Abstract
The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p-value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 105, is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index.
Highlights
Human complex diseases such as neurodegenerative diseases, psychiatric disorders, metabolic syndromes, and cancers are complex traits for which disease susceptibility, disease development, and treatment response are mediated by intricate genetic and environmental factors
We focus on continuous traits and introduce a Scalable Exact AlGorithm for Large-scale set-based gene-environment interactions (G×E) tests (SEAGLE) for performing G×E variance component (VC) tests on biobank data
Our numerical experiments illustrate that SEAGLE produces Type 1 error rates and power identical to those of the original GxE VC
Summary
Human complex diseases such as neurodegenerative diseases, psychiatric disorders, metabolic syndromes, and cancers are complex traits for which disease susceptibility, disease development, and treatment response are mediated by intricate genetic and environmental factors. Understanding the genetic etiology of these complex diseases requires collective consideration of potential genetic and environmental contributors. Studies of gene-environment interactions (G×E) enable understanding of the differences that environmental exposures may have on health outcomes in Biobank-Scale GxE VC Test people with varying genotypes (Ottman, 1996; Hunter, 2005; McAllister et al, 2017). When assessing G×E effects, set-based tests are popular approaches to detecting interactions between an environmental factor and a set of single nucleotide polymorphism (SNPs) in a gene, sliding window, or functional region (Tzeng et al, 2011; Lin et al, 2013; Zhao et al, 2015; Lin et al, 2016; Su et al, 2017). Compared to single-SNP G×E tests, set-based G×E tests can enhance testing performance by reducing multiple-testing burden and by aggregating G×E signals over multiple SNPs that are of moderate effect sizes or of low frequencies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
