Latest Articles Include:
- Keeping the tires warm
- Nature genetics 42(4):277 (2010)
Nature Genetics | Editorial Keeping the tires warm Journal name:Nature GeneticsVolume:42,Page:277Year published:(2010)DOI:doi:10.1038/ng0410-277 Research is a necessity for the Middle Eastern countries currently growing their knowledge economies, and it is the key to their achieving autonomous control of their people's healthcare. To sustain knowledge growth, policymakers need to learn to trust researchers while also insisting upon evidence for the advice they get from them. View full text Additional data - Genome-wide analysis of palindrome formation
- Nature genetics 42(4):279 (2010)
In 2005, we reported a widespread and nonrandom distribution of DNA palindromes in cancer cells acting as a structural basis for subsequent gene amplification. These conclusions were based in part on the development of a new technique for the genome-wide analysis of palindrome formation (GAPF). - Unlocking the pathogenesis of celiac disease
- Nature genetics 42(4):281-282 (2010)
A genome-wide association study reports more than a dozen new susceptibility loci for celiac disease. Analysis of eQTL data from these and previously established risk loci sheds light on the genetic pathways underlying this common autoimmune disease. - Epigenetic marks identify functional elements
- Nature genetics 42(4):282-284 (2010)
Enhancers and transcription factor binding sites that control cell-specific transcription in higher eukaryotes can be found up to hundreds of kilobases from the promoters that they control, making their identification challenging. A new study uses a model based on histone modifications and chromatin dynamics to predict functional elements involved in androgen receptor response. - Protective hemoglobinopathies and Plasmodium falciparum transmission
- Nature genetics 42(4):284-285 (2010)
Human hemoglobin variants are known to protect the host against severe malaria due to P. falciparum. A new study demonstrates that such genetic variation may also be associated with increased transmission of this pathogen from the human host to the Anopheles vector. - Research highlights
- Nature genetics 42(4):287 (2010)
- Common variants at 5q22 associate with pediatric eosinophilic esophagitis
Rothenberg ME Spergel JM Sherrill JD Annaiah K Martin LJ Cianferoni A Gober L Kim C Glessner J Frackelton E Thomas K Blanchard C Liacouras C Verma R Aceves S Collins MH Brown-Whitehorn T Putnam PE Franciosi JP Chiavacci RM Grant SF Abonia JP Sleiman PM Hakonarson H - Nature genetics 42(4):289-291 (2010)
Nature Genetics | Brief Communication Common variants at 5q22 associate with pediatric eosinophilic esophagitis * Marc E Rothenberg1, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan M Spergel2, 3, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph D Sherrill1, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Kiran Annaiah4, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa J Martin5, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Antonella Cianferoni2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Laura Gober2 Search for this author in: * NPG journals * PubMed * Google Scholar * Cecilia Kim4 Search for this author in: * NPG journals * PubMed * Google Scholar * Joseph Glessner4 Search for this author in: * NPG journals * PubMed * Google Scholar * Edward Frackelton4 Search for this author in: * NPG journals * PubMed * Google Scholar * Kelly Thomas4 Search for this author in: * NPG journals * PubMed * Google Scholar * Carine Blanchard1 Search for this author in: * NPG journals * PubMed * Google Scholar * Chris Liacouras3, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Ritu Verma3, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Seema Aceves7 Search for this author in: * NPG journals * PubMed * Google Scholar * Margaret H Collins8 Search for this author in: * NPG journals * PubMed * Google Scholar * Terri Brown-Whitehorn2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Phil E Putnam9 Search for this author in: * NPG journals * PubMed * Google Scholar * James P Franciosi9 Search for this author in: * NPG journals * PubMed * Google Scholar * Rosetta M Chiavacci4 Search for this author in: * NPG journals * PubMed * Google Scholar * Struan F A Grant3, 4, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * J Pablo Abonia1 Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick M A Sleiman4 Search for this author in: * NPG journals * PubMed * Google Scholar * Hakon Hakonarson3, 4, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume:42,Pages:289–291Year published:(2010)DOI:doi:10.1038/ng.547Received22 September 2009Accepted11 February 2010Published online07 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Eosinophilic esophagitis (EoE) is an allergic disorder characterized by the accumulation of eosinophils in the esophagus. We report association of EoE with variants at chromosome 5q22 encompassing TSLP and WDR36 (rs3806932, combined P = 3.19 × 10−9). TSLP is overexpressed in esophageal biopsies from individuals with EoE compared with unaffected individuals, whereas WDR36 expression is unaltered between the two groups. These data implicate the 5q22 locus in the pathogenesis of EoE and identify TSLP as the most likely candidate gene in the region. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Marc E Rothenberg, * Jonathan M Spergel, * Joseph D Sherrill, * Kiran Annaiah & * Lisa J Martin Affiliations * Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA. * Marc E Rothenberg, * Joseph D Sherrill, * Carine Blanchard & * J Pablo Abonia * Division of Allergy and Immunology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Jonathan M Spergel, * Antonella Cianferoni, * Laura Gober & * Terri Brown-Whitehorn * Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Jonathan M Spergel, * Antonella Cianferoni, * Chris Liacouras, * Ritu Verma, * Terri Brown-Whitehorn, * Struan F A Grant & * Hakon Hakonarson * The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Kiran Annaiah, * Cecilia Kim, * Joseph Glessner, * Edward Frackelton, * Kelly Thomas, * Rosetta M Chiavacci, * Struan F A Grant, * Patrick M A Sleiman & * Hakon Hakonarson * Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA. * Lisa J Martin * Division of Gastroenterology and Nutrition, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Chris Liacouras & * Ritu Verma * Division of Allergy and Immunology, Riley Hospital for Children, University of California at San Diego, San Diego, California, USA. * Seema Aceves * Division of Pathology, Hepatology and Nutrition, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA. * Margaret H Collins * Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio, USA. * Phil E Putnam & * James P Franciosi * Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA. * Struan F A Grant & * Hakon Hakonarson Contributions M.E.R., J.M.S., S.A., M.H.C., T.B.-W., P.E.P., J.P.F., S.F.A.G., J.P.A., P.M.A.S. and H.H. designed the study. J.M.S., J.D.S., L.J.M., A.C., L.G., C.K., J.G., E.F., K.T., C.B., C.L., R.V., R.M.C. and H.H. generated the data. K.A., J.D.S., L.J.M. and P.M.A.S. analyzed the data. M.E.R., J.M.S., J.D.S., P.M.A.S. and H.H. prepared the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hakon Hakonarson (hakonarson@chop.edu) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Methods, Supplementary Figures 1–4 and Supplementary Tables 1 and 2 Additional data - Genome-wide association study for ulcerative colitis identifies risk loci at 7q22 and 22q13 (IL17REL)
Franke A Balschun T Sina C Ellinghaus D Häsler R Mayr G Albrecht M Wittig M Buchert E Nikolaus S Gieger C Wichmann HE Sventoraityte J Kupcinskas L Onnie CM Gazouli M Anagnou NP Strachan D McArdle WL Mathew CG Rutgeerts P Vermeire S Vatn MH and the IBSEN study group Krawczak M Rosenstiel P Karlsen TH Schreiber S - Nature genetics 42(4):292-294 (2010)
Nature Genetics | Brief Communication Genome-wide association study for ulcerative colitis identifies risk loci at 7q22 and 22q13 (IL17REL) * Andre Franke1, 18 Search for this author in: * NPG journals * PubMed * Google Scholar * Tobias Balschun1, 18 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Sina2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * David Ellinghaus1 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert Häsler1 Search for this author in: * NPG journals * PubMed * Google Scholar * Gabriele Mayr4 Search for this author in: * NPG journals * PubMed * Google Scholar * Mario Albrecht4 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Wittig1 Search for this author in: * NPG journals * PubMed * Google Scholar * Eva Buchert1 Search for this author in: * NPG journals * PubMed * Google Scholar * Susanna Nikolaus2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Christian Gieger5 Search for this author in: * NPG journals * PubMed * Google Scholar * H Erich Wichmann5, 6, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Jurgita Sventoraityte8 Search for this author in: * NPG journals * PubMed * Google Scholar * Limas Kupcinskas8 Search for this author in: * NPG journals * PubMed * Google Scholar * Clive M Onnie9 Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Gazouli10 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas P Anagnou10 Search for this author in: * NPG journals * PubMed * Google Scholar * David Strachan11 Search for this author in: * NPG journals * PubMed * Google Scholar * Wendy L McArdle12 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher G Mathew9 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Rutgeerts13 Search for this author in: * NPG journals * PubMed * Google Scholar * Séverine Vermeire13 Search for this author in: * NPG journals * PubMed * Google Scholar * Morten H Vatn14, 15 Search for this author in: * NPG journals * PubMed * Google Scholar * and the IBSEN study group17 * Michael Krawczak16 Search for this author in: * NPG journals * PubMed * Google Scholar * Philip Rosenstiel1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tom H Karlsen15, 18 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan Schreiber1, 3, 18 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume:42,Pages:292–294Year published:(2010)DOI:doi:10.1038/ng.553Received07 October 2009Accepted19 February 2010Published online14 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We performed a genome-wide association analysis of 1,897,764 SNPs in 1,043 German ulcerative colitis (UC) cases and 1,703 controls. We discovered new associations at chromosome 7q22 (rs7809799) and at chromosome 22q13 in IL17REL (rs5771069) and confirmed these associations in six replication panels (2,539 UC cases and 5,428 controls) from different regions of Europe (overall study sample Prs7809799 = 8.81 × 10−11 and Prs5771069 = 4.21 × 10−8, respectively). View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Andre Franke, * Tobias Balschun, * Tom H Karlsen & * Stefan Schreiber Affiliations * Institute for Clinical Molecular Biology, University Hospital Schleswig-Holstein, Christian-Albrechts University, Kiel, Germany. * Andre Franke, * Tobias Balschun, * David Ellinghaus, * Robert Häsler, * Michael Wittig, * Eva Buchert, * Philip Rosenstiel & * Stefan Schreiber * PopGen Biobank, University Hospital Schleswig-Holstein, Christian-Albrechts University, Kiel, Germany. * Christian Sina & * Susanna Nikolaus * Department of General Internal Medicine, University Hospital Schleswig-Holstein, Christian-Albrechts University, Kiel, Germany. * Christian Sina, * Susanna Nikolaus & * Stefan Schreiber * Max-Planck Institute for Informatics, Saarbrücken, Germany. * Gabriele Mayr & * Mario Albrecht * Institute of Epidemiology, Helmholtz Centre Munich, German Research Center for Environmental Health, Neuherberg, Germany. * Christian Gieger & * H Erich Wichmann * Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians University, Munich, Germany. * H Erich Wichmann * Klinikum Grosshadern, Munich, Germany. * H Erich Wichmann * Department of Gastroenterology, Kaunas University of Medicine, Kaunas, Lithuania. * Jurgita Sventoraityte & * Limas Kupcinskas * Department of Medical and Molecular Genetics, King's College London School of Medicine, London, UK. * Clive M Onnie & * Christopher G Mathew * Department of Biology, School of Medicine, University of Athens, Athens, Greece. * Maria Gazouli & * Nicholas P Anagnou * St. George's University, Division of Community Health Sciences, London, UK. * David Strachan * Avon Longitudinal Study of Parents and Children, Department of Social Medicine, University of Bristol, Bristol, UK. * Wendy L McArdle * Department of Gastroenterology, University Hospital Gasthuisberg, Leuven, Belgium. * Paul Rutgeerts & * Séverine Vermeire * Faculty of Medicine, Epigen, Akershus University Hospital, Oslo, Norway. * Morten H Vatn * Medical Department, Rikshospitalet University Hospital, Oslo, Norway. * Morten H Vatn & * Tom H Karlsen * Institute of Medical Informatics and Statistics, Christian-Albrechts University, Kiel, Germany. * Michael Krawczak * A full list of members is available in the Supplementary Note. * and the IBSEN study group Consortia * and the IBSEN study group Contributions A.F. and T.B. performed SNP selection, genotyping and data analysis and prepared figures and tables. A.F. helped with data analysis. D.E. performed the imputation and generated the regional association plots. R.H. and P. Rosenstiel performed the expression analyses. E.B. helped with figures. M.W. was responsible for in-house conversion and quality assessment of GWAS data. M.K. helped with statistical analyses and interpretation of the results. S.N. and C.S. coordinated the recruitment and collected phenotype data of panels A and B. G.M. and M.A. performed the in silico protein analyses. C.G. and H.E.W. provided the KORA control samples. J.S., L.K., C.M.O., C.G.M., M.G., N.P.A., D.S., W.L.M., S.V., P. Rutgeerts, M.H.V. and the IBSEN study group provided the European replication samples and respective phenotypes. M.K., E.B., P. Rosenstiel and S.S. edited the manuscript. A.F. supervised the experiment. T.H.K., T.B. and A.F. drafted the manuscript, and all authors approved the f! inal draft. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Andre Franke (a.franke@mucosa.de) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (7M) Supplementary Methods, Supplementary Note, Supplementary Tables 1–6 and Supplementary Figures 1–11 Additional data - Multiple common variants for celiac disease influencing immune gene expression
Dubois PC Trynka G Franke L Hunt KA Romanos J Curtotti A Zhernakova A Heap GA Adány R Aromaa A Bardella MT van den Berg LH Bockett NA de la Concha EG Dema B Fehrmann RS Fernández-Arquero M Fiatal S Grandone E Green PM Groen HJ Gwilliam R Houwen RH Hunt SE Kaukinen K Kelleher D Korponay-Szabo I Kurppa K Macmathuna P Mäki M Mazzilli MC McCann OT Mearin ML Mein CA Mirza MM Mistry V Mora B Morley KI Mulder CJ Murray JA Núñez C Oosterom E Ophoff RA Polanco I Peltonen L Platteel M Rybak A Salomaa V Schweizer JJ Sperandeo MP Tack GJ Turner G Veldink JH Verbeek WH Weersma RK Wolters VM Urcelay E Cukrowska B Greco L Neuhausen SL McManus R Barisani D Deloukas P Barrett JC Saavalainen P Wijmenga C van Heel DA - Nature genetics 42(4):295-302 (2010)
