteins to the cilium, including Arl13b, AC3, Smoothened and Pkd2. Tctn1 forms a complex with multiple ciliopathy proteins associated with Meckel and Joubert syndromes, including Mks1, Tmem216, Tmem67, Cep290, B9d1, Tctn2 and Cc2d2a. Components of this complex co-localize at the transition zone, a region between the basal body and ciliary axoneme. Like Tctn1, loss of Tctn2, Tmem67 or Cc2d2a causes tissue-specific defects in ciliogenesis and ciliary membrane composition. Consistent with a shared function for complex components, we identified a mutation in TCTN1 that causes Joubert syndrome. Thus, a transition zone complex of Meckel and Joubert ! syndrome proteins regulates ciliary assembly and trafficking, suggesting that transition zone dysfunction is the cause of these ciliopathies. View full text Figures at a glance * Figure 1: Tctn1 is required for ciliogenesis in a tissue-dependent manner. () Wild-type and Tctn1−/− E8.5 nodes stained for Arl13b (green) and DNA (DAPI, blue). Scale bar, 10 μm. () SEM of wild-type and Tctn1−/− E8.0 nodes. Scale bars, 50 μm, 5 μm and 0.5 μm in the left, middle and right panels, respectively. () TEM of wild-type and Tctn1−/− E8.5 nodes. Arrows indicate basal bodies. Scale bar, 1 μm. () Wild-type and Tctn1−/− E10.5 ventral neural tube stained for Arl13b (green), γ-tubulin (red) and DNA (DAPI, blue). Scale bar, 10 μm. () SEM of wild-type and Tctn1−/− E9.5 neural tubes. Arrowheads indicate cilia. Scale bars, 1 μm. () TEM of wild-type and Tctn1−/− E9.5 neural tubes. Scale bar, 1 μm. () SEM of notochord cilia from wild-type and Tctn1−/− E8.0 embryos. Scale bar, 1 μm. () We stained E10.5 perineural mesenchyme sections for AcTub (blue), Arl13b (red), γ-tubulin (green) and DNA (gray). Arl13b localization to Tctn1−/− cilia was reduced. Arrowheads indicate cilia. Scale bar, 5 μm. () We stained E11.! 5 hindlimb bud mesenchyme sections as in . Arl13b is also reduced in Tctn1−/− cilia. Arrowheads indicate cilia. Scale bar, 5 μm. () Alcian blue staining of wild-type and Tctn1−/− E14.5 hindlimb cartilage. () In situ hybridization of E10.5 or E11.5 hindlimb buds for expression of the indicated genes. Arrowheads mark the anterior extent of gene expression. () In situ hybridization of E10.5 hindlimb buds for Fgf4 expression shows that Tctn1 is epistatic to Shh. Arrowheads mark the anterior extent of Fgf4 expression. () Immunoblot of E9.5 wild-type, Tctn1+/− and Tctn1−/− embryo extracts with Gli3 antibodies. WT, wild type. * Figure 2: Tctn1 interacts with ciliopathy proteins. () We stained NIH-3T3 cells stably expressing Tctn1-LAP for GFP, part of the LAP tag and the ciliary marker Arl13b. Tctn1-LAP accumulates at the endoplasmic reticulum, and some Tctn1-LAP is seen at the base of some cilia (insets, 3× magnification of the indicated regions). Scale bar, 5 μm. () We purified Tctn1-LAP–associated proteins from stable NIH-3T3 cells and separated them by SDS-PAGE. SYPRO Ruby staining of the gel revealed several major bands, whose identities were elucidated by mass spectrometry and are indicated on the right (a complete list of interactors is presented in Supplementary Table 1). The sizes of molecular weight markers appear on the left. A control purification is also shown, containing a nonspecific human keratin band (asterisk). () Immunoblot analysis of immunoprecipitates from lysates of COS1 cells transfected with constructs expressing tagged proteins as labeled at top showed that Mks1, Tctn2, Tctn3, B9d1, Cc2d2a and Tmem67 can immunoprecipitat! e Tctn1. Size markers in kDa are shown on the right of each panel. () Reciprocal immunoprecipitation indicated that Tctn1-V5 can associate with tagged coexpressed Mks1, Tmem216, B9d1 and Cc2d2a. The locations of these proteins are indicated on the right. The asterisk indicates a pair of non-specific bands. () We purified Tctn1-LAP from NIH-3T3 cells and analyzed it by gel filtration chromatography. We analyzed the eluted fractions for total protein amount (top) and by immunoblot for the indicated proteins. Arrowheads indicate the positions of molecular weight markers in kDa. * Figure 3: Tctn1 and its interactors localize to the ciliary transition zone. () We stained hTERT-RPE1 cells for AcTub (blue), the basal body component γ-tubulin (or Ninein in the CC2D2A panel) (green) and the indicated proteins (red). White arrowheads indicate the position of the transition zone. Note that TCTN1 and its interactors (TCTN2, TCTN3, MKS1, TMEM67, CEP290, CC2D2A and B9D1) are present at the transition zone, although in some cases not exclusively. Scale bar, 1 μm. () MEFs from wild-type and Tctn1−/− embryos were stained as above, except for Septin2, for which detyrosinated tubulin, rather than AcTub, is shown in blue. The Tctn1 interactors Mks1, Tmem67 and Cc2d2a localize to the transition zone of wild-type MEFs, but Mks1 and Tmem67 fail to localize to the transition zone of Tctn1−/− MEFs. Scale bar, 1 μm. () We quantified the intensity of transition zone staining in wild-type and Tctn1−/− MEFs for Nphp1, Mks1 and Tmem67. Data are shown as mean ± s.e.m. Asterisks denote statistical significance according to unpaired Studen! t′s t-tests (*P < 0.01). * Figure 4: Tctn2, like Tctn1, is essential for ciliogenesis in a tissue-dependent manner. () Wild-type and Tctn2−/− E8.0 nodes stained for AcTub (green), Ninein (red) and DNA (DAPI, blue). Scale bar, 5 μm. Arrowheads indicate cilia. () SEM of wild-type and Tctn2−/− E8.0 nodes. Scale bar, 5 μm. () SEM of wild-type and Tctn2−/− E9.5 neural tubes. Scale bar, 0.5 μm. () TEM of wild-type and Tctn2−/− E9.5 neural tubes. Scale bar, 0.5 μm. () Sections of E12.5 hindlimb bud mesenchyme stained for AcTub (green) and γ-tubulin (red). Scale bar, 10 μm. () Wild-type and Tctn2−/− E9.5 perineural mesenchyme stained for Arl13b (red) and DNA (DAPI, blue). Dotted line marks neural tube border. Scale bar, 10 μm. * Figure 5: Tctn1 interactors Cc2d2a and Tmem67 promote ciliogenesis. () Control and Cc2d2a−/− E10.5 mouse embryo littermates. () Control and Cc2d2a−/− E9.5 ventral neural tube stained for Arl13b (red) and DNA (DAPI, blue). () Control and Cc2d2a−/− E9.5 perineural mesenchyme stained for Arl13b (red) and DNA (DAPI, blue). () Cc2d2a−/− primary MEFs generate cilia, marked by AcTub (green), with associated basal bodies, marked by γ-tubulin (red). () Hematoxylin and eosin staining of E18.5 kidney sections from control and Tmem67−/− embryos. Kidney cysts are visible in the mutant (arrows). () Control and Tmem67−/− E18.5 embryonic kidney tubules stained for AcTub (green), Arl13b (red) and DNA (DAPI, blue). Cilia are less abundant in Tmem67−/− tubules but have Arl13b. () Tmem67−/− primary MEFs generate cilia, as stained for AcTub (green), γ-tubulin (red) and DNA (DAPI, blue). () Tctn1 is epistatic to Tmem67. At E14.5, Tmem67−/− mice are indistinguishable from wild-type mice and Tctn1−/−; Tmem67−/− double m! utant mice are indistinguishable from Tctn1−/− mice. Scale bars, 10 μm. * Figure 6: Tctn1 and its interactors control the localization of select ciliary membrane proteins. () We stained MEFs from sibling wild-type and Tctn1−/− embryos for AcTub (blue), γ-tubulin (green) and the indicated ciliary proteins (red). Relative quantifications of the ciliary intensity of these proteins are also shown as means ± s.e.m. Asterisks denote statistical significance according to unpaired Student′s t-tests (*P < 0.01). Scale bar, 1 μm. () We stained E14.5 palatal sections for AcTub, AC3, γ-tubulin and DNA. Ciliary localization of AC3 was evident in wild-type embryos but not in Tctn1−/− embryos. Scale bar, 5 μm. () We stained and analyzed MEFs from sibling wild-type and Tctn2−/− embryos as in . Scale bar 1 μm. () We stained and analyzed MEFs from sibling wild-type and Cc2d2a−/− embryos as in . Scale bar 1 μm. () We stained and analyzed MEFs from sibling Tmem67+/− and Tmem67−/− embryos as in . Scale bar, 1 μm. () We stained E18.5 kidney sections for AcTub, Pkd2 and DNA. Ciliary localization of Pkd2 was evident in both wild-type a! nd Tmem67−/− kidney tubules. Scale bar, 10 μm. () We stained E14.5 perineural mesenchyme for AcTub, Pkd2 and DNA. Ciliary localization of Pkd2 was evident in wild-type embryos but not in Tctn1−/− embryos. Boxed regions have been magnified 4×. Scale bars, 5 μm. * Figure 7: A human TCTN1 mutation is a cause of Joubert syndrome. () The non-parametric logarithmic odds score (NPL score) profile across the genomes of two sisters with JBTS of consanguineous family A2090. SNP positions on human chromosomes are concatenated from p-ter (left) to q-ter (right) on the x axis. Genetic distance is given in cM. Maximum NPL peaks signify regions of homozygosity by descent. A maximum NPL peak on chromosome 12q (arrow) contains the candidate locus TCTN1. () A homozygous mutation (IVS1-2a>g) of the intron 1 obligatory splice acceptor consensus is present in both siblings (II-1 and II-2) with JBTS of family A2090 (arrows) but is absent from a healthy control individual (WT). () A model of how transition zone dysfunction results in human disease. Progressive compromise of TCTN complex function (y axis) results in JBTS, COACH and MKS. Progressive compromise of NPHP complex function (x axis) results in NPHP and SLSN. Mutations that compromise the function of both the TCTN and NPHP complexes may result in syndromes with! elements of both, such as JBTS with NPHP. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Francesc R Garcia-Gonzalo & * Kevin C Corbit Affiliations * Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. * Francesc R Garcia-Gonzalo, * Kevin C Corbit, * Thomas R Noriega, * Allen D Seol & * Jeremy F Reiter * Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California, USA. * Francesc R Garcia-Gonzalo, * Kevin C Corbit, * Allen D Seol & * Jeremy F Reiter * Laboratorio de Morfología Celular, Unidad Mixta Centro de Investigación Príncipe Felipe-Universidad de Valencia, Centro de Investigación Biomédica en Red (CIBERNED), Valencia, Spain. * María Salomé Sirerol-Piquer & * José Manuel García-Verdugo * Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, USA. * Gokul Ramaswami, * Edgar A Otto & * Friedhelm Hildebrandt * Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA. * Gokul Ramaswami, * Edgar A Otto & * Friedhelm Hildebrandt * Center for Human Disease Modeling, Department of Cell Biology, Duke University, Durham, North Carolina, USA. * Jon F Robinson, * Christopher L Bennett & * Nicholas Katsanis * Department of Pediatrics, Duke University, Durham, North Carolina, USA. * Jon F Robinson, * Christopher L Bennett & * Nicholas Katsanis * Department of Clinical Genetics, Guy's Hospital, London, UK. * Dragana J Josifova * Department of Comparative Neurobiology, Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, Valencia, Spain. * José Manuel García-Verdugo * Howard Hughes Medical Institute, Chevy Chase, Maryland, USA. * Friedhelm Hildebrandt Contributions F.R.G.-G. and K.C.C. performed most of the experiments and wrote the manuscript. M.S.S.-P. did most of the electron microscopy and was supervised by J.M.G.-V. Human genetics were supervised by F.H. and N.K. and performed by G.R., E.A.O., D.J.J., C.L.B. and J.F. Robinson. T.R.N. performed the gel filtration chromatography. A.D.S. helped with mouse experiments. J.F. Reiter wrote the manuscript and supervised the work. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jeremy F Reiter Author Details * Francesc R Garcia-Gonzalo Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin C Corbit Search for this author in: * NPG journals * PubMed * Google Scholar * María Salomé Sirerol-Piquer Search for this author in: * NPG journals * PubMed * Google Scholar * Gokul Ramaswami Search for this author in: * NPG journals * PubMed * Google Scholar * Edgar A Otto Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas R Noriega Search for this author in: * NPG journals * PubMed * Google Scholar * Allen D Seol Search for this author in: * NPG journals * PubMed * Google Scholar * Jon F Robinson Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher L Bennett Search for this author in: * NPG journals * PubMed * Google Scholar * Dragana J Josifova Search for this author in: * NPG journals * PubMed * Google Scholar * José Manuel García-Verdugo Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas Katsanis Search for this author in: * NPG journals * PubMed * Google Scholar * Friedhelm Hildebrandt Search for this author in: * NPG journals * PubMed * Google Scholar * Jeremy F Reiter Contact Jeremy F Reiter Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Excel files * Supplementary Table 1 (209K) Tctn1-LAP interactors PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–8. Additional data
• Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study
  - Nat Genet 43(8):785-791 (2011)
  Nature Genetics | Letter Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study * Zsofia Kote-Jarai1 * Ali Amin Al Olama2 * Graham G Giles3, 4 * Gianluca Severi3, 4 * Johanna Schleutker5 * Maren Weischer6 * Daniele Campa7 * Elio Riboli8 * Tim Key9 * Henrik Gronberg10 * David J Hunter11 * Peter Kraft11 * Michael J Thun12 * Sue Ingles13 * Stephen Chanock14, 15 * Demetrius Albanes14 * Richard B Hayes16 * David E Neal17, 18 * Freddie C Hamdy19, 20 * Jenny L Donovan21 * Paul Pharoah2 * Fredrick Schumacher22 * Brian E Henderson22 * Janet L Stanford23, 24 * Elaine A Ostrander25 * Karina Dalsgaard Sorensen26 * Thilo Dörk27 * Gerald Andriole28 * Joanne L Dickinson29 * Cezary Cybulski30 * Jan Lubinski30 * Amanda Spurdle31 * Judith A Clements32 * Suzanne Chambers33 * Joanne Aitken34 * R A Frank Gardiner35 * Stephen N Thibodeau36 * Dan Schaid36 * Esther M John37, 38 * Christiane Maier39, 40 * Walther Vogel40 * Kathleen A Cooney41, 42 * Jong Y Park43 * Lisa Cannon-Albright44, 45 * Hermann Brenner46 * Tomonori Habuchi47 * Hong-Wei Zhang48 * Yong-Jie Lu49 * Radka Kaneva50 * Ken Muir51 * Sara Benlloch2 * Daniel A Leongamornlert1 * Edward J Saunders1 * Malgorzata Tymrakiewicz1 * Nadiya Mahmud1 * Michelle Guy1 * Lynne T O'Brien1 * Rosemary A Wilkinson1 * Amanda L Hall1 * Emma J Sawyer1 * Tokhir Dadaev1 * Jonathan Morrison2 * David P Dearnaley1, 52 * Alan Horwich1, 52 * Robert A Huddart1, 52 * Vincent S Khoo52, 1 * Christopher C Parker52, 1 * Nicholas Van As52 * Christopher J Woodhouse52 * Alan Thompson52 * Tim Christmas52 * Chris Ogden52 * Colin S Cooper1 * Aritaya Lophatonanon51 * Melissa C Southey53 * John L Hopper4 * Dallas R English3, 4 * Tiina Wahlfors54 * Teuvo L J Tammela54 * Peter Klarskov55 * Børge G Nordestgaard56 * M Andreas Røder57 * Anne Tybjærg-Hansen58 * Stig E Bojesen58 * Ruth Travis9 * Federico Canzian7 * Rudolf Kaaks7 * Fredrik Wiklund10 * Markus Aly10, 59 * Sara Lindstrom11 * W Ryan Diver12 * Susan Gapstur12 * Mariana C Stern13 * Roman Corral13 * Jarmo Virtamo60 * Angela Cox61 * Christopher A Haiman22 * Loic Le Marchand62 * Liesel FitzGerald23 * Suzanne Kolb23 * Erika M Kwon25 * Danielle M Karyadi25 * Torben Falck Ørntoft26 * Michael Borre63 * Andreas Meyer27 * Jürgen Serth27 * Meredith Yeager14 * Sonja I Berndt14 * James R Marthick29 * Briony Patterson29 * Dominika Wokolorczyk30 * Jyotsna Batra32 * Felicity Lose31 * Shannon K McDonnell36 * Amit D Joshi37 * Ahva Shahabi37 * Antje E Rinckleb39, 40 * Ana Ray41 * Thomas A Sellers43 * Hui-Yi Lin43 * Robert A Stephenson64 * James Farnham44 * Heiko Muller46 * Dietrich Rothenbacher46 * Norihiko Tsuchiya47 * Shintaro Narita47 * Guang-Wen Cao48 * Chavdar Slavov65 * Vanio Mitev50 * The UK Genetic Prostate Cancer Study Collaborators/British Association of Urological Surgeons' Section of Oncology * The UK ProtecT Study Collaborators, The Australian Prostate Cancer BioResource * The PRACTICAL Consortium * Douglas F Easton2 * Rosalind A Eeles1, 52 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:785–791Year published:(2011)DOI:doi:10.1038/ng.882Received03 March 2011Accepted14 June 2011Published online10 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Prostate cancer (PrCa) is the most frequently diagnosed male cancer in developed countries. We conducted a multi-stage genome-wide association study for PrCa and previously reported the results of the first two stages, which identified 16 PrCa susceptibility loci. We report here the results of stage 3, in which we evaluated 1,536 SNPs in 4,574 individuals with prostate cancer (cases) and 4,164 controls. We followed up ten new association signals through genotyping in 51,311 samples in 30 studies from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. In addition to replicating previously reported loci, we identified seven new prostate cancer susceptibility loci on chromosomes 2p11, 3q23, 3q26, 5p12, 6p21, 12q13 and Xq12 (P = 4.0 × 10−8 to P = 2.7 × 10−24). We also identified a SNP in TERT more strongly associated with PrCa than that previously reported. More than 40 PrCa susceptibility loci, explaini! ng ~25% of the familial risk in this disease, have now been identified. View full text Figures at a glance * Figure 1: Forest plots for the ten SNPs genotyped in stages 1–4. Squares represent the estimated per-allele odds ratio (OR) for individual studies. The area of the square is inversely proportional to the variance of the estimate. Diamonds represent the summary OR estimates for the subgroups indicated. Horizontal lines represent 95% confidence intervals. * Figure 2: Regional plots of four of the associated SNPs at 5p15 (a), 3q23 (b), 5p12 (c) and 6p21 (d). Plots show the genomic regions associated with PrCa and the −log10 association P values of the SNPs. Also shown are the SNP build 36/hg18 coordinates in kb, recombination rates and genes in the region. The intensity of red shading indicates the strength of LD (r2) with the index SNP. Plots are drawn with a modified Rscript from SNAP LDPlot (see URLs). Author information * Author information * Supplementary information Affiliations * The Institute of Cancer Research, Sutton, Surrey, UK. * Zsofia Kote-Jarai, * Daniel A Leongamornlert, * Edward J Saunders, * Malgorzata Tymrakiewicz, * Nadiya Mahmud, * Michelle Guy, * Lynne T O'Brien, * Rosemary A Wilkinson, * Amanda L Hall, * Emma J Sawyer, * Tokhir Dadaev, * David P Dearnaley, * Alan Horwich, * Robert A Huddart, * Vincent S Khoo, * Christopher C Parker, * Colin S Cooper & * Rosalind A Eeles * Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Laboratory, Cambridge, UK. * Ali Amin Al Olama, * Paul Pharoah, * Sara Benlloch, * Jonathan Morrison & * Douglas F Easton * Cancer Epidemiology Centre, The Cancer Council Victoria, Carlton, Victoria, Australia. * Graham G Giles, * Gianluca Severi & * Dallas R English * Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, The University of Melbourne, Carlton, Victoria, Australia. * Graham G Giles, * Gianluca Severi, * John L Hopper & * Dallas R English * Institute of Biomedical Technology, University of Tampere and Centre for Laboratory Medicine, Tampere University Hospital, Tampere, Finland. * Johanna Schleutker * Department of Clinical Biochemistry, Herlev University Hospital, Herlev, Denmark. * Maren Weischer * Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. * Daniele Campa, * Federico Canzian & * Rudolf Kaaks * Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK. * Elio Riboli * Cancer Epidemiology Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK. * Tim Key & * Ruth Travis * Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden. * Henrik Gronberg, * Fredrik Wiklund & * Markus Aly * Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Pubic