MonsterLM: A method to estimate the variance explained by genome-wide interactions with environmental factors

Abstract

Estimations of heritability and variance explained due to environmental exposures and interaction effects help in understanding complex diseases. Current methods to detect such interactions rely on variance component methods. These methods have been neces- sary due to the m » n problem, where the number of predictors (m) vastly outnumbers the number of observations (n). These methods are all computationally intensive, which is further exacerbated when considering gene-environment interactions, as the number of predictors increases from m to 2m+1 in the case of a single environmental exposure. Novel methods are thus needed to enable fast and unbiased calculations of the variance explained (R2) for gene-environment interactions in very large samples on multiple traits. Taking advantage of the large number of participants in contemporary genetic studies, we herein propose a novel method for continuous trait R2 estimates that are up to 20 times faster than current methods. We have devised a novel method, monsterlm, that enables multiple linear regression on large regions encompassing tens of thousands of variants in hundreds of thousands of participants. We tested monsterlm with simulations using real genotypes from the UK Biobank. During simulations we verified the properties of monsterlm to estimate the variance explained by interaction terms. Our preliminary results showcase potential interactions between blood biochemistry biomarkers such as HbA1c, Triglycerides and ApoB with an environmental factor relating to obesity-related lifestyle factor: Waist-hip Ratio (WHR). We further investigate these results to reveal that more than 50% of the interaction variance calculated can be attributed to ∼5% of the single-nucleotide polymorphisms (SNPs) interacting with the environmental trait. Lastly, we showcase the impact of interactions on improving polygenic risk scores.