Nature Genetics | Article Multiple common variants for celiac disease influencing immune gene expression * Patrick C A Dubois1, 39 Search for this author in: * NPG journals * PubMed * Google Scholar * Gosia Trynka2, 39 Search for this author in: * NPG journals * PubMed * Google Scholar * Lude Franke1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Karen A Hunt1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jihane Romanos2 Search for this author in: * NPG journals * PubMed * Google Scholar * Alessandra Curtotti3 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandra Zhernakova4 Search for this author in: * NPG journals * PubMed * Google Scholar * Graham A R Heap1 Search for this author in: * NPG journals * PubMed * Google Scholar * Róza Ádány5 Search for this author in: * NPG journals * PubMed * Google Scholar * Arpo Aromaa6 Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Teresa Bardella7, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Leonard H van den Berg9 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicholas A Bockett1 Search for this author in: * NPG journals * PubMed * Google Scholar * Emilio G de la Concha10 Search for this author in: * NPG journals * PubMed * Google Scholar * Bárbara Dema10 Search for this author in: * NPG journals * PubMed * Google Scholar * Rudolf S N Fehrmann2 Search for this author in: * NPG journals * PubMed * Google Scholar * Miguel Fernández-Arquero10 Search for this author in: * NPG journals * PubMed * Google Scholar * Szilvia Fiatal5, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Elvira Grandone12 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter M Green13 Search for this author in: * NPG journals * PubMed * Google Scholar * Harry J M Groen14 Search for this author in: * NPG journals * PubMed * Google Scholar * Rhian 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* PubMed * Google Scholar * Leena Peltonen15, 30 Search for this author in: * NPG journals * PubMed * Google Scholar * Mathieu Platteel2 Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Rybak31 Search for this author in: * NPG journals * PubMed * Google Scholar * Veikko Salomaa6 Search for this author in: * NPG journals * PubMed * Google Scholar * Joachim J Schweizer23 Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Pia Sperandeo32 Search for this author in: * NPG journals * PubMed * Google Scholar * Greetje J Tack24 Search for this author in: * NPG journals * PubMed * Google Scholar * Graham Turner18 Search for this author in: * NPG journals * PubMed * Google Scholar * Jan H Veldink9 Search for this author in: * NPG journals * PubMed * Google Scholar * Wieke H M Verbeek24 Search for this author in: * NPG journals * PubMed * Google Scholar * Rinse K Weersma33 Search for this author in: * NPG journals * PubMed * Google Scholar * Victorien M Wolters16 Search for this author in: * NPG journals * PubMed * Google Scholar * Elena Urcelay10 Search for this author in: * NPG journals * PubMed * Google Scholar * Bozena Cukrowska34 Search for this author in: * NPG journals * PubMed * Google Scholar * Luigi Greco32 Search for this author in: * NPG journals * PubMed * Google Scholar * Susan L Neuhausen35 Search for this author in: * NPG journals * PubMed * Google Scholar * Ross McManus18 Search for this author in: * NPG journals * PubMed * Google Scholar * Donatella Barisani36 Search for this author in: * NPG journals * PubMed * Google Scholar * Panos Deloukas15 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey C Barrett15 Search for this author in: * NPG journals * PubMed * Google Scholar * Paivi Saavalainen37, 38 Search for this author in: * NPG journals * PubMed * Google Scholar * Cisca Wijmenga2 Search for this author in: * NPG journals * PubMed * Google Scholar * David A van Heel1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume:42,Pages:295–302Year published:(2010)DOI:doi:10.1038/ng.543Received04 December 2009Accepted04 February 2010Published online28 February 2010Corrected online12 March 2010 Abstract * Abstract * Accession codes * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We performed a second-generation genome-wide association study of 4,533 individuals with celiac disease (cases) and 10,750 control subjects. We genotyped 113 selected SNPs with PGWAS < 10−4 and 18 SNPs from 14 known loci in a further 4,918 cases and 5,684 controls. Variants from 13 new regions reached genome-wide significance (Pcombined < 5 × 10−8); most contain genes with immune functions (BACH2, CCR4, CD80, CIITA-SOCS1-CLEC16A, ICOSLG and ZMIZ1), with ETS1, RUNX3, THEMIS and TNFRSF14 having key roles in thymic T-cell selection. There was evidence to suggest associations for a further 13 regions. In an expression quantitative trait meta-analysis of 1,469 whole blood samples, 20 of 38 (52.6%) tested loci had celiac risk variants correlated (P < 0.0028, FDR 5%) with cis gene expression. View full text Accession codes * Abstract * Accession codes * Change history * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GPL96 * GPL570 * GSE20142 * GSE20332 Change history * Abstract * Accession codes * Change history * Author information * Supplementary informationCorrected online 12 March 2010In the version of this article initially published online, the P value ranges in the second paragraph of the Results section under (iii) and (iv) were noted incorrectly. These errors have been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Accession codes * Change history * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Patrick C A Dubois & * Gosia Trynka Affiliations * Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. * Patrick C A Dubois, * Lude Franke, * Karen A Hunt, * Graham A R Heap, * Nicholas A Bockett, * Vanisha Mistry & * David A van Heel * Genetics Department, University Medical Center and Groningen University, Groningen, The Netherlands. * Gosia Trynka, * Lude Franke, * Jihane Romanos, * Rudolf S N Fehrmann, * Elvira Oosterom, * Mathieu Platteel & * Cisca Wijmenga * The Genome Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. * Alessandra Curtotti & * Charles A Mein * Division of Biomedical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands. * Alexandra Zhernakova * Department of Preventive Medicine, University of Debrecen, Debrecen, Hungary. * Róza Ádány & * Szilvia Fiatal * National Institute for Health and Welfare, Helsinki, Finland. * Arpo Aromaa & * Veikko Salomaa * Fondazione IRCCS Ospedale Maggiore Policlinico, Mangiagalli e Regina Elena, Milan, Italy. * Maria Teresa Bardella * Department of Medical Sciences, University of Milan, Milan, Italy. * Maria Teresa Bardella * Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Centre Utrecht, Utrecht, The Netherlands. * Leonard H van den Berg & * Jan H Veldink * Clinical Immunology Department, Hospital Clínico San Carlos, Madrid, Spain. * Emilio G de la Concha, * Bárbara Dema, * Miguel Fernández-Arquero, * Concepción Núñez & * Elena Urcelay * Public Health Research Group of Hungarian Academy of Sciences, Medical & Health Science Center, University of Debrecen, Debrecen, Hungary. * Szilvia Fiatal * Unitá di Aterosclerosi e Trombosi, I.R.C.C.S Casa Sollievo della Sofferenza, S. Giovanni Rotondo, Foggia, Italy. * Elvira Grandone * NIHR GSTFT/KCL Comprehensive Biomedical Research Centre, King's College London School of Medicine, Guy's Hospital, London, UK. * Peter M Green & * Muddassar M Mirza * Department of Pulmonology, University Medical Center and Groningen University, Groningen, The Netherlands. * Harry J M Groen * Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. * Rhian Gwilliam, * Sarah E Hunt, * Owen T McCann, * Katherine I Morley, * Leena Peltonen, * Panos Deloukas & * Jeffrey C Barrett * Department of Paediatric Gastroenterology, University Medical Centre Utrecht, Utrecht, The Netherlands. * Roderick H J Houwen & * Victorien M Wolters * Paediatric Research Centre, University of Tampere Medical School and Tampere University Hospital, Tampere, Finland. * Katri Kaukinen, * Kalle Kurppa & * Markku Mäki * Department of Clinical Medicine, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland. * Dermot Kelleher, * Graham Turner & * Ross McManus * Heim Pal Childrens Hospital, Budapest, Hungary. * Ilma Korponay-Szabo * Department of Pediatrics, Medical and Health Science Center, University of Debrecen, Hungary. * Ilma Korponay-Szabo * Gastrointestinal Unit, Mater Misericordiae University Hospital, Dublin, Ireland. * Padraic MacMathuna * Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy. * Maria Cristina Mazzilli & * Barbara Mora * Department of Paediatrics, Leiden University Medical Centre, Leiden, The Netherlands. * M Luisa Mearin & * Joachim J Schweizer * Department of Gastroenterology, VU Medical Center, Amsterdam, The Netherlands. * Chris J Mulder, * Greetje J Tack & * Wieke H M Verbeek * Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. * Joseph A Murray * Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands. * Roel A Ophoff * Rudolf Magnus Institute, University Medical Center Utrecht, Utrecht, The Netherlands. * Roel A Ophoff * Center for Neurobehavioral Genetics, University of California, Los Angeles, California, USA. * Roel A Ophoff * Pediatric Gastroenterology Department, Hospital La Paz, Madrid, Spain. * Isabel Polanco * Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland. * Leena Peltonen * Department of Gastroenterology, Hepatology and Immunology, Children's Memorial Health Institute, Warsaw, Poland. * Anna Rybak * European Laboratory for Food Induced Disease, University of Naples Federico II, Naples, Italy. * Maria Pia Sperandeo & * Luigi Greco * Department of Gastroenterology and Hepatology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands. * Rinse K Weersma * Department of Pathology, Children's Memorial Health Institute, Warsaw, Poland. * Bozena Cukrowska * Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, California, USA. * Susan L Neuhausen * Department of Experimental Medicine, Faculty of Medicine University of Milano-Bicocca, Monza, Italy. * Donatella Barisani * Department of Medical Genetics, Biomedicum Helsinki, University of Helsinki, Helsinki, Finland. * Paivi Saavalainen * Research Program for Molecular Medicine, Biomedicum Helsinki, University of Helsinki, Helsinki, Finland. * Paivi Saavalainen Contributions D.A.v.H. and C.W. designed, co-ordinated and led the study. Experiments were performed in the labs of C.W., D.A.v.H., C.A.M., P.D. and P.M.G. Major contributions were: (i) DNA sample preparation: P.C.A.D., G.T., K.A.H., J.R., A.Z. and P.S.; (ii) genotyping: P.C.A.D., G.T., K.A.H., A.C., J.R. and R.G.; (iii) expression data generation: H.J.M.G., L.H.v.d.B., R.A.O., R.K.W. and L.F.; (iv) case-control association analyses: P.C.A.D., G.T., L.F., J.C.B. and D.A.v.H.; (v) expression analyses: L.F., G.A.R.H. and R.S.N.F.; (vi) manuscript preparation: P.C.A.D., G.T., L.F., R.S.N.F., G.A.R.H., J.C.B., C.W. and D.A.v.H. Other authors contributed variously to sample collection and all other aspects of the study. All authors reviewed the final manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * David A van Heel (d.vanheel@qmul.ac.uk) or * Lude Franke (lude@cleverfranke.com) Supplementary information * Abstract * Accession codes * Change history * Author information * Supplementary information Excel files * Supplementary Data 1 (1M) Results for the top 1000 markers PDF files * Supplementary Text and Figures (2M) Supplementary Note, Supplementary Tables 1–3 and Supplementary Figures 1–3 Additional data - Mutations in VIPAR cause an arthrogryposis, renal dysfunction and cholestasis syndrome phenotype with defects in epithelial polarization
Cullinane AR Straatman-Iwanowska A Zaucker A Wakabayashi Y Bruce CK Luo G Rahman F Gürakan F Utine E Ozkan TB Denecke J Vukovic J Di Rocco M Mandel H Cangul H Matthews RP Thomas SG Rappoport JZ Arias IM Wolburg H Knisely AS Kelly DA Müller F Maher ER Gissen P - Nature genetics 42(4):303-312 (2010)
Nature Genetics | Article Mutations in VIPAR cause an arthrogryposis, renal dysfunction and cholestasis syndrome phenotype with defects in epithelial polarization * Andrew R Cullinane1 Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Straatman-Iwanowska1, 19 Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Zaucker1, 19 Search for this author in: * NPG journals * PubMed * Google Scholar * Yoshiyuki Wakabayashi2, 19 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher K Bruce1 Search for this author in: * NPG journals * PubMed * Google Scholar * Guanmei Luo1 Search for this author in: * NPG journals * PubMed * Google Scholar * Fatimah Rahman1 Search for this author in: * NPG journals * PubMed * Google Scholar * Figen Gürakan3 Search for this author in: * NPG journals * PubMed * Google Scholar * Eda Utine4 Search for this author in: * NPG journals * PubMed * Google Scholar * Tanju B Özkan5 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonas Denecke6 Search for this author in: * NPG journals * PubMed * Google Scholar * Jurica Vukovic7 Search for this author in: * NPG journals * PubMed * Google Scholar * Maja Di Rocco8 Search for this author in: * NPG journals * PubMed * Google Scholar * Hanna Mandel9 Search for this author in: * NPG journals * PubMed * Google Scholar * Hakan Cangul1, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Randolph P Matthews11 Search for this author in: * NPG journals * PubMed * Google Scholar * Steve G Thomas12 Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua Z Rappoport13 Search for this author in: * NPG journals * PubMed * Google Scholar * Irwin M Arias2 Search for this author in: * NPG journals * PubMed * Google Scholar * Hartwig Wolburg14 Search for this author in: * NPG journals * PubMed * Google Scholar * A S Knisely15 Search for this author in: * NPG journals * PubMed * Google Scholar * Deirdre A Kelly16 Search for this author in: * NPG journals * PubMed * Google Scholar * Ferenc Müller1 Search for this author in: * NPG journals * PubMed * Google Scholar * Eamonn R Maher1, 17 Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Gissen1, 18 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume:42,Pages:303–312Year published:(2010)DOI:doi:10.1038/ng.538Received24 September 2009Accepted25 January 2010Published online28 February 2010 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Arthrogryposis, renal dysfunction and cholestasis syndrome (ARC) is a multisystem disorder associated with abnormalities in polarized liver and kidney cells. Mutations in VPS33B account for most cases of ARC. We identified mutations in VIPAR (also called C14ORF133) in individuals with ARC without VPS33B defects. We show that VIPAR forms a functional complex with VPS33B that interacts with RAB11A. Knockdown of vipar in zebrafish resulted in biliary excretion and E-cadherin defects similar to those in individuals with ARC. Vipar- and Vps33b-deficient mouse inner medullary collecting duct (mIMDC-3) cells expressed membrane proteins abnormally and had structural and functional tight junction defects. Abnormal Ceacam5 expression was due to mis-sorting toward lysosomal degradation, but reduced E-cadherin levels were associated with transcriptional downregulation. The VPS33B-VIPAR complex thus has diverse functions in the pathways regulating apical-basolateral polarity in the liver! and kidney. View full text Figures at a glance * Figure 1: VPS33B interacts with VIPAR. () HEK293 cells were co-transfected with hemagglutinin (HA)-tagged VPS33B, VPS33B (L30P) mutant or VPS33A and with Myc-tagged VIPAR or VPS16 constructs. Coimmunoprecipitation experiments showed that HA-VPS33B and HA-VPS33B (L30P) interact with Myc-VIPAR and that HA-VPS33A interacts with Myc-VPS16. No interaction was seen between HA-VPS33B and Myc-VPS16 or between HA-VPS33A and Myc-VIPAR. Quantification revealed that 28.6% ± 1.5% (s.e.m.) (VPS33B), 23.8% ± 0.8% (VPS33B-L30P) and 28.2% ± 1.1% (VPS33A) of the Myc-VIPAR or Myc-VPS16 input were recovered (n = 3, ± 1 s.e.m.). () HEK293 cells were transfected with either Myc-VIPAR or Myc-VPS16, and a coimmunoprecipitation experiment was carried out to assess for interaction with endogenous VPS33B. Again, VPS33B interacted with Myc-VIPAR but not with Myc-VPS16. (,) Confocal fluorescence photomicrographs of HEK293 cells transfected with YFP-VPS33B and mCherry-VIPAR constructs individually () or with both mCherry-VIPAR and YFP-VPS! 33B, YFP-VPS33B-L30P or YFP-VPS33A (). Nuclei are stained with TO-PRO-3. Scale bars, 15 μm. Individual overexpression of VPS33B or VIPAR demonstrated generalized cytoplasmic distribution. When both are overexpressed, mCherry-VIPAR and YFP-VPS33B form cytoplasmic clusters. No clusters were seen when mCherry-VIPAR was overexpressed with YFP-VPS33B-L30P or HA-VPS33A. * Figure 2: Intrahepatic defects in individuals with ARC and in Vipar-deficient zebrafish larvae. () Immunohistochemical analysis of E-cadherin localization in an individual with ARC26 with VPS33B mutations and in control liver tissue (×400 and ×1,000 magnification; hematoxylin counterstain). Decreased amounts of E-cadherin can be seen at the apical junction complexes in the ARC sample compared with the control. Decreased E-cadherin expression was particularly obvious in hepatocytes cuffing portal tracts (arrow). () Lateral and ventral views of vipar in situ hybridization in 5 d post fertilization (d.p.f.) larvae using DIG-labeled antisense and sense (control) vipar probes. High expression in liver (L, outlined) and small intestine (arrow) is seen. () Brightfield and green fluorescence images of PED-6–treated non-injected (embryos) larvae and vipar ATG morpholino–injected 5-d.p.f. (embryos) larvae. Liver (white arrow), swimbladder (white arrowhead) and gallbladder samples (red arrowhead) are