Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies
Autor: | Chen, Han, Huffman, Jennifer E, Brody, Jennifer A, Wang, Chaolong, Lee, Seunggeun, Li, Zilin, Gogarten, Stephanie M, Sofer, Tamar, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Bowler, Russell P, Cade, Brian E, Cho, Michael H, Correa, Adolfo, Curran, Joanne E, de Vries, Paul S, Glahn, David C, Guo, Xiuqing, Johnson, Andrew D, Kardia, Sharon, Kooperberg, Charles, Lewis, Joshua P, Liu, Xiaoming, Mathias, Rasika A, Mitchell, Braxton D, O'Connell, Jeffrey R, Peyser, Patricia A, Post, Wendy S, Reiner, Alex P, Rich, Stephen S, Rotter, Jerome I, Silverman, Edwin K, Smith, Jennifer A, Vasan, Ramachandran S, Wilson, James G, Yanek, Lisa R, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Hematology and Hemostasis Working Group, Redline, Susan, Smith, Nicholas L, Boerwinkle, Eric, Borecki, Ingrid B, Cupples, L Adrienne, Laurie, Cathy C, Morrison, Alanna C, Rice, Kenneth M, Lin, Xihong |
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Rok vydání: | 2019 |
Předmět: |
Male
relatedness Time Factors Population TOPMed Medical and Health Sciences Chromosomes NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium Genetic Models 2.5 Research design and methodologies (aetiology) and Blood Institute (U.S.) Genetics Humans Precision Medicine Aetiology Lung Genetic Association Studies Genetics & Heredity variant set association test Whole Genome Sequencing Human Genome Fibrinogen rare variants population structure National Heart Cloud Computing Biological Sciences United States Good Health and Well Being Pair 4 Research Design generalized linear mixed model whole-genome sequencing Female Generic health relevance TOPMed Hematology and Hemostasis Working Group Human Biotechnology |
Zdroj: | American journal of human genetics, vol 104, iss 2 |
Popis: | With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform. |
Databáze: | OpenAIRE |
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