Genetics of cardiometabolic disease progression

Abstract

BackgroundGenome-wide association studies have been crucial in gaining insights into the genetics of cardiometabolic diseases. However, little is known about the genetics of cardiometabolic disease progression which may have both a different genetic architecture and significant implications for treatment decisions. Disease progression can be ascertained by the time from the first disease diagnosis to a second qualifying event (e.g. diagnostic lab, code or procedure). While data of this nature have been available in large repositories such as the UK Biobank, large-scale genome-wide screens in a time-to-event setting have been extremely challenging due to various computational and statistical challenges. Methods and ResultsWe applied our method, snpnet-Cox, that has proven to be an effective method for simultaneous variable selection and estimation in high-dimensional settings, to examine the genetic contributions to cardiometabolic disease progression, measured by time from disease diagnosis to time of complication/comorbidity diagnosed or procedure in the UK Biobank. We apply a Cox regression model in a time-to-event setting to compute polygenic hazard scores (PHS). We identified ten new PHS that significantly predict disease progression. One example is the PHS that significantly predicts the time from hyperlipidemia diagnosis to having coronary artery bypass graft (CABG) surgery performed (Hazards Ratio 1.3 per PHS standard deviation: p=4.5x10-9). In this PHS, we identified a common variant, rs11041816 (downstream of LMO1), which protects against this disease progression (beta = -0.05). Conclusionsnpnet-Cox is a fast and reliable tool to compute PHS capturing genetics in the time-to-event setting. The computed PHS can be used to stratify individuals with an underlying diagnosis (e.g. hyperlipidemia) into different trajectories disease progression (e.g CABG) thereby identifying potential points of intervention. With more time-to-event data to be released, this approach can provide great insight into disease progression at the fraction of computational cost necessary. We make available ten polygenic hazard scores that we find to be significant predictors of cardiometabolic disease progression.