indicated. () A bar graph showing that the amount of PED6 detected in the ! gallbladder is significantly lower in ATG and exon 3 morpholino–injected larvae than in non-injected control larvae or exon 3–mismatch control larvae. Injecting vipar mRNA, but not an unrelated mRNA (RFP), after morpholino treatment rescued the phenotype (n = 90 for each treatment group (3 independent injections with 30 larvae in each clutch); error bars, ± 1 s.d., *P < 0.001 by z-test). () Immunostaining of 5-d.p.f. larvae livers for E-cadherin showing markedly reduced E-cadherin staining in morpholino-injected larvae compared with controls. Scale bars, 200 μm (,) or 10 μm (). * Figure 3: Abnormal membrane polarization of mIMCD-3 cells in Vps33b and Vipar deficiencies. () Protein blotting of Ceacam5 (Cea) in biotinylated membrane fraction lysates and whole-cell lysate showing severe reduction of Cea in Vps33b shRNA and Vipar shRNA cells. The unbound fraction contains the unbiotinylated or nonmembrane proteins. () Confocal immunofluorescence photomicrographs of cultured cells treated with shRNA (control shRNA, Vipar shRNA and Vps33b shRNA); shown are xz plane images. Loss of apical distribution can be seen for transfected P75. The basolateral protein Na+-K+ ATPase (endogenous) is correctly localized in all three cell types. Nuclei are stained with TO-PRO-3. Scale bar, 10 μm. () Average TER readings every 24 h after seeding control and knockdown cells onto Transwell supports (n = 4, error bars, ± 1 s.e.m.). Maximum levels of resistance reached by the knockdown cells are ~50% reduced compared with controls. () Paracellular flux of 4 kDa dextran fluorescein isothiocyanate conjugate, with and without calcium in the medium, for all three cell ! types (n = 4, error bars, ± 1 s.e.m., *P < 0.05, **P < 0.001 by a Student's t-test). Similar paracellular flux is achieved in control cells during 'calcium switch' experiments. * Figure 4: Both Vipar and Vps33b are required for apical junction complex formation. () Freeze-fracture images of control, Vipar knockdown and Vps33b knockdown cells showing tight junction strands (scale bar, 0.2 μm). In Vipar shRNA–treated cells and in Vps33b shRNA–treated cells, tight junctions revealed a strong decrease of tight junction strand complexity and/or a decrease of the P-face association, leading to interrupted and blindly ending strands (arrows), when compared with the control cells. Interruptions but no blind ends were seen in the control cells. This can also be seen in the E-face, where continuous networks of grooves with only rare blind ends can be seen (data not shown). () Confocal immunofluorescence photomicrographs in the xy and xz planes of zo-1, claudin-1 and E-cadherin in cells treated with control shRNA,Vps33b shRNA or Vipar shRNA and in cells with stable transfection of GDP-locked Rab11a mutant DN-Rab11a (scale bar, 10 μm). The knockdown cells do not grow in a monolayer with constant height, but instead grow partly on top of n! eighboring cells. Amounts of E-cadherin and, to a lesser extent, claudin-1 are reduced at adherens junctions and tight junctions. () Protein blotting shows results compatible with those from immunostaining, with decreased E-cadherin and claudin-1 levels in the knockdown cells. However, levels of another adherens junction protein, β-catenin, were the same as those in controls. * Figure 5: Abnormalities in cell morphology and growth in Vipar and Vps33b deficiencies. () Phase contrast images of cells treated with control shRNA, Vps33b shRNA or Vipar shRNA growing on tissue culture dishes and showing disordered growth in knockdown cells (particularly obvious in Vps33b shRNA–treated cells). Cavitations are seen only in the control cells (arrow). () Control shRNA, Vps33b shRNA and Vipar shRNA cells grown on Transwell supports. β-actin is stained with phalloidin-TRITC conjugate (scale bar, 100 μm). Cavitations (arrow) are present only in control cells. () mIMCD-3 cells growing in collagen gels (×10). Cells treated with control shRNA form highly branched tubules after 3 d in culture, whereas cells treated with Vipar shRNA or Vps33b shRNA cells form no tubules. () Numbers of cells harvested (mean) from Transwell supports using trypsin after 8 d in culture (n = 3, error bar, ± 1 s.e.m., P < 0.05 by t-test). Knockdown cell numbers are significantly greater than control cell numbers, compatible with loss of contact inhibition. Actual cell p! roliferation is likely underestimated for knockdown cells, in which ongoing spontaneous detachment led to loss into medium before harvesting. * Figure 6: The VPS33B-VIPAR complex interacts with RAB11A. () Confocal fluorescence photomicrographs of HEK293 cells cotransfected with HA-VPS33B (not immunostained), with mCherry-VIPAR and with green fluorescent protein (GFP)-tagged RAB11A (GFP-RAB11A) showing colocalization (inset). Scale bar, 15 μm. () Confocal immunofluorescence photomicrographs of mIMCD-3 cells stained for endogenous Rab11a (green) and Vps33b (red). Nuclei are stained with TO-PRO-3. Scale bar, 15 μm. Colocalization of both markers is seen (inset). () Coimmunoprecipitation of endogenous Vps33b and Rab11a from mIMCD-3 cells after pulldown with Rab11a antibody or Vps33b antibody. Control (IgG molecules) failed to pull down the relevant protein. () HEK293 cells were cotransfected with HA-tagged VPS33B or empty vector, Myc-VIPAR or empty vector, and GFP-RAB11A. Coimmunoprecipitation experiments using HA-VPS33B or Myc-VIPAR as bait show that both VPS33B and VIPAR immunoprecipitate in the same complex as RAB11A. Overexpression of both HA-VPS33B and Myc-VIPAR was nec! essary for interaction with GFP-RAB11A to occur. Quantification of immunoprecipitation revealed that 6.7% (for HA; IP α HA) and 7.9% (for Myc; IP α Myc) of the RAB11A input was recovered. When GFP-tagged dominant negative (DN) RAB11A was used, no interaction between the VPS33B-VIPAR complex and GDP-locked dominant negative RAB11A could be seen. * Figure 7: Investigation of the intracellular trafficking defects in Vps33b- and Vipar-deficient mIMCD-3 cells. () After transfection with E-cadherin-GFP and with Gal-T (a Golgi-resident protein) linked to CFP (Gal-T-CFP), cells were incubated overnight at 37 °C. Exogenously expressed E-cadherin targeted normally to the plasma membrane in the knockdown and control cells. () Cells were infected with apically targeted A-VSVG-CFP and incubated overnight at 40 °C. The cells were next incubated at 20 °C with cycloheximide (10 μg/ml) for 3 h and then, at t = 0, shifted to incubation at 32 °C. At t = 0, A-VSVG-CFP accumulated in the Golgi, but after the temperature switch, A-VSVG-CFP was trafficked normally to the plasma membrane in both knockdown cells and controls. () Membrane biotinylation assay showing reduced membrane mCherry-P75 content in the knockdown cell lines compared to the control cells when incubated with cycloheximide. At t = 0, the membrane content of P75 was similar for all cell lines, suggesting no abnormality in post-Golgi trafficking of P75. No β-actin was detected ! in the membrane fraction, and the whole-cell lysates contained mCherry-P75 in all samples. () Recovery of the Ceacam5 band after overnight treatment with leupeptin (lysosomal degradation inhibitor) is shown in cells treated with Vps33b shRNA or Vipar shRNA. () Lamp-1 immunofluorescent staining of wild-type mIMCD-3 cells, cells treated with Vipar shRNA and cells treated with both leupeptin and Vipar shRNA. Increased Lamp-1 immunostaining is seen in the knockdown cells. Scale bar, 15 μm. (,) Quantitative real-time PCR analysis of Cdh1 (E-cadherin) mRNA () and Cdh1 promoter activation () assessed by luciferase assay in knockdown and wild-type mIMCD-3 cells. Error bars, ± 1 s.e.m. In knockdown cells, mRNA levels were markedly reduced and promoter activity was decreased. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * NM_001001836 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Anna Straatman-Iwanowska, * Andreas Zaucker & * Yoshiyuki Wakabayashi Affiliations * Medical and Molecular Genetics, School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, UK. * Andrew R Cullinane, * Anna Straatman-Iwanowska, * Andreas Zaucker, * Christopher K Bruce, * Guanmei Luo, * Fatimah Rahman, * Hakan Cangul, * Ferenc Müller, * Eamonn R Maher & * Paul Gissen * Cell Biology and Metabolism Program, Eunice Kennedy Shriver National Institute of Child Health and Human Development, US National Institutes of Health, Bethesda, Maryland, USA. * Yoshiyuki Wakabayashi & * Irwin M Arias * Department of Pediatric Gastroenterology, Hepatology and Nutrition, Hacettepe University, Ankara, Turkey. * Figen Gürakan * Clinical Genetics Unit, Department of Pediatrics, Hacettepe University, Ankara, Turkey. * Eda Utine * Department of Pediatric Gastroenterology, Hepatology and Nutrition, Uludag University School of Medicine, Bursa, Turkey. * Tanju B Özkan * Department of Pediatrics, University Hospital of Rostock, Rostock, Germany. * Jonas Denecke * Department of Pediatrics, University Hospital Rebro, Zagreb, Croatia. * Jurica Vukovic * II Pediatric Unit, Gaslini Institute, Genoa, Italy. * Maja Di Rocco * Metabolic Unit, Meyer Children's Hospital, Rambam Medical Center, Technion Faculty of Medicine, Haifa, Israel. * Hanna Mandel * Department of Medical Genetics, Uludag University School of Medicine, Bursa, Turkey. * Hakan Cangul * Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA. * Randolph P Matthews * Birmingham Platelet Group, School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, UK. * Steve G Thomas * School of Biosciences, University of Birmingham, Birmingham, UK. * Joshua Z Rappoport * Institute of Pathology, University of Tübingen, Tübingen, Germany. * Hartwig Wolburg * Institute of Liver Studies, King′s College Hospital, London, UK. * A S Knisely * The Liver Unit, Birmingham Children's Hospital, Birmingham, UK. * Deirdre A Kelly * West Midlands Regional Genetics Service, Birmingham Women's Hospital, Birmingham, UK. * Eamonn R Maher * The Inherited Metabolic Diseases Unit, Birmingham Children's Hospital, Birmingham, UK. * Paul Gissen Contributions A.R.C. designed, conducted and interpreted experiments and wrote the manuscript. A.S.-I., A.Z. and Y.W. designed, conducted and interpreted experiments. C.K.B., G.L., F.R. and H.C. performed experiments. F.G., E.U., T.B.Ö., J.D., J.V., M.D.R., H.M. contributed subjects' DNA. R.P.M., S.G.T., J.Z.R., I.M.A., H.W., A.S.K., D.A.K., F.M. and E.R.M. designed and supervised experiments. P.G. conceived and directed the project, obtained funding and wrote the manuscript. All authors edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Paul Gissen (p.gissen@bham.ac.uk) Supplementary information * Abstract * Accession codes * Author information * Supplementary information Movies * Supplementary Movie 1 (1M) YFP-VP33B FLIP confocal live-cell microscopy. YFP-VPS33B was transfected into mIMCD-3 cells and a Fluorescence Loss In Photobleaching (FLIP) experiment was carried out in live cells. Bleaching was done using a 514 nm laser set to 100% every 5 seconds in an area of one cell, with an image captured every 2.5 seconds for a total of 5 minutes. Removal of the free cytoplasmic YFP-VPS33B pool (by photobleaching) reveals occasional cytoplasmic clusters, which are not as pronounced as when VPS33B is co-overexpressed with polarin. * Supplementary Movie 2 (5M) VSVG-YFP (basolaterally targeted protein), Gal-T-GFP-, and A-VSVG-CFP infected cells were incubated overnight at 40°C. Live cell imaging was performed using a Zeiss 510 inverted confocal microscope with an ×20 objective lens. Pinhole was set fully open. Images were taken every minute. Experiment started 10min after the temperature shift to 32°C (time 0). Relative fluorescence intensities associated with the Golgi (RO1), entire cell (RO2) and plasma membrane (RO3) for one representative cell after shift to 32°C are plotted at 1min intervals. VSVG-YFP infected cells were incubated overnight at 40°C. The experiment was started 10 min after the temperature shift to 32°C (time 0). At 0 min VSVG-YFP intracellular localization of fluorescence was noted. After 140min, VSVG-YFP targeted to the plasma membrane although some intracellular fluorescence remained. Relative fluorescence intensity is calculated using the following equation: (Raw F.I. of - background F.I.) ×100/maximu! m F.I. PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–8 and Supplementary Table Additional data - Mutations in the mitochondrial protease gene AFG3L2 cause dominant hereditary ataxia SCA28
Di Bella D Lazzaro F Brusco A Plumari M Battaglia G Pastore A Finardi A Cagnoli C Tempia F Frontali M Veneziano L Sacco T Boda E Brussino A Bonn F Castellotti B Baratta S Mariotti C Gellera C Fracasso V Magri S Langer T Plevani P Di Donato S Muzi-Falconi M Taroni F - Nature genetics 42(4):313-321 (2010)
Nature Genetics | Article Mutations in the mitochondrial protease gene AFG3L2 cause dominant hereditary ataxia SCA28 * Daniela Di Bella1 Search for this author in: * NPG journals * PubMed * Google Scholar * Federico Lazzaro2 Search for this author in: * NPG journals * PubMed * Google Scholar * Alfredo Brusco3 Search for this author in: * NPG journals * PubMed * Google Scholar * Massimo Plumari1 Search for this author in: * NPG journals * PubMed * Google Scholar * Giorgio Battaglia4 Search for this author in: * NPG journals * PubMed * Google Scholar * Annalisa Pastore5 Search for this author in: * NPG journals * PubMed * Google Scholar * Adele Finardi4 Search for this author in: * NPG journals * PubMed * Google Scholar * Claudia Cagnoli3 Search for this author in: * NPG journals * PubMed * Google Scholar * Filippo Tempia6 Search for this author in: * NPG journals * PubMed * Google Scholar * Marina Frontali7 Search for this author in: * NPG journals * PubMed * Google Scholar * Liana Veneziano7 Search for this author in: * NPG journals * PubMed * Google Scholar * Tiziana Sacco6 Search for this author in: * NPG journals * PubMed * Google Scholar * Enrica Boda6 Search for this author in: * NPG journals * PubMed * Google Scholar * Alessandro Brussino3 Search for this author in: * NPG journals * PubMed * Google Scholar * Florian Bonn8 Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara Castellotti1 Search for this author in: * NPG journals * PubMed * Google Scholar * Silvia Baratta1 Search for this author in: * NPG journals * PubMed * Google Scholar * Caterina Mariotti1 Search for this author in: * NPG journals * PubMed * Google Scholar * Cinzia Gellera1 Search for this author in: * NPG journals * PubMed * Google Scholar * Valentina Fracasso1 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefania Magri1 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Langer8 Search for this author in: * NPG journals * PubMed * Google Scholar * Paolo Plevani2 Search for this author in: * NPG journals * PubMed * Google Scholar * Stefano Di Donato1 Search for this author in: * NPG journals * PubMed * Google Scholar * Marco Muzi-Falconi2 Search for this author in: * NPG journals * PubMed * Google Scholar * Franco Taroni1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume:42,Pages:313–321Year published:(2010)DOI:doi:10.1038/ng.544Received14 October 2009Accepted05 February 2010Published online07 March 2010 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Autosomal dominant spinocerebellar ataxias (SCAs) are genetically heterogeneous neurological disorders characterized by cerebellar dysfunction mostly due to Purkinje cell degeneration. Here we show that AFG3L2 mutations cause SCA type 28. Along with paraplegin, which causes recessive spastic paraplegia, AFG3L2 is a component of the conserved m-AAA metalloprotease complex involved in the maintenance of the mitochondrial proteome. We identified heterozygous missense mutations in five unrelated SCA families and found that AFG3L2 is highly and selectively expressed in human cerebellar Purkinje cells. m-AAA–deficient yeast cells expressing human mutated AFG3L2 homocomplex show respiratory deficiency, proteolytic impairment and deficiency of respiratory chain complex IV. Structure homology modeling indicates that the mutations may affect AFG3L2 substrate handling. This work identifies AFG3L2 as a novel cause of dominant neurodegenerative disease and indicates a previously unknow! n role for this component of the mitochondrial protein quality control machinery in protecting the human cerebellum against neurodegeneration. View full text Figures at a glance * Figure 1: