101
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Jakobsdottir J, van der Lee SJ, Bis JC, Chouraki V, Li-Kroeger D, Yamamoto S, Grove ML, Naj A, Vronskaya M, Salazar JL, DeStefano AL, Brody JA, Smith AV, Amin N, Sims R, Ibrahim-Verbaas CA, Choi SH, Satizabal CL, Lopez OL, Beiser A, Ikram MA, Garcia ME, Hayward C, Varga TV, Ripatti S, Franks PW, Hallmans G, Rolandsson O, Jansson JH, Porteous DJ, Salomaa V, Eiriksdottir G, Rice KM, Bellen HJ, Levy D, Uitterlinden AG, Emilsson V, Rotter JI, Aspelund T, O’Donnell CJ, Fitzpatrick AL, Launer LJ, Hofman A, Wang LS, Williams J, Schellenberg GD, Boerwinkle E, Psaty BM, Seshadri S, Shulman JM, Gudnason V, van Duijn CM. Rare Functional Variant in TM2D3 is Associated with Late-Onset Alzheimer's Disease. PLoS Genet 2016; 12:e1006327. [PMID: 27764101 PMCID: PMC5072721 DOI: 10.1371/journal.pgen.1006327] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 08/26/2016] [Indexed: 12/21/2022] Open
Abstract
We performed an exome-wide association analysis in 1393 late-onset Alzheimer's disease (LOAD) cases and 8141 controls from the CHARGE consortium. We found that a rare variant (P155L) in TM2D3 was enriched in Icelanders (~0.5% versus <0.05% in other European populations). In 433 LOAD cases and 3903 controls from the Icelandic AGES sub-study, P155L was associated with increased risk and earlier onset of LOAD [odds ratio (95% CI) = 7.5 (3.5-15.9), p = 6.6x10-9]. Mutation in the Drosophila TM2D3 homolog, almondex, causes a phenotype similar to loss of Notch/Presenilin signaling. Human TM2D3 is capable of rescuing these phenotypes, but this activity is abolished by P155L, establishing it as a functionally damaging allele. Our results establish a rare TM2D3 variant in association with LOAD susceptibility, and together with prior work suggests possible links to the β-amyloid cascade.
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Affiliation(s)
| | - Sven J. van der Lee
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Vincent Chouraki
- Boston University School of Medicine, Boston, Massachusetts, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - David Li-Kroeger
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital Houston, Texas, United States of America
| | - Shinya Yamamoto
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital Houston, Texas, United States of America
- Program in Developmental Biology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Megan L. Grove
- School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Adam Naj
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Maria Vronskaya
- Institute of Psychological Medicine and Clinical Neurosciences, Medical Research Council (MRC) Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, United Kingdom
| | - Jose L. Salazar
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Anita L. DeStefano
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Albert V. Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland, United States of America
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Rebecca Sims
- Institute of Psychological Medicine and Clinical Neurosciences, Medical Research Council (MRC) Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, United Kingdom
| | - Carla A. Ibrahim-Verbaas
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Erasmus University Medical Center, CA Rotterdam, The Netherlands
| | - Seung-Hoan Choi
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Claudia L. Satizabal
- Boston University School of Medicine, Boston, Massachusetts, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Oscar L. Lopez
- Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
| | - Alexa Beiser
- Boston University School of Medicine, Boston, Massachusetts, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Erasmus University Medical Center, CA Rotterdam, The Netherlands
- Departments of Radiology, Erasmus University Medical Center, CA Rotterdam, The Netherlands
| | - Melissa E. Garcia
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, United States of America
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Tibor V. Varga
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Paul W. Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
- Department of Public Health & Clinical Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Göran Hallmans
- Department of Biobank Research, Umeå University, Umeå, Sweden
| | - Olov Rolandsson
- Department of Public Health & Clinical Medicine, Section for Family Medicine, Umeå University, Umeå, Sweden
| | - Jan-Håkon Jansson
- Department of Public Health & Clinical Medicine, Umeå University Hospital, Umeå, Sweden
- Research Unit, Skellefteå Hospital, Skellefteå, Sweden
| | - David J. Porteous
- Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | | | - Kenneth M. Rice
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Hugo J. Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital Houston, Texas, United States of America
- Program in Developmental Biology, Baylor College of Medicine, Houston, Texas, United States of America
- Howard Hughes Medical Institute, Durham, North Carolina, United States of America
| | - Daniel Levy
- Boston University School of Medicine, Boston, Massachusetts, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United states of America
| | - Andre G. Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, CA Rotterdam, The Netherlands
| | - Valur Emilsson
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavik, Iceland
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute and Departments of Medicine and Pediatrics, Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Thor Aspelund
- Icelandic Heart Association, Kopavogur, Iceland
- Centre for Public Health, University of Iceland, Reykjavik, Iceland
| | | | | | | | - Christopher J. O’Donnell
- Framingham Heart Study, Framingham, Massachusetts, United States of America
- Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United states of America
| | - Annette L. Fitzpatrick
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Collaborative Health Studies Coordinating Center, Seattle, Washington, United States of America
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, United States of America
| | - Albert Hofman
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Li-San Wang
- Department of Pathology and Laboratory of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Julie Williams
- Institute of Psychological Medicine and Clinical Neurosciences, Medical Research Council (MRC) Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, United Kingdom
| | - Gerard D. Schellenberg
- Department of Pathology and Laboratory of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Eric Boerwinkle
- School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Department of Health Services, University of Washington, Seattle, Washington, United States of America
- Group Health Research Institute, Group Health Cooperative, Seattle, Washington, United States of America
| | - Sudha Seshadri
- Boston University School of Medicine, Boston, Massachusetts, United States of America
- Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Joshua M. Shulman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital Houston, Texas, United States of America
- Program in Developmental Biology, Baylor College of Medicine, Houston, Texas, United States of America
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland, United States of America
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
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102
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Wang J, Skoog T, Einarsdottir E, Kaartokallio T, Laivuori H, Grauers A, Gerdhem P, Hytönen M, Lohi H, Kere J, Jiao H. Investigation of rare and low-frequency variants using high-throughput sequencing with pooled DNA samples. Sci Rep 2016; 6:33256. [PMID: 27633116 PMCID: PMC5025741 DOI: 10.1038/srep33256] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 08/24/2016] [Indexed: 11/09/2022] Open
Abstract
High-throughput sequencing using pooled DNA samples can facilitate genome-wide studies on rare and low-frequency variants in a large population. Some major questions concerning the pooling sequencing strategy are whether rare and low-frequency variants can be detected reliably, and whether estimated minor allele frequencies (MAFs) can represent the actual values obtained from individually genotyped samples. In this study, we evaluated MAF estimates using three variant detection tools with two sets of pooled whole exome sequencing (WES) and one set of pooled whole genome sequencing (WGS) data. Both GATK and Freebayes displayed high sensitivity, specificity and accuracy when detecting rare or low-frequency variants. For the WGS study, 56% of the low-frequency variants in Illumina array have identical MAFs and 26% have one allele difference between sequencing and individual genotyping data. The MAF estimates from WGS correlated well (r = 0.94) with those from Illumina arrays. The MAFs from the pooled WES data also showed high concordance (r = 0.88) with those from the individual genotyping data. In conclusion, the MAFs estimated from pooled DNA sequencing data reflect the MAFs in individually genotyped samples well. The pooling strategy can thus be a rapid and cost-effective approach for the initial screening in large-scale association studies.
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Affiliation(s)
- Jingwen Wang
- Department of Biosciences and Nutrition, Karolinska Institutet, SE-14183 Huddinge, Sweden.,Science for Life Laboratory, Stockholm, Sweden
| | - Tiina Skoog
- Department of Biosciences and Nutrition, Karolinska Institutet, SE-14183 Huddinge, Sweden
| | - Elisabet Einarsdottir
- Department of Biosciences and Nutrition, Karolinska Institutet, SE-14183 Huddinge, Sweden.,Molecular Neurology Research Program, University of Helsinki and Folkhälsan Institute of Genetics, Helsinki, Finland
| | - Tea Kaartokallio
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Hannele Laivuori
- Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Anna Grauers
- Department of Orthopedics, Karolinska University Hospital and Department of Clinical Sciences, Intervention and Technology (CLINTEC) Karolinska Institutet, Stockholm, Sweden.,Department of Orthopaedics, Sundsvall and Harnosand County Hospital, Sundsvall, Sweden
| | - Paul Gerdhem
- Department of Orthopedics, Karolinska University Hospital and Department of Clinical Sciences, Intervention and Technology (CLINTEC) Karolinska Institutet, Stockholm, Sweden
| | - Marjo Hytönen
- Department of Veterinary Biosciences, and Research Programs Unit, Molecular Neurology, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
| | - Hannes Lohi
- Department of Veterinary Biosciences, and Research Programs Unit, Molecular Neurology, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
| | - Juha Kere
- Department of Biosciences and Nutrition, Karolinska Institutet, SE-14183 Huddinge, Sweden.,Science for Life Laboratory, Stockholm, Sweden.,Molecular Neurology Research Program, University of Helsinki and Folkhälsan Institute of Genetics, Helsinki, Finland
| | - Hong Jiao
- Department of Biosciences and Nutrition, Karolinska Institutet, SE-14183 Huddinge, Sweden.,Science for Life Laboratory, Stockholm, Sweden
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103
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Sifrim A, Hitz MP, Wilsdon A, Breckpot J, Al Turki SH, Thienpont B, McRae J, Fitzgerald TW, Singh T, Swaminathan GJ, Prigmore E, Rajan D, Abdul-Khaliq H, Banka S, Bauer UMM, Bentham J, Berger F, Bhattacharya S, Bu'Lock F, Canham N, Colgiu IG, Cosgrove C, Cox H, Daehnert I, Daly A, Danesh J, Fryer A, Gewillig M, Hobson E, Hoff K, Homfray T, Kahlert AK, Ketley A, Kramer HH, Lachlan K, Lampe AK, Louw JJ, Manickara AK, Manase D, McCarthy KP, Metcalfe K, Moore C, Newbury-Ecob R, Omer SO, Ouwehand WH, Park SM, Parker MJ, Pickardt T, Pollard MO, Robert L, Roberts DJ, Sambrook J, Setchfield K, Stiller B, Thornborough C, Toka O, Watkins H, Williams D, Wright M, Mital S, Daubeney PEF, Keavney B, Goodship J, Abu-Sulaiman RM, Klaassen S, Wright CF, Firth HV, Barrett JC, Devriendt K, FitzPatrick DR, Brook JD, Hurles M. Distinct genetic architectures for syndromic and nonsyndromic congenital heart defects identified by exome sequencing. Nat Genet 2016; 48:1060-5. [PMID: 27479907 PMCID: PMC5988037 DOI: 10.1038/ng.3627] [Citation(s) in RCA: 281] [Impact Index Per Article: 35.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 06/24/2016] [Indexed: 02/02/2023]
Abstract
Congenital heart defects (CHDs) have a neonatal incidence of 0.8-1% (refs. 1,2). Despite abundant examples of monogenic CHD in humans and mice, CHD has a low absolute sibling recurrence risk (∼2.7%), suggesting a considerable role for de novo mutations (DNMs) and/or incomplete penetrance. De novo protein-truncating variants (PTVs) have been shown to be enriched among the 10% of 'syndromic' patients with extra-cardiac manifestations. We exome sequenced 1,891 probands, including both syndromic CHD (S-CHD, n = 610) and nonsyndromic CHD (NS-CHD, n = 1,281). In S-CHD, we confirmed a significant enrichment of de novo PTVs but not inherited PTVs in known CHD-associated genes, consistent with recent findings. Conversely, in NS-CHD we observed significant enrichment of PTVs inherited from unaffected parents in CHD-associated genes. We identified three genome-wide significant S-CHD disorders caused by DNMs in CHD4, CDK13 and PRKD1. Our study finds evidence for distinct genetic architectures underlying the low sibling recurrence risk in S-CHD and NS-CHD.
