Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Abstract

Polygenic risk scores (PRSs) capture the polygenic architecture of common diseases by aggregating genome-wide genetic variation into a single score that reflects individual's disease risk, affording a new opportunity to identify downstream molecular pathways involved in disease pathogenesis. We performed an integrative analysis to characterise associations of PRSs of five cardiometabolic diseases with 3,442 plasma proteins in 3,175 healthy individuals. Through polygenic association scans we identified 48 plasma proteins whose levels were associated with PRSs for coronary artery disease (CAD), chronic kidney disease (CKD), or type 2 diabetes (T2D). This approach identified both well-known disease-associated proteins as well as those with previously no known link to these diseases. We found that PRSs to protein associations were largely truly polygenic; independent of single loci and genomic regions expected to have strong effects on protein levels. Our integrative analysis and laboratory experiments revealed a role for polygenic effects on several well-known disease proteins and identified several promising novel targets for follow-up studies, including genes which through Mendelian randomization analysis displayed causal evidence for effects on disease risk. Mouse studies highlighted specific tissues and phenotypes for PRSassociated human proteins. We found that implicated genes were responsive to dietary intervention in mice and showed strong evidence of druggability in humans, consistent with PRS-associated proteins having therapeutic potential. Overall, our study provides a framework for polygenic association studies, and demonstrates the power of polygenic scores to unravel novel disease biology. Research (NIHR), the NIHR BioResource

Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes

“…Our findings support a model where higher protein expression of the BCAA catabolic pathway reduces risk of type 2 diabetes. Another interesting causal candidate is WFIKKN2, also supported by a recent MR study using the same outcome data as here but different instruments ( 43 ). WFIKKN2 is a follistatin domain–containing protein that binds GDF8/GDF11 proteins with high affinity ( 46 )—both of which have been implicated in diabetes ( 47 , 48 ).…”

“…As this was the case even when we considered observational estimates for incident type 2 diabetes (thus, protein changes occurring before the onset of disease), these results may suggest that the genetic liability to type 2 diabetes, and the related physiological changes that may develop before overt disease, already have an effect on these proteins and that those effects may be greater than the effects of the proteins themselves on the disease. Others have furthermore suggested that an effect of a disease polygenic risk score on gene or protein levels may represent convergent genetic effects on important disease pathways ( 42 , 43 ). Further work is needed to establish the complex causal chain from individual proteins to convergent pathways, intermediate phenotypes, and overt type 2 diabetes, which may then in turn affect serum protein levels.…”

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

“…Polygenic scores based on disease variants have been alternatively used to identify disease-mediating candidate proteins. Ritchie et al [88] evaluated the influence of genetic predisposition to complex diseases on the plasma proteome, identifying 48 proteins whose levels were modulated by polygenic scores for coronary artery disease, chronic kidney disease, and T2D. A large proportion of pQTLs (Emilsson V. et al [83] report 60% of their discovery set) overlap with known disease-associated loci identified through GWAS, suggesting a common causal variant.…”

“…A large proportion of pQTLs (Emilsson V. et al [83] report 60% of their discovery set) overlap with known disease-associated loci identified through GWAS, suggesting a common causal variant. However, in most cases where pQTLs overlap with variants composing disease GRSs (or with variants in high LD), associations were largely driven by polygenic effects rather than by these overlapping single loci [88].…”

Electronic Health Record-Based Genome-Wide Meta-Analysis and Mendelian Randomization Identify Metabolic and Phenotypic Consequences of Non-Alcoholic Fatty Liver Disease

“…MR has also been used to determine whether a genetic susceptibility to certain chronic diseases in uences other biological traits such as the blood proteome or the blood metabolome. 23,24 Here, we performed a meta-analysis of electronic health record (EHR)-based genome-wide association studies (GWAS) to identify genetic variants robustly associated with NAFLD. We then used a MR study design to identify novel blood proteins/metabolites causally associated with NAFLD.…”