AFG3L2 mutations cause amino acid substitutions in highly conserved regions of the protein. Bottom, genomic organization of the human AFG3L2 gene and domain structure of the protein. AFG3L2 consists of 17 exons spanning 48 kb on chromosome 18p11.21 (5′→3′ map position: 12319108–12367194). The identified mutations in exon 10 and in exon 16 are boxed. Top, ClustalW2 multiple alignments of the human AFG3L2 regions encoded by exon 10 (residues 426–439) or exon 16 (residues 668–708) with members of the m-AAA family from different organisms (Dr. mel., Drosophila melanogaster; C. el., Caenorhabditis elegans; S. cer., S. cerevisiae; T. therm., T. thermophilus). The mutated residues are indicated in red (proteolytic domain) or magenta (ATPase domain) above the alignment. Residues identical to AFG3L2 are framed in black. Conserved, semiconserved and nonconserved substitutions are framed in gray, light gray and white boxes, respectively. The following consensus symbols are used in the alignment to denote the degree of conservation, as defined by the Gonnet Pam250 ! matrix scores observed in each column: (*), residue is identical in all sequences in the alignment; (:), conserved substitutions have been observed; (.), semiconserved substitutions have been observed. MTS, mitochondrial targeting sequence; TM1 and TM2, transmembrane domains 1 and 2, respectively (TMHMM Server v2.0); WA, Walker-A motif (GPPGTGKT, residues 348–355); WB, Walker-B motif (ILFIDEID, residues 403–410); SRH, second region of homology (TNRPDILDPALLRPGRFD, residues 453–470); HEAGH (residues 574–578), protease catalytic site (Pfam 24.0, October 2009; http://pfam.sanger.ac.uk/; the asterisk on the HEAGH site indicates the catalytic Glu575 that is mutated to glutamine in the control proteolytic mutant AFG3L2E575Q; ref. 13). * Figure 2: Complementation studies in S. cerevisiae. Serial dilutions of exponentially growing yeast cultures spotted on plates show oxidative growth phenotype of yta10Δyta12Δ cells expressing normal and mutant human AFG3L2. Substitutions affecting respiration are in bold. Respiratory competence is deduced by the ability to grow on 2% glycerol (YPG). Except where indicated, we scored growth after 3 d incubation at 28 °C. YPD and YPG, YEP plates containing 2% glucose or 2% glycerol, respectively. () AFG3L2 was expressed under control of the strong ADH1 promoter (pYC6/CTADH1-AFG3L2) or the endogenous YTA10 promoter (pYC6/CTYTA10-AFG3L2). Right panels, AFG3L2 levels assessed by immunoblotting. K699, WT yeast strain; pYC6/CT, empty plasmid. () Respiratory phenotype of yta10Δyta12Δ cells expressing either normal (WT) or mutant human AFG3L2. Similar results were obtained after incubation at 37 °C (data not shown). () Coexpression of AFG3L2WT (WT) and AFG3L2E691K (E691K) results in a limited correction of the respiratory-defici! ent phenotype, suggesting a dominant negative effect of the mutation. The graph below shows the growth rates of cells expressing either AFG3L2WT or AFG3L2E691K or coexpressing both forms (WT+E691K). We grew cells for 24 h with cell counting every 4 h. Values on the y axis represent the ratio between cell density (cells/ml) at a given time and cell density at start (t0). Growth rates are calculated by linear regression analysis (trend line). Each value represents the mean of three independent experiments. Error bars indicate ± 1 s.d. We determined statistical significance (P ≤ 0.0005 or P ≤ 0.001) by Student′s t-test. () Respiratory phenotype of yta10Δyta12Δ cells coexpressing either normal or mutant human AFG3L2 with human paraplegin. () Rescue of the AFG3L2N432T respiratory-deficient phenotype by paraplegin is temperature sensitive, with moderate growth at 28 °C but no growth at 37 °C; no growth difference at 37 °C was observed for the other mutants (data not s! hown). () No rescue of the AFG3L2E691K respiratory-deficient p! henotype by paraplegin in long-term culture (7 d). () Both normal and mutant AFG3L2 interact with paraplegin. We immunoprecipitated hemagglutinin (HA)-tagged paraplegin (parapleginHA) with anti-HA from yta10Δyta12Δ cells expressing AFG3L2WT alone (lanes 2 and 7) or coexpressing either wild-type or mutant AFG3L2 with parapleginHA. We analyzed immunoprecipitates (IP) by SDS-PAGE and immunoblotting (IB) using anti-paraplegin (α-paraplegin) or anti-AFG3L2 (α-AFG3L2). AFG3L2 was detected in all the immunoprecipitates from yeast cells coexpressing parapleginHA (lanes 6 and 8–10). Lanes 1–5, immunoblot analysis of cell extracts before immunoprecipitation (input). * Figure 3: Cytochrome c oxidase enzyme activity and protein levels in yeast cells expressing mutant AFG3L2. Yeast strains and mutants are as described in Figure 2. Substitutions affecting respiration are in bold. () COX activity measured in isolated mitochondria from yta10Δyta12Δ m-AAA–deficient yeast cells expressing AFG3L2 alone, in the absence of paraplegin. Activity is expressed as nanomoles of cytochrome cred oxidized per minute per milligram of protein. Values in parentheses indicate percentage of activity compared to that of cells expressing AFG3L2WT (black bars). Bars and vertical lines indicate mean and ± 1 s.d., respectively. Red bars and asterisk indicate a statistically significant (P ≤ 0.01) difference from AFG3L2WT, as determined by Student's t-test (n = 4). Absence of asterisk (blue bars) indicates P > 0.05. () Fluorescence immunoblot analysis and protein quantitation of mitochondria-encoded COX subunits Cox1p, Cox2p and Cox3p, and nuclear-encoded subunit Cox4p, in mitochondrial extracts from cells expressing AFG3L2 alone, in the absence of paraplegin. Single! or double asterisk indicates a statistically significant difference from AFG3L2WT (black bars) with P ≤ 0.01 or P ≤ 0.001, respectively, as determined by Student's t-test (n = 4). Absence of asterisk indicate P > 0.05. (,) COX activity and fluorescence immunoblot analysis as in and for AFG3L2 in the presence of paraplegin. * Figure 4: Proteolytic activity of normal and mutant AFG3L2 in yeast. Yeast strains and mutants are as in Figure 2. Substitutions affecting respiration are in bold. () Fluorescence immunoblot analysis with anti-MrpL32 shows that yeast MrpL32 precursor (p) accumulates in yta10Δyta12Δ m-AAA–deficient cells (lane 2). m, mature MrpL32. () Fluorescence immunoblot analysis of MrpL32 in yeast cells expressing AFG3L2 homo-oligomeric m-AAA, showing accumulation of MrpL32 precursor in AFG3L2 mutants. Histograph reports quantitative results. AFG3L2 proteolytic competence is expressed as the ratio of pMrpL32 level to total (p + m) MrpL32 level. MrpL32 levels were normalized to the loading control protein β-actin. Bars and vertical lines indicate mean and ± 1 s.d., respectively. Red bars and asterisks indicate a statistically significant (P ≤ 0.01) difference from AFG3L2WT (lane 2, black bar) as determined by Student's t-test (n = 4). Blue bars and absence of asterisk indicate P > 0.05. () Fluorescence immunoblot analysis of MrpL32 in m-AAA–defic! ient yeast cells, showing the effects of AFG3L2 and paraplegin coexpression on the accumulation of pMrpL32 (indicated by p/(p + m) ratio as in ). MrpL32 levels were normalized to the loading control protein β-actin. Bars and vertical lines indicate mean and ± 1 s.d., respectively. No statistically significant difference (P > 0.05) was observed between AFG3L2WT-harboring cells (lane 2, black bar) and strain AFG3L2H126Q or mutant strains AFG3L2S674L, AFG3L2A694E and AFG3L2R702Q (lanes 4, 5, 7 and 8, blue bars). By contrast, asterisk (red bars) indicates a statistically significant difference (P ≤ 0.005, n = 4; Student's t-test) between AFG3L2WT cells and SCA28 mutants AFG3L2E691K or AFG3L2N432T, or the control mutant AFG3L2E575Q (ref. 13). * Figure 5: Molecular modeling of normal and mutant AFG3L2. (,) Orthogonal views of the hexameric ring of AFG3L2 built by homology using the coordinates of T. thermophilus FtsH (PDB 2DHR) as a template. Panel shows the view from the protease domain (matrix) side. The monomers are alternately shown in light and dark green. The side chains of the proteolytic domain residues mutated in the affected individuals are shown in red. In , it is clear that the hexamer adopts a flat-cylinder-like shape divided into two disks. The lower disk, containing the protease domain, forms a sixfold-symmetric structure with a zinc binding site. The upper disk is composed of six AAA domains, each of which contains ADP. Short red arrows indicate the locations of substitutions on the matrix side of the protease domain (lower disc); long magenta arrow indicates the location of the N432T substitution in the ATPase domain (upper disc). The monomer (Supplementary Fig. 5a) is boxed in red. IM, inner mitochondrial membrane. (–) Surface representations of the pro! tease side of the homo- and hetero-oligomeric homology models, showing the effect of the E691K substitution on the electrostatic potential of the complex (see also Supplementary Fig. 5c–g). Shown are electrostatic surfaces of wild-type AFG3L2 homohexamer (); homohexamer of AFG3L2E691K (); homohexamer obtained by alternating wild-type AFG3L2 (AFG3L2WT) and mutant AFG3L2E691K (); and heterohexamer obtained by alternating paraplegin with either AFG3L2WT () or AFG3L2E691K (). Blue, positive charge; red, negative charge. The change induced by the E691K charge reversal in the central pore is greatest in the homohexameric mutant AFG3L2E691K-AFG3L2E691K () and in the heterohexameric complex of AFG3L2E691K and paraplegin (), in which the positively charged Lys691 of AFG3L2E691K is not counteracted by the neutral Gln693 of paraplegin. * Figure 6: Expression of AFG3L2 and paraplegin in human and mouse nervous systems. (–) Confocal immunofluorescence of human nervous tissue. In the cerebellum (–), both AFG3L2 (,) and paraplegin (,) are selectively expressed in the Purkinje cell layer (,, arrows). Note the intense expression in the soma and apical dendrites of Purkinje neurons (,, arrowheads). In the cerebral cortex (,) and spinal cord (,), as compared to AFG3L2 (,), paraplegin is more intensely expressed in layer-V pyramidal neurons () and lamina-IX motor neurons (). AFG3L2 staining in spinal motor neurons () is just above background level. Insets in and show enlarged detail. Scale bars, 100 μm (,,,) or 20 μm (–,, insets of and ). (–) In situ hybridization of mouse cerebellum with Afg3l2 (–) and Spg7 (–) riboprobes. Low (,) and high (,) magnification of parasagittal sections of the cerebellar vermis showing a strong expression in Purkinje cells (,, arrowheads) and a weaker expression in granule and Golgi cells. (,) Control sections adjacent to and , hybridized with sense prob! es. Scale bars, 400 μm (,) or 25 μm (,,,). ml, molecular layer; Pcl, Purkinje cell layer; gl, granule cell layer; Go, Golgi cell; wm, white matter. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Entrez Nucleotide * NM_006796.1 * NM_003119.2 GenBank * Q72IK4 Protein Data Bank * 2DHR * 2DHR Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS Istituto Neurologico "Carlo Besta", Milan, Italy. * Daniela Di Bella, * Massimo Plumari, * Barbara Castellotti, * Silvia Baratta, * Caterina Mariotti, * Cinzia Gellera, * Valentina Fracasso, * Stefania Magri, * Stefano Di Donato & * Franco Taroni * Department of Biomolecular Sciences and Biotechnology, University of Milan, Milan, Italy. * Federico Lazzaro, * Paolo Plevani & * Marco Muzi-Falconi * Department of Genetics, Biology, and Biochemistry, University of Turin, and Unit of Medical Genetics, San Giovanni Battista Hospital, Turin, Italy. * Alfredo Brusco, * Claudia Cagnoli & * Alessandro Brussino * Unit of Molecular Neuroanatomy, Fondazione IRCCS Istituto Neurologico "Carlo Besta", Milan, Italy. * Giorgio Battaglia & * Adele Finardi * National Institute for Medical Research, London, UK. * Annalisa Pastore * Section of Physiology of the Department of Neuroscience, University of Turin, and Rita Levi Montalcini Center for Brain Repair, National Institute of Neuroscience, Turin, Italy. * Filippo Tempia, * Tiziana Sacco & * Enrica Boda * Institute of Neurobiology and Molecular Medicine, Consiglio Nazionale delle Ricerche, Rome, Italy. * Marina Frontali & * Liana Veneziano * Institute for Genetics and Center for Molecular Medicine Cologne, University of Cologne, Germany. * Florian Bonn & * Thomas Langer Contributions D.D.B. and F. Taroni identified the disease gene and characterized the mutations in the cells of affected individuals; D.D.B., F.L., V.F. and S.M. carried out the experiments in yeast; A. Brusco, C.C. and A. Brussino performed preliminary genetic screening and generated AFG3L2 expression plasmid; M.P. and C.G. generated antibodies to AFG3L2 and paraplegin and performed biochemical studies in the cells of affected individuals; A.P. performed molecular modeling of the mutant proteins; G.B. and A.F. performed immunohistochemical studies in human nervous tissue; F. Tempia, T.S. and E.B. performed expression studies in mouse cerebellum; C.M. and S.D.D. made clinical diagnoses and collected clinical data and samples; M.F. and L.V. characterized and contributed data from family RM-DS; F.B. and T.L. provided yeast antibodies, shared unpublished observations and advised on handling of yeast data analysis; C.G., B.C. and S.B. performed preliminary genetic screening and subject selecti! on; B.C. performed quantitative analysis of AFG3L2 gene copy number; F. Taroni, M.M.-F. and P.P. conceived and designed the study and provided financial support; F. Taroni wrote the paper; all others received, edited and approved the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Franco Taroni (ftaroni@istituto-besta.it) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Note, Supplementary Tables 1–3 and Supplementary Figures 1–9 Additional data - Rare genetic variation at Zea mays crtRB1 increases β-carotene in maize grain
Yan J Kandianis CB Harjes CE Bai L Kim EH Yang X Skinner DJ Fu Z Mitchell S Li Q Fernandez MG Zaharieva M Babu R Fu Y Palacios N Li J Dellapenna D Brutnell T Buckler ES Warburton ML Rocheford T - Nature genetics 42(4):322-327 (2010)