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Affiliation(s)
| | - Marc-Phillip Hitz
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
- Department of Congenital Heart Disease and Pediatric Cardiology, UKSH Kiel, Germany
- DZHK (German Center for Cardiovascular Research), partner site Berlin/Hamburg/Kiel/Lübeck, Germany
| | - Anna Wilsdon
- School of Life Sciences, University of Nottingham, Queen’s Medical Centre, Nottingham, United Kingdom
| | - Jeroen Breckpot
- Center for Human Genetics, University Hospitals Leuven, Leuven, Belgium
| | - Saeed H. Al Turki
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
- Department of Pathology, King Abdulaziz Medical City, Riyadh, Saudi Arabia
- Harvard Medical School Genetics Training Program, Boston, United States of America
| | - Bernard Thienpont
- Vesalius Research Center, VIB, Leuven, Belgium
- Department of Oncology, Laboratory for Translational Genetics, KU Leuven, Leuven, Belgium
| | - Jeremy McRae
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | | | | | | | - Elena Prigmore
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | - Diana Rajan
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | - Hashim Abdul-Khaliq
- Department of Paediatric Cardiology, Saarland University, Homburg, Germany
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
| | - Siddharth Banka
- Manchester Centre for Genomic Medicine, Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, Manchester, United Kingdom
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Ulrike M. M. Bauer
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
| | - Jamie Bentham
- Department of Paediatric Cardiology, Yorkshire Heart Centre, Leeds, United Kingdom
| | - Felix Berger
- DZHK (German Center for Cardiovascular Research), partner site Berlin/Hamburg/Kiel/Lübeck, Germany
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
- German Heart Institute Berlin, Charité Universitaetsmedizin Berlin, Department of Congenital Heart Disease and Pediatric Cardiology, Berlin, Germany
| | - Shoumo Bhattacharya
- Department of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Frances Bu'Lock
- East Midlands Congenital Heart Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Natalie Canham
- North West Thames Regional Genetics Centre, London North West Healthcare NHS Trust, Harrow, United Kingdom
| | | | - Catherine Cosgrove
- Department of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Helen Cox
- West Midlands Regional Genetics Service, Birmingham Women’s NHS Foundation Trust, Birmingham Women’s Hospital, Birmingham, United Kingdom
| | - Ingo Daehnert
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
- Department of Pediatric Cardiology, Heart Center, University of Leipzig, Germany
| | - Allan Daly
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
| | - John Danesh
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- INTERVAL Coordinating Centre, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Alan Fryer
- Department of Clinical Genetics, Liverpool Women's NHS Foundation Trust, Crown Street, Liverpool, United Kingdom
| | - Marc Gewillig
- Department of Pediatric Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Emma Hobson
- Yorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Department of Clinical Genetics, Chapel Allerton Hospital, Leeds, United Kingdom
| | - Kirstin Hoff
- Department of Congenital Heart Disease and Pediatric Cardiology, UKSH Kiel, Germany
- DZHK (German Center for Cardiovascular Research), partner site Berlin/Hamburg/Kiel/Lübeck, Germany
| | - Tessa Homfray
- South West Thames Regional Genetics Centre, St George’s Healthcare NHS Trust, St George’s, University of London, London, United Kingdom
| | | | - Anne-Karin Kahlert
- Department of Congenital Heart Disease and Pediatric Cardiology, UKSH Kiel, Germany
- DZHK (German Center for Cardiovascular Research), partner site Berlin/Hamburg/Kiel/Lübeck, Germany
- Institute for Clinical Genetics, Carl Gustav Carus Faculty of Medicine, Dresden, Germany
| | - Ami Ketley
- School of Life Sciences, University of Nottingham, Queen’s Medical Centre, Nottingham, United Kingdom
| | - Hans-Heiner Kramer
- Department of Congenital Heart Disease and Pediatric Cardiology, UKSH Kiel, Germany
- DZHK (German Center for Cardiovascular Research), partner site Berlin/Hamburg/Kiel/Lübeck, Germany
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
| | - Katherine Lachlan
- Wessex Clinical Genetics Service, University Hospital Southampton, Princess Anne Hospital, Southampton, United Kingdom
- Wessex Regional Genetics Laboratory, Salisbury NHS Foundation Trust, Salisbury District Hospital, Salisbury, United Kingdom
- Faculty of Medicine, University of Southampton, Southampton, United Kingdom
| | - Anne Katrin Lampe
- South East of Scotland Clinical Genetic Service, IGMM North, Western General Hospital, Edinburgh, United Kingdom
| | - Jacoba J. Louw
- Department of Pediatric Cardiology, University Hospitals Leuven, Leuven, Belgium
| | | | | | - Karen P. McCarthy
- Cardiac Morphology Unit, Royal Brompton Hospital and the National Heart and Lung Institute, Imperial College, United Kingdom
| | - Kay Metcalfe
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Carmel Moore
- INTERVAL Coordinating Centre, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Ruth Newbury-Ecob
- Department of Clinical Genetics, St Michael's Hospital, Bristol, United Kingdom
| | - Seham Osman Omer
- Division of Pediatric Cardiology, King Abdulaziz Cardiac Center, King Abdulaziz Medical City, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
| | - Willem H. Ouwehand
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Long Road, Cambridge, United Kingdom
- NHS Blood and Transplant, Long Road, Cambridge, United Kingdom
| | - Soo-Mi Park
- East Anglian Medical Genetics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Michael J. Parker
- Sheffield Children’s Hospital NHS Foundation Trust, Western Bank, Sheffield
| | - Thomas Pickardt
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
| | | | - Leema Robert
- South East Thames Regional Genetics Centre, Guy’s and St Thomas’ NHS Foundation Trust, Guy’s Hospital, London, United Kingdom
| | - David J. Roberts
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant, John Radcliffe Hospital, Oxford, United Kingdom
- Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Jennifer Sambrook
- INTERVAL Coordinating Centre, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Long Road, Cambridge, United Kingdom
| | - Kerry Setchfield
- School of Life Sciences, University of Nottingham, Queen’s Medical Centre, Nottingham, United Kingdom
| | - Brigitte Stiller
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
- Department of Congenital Heart Defects and Paediatric Cardiology, Heart Centre, University of Freiburg, Germany
| | - Chris Thornborough
- East Midlands Congenital Heart Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Okan Toka
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Pediatric Cardiology, Erlangen, Germany
| | - Hugh Watkins
- Department of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom
| | - Denise Williams
- West Midlands Regional Genetics Service, Birmingham Women’s NHS Foundation Trust, Birmingham Women’s Hospital, Birmingham, United Kingdom
| | - Michael Wright
- Northern Genetics Service, Newcastle upon Tyne Hospitals NHS Foundation Trust, Institute of Human Genetics, International Centre for Life, Newcastle upon Tyne, United Kingdom
| | | | - Piers E. F. Daubeney
- Division of Paediatric Cardiology, Royal Brompton Hospital, London, United Kingdom
- Paediatric Cardiology, Imperial College, London, United Kingdom
| | - Bernard Keavney
- Institute of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
| | - Judith Goodship
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Riyadh Mahdi Abu-Sulaiman
- Division of Pediatric Cardiology, King Abdulaziz Cardiac Center, King Abdulaziz Medical City, Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
| | - Sabine Klaassen
- DZHK (German Center for Cardiovascular Research), partner site Berlin/Hamburg/Kiel/Lübeck, Germany
- Competence Network for Congenital Heart Defects, National Register for Congenital Heart Defects, DZHK (German Center for Cardiovascular Research), Germany
- Experimental and Clinical Research Center (ECRC), Charité Medical Faculty and Max-Delbruck-Center for Molecular Medicine, Berlin, Germany
- Department of Pediatric Cardiology, Charité University Medicine, Berlin, Germany
| | | | - Helen V. Firth
- East Anglian Medical Genetics, Cambridge University Hospitals NHS Foundation Trust, Biomedical Campus, Cambridge, United Kingdom
| | | | | | - David R. FitzPatrick
- Medical Research Council (MRC) Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine (IGMM), University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
| | - J. David Brook
- School of Life Sciences, University of Nottingham, Queen’s Medical Centre, Nottingham, United Kingdom
| | | | - Matthew Hurles
- Wellcome Trust Sanger Institute, Cambridge, United Kingdom
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104
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Jeong CS, Kim D. Inferring Crohn's disease association from exome sequences by integrating biological knowledge. BMC Med Genomics 2016; 9 Suppl 1:35. [PMID: 27535358 PMCID: PMC4989895 DOI: 10.1186/s12920-016-0189-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Background Exome sequencing has been emerged as a primary method to identify detailed sequence variants associated with complex diseases including Crohn’s disease in the protein-coding regions of human genome. However, constructing an interpretable model for exome sequencing data is challenging because of the huge diversity of genomic variation. In addition, it has been known that utilizing biologically relevant information in a rigorous manner is essential for effectively extracting disease-associated information. Results In this paper, we incorporate three different types of biological knowledge such as predicted pathogenicity, disease gene annotation, and functional interaction network of human genes, and integrate them with exome sequence data in non-negative matrix tri-factorization framework. Based on the proposed method, we successfully identified Crohn’s disease patients from exome sequencing data and achieved the area under the receiver operating characteristics curve (AUC) of 0.816, while other clustering methods not using biological information achieved the AUC of 0.786. Moreover, the disease association score derived from our method showed higher correlation with Crohn’s disease genes than other unrelated genes. Conclusions As a consequence, by integrating biological information across multiple levels such as variant, gene, and systems, our method could be useful for identifying disease susceptibility and its associated genes from exome sequencing data.
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Affiliation(s)
- Chan-Seok Jeong
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 34141 Daejeon, Republic of Korea
| | - Dongsup Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, 34141 Daejeon, Republic of Korea.
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Abstract
Juvenile dermatomyositis is a complex illness characterized by vascular/perivascular inflammation, primarily in the skin and muscles. In this review, we discuss how proteomic and genomic technologies have expanded our understanding of the immune pathogenesis of this disease. We will also discuss further directions that the field may take to use existing and developing technologies to further our understanding of this often-perplexing disease.
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Affiliation(s)
- Rie Karasawa
- Department of Pediatrics and Genetics, Genomics & Bioinformatics Program, University at Buffalo
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106
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Chen F, Duggal P, Klein BEK, Lee KE, Truitt B, Klein R, Iyengar SK, Klein AP. Variation in PTCHD2, CRISP3, NAP1L4, FSCB, and AP3B2 associated with spherical equivalent. Mol Vis 2016; 22:783-96. [PMID: 27440996 DOI: pmid/27440996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2015] [Accepted: 07/12/2016] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Ocular refraction is measured in spherical equivalent as the power of the external lens required to focus images on the retina. Myopia (nearsightedness) and hyperopia (farsightedness) are the most common refractive errors, and the leading causes of visual impairment and blindness in the world. The goal of this study is to identify rare and low-frequency variants that influence spherical equivalent. METHODS We conducted variant-level and gene-level quantitative trait association analyses for mean spherical equivalent, using data from 1,560 individuals in the Beaver Dam Eye Study. Genotyping was conducted using the Illumina exome array. We analyzed 34,976 single nucleotide variants and 11,571 autosomal genes across the genome, using single-variant tests as well as gene-based tests. RESULTS Spherical equivalent was significantly associated with five genes in gene-based analysis: PTCHD2 at 1p36.22 (p = 3.6 × 10(-7)), CRISP3 at 6p12.3 (p = 4.3 × 10(-6)), NAP1L4 at 11p15.5 (p = 3.6 × 10(-6)), FSCB at 14q21.2 (p = 1.5 × 10(-7)), and AP3B2 at 15q25.2 (p = 1.6 × 10(-7)). The variant-based tests identified evidence suggestive of association with two novel variants in linkage disequilibrium (pairwise r(2) = 0.80) in the TCTE1 gene region at 6p21.1 (rs2297336, minor allele frequency (MAF) = 14.1%, β = -0.62 p = 3.7 × 10(-6); rs324146, MAF = 16.9%, β = -0.55, p = 1.4 × 10(-5)). In addition to these novel findings, we successfully replicated a previously reported association with rs634990 near GJD2 at 15q14 (MAF = 47%, β = -0.29, p=1.8 × 10(-3)). We also found evidence of association with spherical equivalent on 2q37.1 in PRSS56 at rs1550094 (MAF = 31%, β = -0.33, p = 1.7 × 10(-3)), a region previously associated with myopia. CONCLUSIONS We identified several novel candidate genes that may play a role in the control of spherical equivalent. However, further studies are needed to replicate these findings. In addition, our results contribute to the increasing evidence that variation in the GJD2 and PRSS56 genes influence the development of refractive errors. Identifying that variation in these genes is associated with spherical equivalent may provide further insight into the etiology of myopia and consequent vision loss.
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Affiliation(s)
- Fei Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Barbara E K Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Kristine E Lee
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Barbara Truitt
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH
| | - Ronald Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Sudha K Iyengar
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH
| | - Alison P Klein
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD
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107
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Long D, Deng X, Singh P, Loeb M, Lauring AS, Seielstad M. Identification of genetic variants associated with susceptibility to West Nile virus neuroinvasive disease. Genes Immun 2016; 17:298-304. [PMID: 27170560 PMCID: PMC5215919 DOI: 10.1038/gene.2016.21] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 04/10/2016] [Accepted: 04/12/2016] [Indexed: 11/09/2022]
Abstract
West Nile virus (WNV) infection results in a diverse spectrum of outcomes, and host genetics are likely to influence susceptibility to neuroinvasive disease (West Nile neuroinvasive disease (WNND)). We performed whole-exome sequencing of 44 individuals with WNND and identified alleles associated with severe disease by variant filtration in cases, kernel association testing in cases and controls and single-nucleotide polymorphism (SNP) imputation into a larger cohort of WNND cases and seropositive controls followed by genome-wide association analysis. Variant filtration prioritized genes based on the enrichment of otherwise rare variants, but did not unambiguously implicate variants shared by a majority of cases. Kernel association demonstrated enrichment for risk and protective alleles in the human leukocyte antigen (HLA)-A and HLA-DQB1 loci that have well understood roles in antiviral immunity. Two loci, HERC5 and an intergenic region between CD83 and JARID2, were implicated by multiple imputed SNPs and exceeded genome-wide significance in a discovery cohort (n=862). SNPs at two additional loci, TFCP2L1 and CACNA1H, achieved genome-wide significance after association testing of directly genotyped and imputed SNPs in a discovery cohort (n=862) and a separate replication cohort (n=1387). The context of these loci suggests that immunoregulatory, ion channel and endothelial barrier functions may be important elements of the host response to WNV.
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Affiliation(s)
- D Long
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - X Deng
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- Blood Systems Research Institute, San Francisco, CA, USA
| | - P Singh
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - M Loeb
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - A S Lauring
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - M Seielstad
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- Blood Systems Research Institute, San Francisco, CA, USA
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108
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Chandler MR, Bilgili EP, Merner ND. A Review of Whole-Exome Sequencing Efforts Toward Hereditary Breast Cancer Susceptibility Gene Discovery. Hum Mutat 2016; 37:835-46. [PMID: 27226120 DOI: 10.1002/humu.23017] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2015] [Accepted: 05/18/2016] [Indexed: 01/08/2023]
Abstract
Inherited genetic risk factors contribute toward breast cancer (BC) onset. BC risk variants can be divided into three categories of penetrance (high, moderate, and low) that reflect the probability of developing the disease. Traditional BC susceptibility gene discovery approaches that searched for high- and moderate-risk variants in familial BC cases have had limited success; to date, these risk variants explain only ∼30% of familial BC cases. Next-generation sequencing technologies can be used to search for novel high and moderate BC risk variants, and this manuscript reviews 12 familial BC whole-exome sequencing efforts. Study design, filtering strategies, and segregation and validation analyses are discussed. Overall, only a modest number of novel BC risk genes were identified, and 90% and 97% of the exome-sequenced families and cases, respectively, had no BC risk variants reported. It is important to learn from these studies and consider alternate strategies in order to make further advances. The discovery of new BC susceptibility genes is critical for improved risk assessment and to provide insight toward disease mechanisms for the development of more effective therapies.
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Affiliation(s)
- Madison R Chandler
- Auburn University, Harrison School of Pharmacy, Department of Drug Discovery and Development, Auburn, Alabama, 36849
| | - Erin P Bilgili
- Auburn University, Harrison School of Pharmacy, Department of Drug Discovery and Development, Auburn, Alabama, 36849
| | - Nancy D Merner
- Auburn University, Harrison School of Pharmacy, Department of Drug Discovery and Development, Auburn, Alabama, 36849
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Abstract
A prolonged QT interval is an important risk factor for ventricular arrhythmias and sudden cardiac death. QT prolongation can be caused by drugs. There are multiple risk factors for drug-induced QT prolongation, including genetic variation. QT prolongation is one of the most common reasons for withdrawal of drugs from the market, despite the fact that these drugs may be beneficial for certain patients and not harmful in every patient. Identifying genetic variants associated with drug-induced QT prolongation might add to tailored pharmacotherapy and prevent beneficial drugs from being withdrawn unnecessarily. In this review, our objective was to provide an overview of the genetic background of drug-induced QT prolongation, distinguishing pharmacokinetic and pharmacodynamic pathways. Pharmacokinetic-mediated genetic susceptibility is mainly characterized by variation in genes encoding drug-metabolizing cytochrome P450 enzymes or drug transporters. For instance, the P-glycoprotein drug transporter plays a role in the pharmacokinetic susceptibility of drug-induced QT prolongation. The pharmacodynamic component of genetic susceptibility is mainly characterized by genes known to be associated with QT interval duration in the general population and genes in which the causal mutations of congenital long QT syndromes are located. Ethnicity influences susceptibility to drug-induced QT interval prolongation, with Caucasians being more sensitive than other ethnicities. Research on the association between pharmacogenetic interactions and clinical endpoints such as sudden cardiac death is still limited. Future studies in this area could enable us to determine the risk of arrhythmias more adequately in clinical practice.
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110
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Neřoldová M, Stránecký V, Hodaňová K, Hartmannová H, Piherová L, Přistoupilová A, Mrázová L, Vrablík M, Adámková V, Hubáček JA, Jirsa M, Kmoch S. Rare variants in known and novel candidate genes predisposing to statin-associated myopathy. Pharmacogenomics 2016; 17:1405-14. [PMID: 27296017 DOI: 10.2217/pgs-2016-0071] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM Genetic variants affecting statin uptake, metabolism or predisposing to muscular diseases may confer susceptibility to statin-induced myopathy. Besides the SLCO1B1 rs4149056 genotype, common genetic variants do not seem to determine statin-associated myopathy. Here we aimed to address the potential role of rare variants. METHODS We performed whole exome sequencing in 88 individuals suffering from statin-associated myopathy and assessed the burden of rare variants using candidate-gene and exome-wide association analysis. RESULTS In the novel candidate gene CLCN1, we identified a heterozygote truncating mutation p.R894* in four patients. In addition, we detected predictably pathogenic case-specific variants in MYOT, CYP3A5, SH3TC2, FBXO32 and RBM20. CONCLUSION These findings support the role of rare variants and nominate loci for follow-up studies.