Health, Boston, Massachusetts, USA. * David J Hunter, * Peter Kraft & * Sara Lindstrom * Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA. * Michael J Thun, * W Ryan Diver & * Susan Gapstur * Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, California, USA. * Sue Ingles, * Mariana C Stern & * Roman Corral * Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health (NIH), Bethesda, Maryland, USA. * Stephen Chanock, * Demetrius Albanes, * Meredith Yeager & * Sonja I Berndt * Core Genotyping Facility, SAIC-Frederick, Inc., National Cancer Institute, NIH, Gaithersburg, Maryland, USA. * Stephen Chanock * Division of Epidemiology, Department of Environmental Medicine, New York University (NYU) Langone Medical Center, NYU Cancer Institute, New York, New York, USA. * Richard B Hayes * Surgical Oncology (Uro-Oncology: S4), University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge, UK. * David E Neal * Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK. * David E Neal * Nuffield Department of Surgery, University of Oxford, Oxford, UK. * Freddie C Hamdy * Faculty of Medical Science, University of Oxford, John Radcliffe Hospital, Oxford, UK. * Freddie C Hamdy * School of Social and Community Medicine, University of Bristol, Bristol, UK. * Jenny L Donovan * Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, USA. * Fredrick Schumacher, * Brian E Henderson & * Christopher A Haiman * Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. * Janet L Stanford, * Liesel FitzGerald & * Suzanne Kolb * Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, USA. * Janet L Stanford * National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA. * Elaine A Ostrander, * Erika M Kwon & * Danielle M Karyadi * Department of Molecular Medicine, Aarhus University Hospital, Skejby, Denmark. * Karina Dalsgaard Sorensen & * Torben Falck Ørntoft * Hannover Medical School, Hannover, Germany. * Thilo Dörk, * Andreas Meyer & * Jürgen Serth * Division of Urologic Surgery, Washington University School of Medicine, St. Louis, Missouri, USA. * Gerald Andriole * Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia. * Joanne L Dickinson, * James R Marthick & * Briony Patterson * International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland. * Cezary Cybulski, * Jan Lubinski & * Dominika Wokolorczyk * Molecular Cancer Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia. * Amanda Spurdle & * Felicity Lose * Australian Prostate Cancer Research Centre-Queensland, Institute of Health and Biomedical Innovation and Schools of Life Science and Public Health, Queensland University of Technology, Brisbane, Australia. * Judith A Clements & * Jyotsna Batra * Griffith Health Institute, Griffith University, Gold Coast, Queensland, Australia. * Suzanne Chambers * Viertel Centre for Research in Cancer Control, Cancer Council Queensland, Brisbane, Queensland, Australia. * Joanne Aitken * Centre for Clinical Research, University of Queensland, Brisbane, Queensland, Australia. * R A Frank Gardiner * Mayo Clinic, Rochester, Minnesota, USA. * Stephen N Thibodeau, * Dan Schaid & * Shannon K McDonnell * Cancer Prevention Institute of California, Fremont, California, USA. * Esther M John, * Amit D Joshi & * Ahva Shahabi * Stanford University School of Medicine, Stanford, California, USA. * Esther M John * Department of Urology, University Hospital Ulm, Ulm, Germany. * Christiane Maier & * Antje E Rinckleb * Institute of Human Genetics University Hospital Ulm, Ulm, Germany. * Christiane Maier, * Walther Vogel & * Antje E Rinckleb * Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA. * Kathleen A Cooney & * Ana Ray * Department of Urology, University of Michigan Medical School, Ann Arbor, Michigan, USA. * Kathleen A Cooney * Division of Cancer Prevention and Control, H. Lee Moffitt Cancer Center, Tampa, Florida, USA. * Jong Y Park, * Thomas A Sellers & * Hui-Yi Lin * Division of Genetic Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA. * Lisa Cannon-Albright & * James Farnham * George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah, USA. * Lisa Cannon-Albright * Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany. * Hermann Brenner, * Heiko Muller & * Dietrich Rothenbacher * Department of Urology, Akita University Graduate School of Medicine, Akita, Japan. * Tomonori Habuchi, * Norihiko Tsuchiya & * Shintaro Narita * Epidemiology Department, Second Military Medical University, Shanghai, China. * Hong-Wei Zhang & * Guang-Wen Cao * Centre for Molecular Oncology and Imaging, Barts Cancer Institute, Queen Mary University of London, London, UK. * Yong-Jie Lu * Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University–Sofia, Sofia, Bulgaria. * Radka Kaneva & * Vanio Mitev * University of Warwick, Coventry, UK. * Ken Muir & * Aritaya Lophatonanon * Royal Marsden National Health Service Foundation Trust, Fulham and Sutton, London and Surrey, UK. * David P Dearnaley, * Alan Horwich, * Robert A Huddart, * Vincent S Khoo, * Christopher C Parker, * Nicholas Van As, * Christopher J Woodhouse, * Alan Thompson, * Tim Christmas, * Chris Ogden & * Rosalind A Eeles * Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia. * Melissa C Southey * Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Tampere, Finland. * Tiina Wahlfors & * Teuvo L J Tammela * Department of Urology, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark. * Peter Klarskov * Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev, Denmark. * Børge G Nordestgaard * Department of Urology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark. * M Andreas Røder * Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark. * Anne Tybjærg-Hansen & * Stig E Bojesen * Division of Urology, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institute, Stockholm, Sweden. * Markus Aly * Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland. * Jarmo Virtamo * Department of Oncology, University of Sheffield, Sheffield, UK. * Angela Cox * Epidemiology Program, University of Hawaii Cancer Center, Department of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA. * Loic Le Marchand * Department of Urology, Aarhus University Hospital, Skejby, Denmark. * Michael Borre * Huntsman Cancer Institute, Salt Lake City, Utah, USA. * Robert A Stephenson * Department of Urology and Aleksandrovska University Hospital, Medical University–Sofia, Bulgaria. * Chavdar Slavov Consortia * The UK Genetic Prostate Cancer Study Collaborators/British Association of Urological Surgeons' Section of Oncology * The UK ProtecT Study Collaborators, The Australian Prostate Cancer BioResource * The PRACTICAL Consortium Contributions R.A.E. and D.F.E. designed the study and are joint principal investigators (PIs) on the GWAS. R.A.E. is the PI of the UK Genetic Prostate Cancer Study (UKGPCS) and project managed the overall study. Z.K.-J., R.A.E., D.F.E. and A.A.A.O. wrote the paper, and Z.K.-J. coordinated and managed the stage 3 and the PRACTICAL stage 4 genotyping. Z.K.-J., D.A.L., M.T., E.J.S. and N.M. coordinated sample collation for stage 3 and the PRACTICAL stage 4 set genotyped in the UK. A.A.A.O. and D.F.E. performed the statistical analyses. S.B. collated the dataset. G.G.G., J.L.H., D.R.E. and G.S. are PIs of the Australian studies. M.C. Southey manages the molecular work. J. Schleutker is PI of the Tampere study. T.W. collected clinical data, performed sample selection and collated data. T.L.J.T. coordinated sample collection. M.W. is the PI of the CPCS1 and CPCS2 studies. P. Klarskov, B.G.E., M.A.R., A.T.-H. and S.E.B. have collected samples and data and contributed to the genotyping of this s! tudy. F.C.H., D.E.N. and J.L. Donovan are joint PIs of ProtecT. A.L. is the study coordinator and M.D. the database manager. A.C. assisted with sample selection, retrieval and processing. D.A. and J.V. are PIs of the ATBC Study and were responsible for the original collection of the ATBC DNA samples. S.C. was responsible for assembly and genotyping. S.I.B. is the PI of the PLCO study, A.G. is the PI for the St. Louis screening center for PLCO and M.Y. oversaw the genotyping for PLCO. H.B. is the PI of the ESTHER study; D.R. and C.S. contributed to design and data collection; and H.M. is the study coordinator. C.C. and J.L.H. are PIs of the Poland study. C.C. and D.W. genotyped the samples. C.M. and W.V. are PIs of the Ulm study. A.E.R. identified and collected clinical material, processed samples, undertook genotyping and collated data. T. Dörk is the PI of the Hannover Prostate Cancer Study. A.M. and J.S. coordinated sample collation, provided molecular advice and conduct! ed molecular work. J.L. Dickinson is the PI of the Tasprac stu! dy. J.R.M. performed the Tasmanian genotyping and collated data; B.P. provided molecular advice and assistance with collating data. P. Kraft coordinated data collection and management for the HPFS. T.F.Ø. and K.D.S. are PIs of the Aarhus study. M.B. coordinated sample collection and registration of clinical data. K.D.S. led the sample genotyping. E.R. is PI of EPIC; D.C. and F.C. typed EPIC samples. T.K. is the PI of the EPIC-Oxford cohort and collected clinical material. R.T. collated data. S.M.G. and M.J.T. are the PIs of the ACS CPS-II study, and W.R.D. is the data manager for this study. B.E.H. and L.L.M. are the PIs of the MEC, and C.A.H. and F.S. are co-investigators (CIs). Y.-J.L. and H.-W.Z. are joint PIs of CHSH, Y.-J.L. is study coordinator and H.-W.Z. participated in and closely supervised the CHSH study. J.L.S. is the PI of the Fred Hutchinson study and E.A.O. is PI of the NHGRI genotyping for PROGRESS, and L.M.F. and J.S.K. coordinated data collation. S.A.I. i! s the PI of the USC study, E.M.J. is the PI of the NCCC study and M.C. Stern and R.C. led the genotyping of both studies. S.N.T. and D.S. are PIs of the Mayo clinic study, and S.K.D. coordinated data collation. J.Y.P. is the PI of the Moffitt study, and T.A.S. and H.