Nature Genetics | Article Rare genetic variation at Zea mays crtRB1 increases β-carotene in maize grain * Jianbing Yan1, 2, 3, 13 Search for this author in: * NPG journals * PubMed * Google Scholar * Catherine Bermudez Kandianis4, 5, 13 Search for this author in: * NPG journals * PubMed * Google Scholar * Carlos E Harjes3, 12 Search for this author in: * NPG journals * PubMed * Google Scholar * Ling Bai6 Search for this author in: * NPG journals * PubMed * Google Scholar * Eun-Ha Kim7 Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaohong Yang2 Search for this author in: * NPG journals * PubMed * Google Scholar * Debra J Skinner4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Zhiyuan Fu2 Search for this author in: * NPG journals * PubMed * Google Scholar * Sharon Mitchell3 Search for this author in: * NPG journals * PubMed * Google Scholar * Qing Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Maria G Salas Fernandez3, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Zaharieva1 Search for this author in: * NPG journals * PubMed * Google Scholar * Raman Babu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yang Fu2, 4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Natalia Palacios1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jiansheng Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Dean DellaPenna7 Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Brutnell6 Search for this author in: * NPG journals * PubMed * Google Scholar * Edward S Buckler3, 9, 10 Search for this author in: * NPG journals * PubMed * Google Scholar * Marilyn L Warburton1, 11 Search for this author in: * NPG journals * PubMed * Google Scholar * Torbert Rocheford4, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume:42,Pages:322–327Year published:(2010)DOI:doi:10.1038/ng.551Received23 September 2009Accepted19 February 2010Published online21 March 2010 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Breeding to increase β-carotene levels in cereal grains, termed provitamin A biofortification, is an economical approach to address dietary vitamin A deficiency in the developing world. Experimental evidence from association and linkage populations in maize (Zea mays L.) demonstrate that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels. crtRB1 alleles associated with reduced transcript expression correlate with higher β-carotene concentrations. Genetic variation at crtRB1 also affects hydroxylation efficiency among encoded allozymes, as observed by resultant carotenoid profiles in recombinant expression assays. The most favorable crtRB1 alleles, rare in frequency and unique to temperate germplasm, are being introgressed via inexpensive PCR marker-assisted selection into tropical maize germplasm adapted to developing countries, where it is most needed! for human health. View full text Figures at a glance * Figure 1: Carotenoid biosynthetic pathway and Zea mays crtRB1 gene structure. () Simplified carotenoid biosynthetic pathway in maize and Arabidopsis5, 6, 12, 13 CRTRB, in blue, represents the nonheme di-iron β-carotene hydroxylase (BCH) family in maize, which has at least five members (Supplementary Table 1); the orthologous family in Arabidopsis has two members (BCH1 and BCH2). Carotenoid intermediates highlighted in red are compounds detected by HPLC in this study. () Zea mays crtRB1 is the target gene in the present study. The sequenced region is framed in gray, translated exons are depicted as black boxes and the putative start of transcription (TSS) and poly(A) sites are indicated. Polymorphisms found in original P1 sequence alignments are marked in the diagram, and those that are significantly associated with changes in βC, βC/βCX, βC/Z and βC/ALL are labeled with asterisks. GGPP, geranylgeranyl pyrophosphate; PSY, phytoene synthase; PDS, phytoene desaturase; Z-ISO, ζ-carotene isomerase; ZDS, ζ-carotene desaturase; CRTISO, carotenoid iso! merase; LCYE, lycopene ε-cyclase; LCYB, lycopene β-cyclase; CRTRB, β-carotene hydroxylase family; CYP97A, β-carotene hydroxylase (P450); CYP97C, ε-carotene hydroxylase (P450); ZEP1, zeaxanthin epoxidase; VDE1, violaxanthin de-epoxidase; ABA, abscisic acid. * Figure 2: Mean carotenoid concentration (μg g−1 dry weight, DW) for crtRB1 allele classes across three F2:3 populations. Series represent genotype class average concentrations of βC, ALL and ALL-βC (that is, total carotenoids minus βC). () A619 × SC55; () KI3 × B77; () KI3 × SC55. Error bars, s.e.m. * Figure 3: Allele-specific crtRB1 effects on biochemical activity and transcriptional expression. () crtRB1 quantitative RT-PCR from whole kernel at 15 d after pollination (DAP) and seedling leaf messenger RNA for the six indicated lines of Zea mays. Values are given as expression levels relative to kernel and seedling values for line NC356, most highly expressed in this set. Error bars are s.e.m.; all measurements are based on three replications except Hi27, which was unreplicated. Genetic variation for each inbred line is listed below according to 5′TE, InDel4 and 3′TE differences; allele codes can be found in Table 2 and Supplementary Figure 2. () crtRB1 quantitative RT-PCR for endosperm and embryo at 20 DAP from 42 lines differing in the 3′TE allele. Values are given as expression levels relative to endosperm and embryo values for line BY804 because it was the highest expresser in this panel. Error bars are s.e.m. 1, lines with no 3′TE insertion; 2, lines with 325-bp 3′TE insertion; 3, lines with 1,250-bp 3′TE insertion. () β-carotene hydroxylase product! profiles for the four CRTRB1 allozymes expressed in a recombinant E. coli assay system producing β-carotene. Accumulated carotenoids are expressed as percentage of total carotenoids. βC, β-carotene; βCX, β-cryptoxanthin; Z, zeaxanthin. Error bars are s.d. Genetic variation for each allozyme is listed below according to InDel4 and C-terminal (3′TE) differences; allele codes can be found in Table 2 and Supplementary Figure 2. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * GQ889501 * GQ889872 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Jianbing Yan & * Catherine Bermudez Kandianis Affiliations * International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico. * Jianbing Yan, * Maria Zaharieva, * Raman Babu, * Natalia Palacios & * Marilyn L Warburton * National Maize Improvement Center of China, China Agricultural University, Beijing, China. * Jianbing Yan, * Xiaohong Yang, * Zhiyuan Fu, * Qing Li, * Yang Fu & * Jiansheng Li * Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. * Jianbing Yan, * Carlos E Harjes, * Sharon Mitchell, * Maria G Salas Fernandez & * Edward S Buckler * Department of Crop Sciences, University of Illinois, Urbana, Illinois, USA. * Catherine Bermudez Kandianis, * Debra J Skinner, * Yang Fu & * Torbert Rocheford * Department of Agronomy, Purdue University, West Lafayette, Indiana, USA. * Catherine Bermudez Kandianis, * Debra J Skinner, * Yang Fu & * Torbert Rocheford * Boyce Thompson Institute, Ithaca, New York, USA. * Ling Bai & * Thomas Brutnell * Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA. * Eun-Ha Kim & * Dean DellaPenna * Agronomy Department, Iowa State University, Ames, Iowa, USA. * Maria G Salas Fernandez * United States Department of Agriculture–Agricultural Research Service: Plant, Soil and Nutrition Research Unit, Ithaca, New York, USA. * Edward S Buckler * Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York, USA. * Edward S Buckler * United States Department of Agriculture–Agricultural Research Service: Corn Host Plant Resistance Research Unit, Starkville, Mississippi, USA. * Marilyn L Warburton * Monsanto, Leesburg, Georgia, USA. * Carlos E Harjes Contributions C.E.H. and J.Y. identified the gene. X.Y., Z.F., Y.F., R.B., C.B.K., J.Y., M.G.S.F., M.Z. and S.M. carried out the sequencing and genotyping. L.B., E.-H.K. and X.Y. carried out the transcript expression and biochemical assays. J.Y. and D.J.S. developed the crtRB1 molecular markers. R.B. and J.Y. supervised the field testing. C.B.K., Z.F., Q.L. and N.P. carried out the carotenoid profiling. C.B.K. and X.Y. completed the genetic mapping and QTL analyses. J.Y. and C.B.K. carried out the association and genetic analyses. The study was designed and supervised by J.Y., J.L., D.D.P., T.B., E.S.B., M.L.W. and T.R. The manuscript was prepared by J.Y., C.B.K., D.J.S., M.L.W. and T.R. and was edited by D.D.P., T.B. and E.S.B. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Torbert Rocheford (torbert@purdue.edu) or * Marilyn L Warburton (marilyn.warburton@ars.usda.gov) Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–5 and Supplementary Tables 1–20 Additional data - Genetic variation in human HBB is associated with Plasmodium falciparum transmission
Gouagna LC Bancone G Yao F Yameogo B Dabiré KR Costantini C Simporé J Ouedraogo JB Modiano D - Nature genetics 42(4):328-331 (2010)
Nature Genetics | Letter Genetic variation in human HBB is associated with Plasmodium falciparum transmission * Louis Clement Gouagna1, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Germana Bancone2, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Yao3 Search for this author in: * NPG journals * PubMed * Google Scholar * Bienvenue Yameogo3 Search for this author in: * NPG journals * PubMed * Google Scholar * Kounbobr Roch Dabiré3 Search for this author in: * NPG journals * PubMed * Google Scholar * Carlo Costantini4 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacques Simporé5 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean Bosco Ouedraogo3 Search for this author in: * NPG journals * PubMed * Google Scholar * David Modiano2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume:42,Pages:328–331Year published:(2010)DOI:doi:10.1038/ng.554Received17 June 2009Accepted19 February 2010Published online21 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Genetic factors are known to have a role in determining susceptibility to infectious diseases1, 2, although it is unclear whether they may also influence host efficiency in transmitting pathogens. We examine variants in HBB that have been shown to be protective against malaria3 and test whether these are associated with the transmission of the parasite from the human host to the Anopheles vector. We conducted cross-sectional malariological surveys on 3,739 human subjects and transmission experiments involving 60 children and 6,446 mosquitoes in Burkina Faso, West Africa. Protective hemoglobins C (HbC, β6Glu→Lys)4, 5 and S (β6Glu→Val)6, 7, 8 are associated with a twofold in vivo (odds ratio 2.17, 95% CI 1.57–3.01, P = 1.0 × 10−6) and a fourfold ex vivo (odds ratio 4.12, 95% CI 1.90–9.29, P = 7.0 × 10−5) increase of parasite transmission from the human host to the Anopheles vector. This provides an example of how host genetic variation may influence the transmi! ssion dynamics of an infectious disease. View full text Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Louis Clement Gouagna & * Germana Bancone Affiliations * Institut de Récherche pour le Developpement (IRD), Unité de Recherche 16, Bobo Dioulasso, Burkina Faso. * Louis Clement Gouagna * Department of Public Health Sciences, University of Rome 'La Sapienza', Rome, Italy. * Germana Bancone & * David Modiano * Institut de Récherche en Sciences de la Santé (IRSS), Direction Régionale de Bobo-Dioulasso, Bobo Dioulasso, Burkina Faso. * Frank Yao, * Bienvenue Yameogo, * Kounbobr Roch Dabiré & * Jean Bosco Ouedraogo * IRD, Unité de Recherche 016, Organisation de Coordination pour la lutte contre les Endémies en Afrique Centrale, Yaoundé, Cameroon. * Carlo Costantini * Centre Medical Saint-Camille, Ouagadougou, Burkina Faso. * Jacques Simporé * Istituto Pasteur, Fondazione Cenci Bolognetti, University of Rome 'La Sapienza', Rome, Italy. * David Modiano Contributions L.C.G. organized and supervised the parasitological and entomological surveys in the Bobo Dioulasso area. G.B. performed parasitological, entomological and genetic surveys in the Bobo Dioulasso area. F.Y. and B.Y. participated in parasitological, entomological and genetic surveys. K.R.D. participated to entomological surveys. J.S. organized and performed the parasitological and genetic survey in the Ouagadougou area. J.B.O. coordinated the study in the Bobo Dioulasso area. D.M. proposed the scientific hypothesis and organized and coordinated the study. L.C.G., C.C., J.B.O. and D.M. designed the research procedure. L.C.G., G.B., J.B.O. and D.M. analyzed and interpreted the data. D.M. and L.C.G. wrote the paper. All authors discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * David Modiano (david.modiano@uniroma1.it) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (224K) Supplementary Tables 1–5 Additional data - Genome-wide association identifies multiple ulcerative colitis susceptibility loci
McGovern DP Gardet A Törkvist L Goyette P Essers J Taylor KD Neale BM Ong RT Lagacé C Li C Green T Stevens CR Beauchamp C Fleshner PR Carlson M D'Amato M Halfvarson J Hibberd ML Lördal M Padyukov L Andriulli A Colombo E Latiano A Palmieri O Bernard EJ Deslandres C Hommes DW de Jong DJ Stokkers PC Weersma RK The NIDDK IBD Genetics Consortium Sharma Y Silverberg MS Cho JH Wu J Roeder K Brant SR Schumm LP Duerr RH Dubinsky MC Glazer NL Haritunians T Ippoliti A Melmed GY Siscovick DS Vasiliauskas EA Targan SR Annese V Wijmenga C Pettersson S Rotter JI Xavier RJ Daly MJ Rioux JD Seielstad M - Nature genetics 42(4):332-337 (2010)
Nature Genetics | Letter Genome-wide association identifies multiple ulcerative colitis susceptibility loci * Dermot P B McGovern1 Search for this author in: * NPG journals * PubMed * Google Scholar * Agnès Gardet2 Search for this author in: * NPG journals * PubMed * Google Scholar * Leif Törkvist3 Search for this author in: * NPG journals * PubMed * Google Scholar * Philippe Goyette4 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonah Essers5 Search for this author in: * NPG journals * PubMed * Google Scholar * Kent D Taylor6 Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin M Neale5 Search for this author in: * NPG journals * PubMed * Google Scholar * Rick T H Ong7 Search for this author in: * NPG journals * PubMed * Google Scholar * Caroline Lagacé4 Search for this author in: * NPG journals * PubMed * Google Scholar * Chun Li2 Search for this author in: * NPG journals * PubMed * Google Scholar * Todd Green8 Search for this author in: * NPG journals * PubMed * Google Scholar * Christine R Stevens8 Search for this author in: * NPG journals * PubMed * Google Scholar * Claudine Beauchamp4 Search for this author in: * NPG journals * PubMed * Google Scholar * Phillip R Fleshner1 Search for this author in: * NPG journals * PubMed * Google Scholar * Marie Carlson9 Search for this author in: * NPG journals * PubMed * Google Scholar * Mauro D'Amato10 Search for this author in: * NPG journals * PubMed * Google Scholar * Jonas Halfvarson11 Search for this author in: * NPG journals * PubMed * Google Scholar * Martin L Hibberd12 Search for this author in: * NPG journals * PubMed * Google Scholar * Mikael Lördal13 Search for this author in: * NPG journals * PubMed * Google Scholar * Leonid Padyukov14 Search for this author in: * NPG journals * PubMed * Google Scholar * Angelo Andriulli15 Search for this author in: * NPG journals * PubMed * Google Scholar * Elisabetta Colombo15 Search for this author in: * NPG journals * PubMed * Google Scholar * Anna Latiano15 Search for this author in: * NPG journals * PubMed * Google Scholar * Orazio Palmieri15 Search for this author in: * NPG journals * PubMed * Google Scholar * Edmond-Jean Bernard16 Search for this author in: * NPG journals * PubMed * Google Scholar * Colette Deslandres17 Search for this author in: * NPG journals * PubMed * Google Scholar * Daan W Hommes18 Search for this author in: * NPG journals * PubMed * Google Scholar * Dirk J de Jong19 Search for this author in: * NPG journals * PubMed * Google Scholar * Pieter C Stokkers20 Search for this author in: * NPG journals * PubMed * Google Scholar * Rinse K Weersma21 Search for this author in: * NPG journals * PubMed * Google Scholar * The NIDDK IBD Genetics Consortium22 * Yashoda Sharma23 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark S Silverberg24 Search for this author in: * NPG journals * PubMed * Google Scholar * Judy H Cho23, 25 Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Wu26 Search for this author in: * NPG journals * PubMed * Google Scholar * Kathryn Roeder27 Search for this author in: * NPG journals * PubMed * Google Scholar * Steven R Brant27 Search for this author in: * NPG journals * PubMed * Google Scholar * L Phillip Schumm28 Search for this author in: * NPG journals * PubMed * Google Scholar * Richard H Duerr29 Search for this author in: * NPG journals * PubMed * Google Scholar * Marla C Dubinsky1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nicole L Glazer30 Search for this author in: * NPG journals * PubMed * Google Scholar * Talin Haritunians6 Search for this author in: * NPG journals * PubMed * Google Scholar * Andy Ippoliti1 Search for this author in: * NPG journals * PubMed * Google Scholar * Gil Y Melmed1 Search for this author in: * NPG journals * PubMed * Google Scholar * David S Siscovick30 Search for this author in: * NPG journals * PubMed * Google Scholar * Eric A Vasiliauskas1 Search for this author in: * NPG journals * PubMed * Google Scholar * Stephan R Targan1 Search for this author in: * NPG journals * PubMed * Google Scholar * Vito Annese15 Search for this author in: * NPG journals * PubMed * Google Scholar * Cisca Wijmenga31 Search for this author in: * NPG journals * PubMed * Google Scholar * Sven Pettersson32, 33 Search for this author in: * NPG journals * PubMed * Google Scholar * Jerome I Rotter6 Search for this author in: * NPG journals * PubMed * Google Scholar * Ramnik J Xavier2, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Daly5, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * John D Rioux4 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark Seielstad7, 34, 35 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume:42,Pages:332–337Year published:(2010)DOI:doi:10.1038/ng.549Received25 September 2009Accepted26 January 2010Published online14 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Ulcerative colitis is a chronic, relapsing inflammatory condition of the gastrointestinal tract with a complex genetic and environmental etiology. In an effort to identify genetic variation underlying ulcerative colitis risk, we present two distinct genome-wide association studies of ulcerative colitis and their joint analysis with a previously published scan1, comprising, in aggregate, 2,693 individuals with ulcerative colitis and 6,791 control subjects. Fifty-nine SNPs from 14 independent loci attained an association significance of P < 10−5. Seven of these loci exceeded genome-wide significance (P < 5 × 