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Affiliation(s)
- Magdaléna Neřoldová
- Laboratory of Experimental Hepatology, Center for Experimental Medicine, Institute for Clinical & Experimental Medicine, Prague, Czech Republic
| | - Viktor Stránecký
- Institute of Inherited Metabolic Diseases, First Medical Faculty, Charles University, Prague, Czech Republic
| | - Kateřina Hodaňová
- Institute of Inherited Metabolic Diseases, First Medical Faculty, Charles University, Prague, Czech Republic
| | - Hana Hartmannová
- Institute of Inherited Metabolic Diseases, First Medical Faculty, Charles University, Prague, Czech Republic
| | - Lenka Piherová
- Institute of Inherited Metabolic Diseases, First Medical Faculty, Charles University, Prague, Czech Republic
| | - Anna Přistoupilová
- Institute of Inherited Metabolic Diseases, First Medical Faculty, Charles University, Prague, Czech Republic
| | - Lenka Mrázová
- Laboratory for Atherosclerosis Research, Center for Experimental Medicine, Institute for Clinical & Experimental Medicine, Prague, Czech Republic
| | - Michal Vrablík
- Third Medical Department, First Faculty of Medicine, Charles University & General Faculty Hospital, Prague, Czech Republic
| | - Věra Adámková
- Preventive Cardiology Department, Institute for Clinical & Experimental Medicine, Prague, Czech Republic
| | - Jaroslav A Hubáček
- Laboratory for Atherosclerosis Research, Center for Experimental Medicine, Institute for Clinical & Experimental Medicine, Prague, Czech Republic
| | - Milan Jirsa
- Laboratory of Experimental Hepatology, Center for Experimental Medicine, Institute for Clinical & Experimental Medicine, Prague, Czech Republic
| | - Stanislav Kmoch
- Institute of Inherited Metabolic Diseases, First Medical Faculty, Charles University, Prague, Czech Republic
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111
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Whole-exome sequencing to analyze population structure, parental inbreeding, and familial linkage. Proc Natl Acad Sci U S A 2016; 113:6713-8. [PMID: 27247391 DOI: 10.1073/pnas.1606460113] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Principal component analysis (PCA), homozygosity rate estimations, and linkage studies in humans are classically conducted through genome-wide single-nucleotide variant arrays (GWSA). We compared whole-exome sequencing (WES) and GWSA for this purpose. We analyzed 110 subjects originating from different regions of the world, including North Africa and the Middle East, which are poorly covered by public databases and have high consanguinity rates. We tested and applied a number of quality control (QC) filters. Compared with GWSA, we found that WES provided an accurate prediction of population substructure using variants with a minor allele frequency > 2% (correlation = 0.89 with the PCA coordinates obtained by GWSA). WES also yielded highly reliable estimates of homozygosity rates using runs of homozygosity with a 1,000-kb window (correlation = 0.94 with the estimates provided by GWSA). Finally, homozygosity mapping analyses in 15 families including a single offspring with high homozygosity rates showed that WES provided 51% less genome-wide linkage information than GWSA overall but 97% more information for the coding regions. At the genome-wide scale, 76.3% of linked regions were found by both GWSA and WES, 17.7% were found by GWSA only, and 6.0% were found by WES only. For coding regions, the corresponding percentages were 83.5%, 7.4%, and 9.1%, respectively. With appropriate QC filters, WES can be used for PCA and adjustment for population substructure, estimating homozygosity rates in individuals, and powerful linkage analyses, particularly in coding regions.
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112
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Discovery of rare variants for complex phenotypes. Hum Genet 2016; 135:625-34. [PMID: 27221085 DOI: 10.1007/s00439-016-1679-1] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Accepted: 04/28/2016] [Indexed: 12/27/2022]
Abstract
With the rise of sequencing technologies, it is now feasible to assess the role rare variants play in the genetic contribution to complex trait variation. While some of the earlier targeted sequencing studies successfully identified rare variants of large effect, unbiased gene discovery using exome sequencing has experienced limited success for complex traits. Nevertheless, rare variant association studies have demonstrated that rare variants do contribute to phenotypic variability, but sample sizes will likely have to be even larger than those of common variant association studies to be powered for the detection of genes and loci. Large-scale sequencing efforts of tens of thousands of individuals, such as the UK10K Project and aggregation efforts such as the Exome Aggregation Consortium, have made great strides in advancing our knowledge of the landscape of rare variation, but there remain many considerations when studying rare variation in the context of complex traits. We discuss these considerations in this review, presenting a broad range of topics at a high level as an introduction to rare variant analysis in complex traits including the issues of power, study design, sample ascertainment, de novo variation, and statistical testing approaches. Ultimately, as sequencing costs continue to decline, larger sequencing studies will yield clearer insights into the biological consequence of rare mutations and may reveal which genes play a role in the etiology of complex traits.
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113
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Fujikura K. Global Carrier Rates of Rare Inherited Disorders Using Population Exome Sequences. PLoS One 2016; 11:e0155552. [PMID: 27219052 PMCID: PMC4878778 DOI: 10.1371/journal.pone.0155552] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2016] [Accepted: 04/29/2016] [Indexed: 12/22/2022] Open
Abstract
Exome sequencing has revealed the causative mutations behind numerous rare, inherited disorders, but it is challenging to find reliable epidemiological values for rare disorders. Here, I provide a genetic epidemiology method to identify the causative mutations behind rare, inherited disorders using two population exome sequences (1000 Genomes and NHLBI). I created global maps of carrier rate distribution for 18 recessive disorders in 16 diverse ethnic populations. Out of a total of 161 mutations associated with 18 recessive disorders, I detected 24 mutations in either or both exome studies. The genetic mapping revealed strong international spatial heterogeneities in the carrier patterns of the inherited disorders. I next validated this methodology by statistically evaluating the carrier rate of one well-understood disorder, sickle cell anemia (SCA). The population exome-based epidemiology of SCA [African (allele frequency (AF) = 0.0454, N = 2447), Asian (AF = 0, N = 286), European (AF = 0.000214, N = 4677), and Hispanic (AF = 0.0111, N = 362)] was not significantly different from that obtained from a clinical prevalence survey. A pair-wise proportion test revealed no significant differences between the two exome projects in terms of AF (46/48 cases; P > 0.05). I conclude that population exome-based carrier rates can form the foundation for a prospectively maintained database of use to clinical geneticists. Similar modeling methods can be applied to many inherited disorders.
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Affiliation(s)
- Kohei Fujikura
- Kobe University School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
- * E-mail:
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114
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Pandey MK, Roorkiwal M, Singh VK, Ramalingam A, Kudapa H, Thudi M, Chitikineni A, Rathore A, Varshney RK. Emerging Genomic Tools for Legume Breeding: Current Status and Future Prospects. FRONTIERS IN PLANT SCIENCE 2016; 7:455. [PMID: 27199998 PMCID: PMC4852475 DOI: 10.3389/fpls.2016.00455] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Accepted: 03/24/2016] [Indexed: 05/19/2023]
Abstract
Legumes play a vital role in ensuring global nutritional food security and improving soil quality through nitrogen fixation. Accelerated higher genetic gains is required to meet the demand of ever increasing global population. In recent years, speedy developments have been witnessed in legume genomics due to advancements in next-generation sequencing (NGS) and high-throughput genotyping technologies. Reference genome sequences for many legume crops have been reported in the last 5 years. The availability of the draft genome sequences and re-sequencing of elite genotypes for several important legume crops have made it possible to identify structural variations at large scale. Availability of large-scale genomic resources and low-cost and high-throughput genotyping technologies are enhancing the efficiency and resolution of genetic mapping and marker-trait association studies. Most importantly, deployment of molecular breeding approaches has resulted in development of improved lines in some legume crops such as chickpea and groundnut. In order to support genomics-driven crop improvement at a fast pace, the deployment of breeder-friendly genomics and decision support tools seems appear to be critical in breeding programs in developing countries. This review provides an overview of emerging genomics and informatics tools/approaches that will be the key driving force for accelerating genomics-assisted breeding and ultimately ensuring nutritional and food security in developing countries.
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Affiliation(s)
- Manish K. Pandey
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Manish Roorkiwal
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Vikas K. Singh
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Abirami Ramalingam
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Himabindu Kudapa
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Mahendar Thudi
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Anu Chitikineni
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Abhishek Rathore
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
| | - Rajeev K. Varshney
- International Crops Research Institute for the Semi-Arid TropicsHyderabad, India
- The University of Western AustraliaCrawley, WA, Australia
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115
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Damiati E, Borsani G, Giacopuzzi E. Amplicon-based semiconductor sequencing of human exomes: performance evaluation and optimization strategies. Hum Genet 2016; 135:499-511. [PMID: 27003585 PMCID: PMC4835520 DOI: 10.1007/s00439-016-1656-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 03/12/2016] [Indexed: 02/02/2023]
Abstract
The Ion Proton platform allows to perform whole exome sequencing (WES) at low cost, providing rapid turnaround time and great flexibility. Products for WES on Ion Proton system include the AmpliSeq Exome kit and the recently introduced HiQ sequencing chemistry. Here, we used gold standard variants from GIAB consortium to assess the performances in variants identification, characterize the erroneous calls and develop a filtering strategy to reduce false positives. The AmpliSeq Exome kit captures a large fraction of bases (>94 %) in human CDS, ClinVar genes and ACMG genes, but with 2,041 (7 %), 449 (13 %) and 11 (19 %) genes not fully represented, respectively. Overall, 515 protein coding genes contain hard-to-sequence regions, including 90 genes from ClinVar. Performance in variants detection was maximum at mean coverage >120×, while at 90× and 70× we measured a loss of variants of 3.2 and 4.5 %, respectively. WES using HiQ chemistry showed ~71/97.5 % sensitivity, ~37/2 % FDR and ~0.66/0.98 F1 score for indels and SNPs, respectively. The proposed low, medium or high-stringency filters reduced the amount of false positives by 10.2, 21.2 and 40.4 % for indels and 21.2, 41.9 and 68.2 % for SNP, respectively. Amplicon-based WES on Ion Proton platform using HiQ chemistry emerged as a competitive approach, with improved accuracy in variants identification. False-positive variants remain an issue for the Ion Torrent technology, but our filtering strategy can be applied to reduce erroneous variants.
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Affiliation(s)
- E Damiati
- Unit of Genetics, Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | - G Borsani
- Unit of Genetics, Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | - Edoardo Giacopuzzi
- Unit of Genetics, Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy.
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116
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Ziff JL, Crompton M, Powell HRF, Lavy JA, Aldren CP, Steel KP, Saeed SR, Dawson SJ. Mutations and altered expression of SERPINF1 in patients with familial otosclerosis. Hum Mol Genet 2016; 25:2393-2403. [PMID: 27056980 PMCID: PMC5181625 DOI: 10.1093/hmg/ddw106] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 03/24/2016] [Accepted: 03/25/2016] [Indexed: 01/08/2023] Open
Abstract
Otosclerosis is a relatively common heterogenous condition, characterized by abnormal bone remodelling in the otic capsule leading to fixation of the stapedial footplate and an associated conductive hearing loss. Although familial linkage and candidate gene association studies have been performed in recent years, little progress has been made in identifying disease-causing genes. Here, we used whole-exome sequencing in four families exhibiting dominantly inherited otosclerosis to identify 23 candidate variants (reduced to 9 after segregation analysis) for further investigation in a secondary cohort of 84 familial cases. Multiple mutations were found in the SERPINF1 (Serpin Peptidase Inhibitor, Clade F) gene which encodes PEDF (pigment epithelium-derived factor), a potent inhibitor of angiogenesis and known regulator of bone density. Six rare heterozygous SERPINF1 variants were found in seven patients in our familial otosclerosis cohort; three are missense mutations predicted to be deleterious to protein function. The other three variants are all located in the 5′-untranslated region (UTR) of an alternative spliced transcript SERPINF1-012. RNA-seq analysis demonstrated that this is the major SERPINF1 transcript in human stapes bone. Analysis of stapes from two patients with the 5′-UTR mutations showed that they had reduced expression of SERPINF1-012. All three 5′-UTR mutations are predicted to occur within transcription factor binding sites and reporter gene assays confirmed that they affect gene expression levels. Furthermore, RT-qPCR analysis of stapes bone cDNA showed that SERPINF1-012 expression is reduced in otosclerosis patients with and without SERPINF1 mutations, suggesting that it may be a common pathogenic pathway in the disease.
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Affiliation(s)
- Joanna L Ziff
- UCL Ear Institute, University College London, London WC1X 8EE, UK
| | - Michael Crompton
- UCL Ear Institute, University College London, London WC1X 8EE, UK
| | - Harry R F Powell
- Royal National Throat Nose and Ear Hospital, London WC1X 8EE, UK
| | - Jeremy A Lavy
- Royal National Throat Nose and Ear Hospital, London WC1X 8EE, UK
| | | | | | - Shakeel R Saeed
- UCL Ear Institute, University College London, London WC1X 8EE, UK.,Royal National Throat Nose and Ear Hospital, London WC1X 8EE, UK
| | - Sally J Dawson
- UCL Ear Institute, University College London, London WC1X 8EE, UK,
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A stop-codon of the phosphodiesterase 11A gene is associated with elevated blood pressure and measures of obesity. J Hypertens 2016; 34:445-51; discussion 451. [DOI: 10.1097/hjh.0000000000000821] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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118
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Society for Pediatric Research 2015 Young Investigator Award: genetics of human hematopoiesis-what patients can teach us about blood cell production. Pediatr Res 2016; 79:366-70. [PMID: 26575596 DOI: 10.1038/pr.2015.245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 09/30/2015] [Indexed: 11/09/2022]
Abstract
Blood cell production or hematopoiesis is one of the most well-understood paradigms of cell differentiation in the body. The majority of work on hematopoiesis comes from studies that have primarily been conducted in mice, zebrafish, or other valuable model systems. However, it is clear that such model organisms may not consistently and faithfully mimic what is observed in humans with blood disorders. Moreover, there is significant divergence between species that is increasingly being appreciated at the genomic level. As a result, there is an opportunity to use observations in humans to provide a refined view of hematopoiesis. Here, we discuss vignettes from our work that illustrate how insight from human genetics can improve our understanding of blood cell production and identify promising therapeutic approaches for blood disorders.
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Abstract
Although genetic studies of Bipolar Disorder have been pursued for decades, it has only been in the last several years that clearly replicated findings have emerged. These findings, typically of modest effects, point to a polygenic genetic architecture consisting of multiple common and rare susceptibility variants. While larger genome-wide association studies are ongoing, the advent of whole exome and genome sequencing should lead to the identification of rare, and potentially more penetrant, variants. Progress along both fronts will provide novel insights into the biology of Bipolar Disorder and help usher in a new era of personalized medicine and improved treatments.
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Abstract
For the first time in the history of human genetics research, it is now both technically feasible and economically affordable to screen individual genomes for novel disease-causing mutations at base-pair resolution using "next-generation sequencing" (NGS). One popular aim in many of today's NGS studies is genome resequencing (in part or whole) to identify DNA variants potentially accounting for the "missing heritability" problem observed in many genetically complex traits. Thus far, only relatively few projects have applied these powerful new technologies to search for novel Alzheimer's disease (AD) related sequence variants. In this review, I summarize the findings from the first NGS-based resequencing studies in AD and discuss their potential implications and limitations. Notable recent discoveries using NGS include the identification of rare susceptibility modifying alleles in APP, TREM2, and PLD3. Several other large-scale NGS projects are currently underway so that additional discoveries can be expected over the coming years.
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Mensah-Ablorh A, Lindstrom S, Haiman CA, Henderson BE, Marchand LL, Lee S, Stram DO, Eliassen AH, Price A, Kraft P. Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations. Genet Epidemiol 2015; 40:57-65. [PMID: 26639010 DOI: 10.1002/gepi.21939] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Revised: 09/15/2015] [Accepted: 09/19/2015] [Indexed: 12/30/2022]
Abstract
Several methods have been proposed to increase power in rare variant association testing by aggregating information from individual rare variants (MAF < 0.005). However, how to best combine rare variants across multiple ethnicities and the relative performance of designs using different ethnic sampling fractions remains unknown. In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single-ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single-ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component-based tests had greater power when some causal rare variants were deleterious and some were protective. Multiethnic studies had greater power than single-ethnicity studies at many loci, with inclusion of African Americans providing the largest impact. On average, studies including African Americans had as much as 20% greater power than equivalently sized studies without African Americans. This suggests that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups.
