Impact of multiple sclerosis risk alleles on the plasma proteome

Abstract

Genetic analysis of multiple sclerosis (MS) susceptibility has implicated over 200 loci to date. However, the biological processes that mediate the impact of genetic variation on disease pathogenesis remain uncertain. As most MS risk variants lie outside of exonic regions, they likely act via effects on transcription and translation of target genes and proteins. Clarifying how genetic variants implicated in MS risk affect the plasma proteome could help to elucidate disease pathobiology and identify druggable targets. The recent application of high-throughput proteomic assays to plasma samples from ~54 000 UK Biobank participants has revealed the genetic architecture of the proteome, which can be used to explore the link between genetic variation, plasma proteins, and MS susceptibility using approaches such as Mendelian randomization (MR) and co-localization. In a recent issue of Brain, Lin et al. demonstrated the power of this approach by applying MR to protein quantitative trait locus (pQTL) data from 3301 European-ancestry participants in the INTERVAL study. The authors report that genetically predicted elevation in fibronectin leucine rich transmembrane protein (FLRT3) and TAP binding protein like (TAPBL), and genetically-predicted decreases in Fc receptor-like protein (FCRL3), Iduronidase (IDUA), and mitogen-activated protein kinase 3 (MAPK3) were associated with elevated MS risk. Here, we sought to reproduce and extend this work using pQTL data from a cohort 10 times the size (UK Biobank), providing greater statistical power and decreasing the risk of false positive discoveries. We leveraged pQTL data from 33 533 European-ancestry UK Biobank participants to understand how MS-associated genetic variation impacts the plasma proteome and used co-localization data to infer potential shared causal variants. Using these approaches, we were able to highlight potentially druggable targets for future interventional work.