Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.

TitleEfficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.
Publication TypeJournal Article
Year of Publication2019
AuthorsChen, H, Huffman, JE, Brody, JA, Wang, C, Lee, S, Li, Z, Gogarten, SM, Sofer, T, Bielak, LF, Bis, JC, Blangero, J, Bowler, RP, Cade, BE, Cho, MH, Correa, A, Curran, JE, de Vries, PS, Glahn, DC, Guo, X, Johnson, AD, Kardia, S, Kooperberg, C, Lewis, JP, Liu, X, Mathias, RA, Mitchell, BD, O'Connell, JR, Peyser, PA, Post, WS, Reiner, AP, Rich, SS, Rotter, JI, Silverman, EK, Smith, JA, Vasan, RS, Wilson, JG, Yanek, LR, Redline, S, Smith, NL, Boerwinkle, E, Borecki, IB, L Cupples, A, Laurie, CC, Morrison, AC, Rice, KM, Lin, X
Corporate Authors,
JournalAm J Hum Genet
Volume104
Issue2
Pagination260-274
Date Published2019 Feb 07
ISSN1537-6605
Abstract

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.

DOI10.1016/j.ajhg.2018.12.012
Alternate JournalAm. J. Hum. Genet.
PubMed ID30639324
PubMed Central IDPMC6372261
Grant ListR35 CA197449 / CA / NCI NIH HHS / United States
R01 HL131136 / HL / NHLBI NIH HHS / United States
U19 CA203654 / CA / NCI NIH HHS / United States
R01 EB015611 / EB / NIBIB NIH HHS / United States
R01 HL139553 / HL / NHLBI NIH HHS / United States
U01 HL120393 / HL / NHLBI NIH HHS / United States
R01 HL113338 / HL / NHLBI NIH HHS / United States
P01 CA134294 / CA / NCI NIH HHS / United States
U01 HG009088 / HG / NHGRI NIH HHS / United States
P20 GM121334 / GM / NIGMS NIH HHS / United States
R00 HL130593 / HL / NHLBI NIH HHS / United States