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Affiliation(s)
- Akweley Mensah-Ablorh
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Sara Lindstrom
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Research Center, Honolulu, Hawaii, United States of America
| | - Seunngeun Lee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, California, United States of America
| | - A Heather Eliassen
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Channing Division of Network Medicine, Brigham & Women's Hospital, Boston, Massachusetts, United States of America
| | - Alkes Price
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts, United States of America.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
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122
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Guo T, Chung J, Wang T, McDonald-McGinn D, Kates W, Hawuła W, Coleman K, Zackai E, Emanuel B, Morrow B. Histone Modifier Genes Alter Conotruncal Heart Phenotypes in 22q11.2 Deletion Syndrome. Am J Hum Genet 2015; 97:869-77. [PMID: 26608785 DOI: 10.1016/j.ajhg.2015.10.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 10/23/2015] [Indexed: 12/18/2022] Open
Abstract
We performed whole exome sequence (WES) to identify genetic modifiers on 184 individuals with 22q11.2 deletion syndrome (22q11DS), of whom 89 case subjects had severe congenital heart disease (CHD) and 95 control subjects had normal hearts. Three genes including JMJD1C (jumonji domain containing 1C), RREB1 (Ras responsive element binding protein 1), and SEC24C (SEC24 family member C) had rare (MAF < 0.001) predicted deleterious single-nucleotide variations (rdSNVs) in seven case subjects and no control subjects (p = 0.005; Fisher exact and permutation tests). Because JMJD1C and RREB1 are involved in chromatin modification, we investigated other histone modification genes. Eighteen case subjects (20%) had rdSNVs in four genes (JMJD1C, RREB1, MINA, KDM7A) all involved in demethylation of histones (H3K9, H3K27). Overall, rdSNVs were enriched in histone modifier genes that activate transcription (Fisher exact p = 0.0004, permutations, p = 0.0003, OR = 5.16); however, rdSNVs in control subjects were not enriched. This implicates histone modification genes as influencing risk for CHD in presence of the deletion.
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Luzón-Toro B, Gui H, Ruiz-Ferrer M, Sze-Man Tang C, Fernández RM, Sham PC, Torroglosa A, Kwong-Hang Tam P, Espino-Paisán L, Cherny SS, Bleda M, Enguix-Riego MDV, Dopazo J, Antiñolo G, García-Barceló MM, Borrego S. Exome sequencing reveals a high genetic heterogeneity on familial Hirschsprung disease. Sci Rep 2015; 5:16473. [PMID: 26559152 PMCID: PMC4642299 DOI: 10.1038/srep16473] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 10/14/2015] [Indexed: 11/24/2022] Open
Abstract
Hirschsprung disease (HSCR; OMIM 142623) is a developmental disorder characterized by aganglionosis along variable lengths of the distal gastrointestinal tract, which results in intestinal obstruction. Interactions among known HSCR genes and/or unknown disease susceptibility loci lead to variable severity of phenotype. Neither linkage nor genome-wide association studies have efficiently contributed to completely dissect the genetic pathways underlying this complex genetic disorder. We have performed whole exome sequencing of 16 HSCR patients from 8 unrelated families with SOLID platform. Variants shared by affected relatives were validated by Sanger sequencing. We searched for genes recurrently mutated across families. Only variations in the FAT3 gene were significantly enriched in five families. Within-family analysis identified compound heterozygotes for AHNAK and several genes (N = 23) with heterozygous variants that co-segregated with the phenotype. Network and pathway analyses facilitated the discovery of polygenic inheritance involving FAT3, HSCR known genes and their gene partners. Altogether, our approach has facilitated the detection of more than one damaging variant in biologically plausible genes that could jointly contribute to the phenotype. Our data may contribute to the understanding of the complex interactions that occur during enteric nervous system development and the etiopathology of familial HSCR.
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Affiliation(s)
- Berta Luzón-Toro
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain.,Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain
| | - Hongsheng Gui
- Centre for Genomic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Macarena Ruiz-Ferrer
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain.,Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain
| | - Clara Sze-Man Tang
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Raquel M Fernández
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain.,Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain
| | - Pak-Chung Sham
- Centre for Genomic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,State Key Laboratory of Brain and Cognitive Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Centre for Reproduction, Development, and Growth, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Ana Torroglosa
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain.,Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain
| | - Paul Kwong-Hang Tam
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Centre for Reproduction, Development, and Growth, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Laura Espino-Paisán
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain
| | - Stacey S Cherny
- Centre for Genomic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,State Key Laboratory of Brain and Cognitive Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Marta Bleda
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain.,Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
| | - María Del Valle Enguix-Riego
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain.,Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain
| | - Joaquín Dopazo
- Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain.,Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain.,Functional Genomics Node, (INB) at CIPF, Valencia, Spain
| | - Guillermo Antiñolo
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain.,Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain
| | - María-Mercé García-Barceló
- Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Centre for Reproduction, Development, and Growth, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Salud Borrego
- Department of Genetics, Reproduction and Fetal Medicine, Institute of Biomedicine of Seville (IBIS), University Hospital Virgen del Rocío/CSIC/University of Seville, Seville, Spain.,Centre for Biomedical Network Research on Rare Diseases (CIBERER), Spain
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124
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Prioritization Of Nonsynonymous Single Nucleotide Variants For Exome Sequencing Studies Via Integrative Learning On Multiple Genomic Data. Sci Rep 2015; 5:14955. [PMID: 26459872 PMCID: PMC4602202 DOI: 10.1038/srep14955] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 09/11/2015] [Indexed: 12/16/2022] Open
Abstract
The rapid advancement of next generation sequencing technology has greatly accelerated the progress for understanding human inherited diseases via such innovations as exome sequencing. Nevertheless, the identification of causative variants from sequencing data remains a great challenge. Traditional statistical genetics approaches such as linkage analysis and association studies have limited power in analyzing exome sequencing data, while relying on simply filtration strategies and predicted functional implications of mutations to pinpoint pathogenic variants are prone to produce false positives. To overcome these limitations, we herein propose a supervised learning approach, termed snvForest, to prioritize candidate nonsynonymous single nucleotide variants for a specific type of disease by integrating 11 functional scores at the variant level and 8 association scores at the gene level. We conduct a series of large-scale in silico validation experiments, demonstrating the effectiveness of snvForest across 2,511 diseases of different inheritance styles and the superiority of our approach over two state-of-the-art methods. We further apply snvForest to three real exome sequencing data sets of epileptic encephalophathies and intellectual disability to show the ability of our approach to identify causative de novo mutations for these complex diseases. The online service and standalone software of snvForest are found at http://bioinfo.au.tsinghua.edu.cn/jianglab/snvforest.
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125
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Clark PM, Kunkel M, Monos DS. The dichotomy between disease phenotype databases and the implications for understanding complex diseases involving the major histocompatibility complex. Int J Immunogenet 2015; 42:413-22. [PMID: 26456690 DOI: 10.1111/iji.12236] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Revised: 07/14/2015] [Accepted: 08/16/2015] [Indexed: 01/08/2023]
Abstract
Many genes related to innate and adaptive immunity reside within the major histocompatibility complex (MHC) and have been associated with a multitude of complex, immune-related disorders. Despite years of genetic study, this region has seen few causative determinants discovered for immune-mediated diseases. Reported associations have been curated in various databases including the Genetic Association Database, NCBI database of clinically relevant variants (ClinVar) and the Human Gene Mutation Database and together capture genetic associations and annotated pathogenic loci within the MHC and across the genome for a variety of complex, immune-mediated diseases. A review of these three distinct databases reveals disparate annotations between associated genes and pathogenic loci, alluding to the polygenic, multifactorial nature of immune-mediated diseases and the pleiotropic character of genes within the MHC. The technical limitations and inherent biases imposed by current approaches and technologies in studying the MHC create a strong case for the need to perform targeted deep sequencing of the MHC and other immunologically relevant loci in order to fully elucidate and study the causative elements of complex immune-mediated diseases.
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Affiliation(s)
- P M Clark
- Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - M Kunkel
- Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - D S Monos
- Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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126
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Merner ND, Chandler MR, Bourassa C, Liang B, Khanna AR, Dion P, Rouleau GA, Kahle KT. Regulatory domain or CpG site variation in SLC12A5, encoding the chloride transporter KCC2, in human autism and schizophrenia. Front Cell Neurosci 2015; 9:386. [PMID: 26528127 PMCID: PMC4600830 DOI: 10.3389/fncel.2015.00386] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2015] [Accepted: 09/16/2015] [Indexed: 12/23/2022] Open
Abstract
Many encoded gene products responsible for neurodevelopmental disorders (NDs) like autism spectrum disorders (ASD), schizophrenia (SCZ), intellectual disability (ID), and idiopathic generalized epilepsy (IGE) converge on networks controlling synaptic function. An increase in KCC2 (SLC12A5) Cl− transporter activity drives the developmental GABA excitatory-inhibitory sequence, but the role of KCC2 in human NDs is essentially unknown. Here, we report two rare, non-synonymous (NS), functionally-impairing variants in the KCC2 C-terminal regulatory domain (CTRD) in human ASD (R952H and R1049C) and SCZ (R952H) previously linked with IGE and familial febrile seizures, and another novel NS KCC2 variant in ASD (R1048W) with highly-predicted pathogenicity. Exome data from 2517 simplex families in the ASD Simon Simplex Collection (SSC) revealed significantly more KCC2 CTRD variants in ASD cases than controls, and interestingly, these were more often synonymous and predicted to disrupt or introduce a CpG site. Furthermore, full gene analysis showed ASD cases are more likely to contain rare KCC2 variants affecting CpG sites than controls. These data suggest genetically-encoded dysregulation of KCC2-dependent GABA signaling may contribute to multiple human NDs.
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Affiliation(s)
- Nancy D Merner
- Harrison School of Pharmacy, Department of Drug Discovery and Development, Auburn University Auburn, AL, USA ; Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University Montréal, QC, Canada
| | - Madison R Chandler
- Harrison School of Pharmacy, Department of Drug Discovery and Development, Auburn University Auburn, AL, USA
| | - Cynthia Bourassa
- Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University Montréal, QC, Canada
| | - Bo Liang
- Department of Biological Chemistry and Molecular Pharmacology (BCMP), Harvard Medical School Boston, MA, USA
| | - Arjun R Khanna
- Department of Neurosurgery, Boston Children's Hospital and Harvard Medical School Boston, MA, USA
| | - Patrick Dion
- Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University Montréal, QC, Canada
| | - Guy A Rouleau
- Department of Neurology and Neurosurgery, Montreal Neurological Hospital and Institute, McGill University Montréal, QC, Canada
| | - Kristopher T Kahle
- Department of Neurosurgery, Boston Children's Hospital and Harvard Medical School Boston, MA, USA ; Manton Center for Orphan Disease Research, Boston Children's Hospital Boston, MA, USA
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127
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Jacunski A, Dixon SJ, Tatonetti NP. Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality. PLoS Comput Biol 2015; 11:e1004506. [PMID: 26451775 PMCID: PMC4599967 DOI: 10.1371/journal.pcbi.1004506] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 08/11/2015] [Indexed: 12/22/2022] Open
Abstract
Synthetic lethality is a genetic interaction wherein two otherwise nonessential genes cause cellular inviability when knocked out simultaneously. Drugs can mimic genetic knock-out effects; therefore, our understanding of promiscuous drugs, polypharmacology-related adverse drug reactions, and multi-drug therapies, especially cancer combination therapy, may be informed by a deeper understanding of synthetic lethality. However, the colossal experimental burden in humans necessitates in silico methods to guide the identification of synthetic lethal pairs. Here, we present SINaTRA (Species-INdependent TRAnslation), a network-based methodology that discovers genome-wide synthetic lethality in translation between species. SINaTRA uses connectivity homology, defined as biological connectivity patterns that persist across species, to identify synthetic lethal pairs. Importantly, our approach does not rely on genetic homology or structural and functional similarity, and it significantly outperforms models utilizing these data. We validate SINaTRA by predicting synthetic lethality in S. pombe using S. cerevisiae data, then identify over one million putative human synthetic lethal pairs to guide experimental approaches. We highlight the translational applications of our algorithm for drug discovery by identifying clusters of genes significantly enriched for single- and multi-drug cancer therapies. Synthetic lethality is a genetic interaction that has promising implications for informing novel cancer therapies. Over 200 million pairwise tests would be required to identify all pairwise synthetic lethal interactions in humans–currently, an impossibly large experimental burden. To simplify the process, we have developed a method to predict human synthetic lethal pairs in translation from a well-studied species to one in which synthetic lethality is understudied using both species’ protein-protein interaction networks. Here, we explore the model’s success in translation from S. cerevisiae to S. pombe. We then predict human synthetic lethality and suggest novel areas of inquiry for cancer therapies.
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Affiliation(s)
- Alexandra Jacunski
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Scott J. Dixon
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Nicholas P. Tatonetti
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Department of Biomedical Informatics, Columbia University, New York, New York, United States of America
- Department of Medicine, Columbia University, New York, New York, United States of America
- * E-mail:
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128
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Zheng HF, Forgetta V, Hsu YH, Estrada K, Rosello-Diez A, Leo PJ, Dahia CL, Park-Min KH, Tobias JH, Kooperberg C, Kleinman A, Styrkarsdottir U, Liu CT, Uggla C, Evans DS, Nielson CM, Walter K, Pettersson-Kymmer U, McCarthy S, Eriksson J, Kwan T, Jhamai M, Trajanoska K, Memari Y, Min J, Huang J, Danecek P, Wilmot B, Li R, Chou WC, Mokry LE, Moayyeri A, Claussnitzer M, Cheng CH, Cheung W, Medina-Gómez C, Ge B, Chen SH, Choi K, Oei L, Fraser J, Kraaij R, Hibbs MA, Gregson CL, Paquette D, Hofman A, Wibom C, Tranah GJ, Marshall M, Gardiner BB, Cremin K, Auer P, Hsu L, Ring S, Tung JY, Thorleifsson G, Enneman AW, van Schoor NM, de Groot LCPGM, van der Velde N, Melin B, Kemp JP, Christiansen C, Sayers A, Zhou Y, Calderari S, van Rooij J, Carlson C, Peters U, Berlivet S, Dostie J, Uitterlinden AG, Williams SR, Farber C, Grinberg D, LaCroix AZ, Haessler J, Chasman DI, Giulianini F, Rose LM, Ridker PM, Eisman JA, Nguyen TV, Center JR, Nogues X, Garcia-Giralt N, Launer LL, Gudnason V, Mellström D, Vandenput L, Amin N, van Duijn CM, Karlsson MK, Ljunggren Ö, Svensson O, Hallmans G, Rousseau F, Giroux S, Bussière J, Arp PP, Koromani F, Prince RL, Lewis JR, Langdahl BL, Hermann AP, Jensen JEB, Kaptoge S, Khaw KT, Reeve J, Formosa MM, Xuereb-Anastasi A, Åkesson K, McGuigan FE, Garg G, Olmos JM, Zarrabeitia MT, Riancho JA, Ralston SH, Alonso N, Jiang X, Goltzman D, Pastinen T, Grundberg E, Gauguier D, Orwoll ES, Karasik D, Davey-Smith G, Smith AV, Siggeirsdottir K, Harris TB, Zillikens MC, van Meurs JBJ, Thorsteinsdottir U, Maurano MT, Timpson NJ, Soranzo N, Durbin R, Wilson SG, Ntzani EE, Brown MA, Stefansson K, Hinds DA, Spector T, Cupples LA, Ohlsson C, Greenwood CMT, Jackson RD, Rowe DW, Loomis CA, Evans DM, Ackert-Bicknell CL, Joyner AL, Duncan EL, Kiel DP, Rivadeneira F, Richards JB. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 2015; 526:112-117. [PMID: 26367794 PMCID: PMC4755714 DOI: 10.1038/nature14878 10.1016/j.ajhg.2017.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 06/30/2015] [Indexed: 04/02/2024]
Abstract
The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 × 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 × 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 × 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.
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Affiliation(s)
- Hou-Feng Zheng
- Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montréal H3A 1A2, Canada
- Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal H3T 1E2, Canada
| | - Vincenzo Forgetta
- Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montréal H3A 1A2, Canada
- Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal H3T 1E2, Canada
| | - Yi-Hsiang Hsu
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Broad Institute of MIT and Harvard, Boston, Massachusetts 02115, USA
| | - Karol Estrada
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Broad Institute of MIT and Harvard, Boston, Massachusetts 02115, USA
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Alberto Rosello-Diez
- Developmental Biology Program, Sloan Kettering Institute, New York, New York 10065, USA
| | - Paul J Leo
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
| | - Chitra L Dahia
- Department of Cell and Developmental Biology, Weill Cornell Medical College, New York, New York 10065, USA
- Tissue Engineering, Regeneration and Repair Program, Hospital for Special Surgery, New York 10021, USA
| | - Kyung Hyun Park-Min
- Rheumatology Divison, Hospital for Special Surgery New York, New York 10021, USA
| | - Jonathan H Tobias
- School of Clinical Science, University of Bristol, Bristol BS10 5NB, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | | | - Aaron Kleinman
- Department of Research, 23andMe, Mountain View, California 94041, USA
| | | | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
| | - Charlotta Uggla
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, California 94158, USA
| | - Carrie M Nielson
- Department of Public Health and Preventive Medicine, Oregon Health &Science University, Portland, Oregon 97239, USA
- Bone &Mineral Unit, Oregon Health &Science University, Portland, Oregon 97239, USA
| | - Klaudia Walter
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Ulrika Pettersson-Kymmer
- Departments of Pharmacology and Clinical Neurosciences, Umeå University, Umeå S-901 87, Sweden
- Department of Public Health and Clinical Medicine, Umeå University, Umeå SE-901 87, Sweden
| | - Shane McCarthy
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Joel Eriksson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden
| | - Tony Kwan
- McGill University and Genome Quebec Innovation Centre, Montréal H3A 0G1, Canada
| | - Mila Jhamai
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
| | - Katerina Trajanoska
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
| | - Yasin Memari
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Josine Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Jie Huang
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Petr Danecek
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Beth Wilmot
- Oregon Clinical and Translational Research Institute, Oregon Health &Science University, Portland, Oregon 97239, USA
- Department of Medical and Clinical Informatics, Oregon Health &Science University, Portland, Oregon 97239, USA
| | - Rui Li
- Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montréal H3A 1A2, Canada
- Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal H3T 1E2, Canada
| | - Wen-Chi Chou
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Lauren E Mokry
- Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal H3T 1E2, Canada
| | - Alireza Moayyeri
- Farr Institute of Health Informatics Research, University College London, London NW1 2DA, UK
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - Melina Claussnitzer
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Broad Institute of MIT and Harvard, Boston, Massachusetts 02115, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02115, USA
| | - Chia-Ho Cheng
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA
| | - Warren Cheung
- McGill University and Genome Quebec Innovation Centre, Montréal H3A 0G1, Canada
- Department of Human Genetics, McGill University, Montréal H3A 1B1, Canada
| | - Carolina Medina-Gómez
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands
| | - Bing Ge
- McGill University and Genome Quebec Innovation Centre, Montréal H3A 0G1, Canada
| | - Shu-Huang Chen
- McGill University and Genome Quebec Innovation Centre, Montréal H3A 0G1, Canada
| | - Kwangbom Choi
- Center for Musculoskeletal Research, University of Rochester, Rochester, New York 14642, USA
| | - Ling Oei
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands
| | - James Fraser
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montréal H3G 1Y6, Canada
| | - Robert Kraaij
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands
| | - Matthew A Hibbs
- Center for Musculoskeletal Research, University of Rochester, Rochester, New York 14642, USA
- Department of Computer Science, Trinity University, San Antonio, Texas 78212, USA
| | - Celia L Gregson
- Musculoskeletal Research Unit, University of Bristol, Bristol BS10 5NB, UK
| | - Denis Paquette
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montréal H3G 1Y6, Canada
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands
| | - Carl Wibom
- Department of Radiation Sciences, Umeå University, Umeå S-901 87, Sweden
| | - Gregory J Tranah
- Department of Public Health and Preventive Medicine, Oregon Health &Science University, Portland, Oregon 97239, USA
- Bone &Mineral Unit, Oregon Health &Science University, Portland, Oregon 97239, USA
| | - Mhairi Marshall
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
| | - Brooke B Gardiner
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
| | - Katie Cremin
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
| | - Paul Auer
- School of Public Health, University of Wisconsin, Milwaukee, Wisconsin 53726, USA
| | - Li Hsu
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Sue Ring
- School of Social and Community Medicine, University of Bristol, Bristol BS8 2BN, UK
| | - Joyce Y Tung
- Department of Research, 23andMe, Mountain View, California 94041, USA
| | | | - Anke W Enneman
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
| | - Natasja M van Schoor
- Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam 1007 MB, The Netherlands
| | | | - Nathalie van der Velde
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Internal Medicine, Section Geriatrics, Academic Medical Center, Amsterdam 1105, The Netherlands
| | - Beatrice Melin
- Department of Radiation Sciences, Umeå University, Umeå S-901 87, Sweden
| | - John P Kemp
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | | | - Adrian Sayers
- Musculoskeletal Research Unit, University of Bristol, Bristol BS10 5NB, UK
| | - Yanhua Zhou
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
| | - Sophie Calderari
- Cordeliers Research Centre, INSERM UMRS 1138, Paris 75006, France
- Institute of Cardiometabolism and Nutrition, University Pierre &Marie Curie, Paris 75013, France
| | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands
| | - Chris Carlson
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Ulrike Peters
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Soizik Berlivet
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montréal H3G 1Y6, Canada
| | - Josée Dostie
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montréal H3G 1Y6, Canada
| | - Andre G Uitterlinden
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands
| | - Stephen R Williams
- Departments of Medicine (Cardiovascular Medicine), Centre for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Charles Farber
- Departments of Medicine (Cardiovascular Medicine), Centre for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22908, USA
| | - Daniel Grinberg
- Department of Genetics, University of Barcelona, Barcelona 08028, Spain
- U-720, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Barcelona 28029, Spain
- Department of Human Molecular Genetics, The Institute of Biomedicine of the University of Barcelona (IBUB), Barcelona 08028, Spain
| | - Andrea Z LaCroix
- Women's Health Center of Excellence Family Medicine and Public Health, University of California - San Diego, San Diego, California 92093, USA
| | - Jeff Haessler
- Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
| | - Daniel I Chasman
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - Lynda M Rose
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - Paul M Ridker
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02215, USA
| | - John A Eisman
- Osteoporosis &Bone Biology Program, Garvan Institute of Medical Research, Sydney 2010, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney 6959, Australia
- St. Vincent's Hospital &Clinical School, NSW University, Sydney 2010, Australia
| | - Tuan V Nguyen
- Osteoporosis &Bone Biology Program, Garvan Institute of Medical Research, Sydney 2010, Australia
- St. Vincent's Hospital &Clinical School, NSW University, Sydney 2010, Australia
| | - Jacqueline R Center
- Osteoporosis &Bone Biology Program, Garvan Institute of Medical Research, Sydney 2010, Australia
- St. Vincent's Hospital &Clinical School, NSW University, Sydney 2010, Australia
| | - Xavier Nogues
- Musculoskeletal Research Group, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona 08003, Spain
- Cooperative Research Network on Aging and Fragility (RETICEF), Institute of Health Carlos III, 28029, Spain
- Department of Internal Medicine, Hospital del Mar, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Natalia Garcia-Giralt
- Musculoskeletal Research Group, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona 08003, Spain
- Cooperative Research Network on Aging and Fragility (RETICEF), Institute of Health Carlos III, 28029, Spain
| | - Lenore L Launer
- Neuroepidemiology Section, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Vilmunder Gudnason
- Icelandic Heart Association, Kopavogur IS-201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik IS-101, Iceland
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden
| | - Liesbeth Vandenput
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden
| | - Najaf Amin
- Genetic epidemiology unit, Department of Epidemiology, Erasmus MC, Rotterdam 3000CA, The Netherlands
| | - Cornelia M van Duijn
- Genetic epidemiology unit, Department of Epidemiology, Erasmus MC, Rotterdam 3000CA, The Netherlands
| | - Magnus K Karlsson
- Department of Orthopaedics, Skåne University Hospital Malmö 205 02, Sweden
| | - Östen Ljunggren
- Department of Medical Sciences, University of Uppsala, Uppsala 751 85, Sweden
| | - Olle Svensson
- Department of Surgical and Perioperative Sciences, Umeå Unviersity, Umeå 901 85, Sweden
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Umeå University, Umeå SE-901 87, Sweden
| | - François Rousseau
- Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Québec City G1V 0A6, Canada
- Axe Santé des Populations et Pratiques Optimales en Santé, Centre de recherche du CHU de Québec, Québec City G1V 4G2, Canada
| | - Sylvie Giroux
- Axe Santé des Populations et Pratiques Optimales en Santé, Centre de recherche du CHU de Québec, Québec City G1V 4G2, Canada
| | - Johanne Bussière
- Axe Santé des Populations et Pratiques Optimales en Santé, Centre de recherche du CHU de Québec, Québec City G1V 4G2, Canada
| | - Pascal P Arp
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
| | - Fjorda Koromani
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
| | - Richard L Prince
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands 6009, Australia
- Department of Medicine, University of Western Australia, Perth 6009, Australia
| | - Joshua R Lewis
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands 6009, Australia
- Department of Medicine, University of Western Australia, Perth 6009, Australia
| | - Bente L Langdahl
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus C 8000, Denmark
| | - A Pernille Hermann
- Department of Endocrinology, Odense University Hospital, Odense C 5000, Denmark
| | - Jens-Erik B Jensen
- Department of Endocrinology, Hvidovre University Hospital, Hvidovre 2650, Denmark
| | - Stephen Kaptoge
- Farr Institute of Health Informatics Research, University College London, London NW1 2DA, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge CB2 2QQ, UK
| | - Jonathan Reeve
- Medicine and Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Institute of Musculoskeletal Sciences, The Botnar Research Centre, University of Oxford, Oxford OX3 7LD, UK
| | - Melissa M Formosa
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida MSD 2080, Malta
| | - Angela Xuereb-Anastasi
- Department of Applied Biomedical Science, Faculty of Health Sciences, University of Malta, Msida MSD 2080, Malta
| | - Kristina Åkesson
- Department of Orthopaedics, Skåne University Hospital Malmö 205 02, Sweden
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences Malmö, Lund University, 205 02, Sweden
| | - Fiona E McGuigan
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences Malmö, Lund University, 205 02, Sweden
| | - Gaurav Garg
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences Malmö, Lund University, 205 02, Sweden
| | - Jose M Olmos
- Department of Medicine and Psychiatry, University of Cantabria, Santander 39011, Spain
- Department of Internal Medicine, Hospital U.M. Valdecilla- IDIVAL, Santander 39008, Spain
| | - Maria T Zarrabeitia
- Department of Legal Medicine, University of Cantabria, Santander 39011, Spain
| | - Jose A Riancho
- Department of Medicine and Psychiatry, University of Cantabria, Santander 39011, Spain
- Department of Internal Medicine, Hospital U.M. Valdecilla- IDIVAL, Santander 39008, Spain
| | - Stuart H Ralston
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Nerea Alonso
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Xi Jiang
- Department of Reconstructive Sciences, College of Dental Medicine, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - David Goltzman
- Department of Medicine and Physiology, McGill University, Montréal H4A 3J1, Canada
| | - Tomi Pastinen
- McGill University and Genome Quebec Innovation Centre, Montréal H3A 0G1, Canada
- Department of Human Genetics, McGill University, Montréal H3A 1B1, Canada
| | - Elin Grundberg
- McGill University and Genome Quebec Innovation Centre, Montréal H3A 0G1, Canada
- Department of Human Genetics, McGill University, Montréal H3A 1B1, Canada
| | - Dominique Gauguier
- Cordeliers Research Centre, INSERM UMRS 1138, Paris 75006, France
- Institute of Cardiometabolism and Nutrition, University Pierre &Marie Curie, Paris 75013, France
| | - Eric S Orwoll
- Bone &Mineral Unit, Oregon Health &Science University, Portland, Oregon 97239, USA
- Department of Medicine, Oregon Health &Science University, Portland, Oregon 97239, USA
| | - David Karasik
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA
- Faculty of Medicine in the Galilee, Bar-Ilan University, Safed 13010, Israel
| | - George Davey-Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur IS-201, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik IS-101, Iceland
| | | | - Tamara B Harris
- Laboratory of Epidemiology, National Institute on Aging, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - M Carola Zillikens
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
| | - Unnur Thorsteinsdottir
- Department of Population Genomics, deCODE Genetics, Reykjavik IS-101, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik IS-101, Iceland
| | - Matthew T Maurano
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Richard Durbin
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK
| | - Scott G Wilson
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands 6009, Australia
- School of Medicine and Pharmacology, University of Western Australia, Crawley 6009, Australia
| | - Evangelia E Ntzani
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island 02903, USA
| | - Matthew A Brown
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
| | - Kari Stefansson
- Faculty of Medicine, University of Iceland, Reykjavik IS-101, Iceland
- deCODE Genetics, Reykjavik IS-101, Iceland
| | - David A Hinds
- Department of Research, 23andMe, Mountain View, California 94041, USA
| | - Tim Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
- Framingham Heart Study, Framingham, Massachusetts 01702, USA
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg S-413 45, Sweden
| | - Celia M T Greenwood
- Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montréal H3A 1B1, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal H3A 1A2, Canada
- Department of Oncology, Gerald Bronfman Centre, McGill University, Montréal H2W 1S6, Canada
| | - Rebecca D Jackson
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The Ohio State University, Columbus, Ohio 43210, USA
| | - David W Rowe
- Department of Reconstructive Sciences, College of Dental Medicine, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Cynthia A Loomis
- The Ronald O. Perelman Department of Dermatology and Department of Cell Biology, New York University School of Medicine, New York, New York 10016, USA
| | - David M Evans
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | | | - Alexandra L Joyner
- Developmental Biology Program, Sloan Kettering Institute, New York, New York 10065, USA
| | - Emma L Duncan
- The University of Queensland Diamantina Institute, Translational Research Institute, Princess Alexandra Hospital, Brisbane 4102, Australia
- Department of Diabetes and Endocrinology, Royal Brisbane and Women's Hospital, Brisbane 4029, Australia
| | - Douglas P Kiel
- Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts 02131, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Broad Institute of MIT and Harvard, Boston, Massachusetts 02115, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02115, USA
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam 3015GE, The Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Leiden 2300RC, The Netherlands
| | - J Brent Richards
- Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montréal H3A 1A2, Canada
- Department of Medicine, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal H3T 1E2, Canada
- Department of Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, UK
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Yan S, Yuan S, Xu Z, Zhang B, Zhang B, Kang G, Byrnes A, Li Y. Likelihood-based complex trait association testing for arbitrary depth sequencing data. Bioinformatics 2015; 31:2955-62. [PMID: 25979475 PMCID: PMC4668777 DOI: 10.1093/bioinformatics/btv307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Revised: 05/06/2015] [Accepted: 05/11/2015] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED In next generation sequencing (NGS)-based genetic studies, researchers typically perform genotype calling first and then apply standard genotype-based methods for association testing. However, such a two-step approach ignores genotype calling uncertainty in the association testing step and may incur power loss and/or inflated type-I error. In the recent literature, a few robust and efficient likelihood based methods including both likelihood ratio test (LRT) and score test have been proposed to carry out association testing without intermediate genotype calling. These methods take genotype calling uncertainty into account by directly incorporating genotype likelihood function (GLF) of NGS data into association analysis. However, existing LRT methods are computationally demanding or do not allow covariate adjustment; while existing score tests are not applicable to markers with low minor allele frequency (MAF). We provide an LRT allowing flexible covariate adjustment, develop a statistically more powerful score test and propose a combination strategy (UNC combo) to leverage the advantages of both tests. We have carried out extensive simulations to evaluate the performance of our proposed LRT and score test. Simulations and real data analysis demonstrate the advantages of our proposed combination strategy: it offers a satisfactory trade-off in terms of computational efficiency, applicability (accommodating both common variants and variants with low MAF) and statistical power, particularly for the analysis of quantitative trait where the power gain can be up to ∼60% when the causal variant is of low frequency (MAF < 0.01). AVAILABILITY AND IMPLEMENTATION UNC combo and the associated R files, including documentation, examples, are available at http://www.unc.edu/∼yunmli/UNCcombo/ CONTACT yunli@med.unc.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Song Yan
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Shuai Yuan
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Zheng Xu
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Baqun Zhang
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Bo Zhang
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Guolian Kang
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Andrea Byrnes
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
| | - Yun Li
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA, Merck Research Laboratories, North Wales, PA, USA, School of Statistics, Renmin University of China, Beijing, People's Republic of China, Department of Statistics, North Carolina State University, Raleigh, NC, 27607 USA, Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA and Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
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Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 2015; 526:112-7. [PMID: 26367794 DOI: 10.1038/nature14878] [Citation(s) in RCA: 403] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 06/30/2015] [Indexed: 12/31/2022]
Abstract
The extent to which low-frequency (minor allele frequency (MAF) between 1-5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is mainly unknown. Bone mineral density (BMD) is highly heritable, a major predictor of osteoporotic fractures, and has been previously associated with common genetic variants, as well as rare, population-specific, coding variants. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n = 2,882 from UK10K (ref. 10); a population-based genome sequencing consortium), whole-exome sequencing (n = 3,549), deep imputation of genotyped samples using a combined UK10K/1000 Genomes reference panel (n = 26,534), and de novo replication genotyping (n = 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size fourfold larger than the mean of previously reported common variants for lumbar spine BMD (rs11692564(T), MAF = 1.6%, replication effect size = +0.20 s.d., Pmeta = 2 × 10(-14)), which was also associated with a decreased risk of fracture (odds ratio = 0.85; P = 2 × 10(-11); ncases = 98,742 and ncontrols = 409,511). Using an En1(cre/flox) mouse model, we observed that conditional loss of En1 results in low bone mass, probably as a consequence of high bone turnover. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817(T), MAF = 1.2%, replication effect size = +0.41 s.d., Pmeta = 1 × 10(-11)). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.