-Y.L. are contributors to this study. J.A.C. and A.B.S. are PIs of the molecular genetics arm of the Proscan study, and with J.B. and F.L. coordinated all risk factor data and genetic data collection for prostate cancer cases from Proscan, the Brisbane Retrospective Study, the Australian Prostate Cancer BioResource Brisbane node and controls from two Queensland control sets. S.C., J.A. and R.A.F.G. are PIs of the Proscan study and were responsible for the original platform study initiation, conceptualization and collection of the Proscan study cases. K.A.C. is the PI of the FMHS study. L.C.-A. is the PI of the Utah study, and R.K. is the PI of the PCMUS study. H.G. is PI of CAPS and STHM1; F.K. directed the geno! typing. D.J.H. directs the CPSII. S.I. directs the NCCC. R.B.H. and G.A! . direct the PLCO. P.P. directs the SEARCH study. T.H. is PI of the Japan study. K.M. is co-PI of the PCRF study which provided some controls for the UK sets. A.T., A.D.J., A.L.H., L.T.O., R.A.W., E.C.P., E.J.S., D.P.D., A.H., R.A.H., V.S.K., C.C.P., N.V.A., C.J.W., A.T., T.C., C.O., L.N.K., L.L.M., A.A., A.C., D.M.K., E.M.K., A.D.J., A. Shahabi, T.A.S., J.P.S., S.C., J.A., R.A.F.G., J.B., F.L., A.P., B.P., J. Serth, F.W., T. Dadaev, M.G., J.M., C.S.C., B.G.N., M.A., S.L., S.G., L.F., S.K., S.K.M., R.A.S., A.R., N.T., S.N., G.-W.C., A.M., A.E.R., K.L., A.M.R., E.M.L., J.F., H.K. and C.S. identified and collected clinical material, and V.M. coordinated data collation. Other members of the UK Genetic Prostate Cancer Study Collaborators/British Association of Urological Surgeons' Section of Oncology, The UK ProtecT Study Collaborators and The PRACTICAL Consortium members (membership lists provided in the Supplementary Note) collected clinical samples, assisted in genotyping an! d provided data management. All study acronyms are defined in the Supplementary Note. These authors jointly directed this work Zsofia Kote-Jarai, Ali Amin Al Olama, Douglas F Easton & Rosalind A Eeles. These authors contributed equally to this work Graham G Giles, Gianluca Severi, Johanna Schleutker, Maren Weischer, Daniele Campa, Elio Riboli, Tim Key, Henrik Gronberg, David J Hunter, Peter Kraft, Michael J Thun, Sue Ingles, Stephen Chanock, Demetrius Albanes, Richard B Hayes, David E Neal, Freddie C Hamdy, Jenny L Donovan, Paul Pharoah, Fredrick Schumacher, Brian E Henderson, Janet L Stanford, Elaine A Ostrander, Karina Dalsgaard Sorensen, Thilo Dörk, Gerald Andriole, Joanne L Dickinson, Cezary Cybulski, Jan Lubinski, Amanda Spurdle, Judith A Clements, Suzanne Chambers, Joanne Aitken, R A Frank Gardiner, Stephen N Thibodeau, Dan Schaid, Esther M John, Christiane Maier, Walther Vogel, Kathleen A Cooney, Jong Y Park, Lisa Cannon-Albright, Hermann Brenner, Tomonori Habuchi, Hong-Wei Zhang, Yong-Jie Lu, Radka Kaneva & Ken Muir Competing financial interests The authors declare no competing financial interests. 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Slavov Search for this author in: * NPG journals * PubMed * Google Scholar * Vanio Mitev Search for this author in: * NPG journals * PubMed * Google Scholar * The UK Genetic Prostate Cancer Study Collaborators/British Association of Urological Surgeons' Section of Oncology * The UK ProtecT Study Collaborators, The Australian Prostate Cancer BioResource * The PRACTICAL Consortium * Douglas F Easton Search for this author in: * NPG journals * PubMed * Google Scholar * Rosalind A Eeles Contact Rosalind A Eeles Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (709K) Supplementary Figures 1 and 2, Supplementary Tables 1–5 and Supplementary Note Additional data
• A genome-wide association study identifies two new lung cancer susceptibility loci at 13q12.12 and 22q12.2 in Han Chinese
  - Nat Genet 43(8):792-796 (2011)
  Nature Genetics | Letter A genome-wide association study identifies two new lung cancer susceptibility loci at 13q12.12 and 22q12.2 in Han Chinese * Zhibin Hu1, 2, 3 * Chen Wu4, 19 * Yongyong Shi5, 19 * Huan Guo6, 19 * Xueying Zhao7, 19 * Zhihua Yin8, 19 * Lei Yang9, 19 * Juncheng Dai1 * Lingmin Hu1 * Wen Tan4 * Zhiqiang Li5 * Qifei Deng6 * Jiucun Wang7 * Wei Wu8 * Guangfu Jin1, 2 * Yue Jiang1 * Dianke Yu4 * Guoquan Zhou5 * Hongyan Chen7 * Peng Guan8 * Yijiang Chen10 * Yongqian Shu10 * Lin Xu11 * Xiangyang Liu12 * Li Liu13 * Ping Xu14 * Baohui Han15 * Chunxue Bai16 * Yuxia Zhao17 * Haibo Zhang18 * Ying Yan18 * Hongxia Ma1 * Jiaping Chen1, 2 * Mingjie Chu1 * Feng Lu1 * Zhengdong Zhang1 * Feng Chen1 * Xinru Wang1, 3 * Li Jin7, 19 * Jiachun Lu9, 19 * Baosen Zhou8, 19 * Daru Lu7, 19 * Tangchun Wu6, 19 * Dongxin Lin4, 19 * Hongbing Shen1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:792–796Year published:(2011)DOI:doi:10.1038/ng.875Received10 January 2011Accepted06 June 2011Published online03 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Lung cancer is the leading cause of cancer-related deaths worldwide. To identify genetic factors that modify the risk of lung cancer in individuals of Chinese ancestry, we performed a genome-wide association scan in 5,408 subjects (2,331 individuals with lung cancer (cases) and 3,077 controls) followed by a two-stage validation among 12,722 subjects (6,313 cases and 6,409 controls). The combined analyses identified six well-replicated SNPs with independent effects and significant lung cancer associations (P < 5.0 × 10−8) located in TP63 (rs4488809 at 3q28, P = 7.2 × 10−26), TERT-CLPTM1L (rs465498 and rs2736100 at 5p15.33, P = 1.2 × 10−20 and P = 1.0 × 10−27, respectively), MIPEP-TNFRSF19 (rs753955 at 13q12.12, P = 1.5 × 10−12) and MTMR3-HORMAD2-LIF (rs17728461 and rs36600 at 22q12.2, P = 1.1 × 10−11 and P = 6.2 × 10−13, respectively). Two of these loci (13q12.12 and 22q12.2) were newly identified in the Chinese population. These results suggest that gene! tic variants in 3q28, 5p15.33, 13q12.12 and 22q12.2 may contribute to the susceptibility of lung cancer in Han Chinese. View full text Figures at a glance * Figure 1: Genome-wide association results on lung cancer in Han Chinese individuals. Scatter plot of P values in –log10 scale from the additive model on 591,370 SNPs (from 2,331 cases and 3,077 controls). Note that we did not include 3q13.2 rs1846594 (P = 2.71 × 10−40) or 12q13.11 rs2956467 (P = 3.77 × 10−17) in the plot. We calculated the results for the X chromosome based on 1,611 females (620 cases and 991 controls). The red line represents P = 5.0 × 10−8, and the blue line represents P = 1.0 × 10−6. * Figure 2: Regional plot of the six identified marker SNPs (rs4488809 at 3q28, rs465498 and rs2736100 at 5p15.33, rs753955 at 13q12.12, rs17728461 at 22q12.2 and rs36600 at 22q12.2). Results (−log10P) are shown for SNPs in the region flanking 150 kb on either side of the marker SNPs. The marker SNPs are shown in purple and the r2 values of the rest of the SNPs are indicated by different colors. The genes within the region of interest are annotated and are shown as arrows. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Chen Wu, * Yongyong Shi, * Huan Guo, * Xueying Zhao, * Zhihua Yin, * Lei Yang, * Li Jin, * Jiachun Lu, * Baosen Zhou, * Daru Lu, * Tangchun Wu & * Dongxin Lin Affiliations * Department of Epidemiology and Biostatistics, Ministry of Education (MOE) Key Laboratory of Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing, China. * Zhibin Hu, * Juncheng Dai, * Lingmin Hu, * Guangfu Jin, * Yue Jiang, * Hongxia Ma, * Jiaping Chen, * Mingjie Chu, * Feng Lu, * Zhengdong Zhang, * Feng Chen, * Xinru Wang & * Hongbing Shen * Section of Clinical Epidemiology, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Nanjing Medical University, Nanjing, China. * Zhibin Hu, * Guangfu Jin, * Jiaping Chen & * Hongbing Shen * State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China. * Zhibin Hu, * Xinru Wang & * Hongbing Shen * State Key Laboratory of Molecular Oncology and Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Chen Wu, * Wen Tan, * Dianke Yu & * Dongxin Lin * Bio-X Center and Affiliated Changning Mental Health Center, Ministry of Education Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai JiaoTong University, Shanghai, China. * Yongyong Shi, * Zhiqiang Li & * Guoquan Zhou * Institute of Occupational Medicine and Ministry of Education Key Laboratory for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. * Huan Guo, * Qifei Deng & * Tangchun Wu * State Key Laboratory of Genetic Engineering, Center for Fudan-VARI Genetic Epidemiology and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China. * Xueying Zhao, * Jiucun Wang, * Hongyan Chen, * Li Jin & * Daru Lu * Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China. * Zhihua Yin, * Wei Wu, * Peng Guan & * Baosen Zhou * The Institute for Chemical Carcinogenesis, State Key Laboratory of Respiratory Disease, Guangzhou Medical College, Guangzhou, China. * Lei Yang & * Jiachun Lu * Departments of Thoracic Surgery and Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China. * Yijiang Chen & * Yongqian Shu * Department of Thoracic Surgery, Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Nanjing, China. * Lin Xu * Department of Thoracic Surgery, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. * Xiangyang Liu * Cancer Center of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. * Li Liu * Department of Oncology, Wuhan Iron and Steel Group/Corporation Staff-Worker Hospital, Wuhan, China. * Ping Xu * Department of Respiratory Disease, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China. * Baohui Han * Department