Table 2 Real data estimates for dichotomous outcomes with MonsterLM

From: A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Trait

Additive genetic variance (95% CI)

WHR GxE variance (95% CI)

M10 GxE variance (95% CI)

Permuted exposure GxE (95% CI)

Type 2 diabetes

0.659 (0.562, 0.755)

−0.0015 (−0.0385, 0.0355)

0.0173 (−0.00265, 0.0378)

−0.0201 (−0.0721, 0.0319)

Coronary artery disease

0.181 (0.144, 0.218)

−0.0057 (−0.0407, 0.0293)

−0.0105 (−0.0405, 0.0101)

−0.0331 (−0.0785, 0.0123)

  1. MonsterLM real data results for dichotomous outcomes. Presented are real data estimates for dichotomous outcomes. All estimates are performed using the MonsterLM methodology. Exposures include waist-hip-ratio (WHR), number of days of 10 minutes moderate exercise (M10), and a randomly permuted exposure (Epm). Bolded estimates are significant.
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