Simulated distributions from negative experiments highlight the importance of the body mass index distribution in explaining depression-body mass index genetic risk score interactions

Depression and obesity are complex global health problems. Recent studies suggest that a genetic predisposition to obesity might be accentuated in people with depression, but these analyses are prone to bias. Here, we tested the hypothesis that depression accentuates genetic susceptibility to obesity and applied negative control experiments to test whether any observed interactions were real or driven by confounding and statistical biases. We used data from up to 378 000 Europeans in UK Biobank, a 73 variant body mass index (BMI) genetic risk score, two depression measures [depression symptoms (DS), major depression (MD)] and an antidepressant usage variable available. We tested whether (i) depression and (ii) antidepressant treatment accentuated genetic susceptibility to obesity. Finally, we performed negative control experiments by sampling individuals at random so that they had BMI distributions identical to depression cases and controls. Depression was associated with an accentuation of an individual's genetic risk of obesity with evidence of interactions for both DS and MD (Pinteraction = 7 × 10-4 and 7 × 10-5 respectively). Antidepressant usage within DS cases accentuated genetic obesity risk (Pinteraction = 9 × 10-4), but not for MD (Pinteraction = 0.13). Negative control experiments suggested that the observed interactions for MD (empirical-P = 0.067) may be driven by statistical biases or confounding factors but were not possible with the larger DS groups. Antidepressant usage interaction also appears to be driven by statistical artefacts (empirical-P = 0.510 using MD and 0.162 using DS). We have highlighted the importance of running negative experiments to confirm putative interactions in gene-environment studies. We provide some tentative evidence that depression accentuates an individual's genetic susceptibility to higher BMI but demonstrated that the BMI distributions within cases and controls might drive these interactions.