of Respiratory Disease, Zhongshan Hospital, Fudan University, Shanghai, China. * Chunxue Bai * Department of Radiation Oncology, First Affiliated Hospital of China Medical University, Shenyang, China. * Yuxia Zhao * Department of Radiotherapy, Shenyang Northern Hospital, Shenyang, China. * Haibo Zhang & * Ying Yan Contributions H.S. directed the study, obtained financial support and was responsible for study design, interpretation of results and manuscript writing. Z.H. performed overall project management, statistical analyses with J.D. and drafted the initial manuscript. D. Lin, T.W., Y.S., D. Lu., J.L., B.Z., X.W. and L.J. directed each participating study and jointly organized this study. L.H., Y.J., M.C., J.C. and F.L. were responsible for sample processing and managing the genotyping data. Y.C., Y.S., L.X., G.J., Z.Z. and H.M. were responsible for subject recruitment and sample preparation of Nanjing samples. X.L., C.W., W.T. and D.Y. were responsible for subject recruitment and sample preparation of Beijing samples. L.L., P.X., H.G. and Q.D. were responsible for subject recruitment and sample preparation of Wuhan samples. B.H., C.B., Z.L., X.Z., G.Z., J.W. and H.C. were responsible for subject recruitment and sample preparation of Shanghai samples. Y.Z., H.Z., Y.Y., Z.Y., W.W. and P.G. were ! responsible for subject recruitment and sample preparation of Shenyang samples. L.Y. was responsible for subject recruitment and sample preparation of Guangzhou samples. F.C. oversaw the statistical analyses process. All authors approved the final manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hongbing Shen Author Details * Zhibin Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Chen Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Yongyong Shi Search for this author in: * NPG journals * PubMed * Google Scholar * Huan Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Xueying Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Zhihua Yin Search for this author in: * NPG journals * PubMed * Google Scholar * Lei Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Juncheng Dai Search for this author in: * NPG journals * PubMed * Google Scholar * Lingmin Hu Search for this author in: * NPG journals * PubMed * Google Scholar * Wen Tan Search for this author in: * NPG journals * PubMed * Google Scholar * Zhiqiang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Qifei Deng Search for this author in: * NPG journals * PubMed * Google Scholar * Jiucun Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Wei Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Guangfu Jin Search for this author in: * NPG journals * PubMed * Google Scholar * Yue Jiang Search for this author in: * NPG journals * PubMed * Google Scholar * Dianke Yu Search for this author in: * NPG journals * PubMed * Google Scholar * Guoquan Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Hongyan Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Peng Guan Search for this author in: * NPG journals * PubMed * Google Scholar * Yijiang Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Yongqian Shu Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Xiangyang Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Li Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Ping Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Baohui Han Search for this author in: * NPG journals * PubMed * Google Scholar * Chunxue Bai Search for this author in: * NPG journals * PubMed * Google Scholar * Yuxia Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * Haibo Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Ying Yan Search for this author in: * NPG journals * PubMed * Google Scholar * Hongxia Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Jiaping Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Mingjie Chu Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Zhengdong Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Feng Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Xinru Wang Search for this author in: * NPG journals * PubMed * Google Scholar * Li Jin Search for this author in: * NPG journals * PubMed * Google Scholar * Jiachun Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Baosen Zhou Search for this author in: * NPG journals * PubMed * Google Scholar * Daru Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Tangchun Wu Search for this author in: * NPG journals * PubMed * Google Scholar * Dongxin Lin Search for this author in: * NPG journals * PubMed * Google Scholar * Hongbing Shen Contact Hongbing Shen Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–4 and Supplementary Tables 1–8. 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• Variation in the DEPDC5 locus is associated with progression to hepatocellular carcinoma in chronic hepatitis C virus carriers
  - Nat Genet 43(8):797-800 (2011)
  Nature Genetics | Letter Variation in the DEPDC5 locus is associated with progression to hepatocellular carcinoma in chronic hepatitis C virus carriers * Daiki Miki1, 2, 10 * Hidenori Ochi1, 2, 10 * C Nelson Hayes1, 2 * Hiromi Abe1, 2 * Tadahiko Yoshima1, 3 * Hiroshi Aikata2 * Kenji Ikeda4 * Hiromitsu Kumada4 * Joji Toyota5 * Takashi Morizono6 * Tatsuhiko Tsunoda6 * Michiaki Kubo7 * Yusuke Nakamura8 * Naoyuki Kamatani9 * Kazuaki Chayama1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:797–800Year published:(2011)DOI:doi:10.1038/ng.876Received04 January 2011Accepted07 June 2011Published online03 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Chronic viral hepatitis is the most important risk factor for progression to hepatocellular carcinoma (HCC). To identify genetic risk factors for progression to HCC in individuals with chronic hepatitis C virus (HCV), we analyzed 467,538 SNPs in 212 Japanese individuals with chronic HCV with HCC and 765 individuals with chronic HCV without HCC. We identified one intronic SNP in the DEPDC5 locus on chromosome 22 associated with HCC risk and confirmed the association using an independent case-control population (710 cases and 1,625 controls). The association was highly significant when we analyzed the stages separately as well as together (rs1012068, Pcombined = 1.27 × 10−13, odds ratio = 1.75). The significance level of the association further increased after adjustment for gender, age and platelet count (P = 1.35 × 10−14, odds ratio = 1.96). Our findings suggest that common variants within the DEPDC5 locus affect susceptibility to HCC in Japanese individuals with chron! ic HCV infection. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Daiki Miki & * Hidenori Ochi Affiliations * Laboratory for Digestive Diseases, Center for Genomic Medicine, RIKEN, Hiroshima, Japan. * Daiki Miki, * Hidenori Ochi, * C Nelson Hayes, * Hiromi Abe, * Tadahiko Yoshima & * Kazuaki Chayama * Department of Medicine and Molecular Science, Division of Frontier Medical Science, Programs for Biomedical Research, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan. * Daiki Miki, * Hidenori Ochi, * C Nelson Hayes, * Hiromi Abe, * Hiroshi Aikata & * Kazuaki Chayama * Pharmacology Research Laboratories, Dainippon Sumitomo Pharma Co., Ltd., Osaka, Japan. * Tadahiko Yoshima * Department of Hepatology, Toranomon Hospital, Tokyo, Japan. * Kenji Ikeda & * Hiromitsu Kumada * Department of Gastroenterology, Sapporo Kosei General Hospital, Hokkaido, Japan. * Joji Toyota * Laboratory for Medical Informatics, RIKEN Center for Genomic Medicine, Yokohama, Japan. * Takashi Morizono & * Tatsuhiko Tsunoda * Laboratory for Genotyping Development, RIKEN Center for Genomic Medicine, Yokohama, Japan. * Michiaki Kubo * Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, University of Tokyo, Tokyo, Japan. * Yusuke Nakamura * Laboratory for Statistics, RIKEN Center for Genomic Medicine, Yokohama, Japan. * Naoyuki Kamatani Contributions K.C. conceived the study. D.M., H.O. and K.C. designed the study. D.M. and H.O. performed genotyping. D.M., H.O., C.N.H. and K.C. wrote the manuscript. T.M., T.T., M.K. and N.K. performed data analysis at the genome-wide phase. H. Abe and T.Y. performed functional analyses. H. Aikata, K.I., H.K., J.T. and K.C. managed DNA samples. D.M., H.O. and K.C. summarized the whole results. Y.N., N.K. and K.C. obtained funding for the study. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Kazuaki Chayama Author Details * Daiki Miki Search for this author in: * NPG journals * PubMed * Google Scholar * Hidenori Ochi Search for this author in: * NPG journals * PubMed * Google Scholar * C Nelson Hayes Search for this author in: * NPG journals * PubMed * Google Scholar * Hiromi Abe Search for this author in: * NPG journals * PubMed * Google Scholar * Tadahiko Yoshima Search for this author in: * NPG journals * PubMed * Google Scholar * Hiroshi Aikata Search for this author in: * NPG journals * PubMed * Google Scholar * Kenji Ikeda Search for this author in: * NPG journals * PubMed * Google Scholar * Hiromitsu Kumada Search for this author in: * NPG journals * PubMed * Google Scholar * Joji Toyota Search for this author in: * NPG journals * PubMed * Google Scholar * Takashi Morizono Search for this author in: * NPG journals * PubMed * Google Scholar * Tatsuhiko Tsunoda Search for this author in: * NPG journals * PubMed * Google Scholar * Michiaki Kubo Search for this author in: * NPG journals * PubMed * Google Scholar * Yusuke Nakamura Search for this author in: * NPG journals * PubMed * Google Scholar * Naoyuki Kamatani Search for this author in: * NPG journals * PubMed * Google Scholar * Kazuaki Chayama Contact Kazuaki Chayama Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (680K) Supplementary Figures 1–6 and Supplementary Tables 1–12. Additional data
• Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction
  - Nat Genet 43(8):801-805 (2011)
  Nature Genetics | Letter Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction * Jessica Shea1, 2, 3 * Vineeta Agarwala1, 3, 4, 5 * Anthony A Philippakis1, 3, 4, 5, 6, 7 * Jared Maguire1 * Eric Banks1 * Mark DePristo1 * Brian Thomson1 * Candace Guiducci1 * Robert C Onofrio8 * The Myocardial Infarction Genetics Consortium * Sekar Kathiresan1, 6, 10, 11, 12 * Stacey Gabriel1 * Noël P Burtt1 * Mark J Daly1, 6, 10, 12 * Leif Groop13 * David Altshuler1, 3, 6, 10, 12, 14 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:801–805Year published:(2011)DOI:doi:10.1038/ng.871Received04 January 2011Accepted01 June 2011Published online24 July 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Noncoding variants at human chromosome 9p21 near CDKN2A and CDKN2B are associated with type 2 diabetes1, 2, 3, 4, myocardial infarction5, 6, 7, aneurysm8, vertical cup disc ratio9 and at least five cancers10, 11, 12, 13, 14, 15, 16. Here we compare approaches to more comprehensively assess genetic variation in the region. We carried out targeted sequencing at high coverage in 47 individuals and compared the results to pilot data from the 1000 Genomes Project. We imputed variants into type 2 diabetes and myocardial infarction cohorts directly from targeted sequencing, from a genotyped reference panel derived from sequencing and from 1000 Genomes Project low-coverage data. Polymorphisms with frequency >5% were captured well by all strategies. Imputation of intermediate-frequency polymorphisms required a higher density of tag SNPs in disease samples than is available on first-generation genome-wide association study (GWAS) arrays. Our association analyses identified more compre! hensive sets of variants showing equivalent statistical association with type 2 diabetes or myocardial infarction, but did not identify stronger associations than the original GWAS signals. View full text Figures at a glance * Figure 1: Comparison of targeted sequencing to 1000 Genomes Pilot 1 data. Variant calls were made in all six regions of type 2 diabetes association in the 32 individuals sequenced as part of both this targeted, high-coverage sequencing effort (47 CEU HapMap individuals) and 1000 Genomes Pilot 1 (60 CEU HapMap individuals). * Figure 2: Percentage of variation at chromosome 9p21 captured in the type 2 diabetes disease cohort by different imputation scenarios. (–) MACH imputation quality estimates (,,) and overall percentage of variation captured in type 2 diabetes samples (,,) for different imputation scenarios. (,,) MACH-estimated r2 for each SNP versus genomic position. SNPs not observed in the reference panel are assigned r2 = 0. Recombination rate was estimated from HapMap II. (,,) Percentage of variants captured in type 2 diabetes samples versus MAF and MACH-estimated r2. Imputation scenarios include imputing from HapMap II (n = 238 SNPs in 60 individuals) into SNPs genotyped on Affymetrix 500K array (,), imputing from 112 individuals genotyped at HapMap II sites and validated sequencing sites (total n = 464 SNPs) into SNPs genotyped on Affymetrix 500K array (,) and imputing from the same reference panel as and into SNPs genotyped on Affymetrix 500K array plus additional tag SNPs genotyped in type 2 diabetes cohort (genotyped marker density in type 2 diabetes samples ~1 SNP per 1.5 kb) (,). * Figure 3: Comparison of imputation from a genotyped (Geno) reference panel, directly from high-coverage resequencing data (Seq) and directly from 1000 Genomes Pilot 1 data. () Variants present in the three reference panels and their overlap. Note that the 67 variants present in the genotyped reference panel but not in the high-coverage sequencing reference panel (asterisk) were called in high-coverage sequencing as singletons and were excluded from the sequencing reference panel. 40% of these variants are not singletons in the larger genotyped reference panel. () Percentage of sites within each reference panel captured with MACH-estimated r2 ≥ 0.8. () Overall percentage of known variants captured with MACH-estimated r2 ≥ 0.8 by imputation from each reference panel. * Figure 4: Association results for type 2 diabetes and myocardial infarction at chromosome 9p21. (,) Regional plots of association signal for type 2 diabetes () and myocardial infarction (). Signal for each SNP (represented as –log10P value) versus genomic position. Size of the diamond for each SNP represents the linkage disequilibrium (measured as r2) between that SNP and the original GWAS SNP (rs10811661 for type 2 diabetes and rs4977574 for myocardial infarction). Recombination rate was estimated from HapMap II. Author information * Author information * Supplementary information Affiliations * Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Jessica Shea, * Vineeta Agarwala, * Anthony A Philippakis, * Jared Maguire, * Eric Banks, * Mark DePristo, * Brian Thomson, * Candace Guiducci, * Sekar Kathiresan, * Stacey Gabriel, * Noël P Burtt, * Mark J Daly & * David Altshuler * Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA. * Jessica Shea * Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA. * Jessica Shea, * Vineeta Agarwala, * Anthony A Philippakis & * David Altshuler * Program in Biophysics, Harvard University, Cambridge, Massachusetts, USA. * Vineeta Agarwala & * Anthony A Philippakis * Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA. * Vineeta Agarwala & * Anthony A Philippakis * Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. * Anthony A Philippakis, * Sekar Kathiresan, * Mark J Daly & * David Altshuler * Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA. * Anthony A Philippakis * Genetic Analysis Platform, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Robert C Onofrio * Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA. * Sekar Kathiresan, * Mark J Daly & * David Altshuler * Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA. * Sekar Kathiresan * Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. * Sekar Kathiresan, * Mark J Daly & * David Altshuler * Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden. * Leif Groop * Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA. * David Altshuler Consortia * The Myocardial Infarction Genetics Consortium Contributions J.S., V.A., A.A.P. and D.A. wrote the manuscript; L.G. and the Myocardial Infarction Genomics Consortium provided clinical samples; C.G., R.C.O., N.P.B. and S.G. contributed to next-generation sequencing data generation; A.A.P., J.M., E.B., M.D.P., S.G., M.J.D. and D.A. carried out sequencing analysis and variant calling; J.S., V.A., M.J.D. and D.A. carried out imputation and association analysis; J.S., V.A., B.T., C.G. and N.P.B. carried out genotyping and analysis. A list of members is provided in the Supplementary Note. The Myocardial Infarction Genetics Consortium Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David Altshuler Author Details * Jessica Shea Search for this author in: * NPG journals * PubMed * Google Scholar * Vineeta Agarwala Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony A Philippakis Search for this author in: * NPG journals * PubMed * Google Scholar * Jared Maguire Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Banks Search for this author in: * NPG journals * PubMed * Google Scholar * Mark DePristo Search for this author in: * NPG journals * PubMed * Google Scholar * Brian Thomson Search for this author in: * NPG journals * PubMed * Google Scholar * Candace Guiducci Search for this author in: * NPG journals * PubMed * Google Scholar * Robert C Onofrio Search for this author in: * NPG journals * PubMed * Google Scholar * The Myocardial Infarction Genetics Consortium * Sekar Kathiresan Search for this author in: * NPG journals * PubMed * Google Scholar * Stacey Gabriel Search for this author in: * NPG journals * PubMed * Google Scholar * Noël P Burtt Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Daly Search for this author in: * NPG journals * PubMed * Google Scholar * Leif Groop Search for this author in: * NPG journals * PubMed * Google Scholar * David Altshuler Contact David Altshuler Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 2 (279K) List of PCR primers and hybrid selection baits used in sequencing * Supplementary Table 3 (348K) List of all variants identified in high coverage sequencing * Supplementary Table 4 (119K) Validation analysis for SNPs identified in sequencing on 9p21 * Supplementary Table 5 (147K) List of variants in the genotyped reference panel for 9p21 * Supplementary Table 6 (283K) Imputation and association results for T2D and MI on 9p21 PDF files * Supplementary Text and Figures (5M) Supplementary Table 1, Supplementary Figures 1–11 and Supplementary Note. Additional data
• Mitochondrial aging is accelerated by anti-retroviral therapy through the clonal expansion of mtDNA mutations
  - Nat Genet 43(8):806-810 (2011)
