In addition to conventional single variant–based analysis, we used a complementary polygenic score–based approach, which included partitioned T2D risk scores that capture biological processes relevant to T2D pathophysiology. To increase power to detect GGI effects, we combined recent advances in the fine-mapping of causal T2D risk variants with the increased sample size available within UK Biobank (375,736 unrelated European participants, including 16,430 with T2D). Despite longstanding interest in understanding whether nonlinear interactions between these risk variants additionally influence T2D risk, the ability to detect significant gene-gene interaction (GGI) effects has been limited to date. A growing number of genetic loci have been shown to influence individual predisposition to type 2 diabetes (T2D).
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