10−8). After testing an independent cohort of 2,009 cases of ulcerative colitis and 1,580 controls, we identified 13 loci that were significantly associated with ulcerative colitis (P < 5 × 10−8), including the immunoglobulin receptor gene FCGR2A, 5p15, 2p16 and ORMDL3 (orosomucoid1-like 3). We confirmed association with 14 previously identified ulcerative colitis ! susceptibility loci, and an analysis of acknowledged Crohn's disease loci showed that roughly half of the known Crohn's disease associations are shared with ulcerative colitis. These data implicate approximately 30 loci in ulcerative colitis, thereby providing insight into disease pathogenesis. View full text Author information * Author information * Supplementary information Affiliations * Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. * Dermot P B McGovern, * Phillip R Fleshner, * Marla C Dubinsky, * Andy Ippoliti, * Gil Y Melmed, * Eric A Vasiliauskas & * Stephan R Targan * Center for Computational and Integrative Biology and Gastrointestinal Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Agnès Gardet, * Chun Li & * Ramnik J Xavier * Department for Clinical Science Intervention and Technology, Karolinska Institutet and IBD Clinical Research Group at Karolinska University Hospital, Stockholm, Sweden. * Leif Törkvist * Université de Montréal and the Montreal Heart Institute, Research Center, Montréal, Québec, Canada. * Philippe Goyette, * Caroline Lagacé, * Claudine Beauchamp & * John D Rioux * Center for Human Genetic Research, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. * Jonah Essers, * Benjamin M Neale & * Mark J Daly * Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. * Kent D Taylor, * Talin Haritunians & * Jerome I Rotter * Human Genetics, Genome Institute of Singapore, Singapore. * Rick T H Ong & * Mark Seielstad * The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. * Todd Green, * Christine R Stevens, * Ramnik J Xavier & * Mark J Daly * Department of Medical Sciences, Gastroenterology Research Group, Uppsala University Hospital, Uppsala, Sweden. * Marie Carlson * Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden. * Mauro D'Amato * Division of Gastroenterology, Department of Internal Medicine, Örebro University Hospital, Örebro, Sweden. * Jonas Halfvarson * Infectious Diseases, Genome Institute of Singapore, Singapore. * Martin L Hibberd * Department of Medicine, and IBD Clinical Research Group at Karolinska University Hospital, Stockholm, Sweden. * Mikael Lördal * Rheumatology Unit, Department of Medicine, Karolinska Institutet at Karolinska University Hospital Solna, Stockholm, Sweden. * Leonid Padyukov * Gastroenterologia ed Endoscopia Digestiva, Ospedale 'Casa Sollievo della Sofferenza', Istituto di Ricovero e Cura a Carattere Scientifico, San Giovanni Rotondo, Italy. * Angelo Andriulli, * Elisabetta Colombo, * Anna Latiano, * Orazio Palmieri & * Vito Annese * Université de Montréal and Centre Hospitalier Universitaire de l'Université de Montréal, Montréal, Québec, Canada. * Edmond-Jean Bernard * Department of Gastroenterology, Hôpital Sainte-Justine, Montréal, Québec, Canada. * Colette Deslandres * Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands. * Daan W Hommes * Department of Gastroenterology and Hepatology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. * Dirk J de Jong * Department of Gastroenterology and Hepatology, Academic Medical Center, Amsterdam, The Netherlands. * Pieter C Stokkers * Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. * Rinse K Weersma * National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). * The NIDDK IBD Genetics Consortium * Section of Digestive Diseases, Department of Medicine, Yale University, New Haven, Connecticut, USA. * Yashoda Sharma & * Judy H Cho * Mount Sinai Hospital Inflammatory Bowel Disease Group, University of Toronto, Toronto, Ontario, Canada. * Mark S Silverberg * Department of Genetics, Yale University, New Haven, Connecticut, USA. * Judy H Cho * Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. * Jing Wu * Johns Hopkins University School of Medicine, Department of Medicine, and Johns Hopkins University Bloomberg School of Public Health, Department of Epidemiology, Baltimore, Maryland, USA. * Kathryn Roeder & * Steven R Brant * Department of Health Studies, University of Chicago, Chicago, Illinois, USA. * L Phillip Schumm * Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh School of Medicine, and Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. * Richard H Duerr * Cardiovascular Health Research Unit, Departments of Epidemiology and General Medicine, University of Washington, Seattle, Washington, USA. * Nicole L Glazer & * David S Siscovick * Department of Genetics, University Medical Center Groningen and Groningen University, Groningen, The Netherlands. * Cisca Wijmenga * Department of Microbiology Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden. * Sven Pettersson * Laboratory of Inflammation Biology, Singapore General Hospital, Singapore. * Sven Pettersson * Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. * Mark Seielstad * Institute for Human Genetics, University of California San Fransisco, San Francisco, California, USA. * Mark Seielstad Consortia * The NIDDK IBD Genetics Consortium Contributions D.P.B.M., M.J.D., R.J.X., J.D.R., J.H.C., P.G., R.H.D. and M.S. participated in the study design and conception. D.P.B.M., A.G., M.J.D., R.J.X., J.D.R. and M.S. wrote the manuscript with contributions from R.H.D. and J.I.R. D.P.B.M., L.T., K.D.T., C.Li, C.B., P.R.F., M.C., M.D., J.H., M.L.H., M.L., L.P., A.A., E.C., A.L., O.P., E.-J.B., C.D., D.W.H., D.J.d.J., P.C.S., R.K.W., Y.S., M.S.S., J.H.C., S.R.B., L.P.S., R.H.D., M.C.D., N.L.G., T.H., A.I., G.Y.M., D.S.S., E.A.V., S.R.T., V.A., C.W. and S.P. performed patient diagnosis, patient enrollment and collection of clinical data. Replication genotyping was performed by C. Lagacé, C.R.S. and C.B. in the laboratory of J.D.R. Expression analysis, immunohistochemistry and shRNA studies were designed by A.G. and R.J.X. and performed by C. Li and A.G. M.J.D., J.E., B.M.N., K.R., J.W., J.D.R., P.G., T.G. and R.T.H.O. provided statistical analyses. All authors contributed to the final paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Dermot P B McGovern (mcgovernd@cshs.org) or * John D Rioux (john.david.rioux@umontreal.ca) or * Mark Seielstad (mark.seielstad@ucsf.edu) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Tables 1–5 and Supplementary Figures 1 and 2 Additional data - PHF6 mutations in T-cell acute lymphoblastic leukemia
Van Vlierberghe P Palomero T Khiabanian H Van der Meulen J Castillo M Van Roy N De Moerloose B Philippé J González-García S Toribio ML Taghon T Zuurbier L Cauwelier B Harrison CJ Schwab C Pisecker M Strehl S Langerak AW Gecz J Sonneveld E Pieters R Paietta E Rowe JM Wiernik PH Benoit Y Soulier J Poppe B Yao X Cordon-Cardo C Meijerink J Rabadan R Speleman F Ferrando A - Nature genetics 42(4):338-342 (2010)
Nature Genetics | Letter PHF6 mutations in T-cell acute lymphoblastic leukemia * Pieter Van Vlierberghe1, 2, 3, 21 Search for this author in: * NPG journals * PubMed * Google Scholar * Teresa Palomero1, 4, 21 Search for this author in: * NPG journals * PubMed * Google Scholar * Hossein Khiabanian5 Search for this author in: * NPG journals * PubMed * Google Scholar * Joni Van der Meulen2 Search for this author in: * NPG journals * PubMed * Google Scholar * Mireia Castillo4 Search for this author in: * NPG journals * PubMed * Google Scholar * Nadine Van Roy2 Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara De Moerloose6 Search for this author in: * NPG journals * PubMed * Google Scholar * Jan Philippé7 Search for this author in: * NPG journals * PubMed * Google Scholar * Sara González-García8 Search for this author in: * NPG journals * PubMed * Google Scholar * María L Toribio8 Search for this author in: * NPG journals * PubMed * Google Scholar * Tom Taghon7 Search for this author in: * NPG journals * PubMed * Google Scholar * Linda Zuurbier3 Search for this author in: * NPG journals * PubMed * Google Scholar * Barbara Cauwelier9 Search for this author in: * NPG journals * PubMed * Google Scholar * Christine J Harrison10 Search for this author in: * NPG journals * PubMed * Google Scholar * Claire Schwab10 Search for this author in: * NPG journals * PubMed * Google Scholar * Markus Pisecker11 Search for this author in: * NPG journals * PubMed * Google Scholar * Sabine Strehl11 Search for this author in: * NPG journals * PubMed * Google Scholar * Anton W Langerak12 Search for this author in: * NPG journals * PubMed * Google Scholar * Jozef Gecz13, 14 Search for this author in: * NPG journals * PubMed * Google Scholar * Edwin Sonneveld15 Search for this author in: * NPG journals * PubMed * Google Scholar * Rob Pieters3, 15 Search for this author in: * NPG journals * PubMed * Google Scholar * Elisabeth Paietta16 Search for this author in: * NPG journals * PubMed * Google Scholar * Jacob M Rowe17 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter H Wiernik16 Search for this author in: * NPG journals * PubMed * Google Scholar * Yves Benoit6 Search for this author in: * NPG journals * PubMed * Google Scholar * Jean Soulier18 Search for this author in: * NPG journals * PubMed * Google Scholar * Bruce Poppe2 Search for this author in: * NPG journals * PubMed * Google Scholar * Xiaopan Yao19 Search for this author in: * NPG journals * PubMed * Google Scholar * Carlos Cordon-Cardo4 Search for this author in: * NPG journals * PubMed * Google Scholar * Jules Meijerink3 Search for this author in: * NPG journals * PubMed * Google Scholar * Raul Rabadan5 Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Speleman2, 22 Search for this author in: * NPG journals * PubMed * Google Scholar * Adolfo Ferrando1, 4, 20, 22 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume:42,Pages:338–342Year published:(2010)DOI:doi:10.1038/ng.542Received11 November 2009Accepted03 February 2010Published online14 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Tumor suppressor genes on the X chromosome may skew the gender distribution of specific types of cancer1, 2. T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy with an increased incidence in males3. In this study, we report the identification of inactivating mutations and deletions in the X-linked plant homeodomain finger 6 (PHF6) gene in 16% of pediatric and 38% of adult primary T-ALL samples. Notably, PHF6 mutations are almost exclusively found in T-ALL samples from male subjects. Mutational loss of PHF6 is importantly associated with leukemias driven by aberrant expression of the homeobox transcription factor oncogenes TLX1 and TLX3. Overall, these results identify PHF6 as a new X-linked tumor suppressor in T-ALL and point to a strong genetic interaction between PHF6 loss and aberrant expression of TLX transcription factors in the pathogenesis of this disease. View full text Figures at a glance * Figure 1: Next-generation sequencing and microarray-based comparative genomic hybridization (array-CGH) analysis of the X chromosome identifies PHF6 mutations in human T-cell acute lymphoblastic leukemia (T-ALL). () Overview of mutation screening approach of the human X-chromosome exome in a panel of tumor DNA samples from 12 males with T-ALL using oligonucleotide sequence capture and next-generation sequencing with SOLiD 3. After filtering and confirmation of high-throughput sequencing data, analysis of corresponding remission DNA samples led to the identification of three somatically acquired changes in PHF6. () Schematic overview of the recurrent genomic deletions involving chromosomal band Xq26.3 in eight human T-ALL samples. Specific genes located in Xq26.3 are shown. () Detailed view of a representative oligonucleotide array-CGH plot of leukemia DNA/control DNA ratios (blue trace) versus the dye-swap experiment (red trace) in an individual harboring an Xq26.3 deletion. () DNA quantitative PCR analysis of PHF6 copy number dose in female and male reference genomic DNAs and two primary samples from males with T-ALL harboring Xq26.3 deletions. * Figure 2: PHF6 mutations and expression in T-cell acute lymphoblastic leukemia (T-ALL) lymphoblasts. () Structure of the PHF6 protein, including four nuclear localization signals (NLSs) and two imperfect plant homeodomain (PHD) zinc-finger domains. Overview of all PHF6 mutations identified in primary T-ALL samples and T-ALL cell lines. Filled circles represent nonsense and frameshift mutations, whereas missense mutations are depicted as open circles. The circle filled in gray indicates the mutation identified in a sample from a female with T-ALL. aa, amino acids. () Representative DNA sequencing chromatograms of paired diagnosis and remission genomic T-ALL DNA samples showing a somatic mutation in exon 7 of PHF6. () Protein blot analysis of T-ALL cell lines revealed complete loss of PHF6 protein expression in the PHF6-mutated T-ALL cell lines. () PHF6 immunostaining in the Jurkat and HPB-ALL, wild-type and mutant T-ALL cell lines, respectively. () Protein blot analysis of PHF6 and γ-H2AX expression in HEK293T cells upon PHF6 short hairpin RNA knockdown. Actin concentration! s are shown as a loading control. * Figure 3: PHF6 expression in T-cell acute lymphoblastic leukemia (T-ALL) lymphoblasts. () Sequence analysis of paired genomic DNA and complementary DNA samples shows monoallelic expression of PHF6 SNP rs17317724 in lymphoblasts from a wild-type PHF6 female with T-ALL. () Differential distribution of PHF6 mutations in samples from males and females with T-ALL. () Immunohistochemical analysis of PHF6 expression in wild-type and mutant T-ALL lymphoblasts. * Figure 4: Clinical and biological characteristics associated with PHF6 mutations in T-cell acute lymphoblastic leukemia (T-ALL). () Frequencies of PHF6 mutations in pediatric and adult T-ALL samples. () Differential distribution of PHF6 mutations in TLX1/TLX3-positive and TLX1/TLX3-negative T-ALL samples. () Kaplan-Meier curve of overall survival in pediatric T-ALL cases from Dutch Childhood Oncology Group trials ALL7, ALL8 and ALL9 with and without PHF6 mutations. () Kaplan-Meier survival curve in adult T-ALL cases with and without mutations in PHF6 treated in Eastern Cooperative Oncology Group clinical trial ECOG2993. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Pieter Van Vlierberghe & * Teresa Palomero Affiliations * Institute for Cancer Genetics, Columbia University Medical Center, New York, New York, USA. * Pieter Van Vlierberghe, * Teresa Palomero & * Adolfo Ferrando * Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium. * Pieter Van Vlierberghe, * Joni Van der Meulen, * Nadine Van Roy, * Bruce Poppe & * Frank Speleman * Department of Pediatric Oncology/Hematology, Erasmus MC, Rotterdam, The Netherlands. * Pieter Van Vlierberghe, * Linda Zuurbier, * Rob Pieters & * Jules Meijerink * Department of Pathology, Columbia University Medical Center, New York, New York, USA. * Teresa Palomero, * Mireia Castillo, * Carlos Cordon-Cardo & * Adolfo Ferrando * Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, USA. * Hossein Khiabanian & * Raul Rabadan * Department of Pediatric Hemato-Oncology, Ghent University Hospital, Ghent, Belgium. * Barbara De Moerloose & * Yves Benoit * Department of Clinical Chemistry, Immunology and Microbiology, Ghent University Hospital, Ghent, Belgium. * Jan Philippé & * Tom Taghon * Centro de Biología Molecular "Severo Ochoa", Consejo Superior de Investigaciones Científicas (CSIC), Universidad Autónoma de Madrid (UAM), Madrid, Spain. * Sara González-García & * María L Toribio * Department of Hematology, Hospital St-Jan, Bruges, Belgium. * Barbara Cauwelier * Leukaemia Research Cytogenetics Group, Northern Institute for Cancer Research, Newcastle University, Newcastle, UK. * Christine J Harrison & * Claire Schwab * Children's Cancer Research Institute, St. Anna Kinderkrebsforschung, Vienna, Austria. * Markus Pisecker & * Sabine Strehl * Department of Immunology, Erasmus MC, Rotterdam, The Netherlands. * Anton W Langerak * Department of Genetics and Molecular Pathology, University of Adelaide, Adelaide, Australia. * Jozef Gecz * Department of Pediatrics, University of Adelaide, Adelaide, Australia. * Jozef Gecz * On behalf of the Dutch Childhood Oncology Group (DCOG), The Hague, The Netherlands. * Edwin Sonneveld & * Rob Pieters * Montefiore Medical Center North, Bronx, New York, USA. * Elisabeth Paietta & * Peter H Wiernik * Rambam Medical Center and Technion, Israel Institute of Technology, Haifa, Israel. * Jacob M Rowe * Hematology Laboratory APHP, INSERM U944, Hôpital Saint Louis, Paris, France. * Jean Soulier * Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. * Xiaopan Yao * Department of Pediatrics, Columbia University Medical Center, New York, New York, USA. * Adolfo Ferrando * These authors jointly directed this work. * Frank Speleman & * Adolfo Ferrando Contributions P.V.V. performed array-CGH and mutation analysis of PHF6 and wrote the manuscript. T.P. performed exon capture and next-generation sequencing of T-ALL samples and wrote the manuscript. H.K. analyzed next-generation sequencing data. J.V.d.M. performed additional array-CGH analysis and PHF6 mutation screening in T-ALL and BCP-ALL samples. T.T., N.V.R. and A.W.L. performed experiments. M.C. and C.C.-C. performed and analyzed histological and immunohistochemical staining. J.P. collaborated on PHF6 mutation screening in BCP-ALL samples. C.J.H. and C.S. collaborated on additional screening for genomic PHF6 deletions in T-ALL. Y.B., B.D.M. and B.C. collaborated on the PHF6 mutation screening. R.P., M.P., S.S. and J.S. collaborated on the multicenter array-CGH study. S.G.-G. and M.L.T. performed the isolation of T-cell progenitor cells for expression analysis of PHF6. X.Y. performed survival analysis of ECOG T-ALL patients. J.G. provided critical reagents and discussion. E.S. provid! ed samples and correlative clinical data from DCOG. E.P., J.M.R. and P.H.W. provided samples and correlative clinical data from ECOG. J.M. and L.Z. collaborated on the multicenter array-CGH study and PHF6 mutation analysis, provided molecular data on the characterization of T-ALL and performed survival analysis of PHF6 mutations in the DCOG series. R.R. designed and directed the analysis of next-generation sequencing results. F.S. and B.P. designed the studies and directed research. A.F. designed the studies, directed research and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Adolfo Ferrando (af2196@columbia.edu) Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (504K) Supplementary Figure 1 and Supplementary Tables 1–7 Additional data - Nucleosome dynamics define transcriptional enhancers