  Nature Genetics | Letter Mitochondrial aging is accelerated by anti-retroviral therapy through the clonal expansion of mtDNA mutations * Brendan A I Payne1, 2 * Ian J Wilson1 * Charlotte A Hateley1 * Rita Horvath1 * Mauro Santibanez-Koref1 * David C Samuels3 * D Ashley Price2 * Patrick F Chinnery1 * Affiliations * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:806–810Year published:(2011)DOI:doi:10.1038/ng.863Received08 February 2011Accepted23 May 2011Published online26 June 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg There is emerging evidence that people with successfully treated HIV infection age prematurely, leading to progressive multi-organ disease1, but the reasons for this are not known. Here we show that patients treated with commonly used nucleoside analog anti-retroviral drugs progressively accumulate somatic mitochondrial DNA (mtDNA) mutations, mirroring those seen much later in life caused by normal aging2, 3. Ultra-deep re-sequencing by synthesis, combined with single-cell analyses, suggests that the increase in somatic mutation is not caused by increased mutagenesis but might instead be caused by accelerated mtDNA turnover. This leads to the clonal expansion of preexisting age-related somatic mtDNA mutations and a biochemical defect that can affect up to 10% of cells. These observations add weight to the role of somatic mtDNA mutations in the aging process and raise the specter of progressive iatrogenic mitochondrial genetic disease emerging over the next decade. View full text Figures at a glance * Figure 1: COX (cytochrome c oxidase) deficiency in single skeletal muscle fibers. () COX histochemistry from a representative healthy control subject (HIV−) showing normal COX activity, whereas a nucleoside analog treated HIV-infected patient (HIV+/NRTI+) shows multiple COX-deficient fibers (counterstained blue by residual SDH (succinate dehydrogenase) activity). Scale bars, 100 μm. () COX defects observed in each subject group (HIV+/NRTI−, HIV-infected treatment-naïve subjects; each dot represents an individual patient biopsy; ≥500 fibers sampled per biopsy). * Figure 2: Mitochondrial DNA analysis of single skeletal muscle fibers. () Mitochondrial DNA (mtDNA) content in individual COX (cytochrome c oxidase)-deficient muscle fibers from nucleoside analog treated HIV-infected (HIV+/NRTI+) subjects, expressed relative to mtDNA content in adjacent fibers of normal COX activity from the same subject. A few fibers show reduced mtDNA content, whereas the majority show increased content (geometric mean of 2.1-fold proliferation, maximum 21.3-fold; P < 0.001 for difference in mean mtDNA content between COX-deficient and normal fibers). () The majority of COX-deficient fibers (COX−) contained high percentage levels of mtDNA containing a large-scale deletion of the major arc, causing the COX defect; whereas no deleted mtDNA was detected in adjacent COX positive fibers (COX+) (P < 0.001). () Schematic representation of mtDNA large-scale deletion breakpoints in COX-deficient fibers from HIV+/NRTI+ patients relative to the mtDNA gene positions (transfer RNA and ribosomal RNA not shown). Each line represents an in! dividual deleted region. OL, origin of light chain replication; OH, origin of heavy chain replication. (n = 15 fibers from four patients). * Figure 3: Proportional level of mt.δ4977 'common deletion' (CD) in homogenized skeletal muscle from HIV-infected subjects. HIV+/NRTI+, HIV-infected, nucleoside analog exposed; HIV+/NRTI−, HIV-infected, treatment-naïve. The dashed line represents the lower threshold of the assay. NRTI-treated subjects showed significantly higher mean levels of common deletion than untreated subjects (HIV+/NRTI+ (mean ± s.e.m.), −3.45 ± 0.25 log10(/mtDNA); HIV+/NRTI−, −4.56 ± 0.31 log10(/mtDNA); P = 0.012). Box and whisker plot. * Figure 4: Ultra-deep re-sequencing by synthesis (UDS) of skeletal muscle mtDNA. UDS (Roche 454 FLX GS) shows no difference in burden of low-level mtDNA point variants (exceeding 0.2% frequency) between HIV-infected nucleoside analog treated (HIV+/NRTI+, n = 8), HIV-infected treatment-naïve (HIV+/NRTI−, n = 4) and control (HIV−, n = 4) subjects in two amplicons located in mtDNA hypervariable segment 2 (MT-HV2) and mtDNA COX subunit 3 (MT-CO3). In contrast, positive control subjects with inherited POLG defects (POLG, n = 4) show an increased burden of low-level mutations compared with healthy controls in MT-HV2 (OR = 2.33, P = 0.002). * Figure 5: Simulations of the effects of partial mitochondrial DNA (mtDNA) replication failure caused by nucleoside analog (NRTI) exposure. Using a validated computer model of mtDNA replication based solely on experimentally derived parameters22, we incorporated a finite period of partial replication failure caused by the mtDNA chain-terminating effects of NRTI exposure9, assigning a probability of failure per mtDNA replication event. All other parameters remained constant, including the de novo mutation rate22. We simulated 2,000 cells for 80 years. () The amount of mtDNA depletion during the NRTI exposure period caused by 25% and 45% probability of replication failure between 20 and 30 years of age. (>50% failure led to the complete loss of mtDNA.) The range of mtDNA depletion predicted is in keeping with published in vivo data12, 23. () This led to a persistent increase in the frequency of COX (cytochrome c oxidase)-deficient cells through the accelerated clonal expansion of preexisting somatic mtDNA mutations. () Direct simulation of the effects of NRTI exposure within our study population (two different per! iods, 10 and 3 years, starting at age 20, of replication failure with 45% probability). The range of COX defects predicted closely fits our empiric data. () Late exposure (40–50 years) had a more pronounced effect than early exposure (20–30 years) (with 45% probability of replication failure) caused by the higher number of preexisting (age-related) somatic mtDNA mutations at the time of exposure. Author information * Author information * Supplementary information Affiliations * Mitochondrial Research Group, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK. * Brendan A I Payne, * Ian J Wilson, * Charlotte A Hateley, * Rita Horvath, * Mauro Santibanez-Koref & * Patrick F Chinnery * Department of Infection and Tropical Medicine, Royal Victoria Infirmary, Newcastle upon Tyne, UK. * Brendan A I Payne & * D Ashley Price * Centre for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA. * David C Samuels Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Patrick F Chinnery Author Details * Brendan A I Payne Search for this author in: * NPG journals * PubMed * Google Scholar * Ian J Wilson Search for this author in: * NPG journals * PubMed * Google Scholar * Charlotte A Hateley Search for this author in: * NPG journals * PubMed * Google Scholar * Rita Horvath Search for this author in: * NPG journals * PubMed * Google Scholar * Mauro Santibanez-Koref Search for this author in: * NPG journals * PubMed * Google Scholar * David C Samuels Search for this author in: * NPG journals * PubMed * Google Scholar * D Ashley Price Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick F Chinnery Contact Patrick F Chinnery Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (573K) Supplementary Note, Supplementary Figures 1–4 and Supplementary Tables 1, 2 and 4. * Supplementary Table 3 (2M) UDS (Roche 454 FLX GS) outputs Additional data
• Dynamic CpG island methylation landscape in oocytes and preimplantation embryos
  - Nat Genet 43(8):811-814 (2011)
  Nature Genetics | Letter Dynamic CpG island methylation landscape in oocytes and preimplantation embryos * Sébastien A Smallwood1 * Shin-ichi Tomizawa1 * Felix Krueger2 * Nico Ruf1 * Natasha Carli1 * Anne Segonds-Pichon2 * Shun Sato3 * Kenichiro Hata3 * Simon R Andrews2 * Gavin Kelsey1, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume: 43,Pages:811–814Year published:(2011)DOI:doi:10.1038/ng.864Received18 February 2011Accepted25 May 2011Published online26 June 2011 Article tools * Full text * 日本語要約 * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Elucidating how and to what extent CpG islands (CGIs) are methylated in germ cells is essential to understand genomic imprinting and epigenetic reprogramming1, 2, 3. Here we present, to our knowledge, the first integrated epigenomic analysis of mammalian oocytes, identifying over a thousand CGIs methylated in mature oocytes. We show that these CGIs depend on DNMT3A and DNMT3L4, 5 but are not distinct at the sequence level, including in CpG periodicity6. They are preferentially located within active transcription units and are relatively depleted in H3K4me3, supporting a general transcription-dependent mechanism of methylation. Very few methylated CGIs are fully protected from post-fertilization reprogramming but, notably, the majority show incomplete demethylation in embryonic day (E) 3.5 blastocysts. Our study shows that CGI methylation in gametes is not entirely related to genomic imprinting but is a strong factor in determining methylation status in preimplantation embryo! s, suggesting a need to reassess mechanisms of post-fertilization demethylation. View full text Figures at a glance * Figure 1: DNA methylation landscape in oocytes and sperm determined by RRBS. (,) Distribution of CpG methylation levels across the genome (, left), within CGIs (, right) and CGI methylation () in immature (day 5), mature (germinal vesicle and MII) oocytes and sperm (***P < 0.001, χ2 test). The number of CpGs and CGIs analyzed is indicated in Supplementary Figure 1b. () Chromosome distribution of the 1,062 CGIs methylated in oocytes and 185 CGIs methylated in sperm (with 100 CGIs in both). () CpG methylation levels (percentage of all cytosines called methylated) at the Dnmt3b and Dnmt1s promoter CGIs in germinal vesicle oocytes. The gray vertical lines represent the sequencing read depth of individual cytosines; below, the percentage methylation of the corresponding CpGs is represented by colored vertical lines. * Figure 2: Mechanism of DNA methylation establishment in oocytes. () Distribution of CpG methylation levels across the genome in Dnmt3a−/− and Dnmt3L−/− oocytes and their wild-type counterparts (+/+); the number of CpGs analyzed is indicated in Supplementary Figure 1b (***P < 0.001, χ2 test). (,) Methylation levels of CGIs in Dnmt3a−/− and Dnmt3L−/− oocytes; only those CGIs for which methylation was ≥75% in the corresponding wild-type oocytes are shown. () Overall