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131
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Lee S, Fuchsberger C, Kim S, Scott L. An efficient resampling method for calibrating single and gene-based rare variant association analysis in case-control studies. Biostatistics 2015; 17:1-15. [PMID: 26363037 DOI: 10.1093/biostatistics/kxv033] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Accepted: 08/10/2015] [Indexed: 11/14/2022] Open
Abstract
For aggregation tests of genes or regions, the set of included variants often have small total minor allele counts (MACs), and this is particularly true when the most deleterious sets of variants are considered. When MAC is low, commonly used asymptotic tests are not well calibrated for binary phenotypes and can have conservative or anti-conservative results and potential power loss. Empirical p-values obtained via resampling methods are computationally costly for highly significant p-values and the results can be conservative due to the discrete nature of resampling tests. Based on the observation that only the individuals containing minor alleles contribute to the score statistics, we develop an efficient resampling method for single and multiple variant score-based tests that can adjust for covariates. Our method can improve computational efficiency >1000-fold over conventional resampling for low MAC variant sets. We ameliorate the conservativeness of results through the use of mid-p-values. Using the estimated minimum achievable p-value for each test, we calibrate QQ plots and provide an effective number of tests. In analysis of a case-control study with deep exome sequence, we demonstrate that our methods are both well calibrated and also reduce computation time significantly compared with resampling methods.
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Affiliation(s)
- Seunggeun Lee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sehee Kim
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Laura Scott
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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132
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Phenotypic extremes in rare variant study designs. Eur J Hum Genet 2015; 24:924-30. [PMID: 26350511 DOI: 10.1038/ejhg.2015.197] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Revised: 07/17/2015] [Accepted: 08/04/2015] [Indexed: 12/16/2022] Open
Abstract
Currently, next-generation sequencing studies aim to identify rare and low-frequency variation that may contribute to disease. For a given effect size, as the allele frequency decreases, the power to detect genes or variants of interest also decreases. Although many methods have been proposed for the analysis of such data, study design and analytic issues still persist in data interpretation. In this study we present sequencing data for ABCA1 that has known rare variants associated with high-density lipoprotein cholesterol (HDL-C). We contrast empirical findings from two study designs: a phenotypic extreme sample and a population-based random sample. We found differing strengths of association with HDL-C across the two study designs (P=0.0006 with n=701 phenotypic extremes vs P=0.03 with n=1600 randomly sampled individuals). To explore this apparent difference in evidence for association, we performed a simulation study focused on the impact of phenotypic selection on power. We demonstrate that the power gain for an extreme phenotypic selection study design is much greater in rare variant studies than for studies of common variants. Our study confirms that studying phenotypic extremes is critical in rare variant studies because it boosts power in two ways: the typical increases from extreme sampling and increasing the proportion of relevant functional variants ascertained and thereby tested for association. Furthermore, we show that when combining statistical evidence through meta-analysis from an extreme-selected sample and a second separate population-based random sample, power is lower when a traditional sample size weighting is used compared with weighting by the noncentrality parameter.
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133
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Pranckevičiene E, Rančelis T, Pranculis A, Kučinskas V. Challenges in exome analysis by LifeScope and its alternative computational pipelines. BMC Res Notes 2015; 8:421. [PMID: 26346699 PMCID: PMC4562342 DOI: 10.1186/s13104-015-1385-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2015] [Accepted: 08/24/2015] [Indexed: 12/22/2022] Open
Abstract
Background Every next generation sequencing (NGS) platform relies on proprietary and open source computational tools to analyze sequencing data. NGS tools for Illumina platforms are well documented which is not the case with AB SOLiD systems. We applied several computational and variant calling pipelines to analyse targeted exome sequencing data obtained using AB SOLiD 5500 system. Our investigated tools comprised proprietary LifeScope’s pipeline in combination with open source color-space competent mapping programs and a variant caller. We present instrumental details of the pipelines that were used and quantitative comparative analysis of variant lists generated by LifeScope’s pipeline versus open source tools. Results Sufficient coverage of targeted regions was achieved by all investigated pipelines. High variability was observed in identities of variants across the mapping programs. We observed less than 50 % concordance of variant lists produced by approaches based on different mapping algorithms. We summarized different approaches with regards to coverage (DP) and quality (QUAL) properties of the variants provided by GATK and found that LifeScope’s computational pipeline is superior. Fusion of information on mapping profiles (pileup) at genomic positions of variants in several different alignments proved to be a useful strategy to assess questionable singleton variants. Conclusions We quantitatively supported a conclusion that Lifescope’s pipeline is superior for processing sequencing data obtained by AB SOLiD 5500 system. Nevertheless the use of alternative pipelines is encouraged because aggregation of information from other mapping and variant calling approaches helps to resolve questionable calls and increases the confidence of the call. It was noted that a coverage threshold for variant to be considered for further analysis has to be chosen in data-driven way to prevent a loss of important information.
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Affiliation(s)
- Erinija Pranckevičiene
- Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania.
| | - Tautvydas Rančelis
- Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania.
| | - Aidas Pranculis
- Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania.
| | - Vaidutis Kučinskas
- Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Santariskiu str. 2, LT-08661, Vilnius, Lithuania.
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134
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Tada H, Kawashiri MA, Konno T, Yamagishi M, Hayashi K. Common and Rare Variant Association Study for Plasma Lipids and Coronary Artery Disease. J Atheroscler Thromb 2015; 23:241-56. [PMID: 26347050 DOI: 10.5551/jat.31393] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Blood lipid levels are highly heritable and modifiable risk factors for coronary artery disease (CAD), and are the leading cause of death worldwide. These facts have motivated human genetic association studies that have the substantial potential to define the risk factors that are causal and to identify pathways and therapeutic targets for lipids and CAD.The success of the HapMap project that provided an extensive catalog of human genetic variations and the development of microarray based genotyping chips (typically containing variations with allele frequencies > 5%) facilitated common variant association study (CVAS; formerly termed genome-wide association study, GWAS) identifying disease-associated variants in a genome-wide manner. To date, 157 loci associated with blood lipids and 46 loci with CAD have been successfully identified, accounting for approximately 12%-14% of heritability for lipids and 10% of heritability for CAD. However, there is yet a major challenge termed "missing heritability problem," namely the observation that loci detected by CVAS explain only a small fraction of the inferred genetic variations. To explain such missing portions, focuses in genetic association studies have shifted from common to rare variants. However, it is challenging to apply rare variant association study (RVAS) in an unbiased manner because such variants typically lack the sufficient number to be identified statistically.In this review, we provide a current understanding of the genetic architecture mostly derived from CVAS, and several updates on the progress and limitations of RVAS for lipids and CAD.
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Affiliation(s)
- Hayato Tada
- Division of Cardiovascular Medicine, Kanazawa University Graduate School of Medicine
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135
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Hamrefors V. Common genetic risk factors for coronary artery disease: new opportunities for prevention? Clin Physiol Funct Imaging 2015; 37:243-254. [DOI: 10.1111/cpf.12289] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2015] [Accepted: 07/03/2015] [Indexed: 12/20/2022]
Affiliation(s)
- Viktor Hamrefors
- Department of Clinical Sciences; Faculty of Medicine; Lund University; Malmö Sweden
- Department of Medical Imaging and Physiology; Skåne University Hospital; Malmö Sweden
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136
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Laissue P. Aetiological coding sequence variants in non-syndromic premature ovarian failure: From genetic linkage analysis to next generation sequencing. Mol Cell Endocrinol 2015; 411:243-57. [PMID: 25960166 DOI: 10.1016/j.mce.2015.05.005] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 04/14/2015] [Accepted: 05/04/2015] [Indexed: 01/19/2023]
Abstract
Premature ovarian failure (POF) is a frequent pathology affecting 1-1.5% of women under 40 years old. Despite advances in diagnosing and treating human infertility, POF is still classified as being idiopathic in 50-80% of cases, strongly suggesting a genetic origin for the disease. Different types of autosomal and X-linked genetic anomalies can originate the phenotype in syndromic and non-syndromic POF cases. Particular interest has been focused on research into non-syndromic POF causative coding variants during the past two decades. This has been based on the assumption that amino acid substitutions might modify the intrinsic physicochemical properties of functional proteins, thereby inducing pathological phenotypes. In this case, a restricted number of mutations might originate the disease. However, like other complex pathologies, POF might result from synergistic/compensatory effects caused by several low-to-mildly drastic mutations which have frequently been classified as non-functional SNPs. Indeed, reproductive phenotypes can be considered as quantitative traits resulting from the subtle interaction of many genes. Although numerous sequencing projects have involved candidate genes, only a few coding mutations explaining a low percentage of cases have been described. Such apparent failure to identify aetiological coding sequence variations might have been due to the inherent molecular complexity of mammalian reproduction and to the difficulty of simultaneously analysing large genomic regions by Sanger sequencing. The purpose of this review is to present the molecular and cellular effects caused by non-synonymous mutations which have been formally associated, by functional tests, with the aetiology of hypergonadotropic non-syndromic POF. Considerations have also been included regarding the polygenic nature of reproduction and POF, as well as future approaches for identifying novel aetiological genes based on next generation sequencing (NGS).
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Affiliation(s)
- Paul Laissue
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia.
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137
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Shigemizu D, Momozawa Y, Abe T, Morizono T, Boroevich KA, Takata S, Ashikawa K, Kubo M, Tsunoda T. Performance comparison of four commercial human whole-exome capture platforms. Sci Rep 2015; 5:12742. [PMID: 26235669 PMCID: PMC4522667 DOI: 10.1038/srep12742] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Accepted: 07/08/2015] [Indexed: 12/16/2022] Open
Abstract
Whole exome sequencing (WXS) is widely used to identify causative genetic mutations of diseases. However, not only have several commercial human exome capture platforms been developed, but substantial updates have been released in the past few years. We report a performance comparison for the latest release of four commercial platforms, Roche/NimbleGen’s SeqCap EZ Human Exome Library v3.0, Illumina’s Nextera Rapid Capture Exome (v1.2), Agilent’s SureSelect XT Human All Exon v5 and Agilent’s SureSelect QXT, using the same DNA samples. Agilent XT showed the highest target enrichment efficiency and the best SNV and short indel detection sensitivity in coding regions with the least amount of sequencing. Agilent QXT had slightly inferior target enrichment than Agilent XT. Illumina, with additional sequencing, detected SNVs and short indels at the same quality as Agilent XT, and showed the best performance in coverage of medically interesting mutations. NimbleGen detected more SNVs and indels in untranslated regions than the others. We also found that the platforms, which enzymatically fragment the genomic DNA (gDNA), detected more homozygous SNVs than those using sonicated gDNA. We believe that our analysis will help investigators when selecting a suitable exome capture platform for their particular research.
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Affiliation(s)
- Daichi Shigemizu
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Testuo Abe
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Takashi Morizono
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Keith A Boroevich
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Sadaaki Takata
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kyota Ashikawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Michiaki Kubo
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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138
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Gribble MO, Voruganti VS, Cole SA, Haack K, Balakrishnan P, Laston SL, Tellez-Plaza M, Francesconi KA, Goessler W, Umans JG, Thomas DC, Gilliland F, North KE, Franceschini N, Navas-Acien A. Linkage Analysis of Urine Arsenic Species Patterns in the Strong Heart Family Study. Toxicol Sci 2015. [PMID: 26209557 DOI: 10.1093/toxsci/kfv164] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Arsenic toxicokinetics are important for disease risks in exposed populations, but genetic determinants are not fully understood. We examined urine arsenic species patterns measured by HPLC-ICPMS among 2189 Strong Heart Study participants 18 years of age and older with data on ~400 genome-wide microsatellite markers spaced ~10 cM and arsenic speciation (683 participants from Arizona, 684 from Oklahoma, and 822 from North and South Dakota). We logit-transformed % arsenic species (% inorganic arsenic, %MMA, and %DMA) and also conducted principal component analyses of the logit % arsenic species. We used inverse-normalized residuals from multivariable-adjusted polygenic heritability analysis for multipoint variance components linkage analysis. We also examined the contribution of polymorphisms in the arsenic metabolism gene AS3MT via conditional linkage analysis. We localized a quantitative trait locus (QTL) on chromosome 10 (LOD 4.12 for %MMA, 4.65 for %DMA, and 4.84 for the first principal component of logit % arsenic species). This peak was partially but not fully explained by measured AS3MT variants. We also localized a QTL for the second principal component of logit % arsenic species on chromosome 5 (LOD 4.21) that was not evident from considering % arsenic species individually. Some other loci were suggestive or significant for 1 geographical area but not overall across all areas, indicating possible locus heterogeneity. This genome-wide linkage scan suggests genetic determinants of arsenic toxicokinetics to be identified by future fine-mapping, and illustrates the utility of principal component analysis as a novel approach that considers % arsenic species jointly.