He HH Meyer CA Shin H Bailey ST Wei G Wang Q Zhang Y Xu K Ni M Lupien M Mieczkowski P Lieb JD Zhao K Brown M Liu XS - Nature genetics 42(4):343-347 (2010)
Nature Genetics | Letter Nucleosome dynamics define transcriptional enhancers * Housheng Hansen He1, 2, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Clifford A Meyer1, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Hyunjin Shin1 Search for this author in: * NPG journals * PubMed * Google Scholar * Shannon T Bailey2 Search for this author in: * NPG journals * PubMed * Google Scholar * Gang Wei3 Search for this author in: * NPG journals * PubMed * Google Scholar * Qianben Wang2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Yong Zhang1, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Kexin Xu2 Search for this author in: * NPG journals * PubMed * Google Scholar * Min Ni2 Search for this author in: * NPG journals * PubMed * Google Scholar * Mathieu Lupien2, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Piotr Mieczkowski4 Search for this author in: * NPG journals * PubMed * Google Scholar * Jason D Lieb4 Search for this author in: * NPG journals * PubMed * Google Scholar * Keji Zhao3 Search for this author in: * NPG journals * PubMed * Google Scholar * Myles Brown2 Search for this author in: * NPG journals * PubMed * Google Scholar * X Shirley Liu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume:42,Pages:343–347Year published:(2010)DOI:doi:10.1038/ng.545Received02 October 2009Accepted09 February 2010Published online07 March 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Chromatin plays a central role in eukaryotic gene regulation. We performed genome-wide mapping of epigenetically marked nucleosomes to determine their position both near transcription start sites and at distal regulatory elements, including enhancers. In prostate cancer cells, where androgen receptor binds primarily to enhancers, we found that androgen treatment dismisses a central nucleosome present at androgen receptor binding sites that is flanked by a pair of marked nucleosomes. A new quantitative model built on the behavior of such nucleosome pairs correctly identified regions bound by the regulators of the immediate androgen response, including androgen receptor and FOXA1. More importantly, this model also correctly predicted previously unidentified binding sites for other transcription factors present after prolonged androgen stimulation, including OCT1 and NKX3-1. Therefore, quantitative modeling of enhancer structure provides a powerful predictive method to infer th! e identity of transcription factors involved in cellular responses to specific stimuli. View full text Figures at a glance * Figure 1: Signal distribution and nucleosome position analysis in the androgen receptor and FOXA1 binding regions identified by ChIP-chip experiments and the TSS. (–) H3K4me2 signal distribution relative to the center of the androgen receptor (AR) motif (,) and FOXA1 motif (,) in the binding regions. The x axis represents the distance to the center of the best AR or FOXA1 motif match in a given binding site. The y axis represents normalized ChIP-Seq tag count numbers. Veh, unstimulated condition; 4 h, stimulated conditions with treatment of DHT for 4 h. () Distance from the AR motif to the center of the nearest nucleosome in the AR binding sites under vehicle (red) and 4 h after DHT stimulation (blue). () H3K4me2 and H3K4me3 signal distribution relative to the TSS. * Figure 2: qPCR validation of the nucleosomes stabilized-destabilized around androgen receptor (AR) binding sites. () Five AR binding sites near the genes TMPRSS2, STK39, KLK3, TMC6 and TRIM35. AR ChIP-chip, AR ChIP-chip signals; H3K4me2 Veh and H3K4me2 4h, H3K4me2 ChIP-Seq signals before and after 4 h of DHT treatment. Input 4 h/Veh, qPCR assay of nucleosome fold change for DHT treatment relative to vehicle; H3K4me2 4 h/Veh, qPCR assay of fold change for H3K4me2 signal for DHT treatment relative to vehicle. Error bar, s.d. Each horizontal bar represents a NPS peak region. () Detailed qPCR analysis of the AR binding sites near TMPRSS2 and STK39. Each horizontal bar represents a qPCR amplification region. * Figure 3: Motif analysis in the paired nucleosome regions. () Flowchart of the prediction model. The formula for the NSD score is described in the Online Methods. Treatment and control refer to treatment and vehicle control conditions. Flank refers to the 200 bp of sequence centered on each flanking nucleosome, and central refers to the sequence between these regions. () The fraction of androgen receptor binding sites in NSD score ranked paired nucleosome bins with decreasing score (at 4 h compared to vehicle). Paired nucleosome regions are ranked by scores representing the differences in H3K4me2 tag counts before and after DHT treatment. These ranked regions are grouped into bins of 500. Shown here is the number of regions in each bin that overlap with androgen receptor ChIP-chip–enriched regions. () Evolutionary conservation in the vicinity of the 5,000 highest-scoring nucleosome pairs. Mean phastCons scores representing DNA sequence conservation over 17 species is plotted as a function of the distance from the midpoint between ! paired nucleosomes. () DNA sequence content associated with nucleosome positioning. The 5,000 highest-scoring paired nucleosome regions, aligned at the midpoint, were analyzed for simple DNA sequence features: the distribution of A/T mononucleotides (black), GC dinucleotides (red) or AT dinucleotides (green). () Logos of androgen receptor (AR), FOXA1, NKX3-1 and OCT1 motifs from TRANSFAC library. () The fraction of AR binding sites in score-ranked paired nucleosome bins with decreasing score (at 16 h compared to 4 h). * Figure 4: ChIP-qPCR and gene expression analysis of NSD scoring sites. (–) ChIP-qPCR validation of predicted androgen receptor (AR) (), NKX3-1 () and OCT1 () binding sites. Box plots were generated from ChIP-qPCR data obtained from three independent experiments testing 10 sites for AR, 22 sites for NKX3-1 and 9 sites for OCT1. The individual ChIP-qPCR assays are shown in Supplementary Figure 5. () Correlation of paired nucleosome regions with gene expression. The fraction of differentially regulated genes with paired nucleosome regions within 20 kb is shown. The top 5,000 paired nucleosome regions were selected under the conditions of DHT treatment for 4 h versus vehicle and DHT treatment for 16 h versus DHT treatment for 4 h. Differentially regulated genes were identified as described in the Online Methods. 4 h regulated, fraction of DHT 4 h versus vehicle-treated differentially regulated genes having at least one DHT 4 h versus vehicle-paired nucleosome region within 20 kb of the transcription start site. 4 h non-regulated, fraction of non-! regulated genes under the same condition. 16 h regulated and 16 h non-regulated, fractions under DHT treatment for 16 h versus 4 h. (,) Correlation of score and number of NSD scoring sites and upregulated gene expression, 4 h versus vehicle treatment () and 16 h versus 4 h treatment (). The x axis represents the lower bound n of the number of sites within 20 kb of the TSS of a gene, and the y axis represents the odds ratio calculated by the formula (upregulated genes with at least n sites/non-regulated genes with at least n sites)/(all upregulated genes/all non-regulated genes). Red, green and blue dots represent the top 5,000, 10,000 and 20,000 NSD score sites, respectively. Accession codes * Accession codes * Author information * Supplementary information Referenced accessions GenBank * GSE20042 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Housheng Hansen He & * Clifford A Meyer Affiliations * Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA. * Housheng Hansen He, * Clifford A Meyer, * Hyunjin Shin, * Yong Zhang & * X Shirley Liu * Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA. * Housheng Hansen He, * Shannon T Bailey, * Qianben Wang, * Kexin Xu, * Min Ni, * Mathieu Lupien & * Myles Brown * Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA. * Gang Wei & * Keji Zhao * Department of Biology, Carolina Center for the Genome Sciences and Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA. * Piotr Mieczkowski & * Jason D Lieb * Present addresses: Department of Molecular and Cellular Biochemistry and the Comprehensive Cancer Center, Ohio State University College of Medicine, Columbus, Ohio, USA (Q.W.); School of Life Science and Technology, Tongji University, Shanghai, China (Y.Z.); and Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, New Hampshire, USA (M.L.). * Qianben Wang, * Yong Zhang & * Mathieu Lupien Contributions H.H.H., C.A.M., K.Z., J.D.L., X.S.L. and M.B. designed the experiments. H.H.H., S.T.B., G.W., Q.W., K.X., M.N., M.L. and P.M. performed the experiments. C.A.M., H.H.H., H.S. and Y.Z. performed data analysis. C.A.M., H.H.H., X.S.L., M.B., J.D.L. and M.L. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * X Shirley Liu (xsliu@jimmy.harvard.edu) or * Myles Brown (myles_brown@dfci.harvard.edu) Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–7 and Supplementary Tables 1–3 Additional data - Variance component model to account for sample structure in genome-wide association studies
Kang HM Sul JH Service SK Zaitlen NA Kong SY Freimer NB Sabatti C Eskin E - Nature genetics 42(4):348-354 (2010)
Nature Genetics | Technical Report Variance component model to account for sample structure in genome-wide association studies * Hyun Min Kang1, 2, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Jae Hoon Sul3, 8 Search for this author in: * NPG journals * PubMed * Google Scholar * Susan K Service4 Search for this author in: * NPG journals * PubMed * Google Scholar * Noah A Zaitlen5 Search for this author in: * NPG journals * PubMed * Google Scholar * Sit-yee Kong4 Search for this author in: * NPG journals * PubMed * Google Scholar * Nelson B Freimer4 Search for this author in: * NPG journals * PubMed * Google Scholar * Chiara Sabatti6 Search for this author in: * NPG journals * PubMed * Google Scholar * Eleazar Eskin3, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature GeneticsVolume:42,Pages:348–354Year published:(2010)DOI:doi:10.1038/ng.548Received23 July 2009Accepted09 February 2010Published online07 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust C! ase Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure. View full text Figures at a glance * Figure 1: Scatter plots of the first two principal components against latitude and longitude. Only individuals of known ancestry are included in the plot. Latitude and longitude are defined as the average latitude and longitude of the parents' birthplaces. Colors indicate linguistic or geographic subgroups. * Figure 2: The genomic control parameters for ten traits change with the number of principal components used for adjustment. Sig PC, significant principal components, includes the principal components (PC) that have a t-test P value < 0.005 as predictors for each of the phenotypes. LDL, low density lipoprotein; SBP, systolic blood pressure; HDL, high-density lipoprotein; GLU, glucose; BMI, body mass index; DBP, diastolic blood pressure; INS, insulin plasma levels; TG, triglyceride; CRP, C-reactive protein. * Figure 3: Comparison of P value distributions across different methods with NFBC66 data. () Quantile-quantile plot of the height phenotype, which shows the largest inflation of test statistics, before application of genomic control. The shadowed region represents a conservative 95% confidence interval (CI) computed from the beta distribution assuming independence markers. ES100 indicates EIGENSOFT correcting for 100 principal components. () Comparison of LDL association P values between uncorrected and EMMAX analysis after application of genomic control in a logarithmic scale. * Figure 4: Rank concordance comparison of strongly associated SNPs between different methods. The ten NFBC66 phenotypes (abbreviated as in Fig. 2) are ordered by their genomic control inflation factors. Rank concordance is presented as CAT plots45. The proportion of SNPs shared between sets of the top k SNPs for different methods are shown for 10 ≤ k ≤ 5000. Pairs of sets being compared are indicated in key at bottom; for example, Uncorr-EMMAX, comparison of uncorrected set and EMMAX set. ES100 indicates EIGENSOFT correcting for 100 principal components. * Figure 5: Distribution of the marker-specific inflation factors from NFBC66 data sets. () Box plots of the marker-specific inflation factors across ten phenotypes, in addition to the genomic control inflation factor for each phenotype. Abbreviations are as in Figure 2. (,) Distributions of P values of the height phenotype association when the estimated per-marker inflation factors are less than 1.05 (35,988 SNPs; ) and when they are greater than 1.2 (15,874 SNPs; ). Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hyun Min Kang & * Jae Hoon Sul Affiliations * Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA. * Hyun Min Kang * Center for Computational Medicine and Bioinformatics, The University of Michigan Medical School, Ann Arbor, Michigan, USA. * Hyun Min Kang * Computer Science Department, University of California, Los Angeles, California, USA. * Jae Hoon Sul & * Eleazar Eskin * Center for Neurobehavioral Genetics, University of California, Los Angeles, California, USA. * Susan K Service, * Sit-yee Kong & * Nelson B Freimer * Department of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. * Noah A Zaitlen * Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA. * Chiara Sabatti * Department of Human Genetics, University of California, Los Angeles, California, USA. * Eleazar Eskin Contributions H.M.K., J.H.S., C.S. and E.E. designed the methods and experiments; H.M.K., J.H.S., S.K.S., S.-y.K., N.B.F., C.S. and E.E. jointly analyzed the NFBC66 data set; H.M.K., J.H.S., N.A.Z., C.S. and E.E. jointly analyzed the WTCCC data set; H.M.K., J.H.S., S.K.S., N.B.F., C.S. and E.E. wrote the manuscript; all authors contributed their critical reviews of the manuscript during its preparation. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Chiara Sabatti (sabatti@stanford.edu) or * Eleazar Eskin (eeskin@cs.ucla.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Tables 1–3, Supplementary Figures 1–6 and Supplementary Note Additional data - Mixed linear model approach adapted for genome-wide association studies