correlation between H3K4me3 enrichment determined in day 15 oocytes by ChIP-seq and methylation status (meth., methylated; unmeth., unmethylated) of CGIs (all CGIs irrespective of genomic location). Each group is shown as a box plot (plotted using default settings in SPSS) with the median values shown as thick horizontal lines. The box covers the twenty-fifth to seventy-fifth percentiles, and the whiskers outside the box extend to the highest and lowest value within 1.5 times the interquartile range. Points outside the whiskers are outliers. The difference in t! he median values between groups was tested using the Mann-Whitney U test (***P < 0.001). * Figure 3: Biological significance and fate of CGI methylation in oocytes. () mRNA expression levels in day 10 and germinal vesicle (GV) oocytes of the genes associated with methylated CGIs, either promoter (red, n = 410) or intragenic (blue, n = 555). () Methylation levels in blastocysts of the CGIs identified as methylated in mature oocytes; twelve known germline DMRs with informative coverage are displayed in red (range 45.2–58.7%). () Range of methylation in blastocysts of the CGIs methylated specifically in oocytes (oo) (n = 803) or sperm (sp) (n = 51), methylated in both oocytes and sperm (n = 86) and unmethylated in gametes (n = 11,512). Each group is shown as a box plot (plotted using default settings in SPSS), with the median values shown as thick horizontal lines. The box covers the twenty-fifth to seventy-fifth percentiles, and the whiskers outside the box extend to the highest and lowest value within 1.5 times the interquartile range. Points outside the whiskers are outliers, with asterisks indicating those >3 times outside the interq! uartile range. () Bisulphite sequencing in germinal vesicle oocytes, sperm and C57BL/6J × CAST/Ei hybrid E3.5 blastocysts of the Syt2 CGI. We discriminated bisulphite sequence profiles from the maternal (mat.) and paternal (pat.) alleles in blastocysts by polymorphisms between C57BL/6J and CAST/Ei. Open circles represent unmethylated CpGs, and filled circles represent methylated CpGs. Author information * Author information * Supplementary information Affiliations * Epigenetics Programme, The Babraham Institute, Cambridge, UK. * Sébastien A Smallwood, * Shin-ichi Tomizawa, * Nico Ruf, * Natasha Carli & * Gavin Kelsey * Bioinformatics Group, The Babraham Institute, Cambridge, UK. * Felix Krueger, * Anne Segonds-Pichon & * Simon R Andrews * Department of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Setagaya, Tokyo, Japan. * Shun Sato & * Kenichiro Hata * Centre for Trophoblast Research, University of Cambridge, Cambridge, UK. * Gavin Kelsey Contributions S.A.S. designed the study, performed RRBS, mRNA-Seq, direct BS-PCR experiments, data analysis and wrote the manuscript. S.-i.T. contributed to direct bisulphite sequencing PCR experiments and performed oocyte collections. F.K. and S.R.A. performed CpG methylation calls, general Illumina sequence alignments and data analysis. N.R. performed ChIP-Seq experiments. N.C. analyzed data. A.S.-P. performed statistical analysis. S.S. and K.H. provided Dnmt3L wild-type and knockout oocytes. G.K. designed and supervised the study and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Gavin Kelsey Author Details * Sébastien A Smallwood Search for this author in: * NPG journals * PubMed * Google Scholar * Shin-ichi Tomizawa Search for this author in: * NPG journals * PubMed * Google Scholar * Felix Krueger Search for this author in: * NPG journals * PubMed * Google Scholar * Nico Ruf Search for this author in: * NPG journals * PubMed * Google Scholar * Natasha Carli Search for this author in: * NPG journals * PubMed * Google Scholar * Anne Segonds-Pichon Search for this author in: * NPG journals * PubMed * Google Scholar * Shun Sato Search for this author in: * NPG journals * PubMed * Google Scholar * Kenichiro Hata Search for this author in: * NPG journals * PubMed * Google Scholar * Simon R Andrews Search for this author in: * NPG journals * PubMed * Google Scholar * Gavin Kelsey Contact Gavin Kelsey Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information Excel files * Supplementary Table 1 (14M) CpG Island methylation calls (separate Excel file). PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–9 and Supplementary Tables 2–4. Additional data
• Erratum: CTCF-mediated functional chromatin interactome in pluripotent cells
  - Nat Genet 43(8):815 (2011)
  Nature Genetics | Erratum Erratum: CTCF-mediated functional chromatin interactome in pluripotent cells * Lusy Handoko * Han Xu * Guoliang Li * Chew Yee Ngan * Elaine Chew * Marie Schnapp * Charlie Wah Heng Lee * Chaopeng Ye * Joanne Lim Hui Ping * Fabianus Mulawadi * Eleanor Wong * Jianpeng Sheng * Yubo Zhang * Thompson Poh * Chee Seng Chan * Galih Kunarso * Atif Shahab * Guillaume Bourque * Valere Cacheux-Rataboul * Wing-Kin Sung * Yijun Ruan * Chia-Lin WeiJournal name:Nature GeneticsVolume: 43,Page:815Year published:(2011)DOI:doi:10.1038/ng0811-815aPublished online27 July 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.43, 630–638 (2011); published online 19 June; corrected after print 11 July 2011 In the version of this article initially published, the accession codes section contained inaccuracies. The raw sequences and processed data generated from this study can be downloaded with accession number GSE28247. The previously published histone modification data used in this study are found under accession numbers GSE12241 and GSE11172. The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * Lusy Handoko Search for this author in: * NPG journals * PubMed * Google Scholar * Han Xu Search for this author in: * NPG journals * PubMed * Google Scholar * Guoliang Li Search for this author in: * NPG journals * PubMed * Google Scholar * Chew Yee Ngan Search for this author in: * NPG journals * PubMed * Google Scholar * Elaine Chew Search for this author in: * NPG journals * PubMed * Google Scholar * Marie Schnapp Search for this author in: * NPG journals * PubMed * Google Scholar * Charlie Wah Heng Lee Search for this author in: * NPG journals * PubMed * Google Scholar * Chaopeng Ye Search for this author in: * NPG journals * PubMed * Google Scholar * Joanne Lim Hui Ping Search for this author in: * NPG journals * PubMed * Google Scholar * Fabianus Mulawadi Search for this author in: * NPG journals * PubMed * Google Scholar * Eleanor Wong Search for this author in: * NPG journals * PubMed * Google Scholar * Jianpeng Sheng Search for this author in: * NPG journals * PubMed * Google Scholar * Yubo Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Thompson Poh Search for this author in: * NPG journals * PubMed * Google Scholar * Chee Seng Chan Search for this author in: * NPG journals * PubMed * Google Scholar * Galih Kunarso Search for this author in: * NPG journals * PubMed * Google Scholar * Atif Shahab Search for this author in: * NPG journals * PubMed * Google Scholar * Guillaume Bourque Search for this author in: * NPG journals * PubMed * Google Scholar * Valere Cacheux-Rataboul Search for this author in: * NPG journals * PubMed * Google Scholar * Wing-Kin Sung Search for this author in: * NPG journals * PubMed * Google Scholar * Yijun Ruan Search for this author in: * NPG journals * PubMed * Google Scholar * Chia-Lin Wei Search for this author in: * NPG journals * PubMed * Google Scholar
• Corrigendum: A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL)
  - Nat Genet 43(8):815 (2011)
  Nature Genetics | Corrigendum Corrigendum: A cooperative microRNA-tumor suppressor gene network in acute T-cell lymphoblastic leukemia (T-ALL) * Konstantinos J Mavrakis * Joni Van Der Meulen * Andrew L Wolfe * Xiaoping Liu * Evelien Mets * Tom Taghon * Aly A Khan * Manu Setti * Pieter Rondou * Peter Vandenberghe * Eric Delabesse * Yves Benoit * Nicholas B Socci * Christina S Leslie * Pieter Van Vlierberghe * Frank Speleman * Hans-Guido WendelJournal name:Nature GeneticsVolume: 43,Page:815Year published:(2011)DOI:doi:10.1038/ng0811-815bPublished online27 July 2011 Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.43, 673–678 (2011); published online 5 June; corrected after print 11 July 2011 In the version of this article initially published, the name of author Manu Setty was incorrectly spelled as Manu Setti. The error has been corrected in the HTML and PDF versions of the article. Additional data Author Details * Konstantinos J Mavrakis Search for this author in: * NPG journals * PubMed * Google Scholar * Joni Van Der Meulen Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew L Wolfe Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaoping Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Evelien Mets Search for this author in: * NPG journals * PubMed * Google Scholar * Tom Taghon Search for this author in: * NPG journals * PubMed * Google Scholar * Aly A Khan Search for this author in: * NPG journals * PubMed * Google Scholar * Manu Setti Search for this author in: * NPG journals * PubMed * Google Scholar * Pieter Rondou Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Vandenberghe Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Delabesse Search for this author in: * NPG journals * PubMed * Google Scholar * Yves Benoit Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas B Socci Search for this author in: * NPG journals * PubMed * Google Scholar * Christina S Leslie Search for this author in: * NPG journals * PubMed * Google Scholar * Pieter Van Vlierberghe Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Speleman Search for this author in: * NPG journals * PubMed * Google Scholar * Hans-Guido Wendel Search for this author in: * NPG journals * PubMed * Google Scholar