Bridget M Lin1, Kelsey E Grinde2, Jennifer A Brody3, Charles E Breeze4, Laura M Raffield5, Josyf C Mychaleckyj6, Timothy A Thornton7, James A Perry8, Leslie J Baier9, Lisa de Las Fuentes10, Xiuqing Guo11, Benjamin D Heavner7, Robert L Hanson9, Yi-Jen Hung12, Huijun Qian13, Chao A Hsiung14, Shih-Jen Hwang15, Margaret R Irvin16, Deepti Jain7, Tanika N Kelly17, Sayuko Kobes9, Leslie Lange18, James P Lash19, Yun Li20, Xiaoming Liu21, Xuenan Mi17, Solomon K Musani22, George J Papanicolaou23, Afshin Parsa24, Alex P Reiner25, Shabnam Salimi26, Wayne H-H Sheu27, Alan R Shuldiner8, Kent D Taylor11, Albert V Smith28, Jennifer A Smith29, Adrienne Tin30, Dhananjay Vaidya31, Robert B Wallace32, Kenichi Yamamoto33, Saori Sakaue34, Koichi Matsuda35, Yoichiro Kamatani36, Yukihide Momozawa37, Lisa R Yanek31, Betsi A Young38, Wei Zhao29, Yukinori Okada39, Gonzalo Abecasis40, Bruce M Psaty41, Donna K Arnett42, Eric Boerwinkle43, Jianwen Cai1, Ida Yii-Der Chen11, Adolfo Correa22, L Adrienne Cupples44, Jiang He17, Sharon Lr Kardia29, Charles Kooperberg25, Rasika A Mathias31, Braxton D Mitchell45, Deborah A Nickerson46, Steve T Turner47, Vasan S Ramachandran48, Jerome I Rotter11, Daniel Levy15, Holly J Kramer49, Anna Köttgen50, Stephen S Rich6, Dan-Yu Lin1, Sharon R Browning7, Nora Franceschini51. 1. Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States. 2. Department of Mathematics, Statistics, and Computer Science, Macalester College, St. Paul, MN, United States. 3. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States. 4. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department Health and Human Services, Bethesda, MD, United States; UCL Cancer Institute, University College London, London WC1E 6BT, United Kingdom; Altius Institute for Biomedical Sciences, Seattle, WA 98121, United States. 5. Department of Genetics, University of North Carolina, Chapel Hill, NC, United States. 6. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States. 7. Department of Biostatistics, University of Washington, Seattle, WA, United States. 8. Division of Endocrinology, Diabetes and Nutrition, and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, United States. 9. Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, United States. 10. Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, United States. 11. The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA United States. 12. Endocrinology and Metabolism, Tri-Service General Hospital Songshan branch, Taipei, Taiwan. 13. Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, United States. 14. Endocrinology and Metabolism, National Taiwan University Hospital, Taipei, Taiwan. 15. National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, United States; National Heart, Lung and Blood Institute, Population Sciences Branch, Division of Intramural Research, Bethesda, MD, United States. 16. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, United States. 17. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States. 18. Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Denver, CO, United States. 19. Department of Medicine, University of Illinois, Chicago, IL, United States. 20. Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States; Department of Genetics, University of North Carolina, Chapel Hill, NC, United States. 21. USF Genomics & College of Public Health, University of South Florida, Tampa, FL, United States. 22. Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States. 23. Epidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, MA, United States. 24. Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MA, United States. 25. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States. 26. Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, United States. 27. Endocrinology & Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan. 28. Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States. 29. Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States. 30. Department of Medicine, University of Mississippi Medical Center, Jackson, MS, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States. 31. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States. 32. University of Iowa College of Public Health, Iowa City, IA, United States. 33. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Department of Pediatrics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan. 34. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo 113-8655, Japan. 35. Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo, Tokyo 108-8639, Japan. 36. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo 108-8639, Japan. 37. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan. 38. Kidney Research Institute and Division of Nephrology, University of Washington, Seattle, WA, United States. 39. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan; Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan; Laboratory of Statistical Immunology, World Premier International Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan. 40. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, An Arbor, MI, United States; Regeneron Pharmaceuticals, Tarrytown, NY, United States. 41. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, United States; Departments of Epidemiology and Health Services, University of Washington, Seattle, WA, United States. 42. College of Public Health, Dean's Office, University of Kentucky, Lexington, KY, United States. 43. Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States. 44. National Heart, Lung, and Blood Institute Framingham Heart Study, Framingham, MA, United States; Department of Biostatistics, Boston University, Boston, MA, United States. 45. Division of Endocrinology, Diabetes and Nutrition, and Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, United States; Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, United States. 46. Department of Genome Sciences, University of Washington, Seattle, WA, United States. 47. Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States. 48. Division of Preventive Medicine and Epidemiology and Cardiology, Boston University School of Medicine, Boston, MA, United States. 49. Department of Public Health Sciences and Medicine, Loyola University Chicago, Maywood, IL, United States; Division of Nephrology and Hypertension, Loyola University Chicago, Maywood, IL, United States. 50. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany. 51. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States. Electronic address: noraf@unc.edu.
Abstract
BACKGROUND: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. METHODS: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. FINDINGS: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10-11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10-9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10-9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10-9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10-9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. INTERPRETATION: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.
BACKGROUND: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants. METHODS: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity. FINDINGS: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10-11; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10-9; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10-9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10-9, nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10-9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants. INTERPRETATION: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.
Authors: Luang Xu; Xinyu Liu; Na Sheng; Kyaw Soe Oo; Junxin Liang; Yok Hian Chionh; Juan Xu; Fuzhou Ye; Yong-Gui Gao; Peter C Dedon; Xin-Yuan Fu Journal: J Biol Chem Date: 2017-06-27 Impact factor: 5.157
Authors: Andrew P Morris; Thu H Le; Haojia Wu; Artur Akbarov; Peter J van der Most; Gibran Hemani; George Davey Smith; Anubha Mahajan; Kyle J Gaulton; Girish N Nadkarni; Adan Valladares-Salgado; Niels Wacher-Rodarte; Josyf C Mychaleckyj; Nicole D Dueker; Xiuqing Guo; Yang Hai; Jeffrey Haessler; Yoichiro Kamatani; Adrienne M Stilp; Gu Zhu; James P Cook; Johan Ärnlöv; Susan H Blanton; Martin H de Borst; Erwin P Bottinger; Thomas A Buchanan; Sylvia Cechova; Fadi J Charchar; Pei-Lun Chu; Jeffrey Damman; James Eales; Ali G Gharavi; Vilmantas Giedraitis; Andrew C Heath; Eli Ipp; Krzysztof Kiryluk; Holly J Kramer; Michiaki Kubo; Anders Larsson; Cecilia M Lindgren; Yingchang Lu; Pamela A F Madden; Grant W Montgomery; George J Papanicolaou; Leslie J Raffel; Ralph L Sacco; Elena Sanchez; Holger Stark; Johan Sundstrom; Kent D Taylor; Anny H Xiang; Aleksandra Zivkovic; Lars Lind; Erik Ingelsson; Nicholas G Martin; John B Whitfield; Jianwen Cai; Cathy C Laurie; Yukinori Okada; Koichi Matsuda; Charles Kooperberg; Yii-Der Ida Chen; Tatjana Rundek; Stephen S Rich; Ruth J F Loos; Esteban J Parra; Miguel Cruz; Jerome I Rotter; Harold Snieder; Maciej Tomaszewski; Benjamin D Humphreys; Nora Franceschini Journal: Nat Commun Date: 2019-01-03 Impact factor: 14.919