2021 journal article

SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data

FRONTIERS IN GENETICS, 12.

By: J. Chi*, I. Ipsen n , T. Hsiao *, C. Lin, L. Wang *, W. Lee *, T. Lu *, J. Tzeng n

author keywords: gene-based GxE test for biobank data; GxE collapsing test for biobank data; GxE test for large-scale sequencing data; scalable GEI test; gene-environment variance component test; gene-environment kernel test; regional-based gene-environment test
TL;DR: SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, is presented to permit GxE VC tests for biobank-scale data to explore the interaction of gene and physical activity status on body mass index and demonstrates its utility by conducting genome-wide gene-basedG×E analysis on the Taiwan Biobank data. (via Semantic Scholar)
UN Sustainable Development Goal Categories
14. Life Below Water (OpenAlex)
Source: Web Of Science
Added: November 29, 2021

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, aScalableExactAlGorithm forLarge-scale set-based G×Etests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic andp-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.