Improvement of polygenic modeling of blood pressure traits using lifestyle information in the UK Biobank.

Abstract

Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to collinearity between the genetic and environmental components of the phenotype, which violates the assumptions of most statistical methods for polygenic modeling (i.e., environmental factors are non-randomized over the genetic factors). This phenomenon is also known as "reverse causation", and could lead to biased predictions due to the difficulty in disentangling the genetic and environmental effects. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted for genetic and other non-genetic factors. The results show that proper adjustment of the lifestyle variables allows for improved model performance and reduces the bias generated by reverse causation. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables.