Identification of genetic variants regulating the abundance of clinically relevant plasma proteins using the Diversity Outbred mouse model

Abstract

Although there have been numerous expression quantitative trait loci (eQTL) studies, the effect of genetic variants on the levels of multiple plasma proteins still warrants more systematic investigation. To identify genetic modifiers that influence the levels of clinically relevant plasma proteins, we performed protein quantitative trait locus (pQTL) mapping on 92 proteins using the Diversity Outbred (DO) mouse population and identified 12 significant cis and 6 trans pQTL. Among them, we discovered coding variants in a cis-pQTL in Ahr and a trans-pQTL in Rfx1 for the IL-17A protein. Our study reports an innovative pipeline for the identification of genetic modifiers that may be targeted for drug development. Author SummaryBlood plasma is a body fluid that can be collected in a noninvasive way to detect diseases, such as autoimmune disease. However, it is known that plasma protein levels are affected by both the environment and genetic background. To determine the effect of genetics on plasma protein levels in human, one needs a rather large sample size. To overcome this critical issue, a mouse model, the Diversity Outbred (DO), was established that is genetically as diverse as the human population. In this study, we used N=140 DO mice and genotyped over 140,000 variants. In addition, we measured the levels of 92 proteins in plasma of these DO mice using Olink Proteomics technology. The proteins detected in this panel are known to be detectable in human plasma, making our study translatable to human. We identified 18 significant protein quantitative trait loci. Furthermore, we describe an analysis pipeline that allows for the detection of a single gene in the locus that is responsible for the differences in protein levels. We identified how variants in the Regulatory Factor X1 (Rfx1) gene regulates Interleukin-17A (IL-17A) plasma levels. Our study reports an innovative approach to identify genetic modifiers that may be targeted for drug development.