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Affiliation(s)
- Matthew O Gribble
- *Department of Preventive Medicine, University of Southern California, Los Angeles, California;
| | - Venkata Saroja Voruganti
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina; UNC Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, North Carolina
| | - Shelley A Cole
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas
| | - Karin Haack
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas
| | - Poojitha Balakrishnan
- Department of Environmental Health Sciences, Johns Hopkins University, Baltimore, Maryland; Department of Epidemiology, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Sandra L Laston
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center, San Antonio-Regional Academic Health Center, Brownsville, Texas
| | - Maria Tellez-Plaza
- Department of Environmental Health Sciences, Johns Hopkins University, Baltimore, Maryland; Biomedical Research Institute, Hospital Clinic de Valencia-INCLIVA, Valencia, Spain
| | - Kevin A Francesconi
- Institute of Chemistry-Analytical Chemistry, University of Graz, Graz, Austria
| | - Walter Goessler
- Institute of Chemistry-Analytical Chemistry, University of Graz, Graz, Austria
| | - Jason G Umans
- Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, District of Columbia; MedStar Health Research Institute, Hyattsville, Maryland
| | - Duncan C Thomas
- *Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | - Frank Gilliland
- *Department of Preventive Medicine, University of Southern California, Los Angeles, California
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina
| | - Ana Navas-Acien
- Department of Environmental Health Sciences, Johns Hopkins University, Baltimore, Maryland; Department of Epidemiology, Johns Hopkins Medical Institutions, Baltimore, Maryland; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland; Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, Maryland
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139
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Leu C, Balestrini S, Maher B, Hernández-Hernández L, Gormley P, Hämäläinen E, Heggeli K, Schoeler N, Novy J, Willis J, Plagnol V, Ellis R, Reavey E, O'Regan M, Pickrell WO, Thomas RH, Chung SK, Delanty N, McMahon JM, Malone S, Sadleir LG, Berkovic SF, Nashef L, Zuberi SM, Rees MI, Cavalleri GL, Sander JW, Hughes E, Helen Cross J, Scheffer IE, Palotie A, Sisodiya SM. Genome-wide Polygenic Burden of Rare Deleterious Variants in Sudden Unexpected Death in Epilepsy. EBioMedicine 2015; 2:1063-70. [PMID: 26501104 PMCID: PMC4588398 DOI: 10.1016/j.ebiom.2015.07.005] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Revised: 07/01/2015] [Accepted: 07/01/2015] [Indexed: 02/06/2023] Open
Abstract
Sudden unexpected death in epilepsy (SUDEP) represents the most severe degree of the spectrum of epilepsy severity and is the commonest cause of epilepsy-related premature mortality. The precise pathophysiology and the genetic architecture of SUDEP remain elusive. Aiming to elucidate the genetic basis of SUDEP, we analysed rare, protein-changing variants from whole-exome sequences of 18 people who died of SUDEP, 87 living people with epilepsy and 1479 non-epilepsy disease controls. Association analysis revealed a significantly increased genome-wide polygenic burden per individual in the SUDEP cohort when compared to epilepsy (P = 5.7 × 10(- 3)) and non-epilepsy disease controls (P = 1.2 × 10(- 3)). The polygenic burden was driven both by the number of variants per individual, and over-representation of variants likely to be deleterious in the SUDEP cohort. As determined by this study, more than a thousand genes contribute to the observed polygenic burden within the framework of this study. Subsequent gene-based association analysis revealed five possible candidate genes significantly associated with SUDEP or epilepsy, but no one single gene emerges as common to the SUDEP cases. Our findings provide further evidence for a genetic susceptibility to SUDEP, and suggest an extensive polygenic contribution to SUDEP causation. Thus, an overall increased burden of deleterious variants in a highly polygenic background might be important in rendering a given individual more susceptible to SUDEP. Our findings suggest that exome sequencing in people with epilepsy might eventually contribute to generating SUDEP risk estimates, promoting stratified medicine in epilepsy, with the eventual aim of reducing an individual patient's risk of SUDEP.
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Affiliation(s)
- Costin Leu
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | - Simona Balestrini
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK ; The Epilepsy Society, Chalfont-St-Peter, Bucks, UK ; Neuroscience Department, Polytechnic University of Marche, Ancona, Italy
| | - Bridget Maher
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK ; The Epilepsy Society, Chalfont-St-Peter, Bucks, UK
| | - Laura Hernández-Hernández
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK ; The Epilepsy Society, Chalfont-St-Peter, Bucks, UK
| | - Padhraig Gormley
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA ; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA ; The Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA ; Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Eija Hämäläinen
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Kristin Heggeli
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | - Natasha Schoeler
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | - Jan Novy
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Joseph Willis
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | | | - Rachael Ellis
- Paediatric Neurosciences Research Group, Royal Hospital for Sick Children, Glasgow, UK ; West of Scotland Genetic Services, Southern General Hospital, Glasgow, UK
| | - Eleanor Reavey
- Paediatric Neurosciences Research Group, Royal Hospital for Sick Children, Glasgow, UK ; West of Scotland Genetic Services, Southern General Hospital, Glasgow, UK
| | - Mary O'Regan
- Paediatric Neurosciences Research Group, Royal Hospital for Sick Children, Glasgow, UK
| | - William O Pickrell
- Wales Epilepsy Research Network, Institute of Life Science, College of Medicine, Swansea University, Swansea, UK
| | - Rhys H Thomas
- Wales Epilepsy Research Network, Institute of Life Science, College of Medicine, Swansea University, Swansea, UK
| | - Seo-Kyung Chung
- Wales Epilepsy Research Network, Institute of Life Science, College of Medicine, Swansea University, Swansea, UK
| | - Norman Delanty
- Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Jacinta M McMahon
- Departments of Medicine and Neurology, University of Melbourne, Austin Health, Melbourne, Australia
| | - Stephen Malone
- Department of Neurosciences, Lady Cilento Children's Hospital, Brisbane, Queensland, Australia
| | - Lynette G Sadleir
- Department of Paediatrics, School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand
| | - Samuel F Berkovic
- Departments of Medicine and Neurology, University of Melbourne, Austin Health, Melbourne, Australia
| | - Lina Nashef
- Department of Neurology, King's College Hospital, London, UK
| | - Sameer M Zuberi
- Paediatric Neurosciences Research Group, Royal Hospital for Sick Children, Glasgow, UK ; School of Medicine, University of Glasgow, Glasgow, UK
| | - Mark I Rees
- Wales Epilepsy Research Network, Institute of Life Science, College of Medicine, Swansea University, Swansea, UK
| | - Gianpiero L Cavalleri
- Molecular and Cellular Therapeutics Department, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Josemir W Sander
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK ; The Epilepsy Society, Chalfont-St-Peter, Bucks, UK
| | - Elaine Hughes
- Children's Neurosciences, Evelina Children's Hospital at Guys and St Thomas' NHS Foundation Trust, Kings Health Partners Academic Health Science Centre, London, UK
| | - J Helen Cross
- UCL Institute of Child Health, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK ; Young Epilepsy, Lingfield, UK
| | - Ingrid E Scheffer
- Departments of Medicine and Neurology, University of Melbourne, Austin Health, Melbourne, Australia ; Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Parkville, Australia ; Florey Institute of Neuroscience and Mental Health, Melbourne, Australia
| | - Aarno Palotie
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA ; Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA ; The Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA ; Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA ; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland ; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sanjay M Sisodiya
- NIHR University College London Hospitals Biomedical Research Centre, Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK ; The Epilepsy Society, Chalfont-St-Peter, Bucks, UK
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140
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Ferrarini A, Xumerle L, Griggio F, Garonzi M, Cantaloni C, Centomo C, Vargas SM, Descombes P, Marquis J, Collino S, Franceschi C, Garagnani P, Salisbury BA, Harvey JM, Delledonne M. The Use of Non-Variant Sites to Improve the Clinical Assessment of Whole-Genome Sequence Data. PLoS One 2015; 10:e0132180. [PMID: 26147798 PMCID: PMC4492948 DOI: 10.1371/journal.pone.0132180] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Accepted: 06/10/2015] [Indexed: 12/19/2022] Open
Abstract
Genetic testing, which is now a routine part of clinical practice and disease management protocols, is often based on the assessment of small panels of variants or genes. On the other hand, continuous improvements in the speed and per-base costs of sequencing have now made whole exome sequencing (WES) and whole genome sequencing (WGS) viable strategies for targeted or complete genetic analysis, respectively. Standard WGS/WES data analytical workflows generally rely on calling of sequence variants respect to the reference genome sequence. However, the reference genome sequence contains a large number of sites represented by rare alleles, by known pathogenic alleles and by alleles strongly associated to disease by GWAS. It’s thus critical, for clinical applications of WGS and WES, to interpret whether non-variant sites are homozygous for the reference allele or if the corresponding genotype cannot be reliably called. Here we show that an alternative analytical approach based on the analysis of both variant and non-variant sites from WGS data allows to genotype more than 92% of sites corresponding to known SNPs compared to 6% genotyped by standard variant analysis. These include homozygous reference sites of clinical interest, thus leading to a broad and comprehensive characterization of variation necessary to an accurate evaluation of disease risk. Altogether, our findings indicate that characterization of both variant and non-variant clinically informative sites in the genome is necessary to allow an accurate clinical assessment of a personal genome. Finally, we propose a highly efficient extended VCF (eVCF) file format which allows to store genotype calls for sites of clinical interest while remaining compatible with current variant interpretation software.
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Affiliation(s)
- Alberto Ferrarini
- Functional Genomics Center, Department of Biotechnology, University of Verona, 37134, Verona, Italy
| | - Luciano Xumerle
- Personal Genomics s.r.l, Strada le Grazie 15, 37134, Verona, Italy
| | - Francesca Griggio
- Functional Genomics Center, Department of Biotechnology, University of Verona, 37134, Verona, Italy
- Personal Genomics s.r.l, Strada le Grazie 15, 37134, Verona, Italy
| | - Marianna Garonzi
- Functional Genomics Center, Department of Biotechnology, University of Verona, 37134, Verona, Italy
| | - Chiara Cantaloni
- Functional Genomics Center, Department of Biotechnology, University of Verona, 37134, Verona, Italy
- Personal Genomics s.r.l, Strada le Grazie 15, 37134, Verona, Italy
| | - Cesare Centomo
- Functional Genomics Center, Department of Biotechnology, University of Verona, 37134, Verona, Italy
| | - Sergio Marin Vargas
- Functional Genomics Center, Department of Biotechnology, University of Verona, 37134, Verona, Italy
| | - Patrick Descombes
- Functional Genomics, Nestlé Institute of Health Sciences SA, EPFL Innovation Park, bâtiment G, 1015, Lausanne, Switzerland
| | - Julien Marquis
- Functional Genomics, Nestlé Institute of Health Sciences SA, EPFL Innovation Park, bâtiment G, 1015, Lausanne, Switzerland
| | - Sebastiano Collino
- Molecular Biomarkers, Nestlé Institute of Health Sciences SA, EPFL Innovation Park, bâtiment H, 1015, Lausanne, Switzerland
| | - Claudio Franceschi
- Personal Genomics s.r.l, Strada le Grazie 15, 37134, Verona, Italy
- Department of Experimental, Diagnostic and Specialty Medicine Experimental Pathology, University of Bologna, Via S. Giacomo 12, 40126, Bologna, Italy
- Interdepartmental Centre “L. Galvani” (CIG), University of Bologna, Piazza di Porta S. Donato 1, 40126, Bologna, Italy
- IRCCS, Institute of Neurological Sciences of Bologna, Ospedale Bellaria, Via Altura 3, 40139, Bologna, Italy
| | - Paolo Garagnani
- Personal Genomics s.r.l, Strada le Grazie 15, 37134, Verona, Italy
- Department of Experimental, Diagnostic and Specialty Medicine Experimental Pathology, University of Bologna, Via S. Giacomo 12, 40126, Bologna, Italy
- Interdepartmental Centre “L. Galvani” (CIG), University of Bologna, Piazza di Porta S. Donato 1, 40126, Bologna, Italy
- Center for Applied Biomedical Research, St. Orsola-Malpighi University Hospital, 40138, Bologna, Italy
| | | | - John Max Harvey
- Knome Inc., Waltham, Massachusetts, 02451, United States of America
| | - Massimo Delledonne
- Functional Genomics Center, Department of Biotechnology, University of Verona, 37134, Verona, Italy
- Personal Genomics s.r.l, Strada le Grazie 15, 37134, Verona, Italy
- * E-mail:
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141
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Basmanav FB, Forstner AJ, Fier H, Herms S, Meier S, Degenhardt F, Hoffmann P, Barth S, Fricker N, Strohmaier J, Witt SH, Ludwig M, Schmael C, Moebus S, Maier W, Mössner R, Rujescu D, Rietschel M, Lange C, Nöthen MM, Cichon S. Investigation of the role of TCF4 rare sequence variants in schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2015; 168B:354-62. [PMID: 26010163 DOI: 10.1002/ajmg.b.32318] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 04/13/2015] [Indexed: 12/20/2022]
Abstract
Transcription factor 4 (TCF4) is one of the most robust of all reported schizophrenia risk loci and is supported by several genetic and functional lines of evidence. While numerous studies have implicated common genetic variation at TCF4 in schizophrenia risk, the role of rare, small-sized variants at this locus-such as single nucleotide variants and short indels which are below the resolution of chip-based arrays requires further exploration. The aim of the present study was to investigate the association between rare TCF4 sequence variants and schizophrenia. Exon-targeted resequencing was performed in 190 German schizophrenia patients. Six rare variants at the coding exons and flanking sequences of the TCF4 gene were identified, including two missense variants and one splice site variant. These six variants were then pooled with nine additional rare variants identified in 379 European participants of the 1000 Genomes Project, and all 15 variants were genotyped in an independent German sample (n = 1,808 patients; n = 2,261 controls). These data were then analyzed using six statistical methods developed for the association analysis of rare variants. No significant association (P < 0.05) was found. However, the results from our association and power analyses suggest that further research into the possible involvement of rare TCF4 sequence variants in schizophrenia risk is warranted by the assessment of larger cohorts with higher statistical power to identify rare variant associations.
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Affiliation(s)
- F Buket Basmanav
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany
| | - Heide Fier
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany.,Department of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Stefan Herms
- Department of Genomics, Life and Brain Center, Bonn, Germany.,Division of Medical Genetics, University Hospital Basel and Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Sandra Meier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.,National Center for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany.,Division of Medical Genetics, University Hospital Basel and Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Neuroscience and Medicine INM-1, Research Center Juelich, Juelich, Germany
| | - Sandra Barth
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany
| | - Nadine Fricker
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany
| | - Jana Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Michael Ludwig
- Department of Clinical Chemistry and Clinical Pharmacology, University of Bonn, Bonn, Germany
| | - Christine Schmael
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Susanne Moebus
- Centre of Urban Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, Essen, Germany
| | - Wolfgang Maier
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Rainald Mössner
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany.,Department of Psychiatry, University of Tübingen
| | - Dan Rujescu
- Department of Psychiatry, University of Halle, Halle, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Christoph Lange
- Department of Genomic Mathematics, University of Bonn, Bonn, Germany.,German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany
| | - Sven Cichon
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, Bonn, Germany.,Division of Medical Genetics, University Hospital Basel and Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Neuroscience and Medicine INM-1, Research Center Juelich, Juelich, Germany
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142
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Shigemizu D, Aiba T, Nakagawa H, Ozaki K, Miya F, Satake W, Toda T, Miyamoto Y, Fujimoto A, Suzuki Y, Kubo M, Tsunoda T, Shimizu W, Tanaka T. Exome Analyses of Long QT Syndrome Reveal Candidate Pathogenic Mutations in Calmodulin-Interacting Genes. PLoS One 2015; 10:e0130329. [PMID: 26132555 PMCID: PMC4488844 DOI: 10.1371/journal.pone.0130329] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2015] [Accepted: 05/19/2015] [Indexed: 12/20/2022] Open
Abstract
Long QT syndrome (LQTS) is an arrhythmogenic disorder that can lead to sudden death. To date, mutations in 15 LQTS-susceptibility genes have been implicated. However, the genetic cause for approximately 20% of LQTS patients remains elusive. Here, we performed whole-exome sequencing analyses on 59 LQTS and 61 unaffected individuals in 35 families and 138 unrelated LQTS cases, after genetic screening of known LQTS genes. Our systematic analysis of familial cases and subsequent verification by Sanger sequencing identified 92 candidate mutations in 88 genes for 23 of the 35 families (65.7%): these included eleven de novo, five recessive (two homozygous and three compound heterozygous) and seventy-three dominant mutations. Although no novel commonly mutated gene was identified other than known LQTS genes, protein-protein interaction (PPI) network analyses revealed ten new pathogenic candidates that directly or indirectly interact with proteins encoded by known LQTS genes. Furthermore, candidate gene based association studies using an independent set of 138 unrelated LQTS cases and 587 controls identified an additional novel candidate. Together, mutations in these new candidates and known genes explained 37.1% of the LQTS families (13 in 35). Moreover, half of the newly identified candidates directly interact with calmodulin (5 in 11; comparison with all genes; p=0.042). Subsequent variant analysis in the independent set of 138 cases identified 16 variants in the 11 genes, of which 14 were in calmodulin-interacting genes (87.5%). These results suggest an important role of calmodulin and its interacting proteins in the pathogenesis of LQTS.
