Citations 2
The genetic determinants of recurrent somatic mutations in 43,693 blood genomes
Weinstock, Joshua S. , Laurie, Cecelia , Broome, Jai , Taylor, Kent D. , Guo, Xiuqing , Shuldiner, Alan R. , O’Connell, Jeffrey R. , Lewis, Joshua P. , Boerwinkle, Eric , Barnes, Kathleen C. , Chami, Nathalie , Loos, Ruth J. F. , Fornage, Myriam , Redline, Susan , Cade, Brian E. , Chen, Zhanghua , Gauderman, W. James , Schleimer, Robert P. , Bis, Joshua C. , Brody, Jennifer A. , Silverman, Edwin K. , Qiao, Dandi , Lasky‐Su, Jessica , DeMeo, Dawn L. , Palmer, Nicholette D. , Bowden, Donald W. , Johnson, Andrew D. , Yanek, Lisa R. , Becker, Lewis C. , Kardia, Sharon L.R., Kaplan, Robert C. , Heckbert, Susan R. , Smith, Nicholas L. , Arnett, Donna K. , Irvin, Marguerite R. , Tiwari, Hemant K., Raffield, Laura M. , Gao, Yan , Rotter, Jerome I. , Wheeler, Marsha M. , Duggirala, Ravindranath , Xiao, Shujie, Williams, L. Keoki , Meyers, Deborah A. , Ortega, Victor E. , McGarvey, Stephen T. , Chen, Yii‐Der Ida , Lee, Wen‐Jane , Shoemaker, M. Benjamin , Roden, Dan M. , Blackwell, Thomas W. , Smith, Albert V. , Kang, Hyun Min , Mathias, Rasika A. , Natarajan, Pradeep , Jaiswal, Siddhartha , Reiner, Alex P. , Bick, Alexander G. , Kaplan, Robert C. , Broome, Jai , Kenny, Eimear E , Loos, Ruth J F , Fornage, Myriam , Redline, Susan , Cade, Brian E , Gauderman, W James , Grammer, Leslie , Schleimer, Robert P , Bis, Joshua C , Brody, Jennifer A , Silverman, Edwin K , Yun, Jeong H , Qiao, Dandi , Weiss, Scott T , Palmer, Nicholette D , Freedman, Barry I , Cho, Michael H , Vasan, Ramachandran S , Johnson, Andrew D , Yanek, Lisa R , Becker, Lewis C , He, Jiang , Heckbert, Susan R , Wiggins, Kerri L , Arnett, Donna K , Correa, Adolfo , Raffield, Laura M , Gao, Yan , Rotter, Jerome I , Rich, Stephen S , Manichaikul, Ani W , Konkle, Barbara A , Johnsen, Jill M , Custer, Brian S , Duggirala, Ravindranath , Curran, Joanne E , Li, Xingnan , McGarvey, Stephen T , Gu, C Charles , Lee, Wen-Jane , Darbar, Dawood , Roden, Dan M. , Kooperberg, Charles , Desai, Pinkal M , Smith, Albert V , Kang, Hyun M , Mathias, Rasika A , Natarajan, Pradeep , Jaiswal, Siddhartha , Bick, Alexander G , Weinstock, Joshua S , Shuldiner, Alan R , Taylor, Kent D , Guo, Xiuqing , Lasky-Su, Jessica , Gilliland, Frank D. , Kumar, Rajesh , Psaty, Bruce M. , Yun, Jeong H. , Weiss, Scott T. , Freedman, Barry I. , Cho, Michael H. , Vasan, Ramachandran S. , He, Jiang , Wiggins, Kerri L. , Andrade, Mariza de , Rich, Stephen S. , Manichaikul, Ani , Konkle, Barbara A. , Custer, Brian , Curran, Joanne E. , Blangero, John , Gui, Hongsheng , Li, Xingnan , Darbar, Dawood , Albert, Christine M. , Kooperberg, Charles , Desai, Pinkal , Abecasis, Gonçalo R.
Science Advances | April 26, 2023 | Volume: 9 | Issue: 17 | Pages: No page available
Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.
Robust, flexible, and scalable tests for Hardy–Weinberg equilibrium across diverse ancestries
Kwong, Alan , Blackwell, Thomas W. , LeFaive, Jonathon , Barnes, Kathleen C. , Boerwinkle, Eric , Cade, Brian E. , Chen, Han , Conomos, Matthew P. , Eng, Celeste , Gao, Yan , Irvin, Marguerite R. , Kelly, Tanika N. , Kim, Wonji , Lubitz, Steven A. , Mathias, Rasika A. , Montasser, May E. , Musani, Solomon K., Palmer, Nicholette D, Qiao, Dandi , Reiner, Alex P. , Roden, Dan M. , Shoemaker, M. Benjamin , Smith, Jennifer A. , Smith, Nicholas L. , Lasky‐Su, Jessica , Tiwari, Hemant K., Weeks, Daniel E. , Group, TOPMed Analysis Working, Scott, Laura J. , Smith, Albert V. , Boehnke, Michael , Kang, Hyun Min , Palmer, Nicholette D. , Smith, Jennifer A , Lubitz, Steven A , Smith, Albert V , Kelly, Tanika N , Weeks, Daniel E , Palmer, Nicholette D , Mathias, Rasika A , Cade, Brian E , Lasky-Su, Jessica , Andrade, Mariza de , Barnard, John , Blangero, John , Burchard, Esteban G. , Chasman, Daniel I. , Cupples, L. Adrienne , Ellinor, Patrick T. , Guo, Xiuqing , Kooperberg, Charles , Mak, Angel C. Y. , Manichaikul, Ani , Montgomery, Courtney G. , Peloso, Gina M. , Roden, Dan M. , Weiss, Scott T. , Group, TOPMed Analysis Working, Abecasis, Gonçalo R.
Genetics | March 15, 2021 | Volume: 218 | Issue: 1 | Pages: No page available
Abstract Traditional Hardy–Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.