SEAGLE - Scalable Exact Algorithm for Large-Scale Set-Based
Gene-Environment Interaction Tests
The explosion of biobank data offers immediate
opportunities for gene-environment (GxE) interaction studies of
complex diseases because of the large sample sizes and rich
collection in genetic and non-genetic information. However, the
extremely large sample size also introduces new computational
challenges in GxE assessment, especially for set-based GxE
variance component (VC) tests, a widely used strategy to boost
overall GxE signals and to evaluate the joint GxE effect of
multiple variants from a biologically meaningful unit (e.g.,
gene). We present 'SEAGLE', a Scalable Exact AlGorithm for
Large-scale Set-based GxE tests, to permit GxE VC test scalable
to biobank data. 'SEAGLE' employs modern matrix computations to
achieve the same “exact” results as the original GxE VC tests,
and does not impose additional assumptions nor relies on
approximations. 'SEAGLE' can easily accommodate sample sizes in
the order of 10^5, is implementable on standard laptops, and
does not require specialized equipment. The accompanying
manuscript for this package can be found at Chi, Ipsen, Hsiao,
Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.