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Affiliation(s)
- Daichi Shigemizu
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Takeshi Aiba
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Hidewaki Nakagawa
- Laboratory for Genome Sequencing Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kouichi Ozaki
- Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Fuyuki Miya
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Wataru Satake
- Division of Neurology/Molecular Brain Science, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tatsushi Toda
- Division of Neurology/Molecular Brain Science, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yoshihiro Miyamoto
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Akihiro Fujimoto
- Laboratory for Genome Sequencing Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yutaka Suzuki
- Department of Computational Biology, Division of Biosystem Sciences, University of Tokyo, Chiba, Japan
| | - Michiaki Kubo
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Wataru Shimizu
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Cardiovascular Medicine, Nippon Medical School, Tokyo, Japan
- * E-mail: (WS); (TT)
| | - Toshihiro Tanaka
- Laboratory for Cardiovascular Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Human Genetics and Disease Diversity, Tokyo Medical and Dental University Graduate School of Medical and Dental Sciences, Tokyo, Japan
- * E-mail: (WS); (TT)
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143
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McLaren PJ, Carrington M. The impact of host genetic variation on infection with HIV-1. Nat Immunol 2015; 16:577-83. [PMID: 25988890 PMCID: PMC6296468 DOI: 10.1038/ni.3147] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Accepted: 03/12/2015] [Indexed: 12/16/2022]
Abstract
The outcome after infection with the human immunodeficiency virus type 1 (HIV-1) is a complex phenotype determined by interactions among the pathogen, the human host and the surrounding environment. An impact of host genetic variation on HIV-1 susceptibility was identified early in the pandemic, with a major role attributed to the genes encoding class I human leukocyte antigens (HLA) and the chemokine receptor CCR5. Studies using genome-wide data sets have underscored the strength of these associations relative to variants located throughout the rest of the genome. However, the extent to which additional polymorphisms influence HIV-1 disease progression, and how much of the variability in outcome can be attributed to host genetics, remain largely unclear. Here we discuss findings concerning the functional impact of associated variants, outline methods for quantifying the host genetic component and examine how available genome-wide data sets may be leveraged to discover gene variants that affect the outcome of HIV-1 infection.
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Affiliation(s)
- Paul J McLaren
- School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Mary Carrington
- 1] Cancer and Inflammation Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, USA. [2] The Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
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144
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Gorokhova S, Biancalana V, Lévy N, Laporte J, Bartoli M, Krahn M. Clinical massively parallel sequencing for the diagnosis of myopathies. Rev Neurol (Paris) 2015; 171:558-71. [PMID: 26022190 DOI: 10.1016/j.neurol.2015.02.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Revised: 01/28/2015] [Accepted: 02/04/2015] [Indexed: 02/07/2023]
Abstract
Massively parallel sequencing, otherwise known as high-throughput or next-generation sequencing, is rapidly gaining wide use in clinical practice due to possibility of simultaneous exploration of multiple genomic regions. More than 300 genes have been implicated in neuromuscular disorders, meaning that many genes need to be considered in a differential diagnosis for a patient affected with myopathy. By providing sequencing information for numerous genes at the same time, massively parallel sequencing greatly accelerates the diagnostic processes of myopathies compared to the classical "gene-after-gene" approach by Sanger sequencing. In this review, we describe multiple advantages of this powerful sequencing method for applications in myopathy diagnosis. We also outline recent studies that used this approach to discover new myopathy-causing genes and to diagnose cohorts of patients with muscular disorders. Finally, we highlight the key aspects and limitations of massively parallel sequencing that a neurologist considering this test needs to know in order to interpret the results of the test and to deal with other issues concerning the test.
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Affiliation(s)
- S Gorokhova
- Aix Marseille Université, INSERM, GMGF, UMR_S 910, Faculté de Médecine, secteur Timone, 27, boulevard Jean-Moulin, 13385 Marseille cedex, France
| | - V Biancalana
- Laboratoire Diagnostic Génétique, Nouvel Hôpital Civil, 1, place de l'Hôpital, BP 426, 67091 Strasbourg cedex, France; Department of Translational Medicine and Neurogenetics, I.G.B.M.C., INSERM U964, CNRS UMR7104, Strasbourg University, 1, rue Laurent-Fries, 67404 Illkirch, France
| | - N Lévy
- Aix Marseille Université, INSERM, GMGF, UMR_S 910, Faculté de Médecine, secteur Timone, 27, boulevard Jean-Moulin, 13385 Marseille cedex, France; AP-HM, Département de Génétique Médicale, Hôpital Timone Enfants, 264, rue Saint-Pierre, 13385 Marseille cedex 05, France
| | - J Laporte
- Department of Translational Medicine and Neurogenetics, I.G.B.M.C., INSERM U964, CNRS UMR7104, Strasbourg University, 1, rue Laurent-Fries, 67404 Illkirch, France
| | - M Bartoli
- Aix Marseille Université, INSERM, GMGF, UMR_S 910, Faculté de Médecine, secteur Timone, 27, boulevard Jean-Moulin, 13385 Marseille cedex, France; AP-HM, Département de Génétique Médicale, Hôpital Timone Enfants, 264, rue Saint-Pierre, 13385 Marseille cedex 05, France
| | - M Krahn
- Aix Marseille Université, INSERM, GMGF, UMR_S 910, Faculté de Médecine, secteur Timone, 27, boulevard Jean-Moulin, 13385 Marseille cedex, France; AP-HM, Département de Génétique Médicale, Hôpital Timone Enfants, 264, rue Saint-Pierre, 13385 Marseille cedex 05, France.
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145
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Fonseca DJ, Patiño LC, Suárez YC, de Jesús Rodríguez A, Mateus HE, Jiménez KM, Ortega-Recalde O, Díaz-Yamal I, Laissue P. Next generation sequencing in women affected by nonsyndromic premature ovarian failure displays new potential causative genes and mutations. Fertil Steril 2015; 104:154-62.e2. [PMID: 25989972 DOI: 10.1016/j.fertnstert.2015.04.016] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Revised: 04/07/2015] [Accepted: 04/12/2015] [Indexed: 01/08/2023]
Abstract
OBJECTIVE To identify new molecular actors involved in nonsyndromic premature ovarian failure (POF) etiology. DESIGN This is a retrospective case-control cohort study. SETTING University research group and IVF medical center. PATIENT(S) Twelve women affected by nonsyndromic POF. The control group included 176 women whose menopause had occurred after age 50 and had no antecedents regarding gynecological disease. A further 345 women from the same ethnic origin (general population group) were also recruited to assess allele frequency for potentially deleterious sequence variants. INTERVENTION(S) Next generation sequencing (NGS), Sanger sequencing, and bioinformatics analysis. MAIN OUTCOME MEASURE(S) The complete coding regions of 70 candidate genes were massively sequenced, via NGS, in POF patients. Bioinformatics and genetics were used to confirm NGS results and to identify potential sequence variants related to the disease pathogenesis. RESULT(S) We have identified mutations in two novel genes, ADAMTS19 and BMPR2, that are potentially related to POF origin. LHCGR mutations, which might have contributed to the phenotype, were also detected. CONCLUSION(S) We thus recommend NGS as a powerful tool for identifying new molecular actors in POF and for future diagnostic/prognostic purposes.
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Affiliation(s)
- Dora Janeth Fonseca
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia
| | - Liliana Catherine Patiño
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia
| | - Yohjana Carolina Suárez
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia
| | - Asid de Jesús Rodríguez
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia
| | - Heidi Eliana Mateus
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia
| | - Karen Marcela Jiménez
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia
| | - Oscar Ortega-Recalde
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia
| | | | - Paul Laissue
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogota, Colombia.
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146
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Wang Y, Liu A, Mills JL, Boehnke M, Wilson AF, Bailey-Wilson JE, Xiong M, Wu CO, Fan R. Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models. Genet Epidemiol 2015; 39:259-75. [PMID: 25809955 PMCID: PMC4443751 DOI: 10.1002/gepi.21895] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 01/28/2015] [Accepted: 01/28/2015] [Indexed: 10/23/2022]
Abstract
In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case.
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Affiliation(s)
- Yifan Wang
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Aiyi Liu
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - James L. Mills
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Michael Boehnke
- Department of Biostatistics, School of Public Health, The University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alexander F. Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Joan E. Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Momiao Xiong
- Human Genetics Center, University of Texas - Houston, Houston, Texas, United States of America
| | - Colin O. Wu
- Office of Biostatistics Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Ruzong Fan
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
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147
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The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet 2015; 11:e1005165. [PMID: 25906071 PMCID: PMC4407972 DOI: 10.1371/journal.pgen.1005165] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Accepted: 03/20/2015] [Indexed: 01/09/2023] Open
Abstract
Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci. Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.
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148
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Ellis MK, Elliott KS, Rautanen A, Crook DW, Hill AVS, Chapman SJ. Rare variants in MYD88, IRAK4 and IKBKG and susceptibility to invasive pneumococcal disease: a population-based case-control study. PLoS One 2015; 10:e0123532. [PMID: 25886387 PMCID: PMC4401548 DOI: 10.1371/journal.pone.0123532] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Accepted: 02/19/2015] [Indexed: 12/16/2022] Open
Abstract
Although rare variants within the Toll-like receptor signalling pathway genes have been found to underlie human primary immunodeficiencies associated with selective predisposition to invasive pneumococcal disease (IPD), the contribution of variants in these genes to IPD susceptibility at the population level remains unknown. Complete re-sequencing of IRAK4, MYD88 and IKBKG genes was undertaken in 164 IPD cases from the UK and 164 geographically-matched population-based controls. 233 single-nucleotide variants (SNVs) were identified, of which ten were in coding regions. Four rare coding variants were predicted to be deleterious, two variants in MYD88 and two in IRAK4. The predicted deleterious variants in MYD88 were observed as two heterozygote cases but not seen in controls. Frequencies of predicted deleterious IRAK4 SNVs were the same in cases and controls. Our findings suggest that rare, functional variants in MYD88, IRAK4 or IKBKG do not significantly contribute to IPD susceptibility in adults at the population level.
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Affiliation(s)
- Magda K. Ellis
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
- Queensland Institute of Medical Research, Brisbane, Australia
- Centenary Institute and Sydney Medical School, University of Sydney, Sydney Australia
- * E-mail:
| | | | - Anna Rautanen
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
| | - Derrick W. Crook
- Department of Microbiology, John Radcliffe Hospital, Oxford, United Kingdom
| | - Adrian V. S. Hill
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
| | - Stephen J. Chapman
- Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
- Oxford Centre for Respiratory Medicine, Churchill Hospital Site, Oxford University Hospitals, Oxford, United Kingdom
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149
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Whole-exome sequencing implicates UBE3D in age-related macular degeneration in East Asian populations. Nat Commun 2015; 6:6687. [DOI: 10.1038/ncomms7687] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 02/20/2015] [Indexed: 12/13/2022] Open
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150
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Zuo X, Sun L, Yin X, Gao J, Sheng Y, Xu J, Zhang J, He C, Qiu Y, Wen G, Tian H, Zheng X, Liu S, Wang W, Li W, Cheng Y, Liu L, Chang Y, Wang Z, Li Z, Li L, Wu J, Fang L, Shen C, Zhou F, Liang B, Chen G, Li H, Cui Y, Xu A, Yang X, Hao F, Xu L, Fan X, Li Y, Wu R, Wang X, Liu X, Zheng M, Song S, Ji B, Fang H, Yu J, Sun Y, Hui Y, Zhang F, Yang R, Yang S, Zhang X. Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis. Nat Commun 2015; 6:6793. [PMID: 25854761 PMCID: PMC4403312 DOI: 10.1038/ncomms7793] [Citation(s) in RCA: 103] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Accepted: 02/28/2015] [Indexed: 12/30/2022] Open
Abstract
Genome-wide association studies (GWASs) have reproducibly associated ∼40 susceptibility loci with psoriasis. However, the missing heritability is evident and the contributions of coding variants have not yet been systematically evaluated. Here, we present a large-scale whole-exome array analysis for psoriasis consisting of 42,760 individuals. We discover 16 SNPs within 15 new genes/loci associated with psoriasis, including C1orf141, ZNF683, TMC6, AIM2, IL1RL1, CASR, SON, ZFYVE16, MTHFR, CCDC129, ZNF143, AP5B1, SYNE2, IFNGR2 and 3q26.2-q27 (P<5.00 × 10(-08)). In addition, we also replicate four known susceptibility loci TNIP1, NFKBIA, IL12B and LCE3D-LCE3E. These susceptibility variants identified in the current study collectively account for 1.9% of the psoriasis heritability. The variant within AIM2 is predicted to impact protein structure. Our findings increase the number of genetic risk factors for psoriasis and highlight new and plausible biological pathways in psoriasis.
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Affiliation(s)
- Xianbo Zuo
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Liangdan Sun
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Xianyong Yin
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Jinping Gao
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yujun Sheng
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Jinhua Xu
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
| | - Jianzhong Zhang
- Department of Dermatology, Peking University People’s Hospital, Beijing 100044, China
| | - Chundi He
- Department of Dermatology, No.1 Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Ying Qiu
- Department of Dermatology, Jining No. 1 People’s Hospital, Jining, Shandong 272011, China
| | - Guangdong Wen
- Department of Dermatology, Peking University People’s Hospital, Beijing 100044, China
| | - Hongqing Tian
- Shandong Provincial Institute of Dermatology and Venereology, Jinan, Shandong 250022, China
| | - Xiaodong Zheng
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Shengxiu Liu
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Wenjun Wang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Weiran Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yuyan Cheng
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Longdan Liu
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yan Chang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Zaixing Wang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Zenggang Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Longnian Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Jianping Wu
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Ling Fang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Changbing Shen
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Fusheng Zhou
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Bo Liang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Gang Chen
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Hui Li
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yong Cui
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Aie Xu
- The Third People's Hospital of Hangzhou, Hangzhou, Zhejiang 310009, China
| | - Xueqin Yang
- Department of Dermatology, General Hospital of PLA Air Force, Beijing 100036, China
| | - Fei Hao
- Department of Dermatology, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Limin Xu
- Department of Dermatology, Tianjin Changzheng Hospital, Tianjin 300106, China
| | - Xing Fan
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Yuzhen Li
- Department of Dermatology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Rina Wu
- Department of Dermatology, The Affiliated Hospital of Inner Mongolia Medical College, Huhehot, Inner Mongolia 010050, China
| | - Xiuli Wang
- Shanghai Skin Diseases and STD Hospital, Shanghai 200050, China
| | - Xiaoming Liu
- Department of Dermatology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, China
| | - Min Zheng
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhenjiang 310009, China
| | - Shunpeng Song
- Department of Dermatology, Dalian Dermatosis Hosptial, Liaoning 116011, China
| | - Bihua Ji
- Department of Dermatology, Yijishan Hospital of Wannan Medical College, Wuhu, Anhui 241000, China
| | - Hong Fang
- Department of Dermatology, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhenjiang 310006, China
| | - Jianbin Yu
- Department of Dermatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China
| | - Yongxin Sun
- Department of Dermatology, Anshan Tanggangzi hosptial, Liaoning 210300, China
| | - Yan Hui
- Department of Dermatology, First Affiliated Hospital of Xinjiang Medical University, Xinjiang 830054, China
| | - Furen Zhang
- Shandong Provincial Institute of Dermatology and Venereology, Jinan, Shandong 250022, China
| | - Rongya Yang
- Department of Dermatology, General Hospital of Beijing Military Command, Beijing 100010, China
| | - Sen Yang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
| | - Xuejun Zhang
- Institute of Dermatology and Department of Dermatology, No. 1 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Department of Dermatology, Huashan Hospital, Fudan University, Shanghai 200040, China
- Department of Dermatology, No.2 Hospital, Anhui Medical University, Hefei, Anhui 230022, China
- Collaborative Innovation Center of Complex and Severe Skin Disease, Anhui Medical University, Hefei, Anhui 230032, China
- State Key Lab Incubation of Dermatology, Ministry of Science and Technology, Hefei, Anhui 230032, China
- Key Lab of Dermatology, Ministry of Education, Hefei, Anhui 230032, China
- Key Lab of Gene Resources Utilization for Severe Inherited Disorders, Anhui 230032, China
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