A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank

Abstract

The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G 3 E) effects. Compared with marginal genetic association studies, G 3 E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G 3 E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (Sad-dlePoint Approximation implementation of G 3 E analysis), that is applicable for genome-wide scale phenome-wide G 3 E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios.

Efficient gene–environment interaction tests for large biobank‐scale sequencing studies

“…For example, statistical power for testing GEI with a binary environmental exposure depends on the minor allele counts in both exposed and unexposed groups. Although computationally efficient GEI tests for biobank‐scale studies have been developed recently in the context of single‐variant tests on unrelated individuals (Bi et al, 2019), critical methodological bottlenecks still exist to expand the sample size and scope of rare variant GEI analyses in large biobank‐scale sequencing studies. To increase power for rare variants, various set‐based methods have been developed to collapse variants in a particular gene or functional region to investigate how variants in a set affect a phenotype synergistically (Chen et al, 2019; Lee, Wu, & Lin, 2012; Pan, Kim, Zhang, Shen, & Wei, 2014; Sun, Zheng, & Hsu, 2013), and to demonstrate whether genetic associations with the phenotype are modified by environment factors in GEI studies (Chen, Meigs, & Dupuis, 2014; Lin et al, 2016; Su, Di, & Hsu, 2017).…”

Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data

“…This strategy can also be applied to other cases. Recently, scalable methods were proposed for a genome-wide G × E analysis ( Bi et al, 2019 ; Wang et al, 2020 ). When testing G × E effect, the null model should include marginal genetic effect.…”

“…However, the matrix projection approach might be inaccurate if the marginal genetic effect is large. To balance the computational efficiency and accuracy, SPAGE ( Bi et al, 2019 ) uses a hybrid strategy as follows. If the marginal genetic effect is small or moderate (e.g., p value > 5e-3), the matrix projection is used.…”

Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes

“…8,10 Another resource-efficient approach, fastGWA, is to use sparse GRM to adjust for the sample relatedness. 11 For binary phenotype analysis, unbalanced case-control ratio can result in inflated type I error rates and saddlepoint approximation (SPA) has been demonstrated to be more accurate for single-variant analysis 7,8 , region-based analysis 12,13 , and gene-environment interaction analysis 14 . Similarly, the sample size distribution in ordinal categorical data could also be highly unbalanced, that is, the sample size in one category could be dozens of times more than the that in other categories.…”