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+) .
| Citation | SEAGLE citation info |
| github.com/jocelynchi/SEAGLE | |