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Multitrait transcriptome‐wide association study (TWAS) tests
Author(s) -
Feng Helian,
Mancuso Nicholas,
Pasaniuc Bogdan,
Kraft Peter
Publication year - 2021
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22391
Subject(s) - bonferroni correction , multiple comparisons problem , biology , single nucleotide polymorphism , genome wide association study , trait , genetic association , statistical power , type i and type ii errors , snp , genetics , quantitative trait locus , false discovery rate , phenotype , computational biology , statistics , gene , mathematics , computer science , genotype , programming language
Multitrait tests can improve power to detect associations between individual single‐nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi‐SNP transcriptome‐wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single‐SNP setting. Thus, established methods for combining single‐SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene‐phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well‐calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single‐trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni‐corrected single‐trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single‐trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome‐wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.