Zhang Z Ersoz E Lai CQ Todhunter RJ Tiwari HK Gore MA Bradbury PJ Yu J Arnett DK Ordovas JM Buckler ES - Nature genetics 42(4):355-360 (2010)
Nature Genetics | Technical Report Mixed linear model approach adapted for genome-wide association studies * Zhiwu Zhang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Elhan Ersoz1 Search for this author in: * NPG journals * PubMed * Google Scholar * Chao-Qiang Lai2 Search for this author in: * NPG journals * PubMed * Google Scholar * Rory J Todhunter3 Search for this author in: * NPG journals * PubMed * Google Scholar * Hemant K Tiwari4 Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Gore5 Search for this author in: * NPG journals * PubMed * Google Scholar * Peter J Bradbury6 Search for this author in: * NPG journals * PubMed * Google Scholar * Jianming Yu7 Search for this author in: * NPG journals * PubMed * Google Scholar * Donna K Arnett8 Search for this author in: * NPG journals * PubMed * Google Scholar * Jose M Ordovas2, 9 Search for this author in: * NPG journals * PubMed * Google Scholar * Edward S Buckler1, 6 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature GeneticsVolume:42,Pages:355–360Year published:(2010)DOI:doi:10.1038/ng.546Received22 September 2009Accepted09 February 2010Published online07 March 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these metho! ds available within an implementation of the software program TASSEL. View full text Figures at a glance * Figure 1: The forms of MLM classified by the random effect size and types of kinship. The GLM and standard MLM are the two extremes of the compressed MLM with the number of groups determined as 1 and n (number of individuals), respectively. The sire model is a special case of the compressed MLM, with the groups determined as the sires derived from pedigrees. Kinship used in Henderson's MLM15 was calculated from the pedigrees. It was extended to marker-based kinship in the unified MLM5. The GLM approach appears in many formats in various GWAS, including structure association (SA)2, genomic control (GC)3 and the quantitative transmission disequilibrium test (QTDT)4. The compressed MLM can be flexibly applied to the entire area by varying the number of groups (s), including the area investigated previously (shaded area) and the area proposed in this study (open area). * Figure 2: Quantile-quantile plots of type I error (false positive) rates of association tests using the compressed MLM under different compression levels. The observed phenotypes are height in humans, hip dysplasia (Norberg angle) in dogs and flowering time (days to pollination) in maize. The distributions of P values are shown by plotting the observed P values against the cumulative P values in the negative log10 scale. Under the assumption that this set of genetic markers are unlinked to the polymorphism controlling the phenotypes, the P values of the association tests have a uniform distribution, indicated by the expected diagonal line (Exp)5. A statistical approach that has a distribution closer to the diagonal line indicates a better control for type I errors. The GLM that is equivalent to the compressed MLM at the maximum compression level had the most type I errors. For all the species, at least one compression level was found at which the compressed MLM performed better than the standard MLM, which is equivalent to the compressed MLM with compression level of 1. * Figure 3: The performance of the compressed MLM under different compression levels (horizontal axis). The two extremes of the compression level at 1 and n (the number of individuals) correspond to the standard MLM and the GLM, respectively. Performances were examined based on model fit, statistical power and computing time (s). The observed phenotypes are height in humans, hip dysplasia (Norberg angle) in dogs and flowering time (days to pollination) in maize. Individuals in each of the datasets were clustered into groups according to kinship by using the UPGMA algorithm implemented by proc cluster in SAS26. Model fit was evaluated using negative log likelihood (–2LL), adjusted Akaike information criterion (AICC) and Bayesian information content (BIC). Smaller values of –2LL, AICC and BIC indicate better fit. The statistical power was evaluated for QTNs with different size effect. The size of QTN effect is expressed in the unit of phenotypic standard deviation (s.d.). The average computing time was calculated from the observed CPU time for association tests on 647 marker! s in human datasets; 1,000 markers in dog datasets; and 553 markers in maize datasets. The computations were performed by proc mixed in SAS26 on a computer from Dell (Optiplex 755) with two physical CPUs (E6850 @ 3.00 GHz) and 3.25 GB RAM operated under Windows XP. * Figure 4: The P values and statistical power of association tests obtained by using the one-step MLM with the full optimization (full OPT) for all unknown parameters compared to P3D on a maize phenotype simulated with different epistatic effects (E). The phenotype was controlled by 20 QTNs, which were randomly assigned to the SNPs from the maize dataset5. Heritability was defined as the proportion of additive genetic variance over the total variance (the sum of additive genetic variance, epistatic variance and residual variance) and was set at 0.5. Because all maize used here belonged to inbred lines, no dominance effect was included. The experiment was repeated 1,000 times. For each replicate, the number of non-causal SNPs that were randomly sampled was the same as the number of causal QTNs. The top two panels display the P values using the full OPT (x axis) and P3D (y axis). Each dot represents a test on a non-causal SNP (top) and a causal QTN (middle). The P values from P3D are highly correlated with the ones from the full OPT for the non-causal SNPs and causal QTNs (r2 > 99%). The empirical statistical power for detecting the causal QTNs is displayed (bottom) as a function of the proportion of the total variation exp! lained (x axis). The P3D approach and the full OPT had approximately the same statistical power for detecting the causal QTNs. Author information * Abstract * Author information * Supplementary information Affiliations * Institute for Genomic Diversity, Cornell University, Ithaca, New York, USA. * Zhiwu Zhang, * Elhan Ersoz & * Edward S Buckler * Nutrition and Genomics Laboratory, Jean Mayer–US Department of Agriculture (USDA) Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts, USA. * Chao-Qiang Lai & * Jose M Ordovas * Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA. * Rory J Todhunter * Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Hemant K Tiwari * USDA–Agricultural Research Service (ARS), Arid-Land Agricultural Research Center, Maricopa, Arizona, USA. * Michael A Gore * USDA-ARS, Ithaca, New York, USA. * Peter J Bradbury & * Edward S Buckler * Department of Agronomy, Kansas State University, Manhattan, Kansas, USA. * Jianming Yu * Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA. * Donna K Arnett * Department of Cardiovascular Epidemiology and Population Genetics, National Center for Cardiovascular Investigation (CNIC), Madrid, Spain. * Jose M Ordovas Contributions Z.Z. conceptualized the study, performed the data analyses and wrote the manuscript. E.E., M.A.G. and J.Y. participated in the data analyses and wrote the manuscript. P.J.B. implemented the two new methods in the TASSEL software package. C.L., H.K.T., D.K.A. and J.M.O. provided the human data and supervised its analyses. R.J.T. provided the dog data and supervised its analyses. E.S.B designed and supervised the project. All authors edited the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Zhiwu Zhang (zz19@cornell.edu) Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–5 and Supplementary Note Additional data - Erratum: Etv4 and Etv5 are required downstream of GDNF and Ret for kidney branching morphogenesis
- Nature genetics 42(4):361 (2010)
Nature Genetics | Erratum Erratum: Etv4 and Etv5 are required downstream of GDNF and Ret for kidney branching morphogenesis * Benson C Lu Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina Cebrian Search for this author in: * NPG journals * PubMed * Google Scholar * Xuan Chi Search for this author in: * NPG journals * PubMed * Google Scholar * Satu Kuure Search for this author in: * NPG journals * PubMed * Google Scholar * Richard Kuo Search for this author in: * NPG journals * PubMed * Google Scholar * Carlton M Bates Search for this author in: * NPG journals * PubMed * Google Scholar * Silvia Arber Search for this author in: * NPG journals * PubMed * Google Scholar * John Hassell Search for this author in: * NPG journals * PubMed * Google Scholar * Lesley MacNeil Search for this author in: * NPG journals * PubMed * Google Scholar * Masato Hoshi Search for this author in: * NPG journals * PubMed * Google Scholar * Sanjay Jain Search for this author in: * NPG journals * PubMed * Google Scholar * Naoya Asai Search for this author in: * NPG journals * PubMed * Google Scholar * Masahide Takahashi Search for this author in: * NPG journals * PubMed * Google Scholar * Kai M Schmidt-Ott Search for this author in: * NPG journals * PubMed * Google Scholar * Jonathan Barasch Search for this author in: * NPG journals * PubMed * Google Scholar * Vivette D'Agati Search for this author in: * NPG journals * PubMed * Google Scholar * Frank Costantini Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature GeneticsVolume:42,Page:361Year published:(2010)DOI:doi:10.1038/ng0410-361d Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.41, 1295–1302 (2009); published online 8 November 2009; corrected after print 5 February 2010. In the version of this article initially published, the sentence under Table 1 on p. 1296 should stop after the words "and 430A arrays," and the words "or >3 if represented on only one array" should be removed. In addition, on p. 1298, the second parenthesis after "Tg(Hoxb7-myrVenus)" is missing. These errors have been corrected in the HTML and PDF versions of the article. Additional data - Corrigendum: Mutations in the formin gene INF2 cause focal segmental glomerulosclerosis
- Nature genetics 42(4):361 (2010)
Nature Genetics | Corrigendum Corrigendum: Mutations in the formin gene INF2 cause focal segmental glomerulosclerosis * Elizabeth J Brown Search for this author in: * NPG journals * PubMed * Google Scholar * Johannes S Schlöndorff Search for this author in: * NPG journals * PubMed * Google Scholar * Daniel J Becker Search for this author in: * NPG journals * PubMed * Google Scholar * Hiroyasu Tsukaguchi Search for this author in: * NPG journals * PubMed * Google Scholar * Andrea L Uscinski Search for this author in: * NPG journals * PubMed * Google Scholar * Henry N Higgs Search for this author in: * NPG journals * PubMed * Google Scholar * Joel M Henderson Search for this author in: * NPG journals * PubMed * Google Scholar * Martin R Pollak Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature GeneticsVolume:42,Page:361Year published:(2010)DOI:doi:10.1038/ng0410-361b Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.42, 72–76 (2010); published online 20 December 2009; corrected after print 5 February 2010. In the version of this article initially published, Stephen J. Tonna was inadvertently omitted from the author list, and the fourth author (Hiroyasu Tsukaguchi) was missing one of his affiliations. These errors have been corrected in the HTML and PDF versions of the article. Additional data - Corrigendum: Comparative genomic and phylogeographic analysis of Mycobacterium leprae
- Nature genetics 42(4):361 (2010)
Nature Genetics | Corrigendum Corrigendum: Comparative genomic and phylogeographic analysis of Mycobacterium leprae * Marc Monot Search for this author in: * NPG journals * PubMed * Google Scholar * Nadine Honoré Search for this author in: * NPG journals * PubMed * Google Scholar * Thierry Garnier Search for this author in: * NPG journals * PubMed * Google Scholar * Nora Zidane Search for this author in: * NPG journals * PubMed * Google Scholar * Diana Sherafi Search for this author in: * NPG journals * PubMed * Google Scholar * Alberto Paniz-Mondolfi Search for this author in: * NPG journals * PubMed * Google Scholar * Masanori Matsuoka Search for this author in: * NPG journals * PubMed * Google Scholar * G Michael Taylor Search for this author in: * NPG journals * PubMed * Google Scholar * Helen D Donoghue Search for this author in: * NPG journals * PubMed * Google Scholar * Abi Bouwman Search for this author in: * NPG journals * PubMed * Google Scholar * Simon Mays Search for this author in: * NPG journals * PubMed * Google Scholar * Claire Watson Search for this author in: * NPG journals * PubMed * Google Scholar * Diana Lockwood Search for this author in: * NPG journals * PubMed * Google Scholar * Ali Khamispour Search for this author in: * NPG journals * PubMed * Google Scholar * Yahya Dowlati Search for this author in: * NPG journals * PubMed * Google Scholar * Shen Jianping Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas H Rea Search for this author in: * NPG journals * PubMed * Google Scholar * Lucio Vera-Cabrera Search for this author in: * NPG journals * PubMed * Google Scholar * Mariane M Stefani Search for this author in: * NPG journals * PubMed * Google Scholar * Sayera Banu Search for this author in: * NPG journals * PubMed * Google Scholar * Murdo Macdonald Search for this author in: * NPG journals * PubMed * Google Scholar * Bishwa Raj Sapkota Search for this author in: * NPG journals * PubMed * Google Scholar * John S Spencer Search for this author in: * NPG journals * PubMed * Google Scholar * Jérôme Thomas Search for this author in: * NPG journals * PubMed * Google Scholar * Keith Harshman Search for this author in: * NPG journals * PubMed * Google Scholar * Pushpendra Singh Search for this author in: * NPG journals * PubMed * Google Scholar * Philippe Busso Search for this author in: * NPG journals * PubMed * Google Scholar * Alexandre Gattiker Search for this author in: * NPG journals * PubMed * Google Scholar * Jacques Rougemont Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick J Brennan Search for this author in: * NPG journals * PubMed * Google Scholar * Stewart T Cole Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature GeneticsVolume:42,Page:361Year published:(2010)DOI:doi:10.1038/ng0410-361a Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.41, 1282–1289 (2009); published online 1 November 2009; corrected after print 5 February 2010. In the version of this article initially published 1 November 2009, Ali Khamesipour was misspelled in the author list. The error has been corrected in the HTML and PDF versions of the article. Additional data - Corrigendum: Widespread and nonrandom distribution of DNA palindromes in cancer cells provides a structural platform for subsequent gene amplification
- Nature genetics 42(4):361 (2010)
Nature Genetics | Corrigendum Corrigendum: Widespread and nonrandom distribution of DNA palindromes in cancer cells provides a structural platform for subsequent gene amplification * Hisashi Tanaka Search for this author in: * NPG journals * PubMed * Google Scholar * Donald A Bergstrom Search for this author in: * NPG journals * PubMed * Google Scholar * Meng-Chao Yao Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen J Tapscott Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature GeneticsVolume:42,Page:361Year published:(2010)DOI:doi:10.1038/ng0410-361c Article tools * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Nat. Genet.37, 320–327 (2005); published online 13 February 2005; corrected after print 29 March 2010 The GAPF technique identifies palindromes associated with gene amplicons in cancers; however, the initial publication over stimated the frequency of palindrome formation and the frequency of palindromes occurring in similar locations among different cancers. The performance of the modified technique is discussed in the accompanying Correspondence1. References * Diede, S. J., Tanaka, H., Bergstrom, D.A., Yao, M.-C. & Tapscott, S.J.Genome-wide analysis of palindrome formation. Nat. Genet.42, 279 (2010). * Article Download references Additional data
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