posted on 2020-08-21, 22:00authored byAda AdminAda Admin, Abhishek Nag, Mark I McCarthy, Anubha Mahajan
A growing number of
genetic loci have been shown to influence individual predisposition to type 2
diabetes (T2D). Despite longstanding interest in understanding whether non-linear
interactions between these risk-variants additionally influence T2D-risk, the
ability to detect significant gene-gene interaction (GGI) effects has to date
been limited. 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 T2D cases). In addition to conventional single variant-based analysis,
we employed a complementary polygenic score-based approach which included
partitioned T2D-risk scores that capture biological processes relevant to T2D
pathophysiology. Nevertheless, we
found no evidence in support of GGI effects influencing T2D-risk. The present
study was powered to detect interactions between common variants with odds
ratios >1.2, so these findings place limits on the contribution of GGIs to
the overall heritability of T2D.
Funding
This study received funding from NIDDK (U01-DK105535) and the Wellcome Trust (090532, 098381, 106130, 203141, 212259). M.I.M. was a Wellcome Investigator and an NIHR Senior Investigator.