Improved understanding of the proteome can facilitate the identification of causal mechanisms for complex traits. We conducted a comprehensive analysis of the common variant cis-regulatory genetic architecture of 4,665 plasma proteins from 7,213 European Americans (EA) and 1,871 African Americans (AA) from the Atherosclerosis Risk in Communities (ARIC) cohort study. We identified and fine-mapped 1,992 plasma proteins in EA and 1,605 in AA, which had significant cis-single-nucleotide polymorphism (SNP) associations. Estimates of cis-heritability (cis-h 2 ) for plasma proteins were similar across EA and AA (median cis-h 2 =0.09 for EA and 0.10 for AA).Elastic-net-based models for cis-SNP-based protein prediction produced high accuracy (median R 2 /cis-h 2 =0.79 for EA and 0.69 for AA). We illustrate the application of these models to conduct proteome-wide association studies (PWAS) for two related complex traits, serum urate and gout, and further conduct conditional analyses to interpret findings in the context of those from transcriptome-wide association studies (TWAS).

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait‐associated variants within genes or regions of interest. Existing multiphenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here, we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi‐SKAT). Multi‐SKAT models affect sizes of variants on the phenotypes through a kernel matrix and perform a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi‐SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed P value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome‐array data from the METabolic Syndrome In Men (METSIM) study, we show that Multi‐SKAT can improve power over single‐phenotype SKAT‐O test and existing multiple‐phenotype tests, while maintaining Type I error rate.

Heart failure is a major public health problem affecting over 23 million people worldwide. In this study, we present the results of a large scale meta-analysis of heart failure GWAS and replication in a comparable sized cohort to identify one known and two novel loci associated with heart failure. Heart failure sub-phenotyping shows that a new locus in chromosome 1 is associated with left ventricular adverse remodeling and clinical heart failure, in response to different initial cardiac muscle insults. Functional characterization and fine-mapping of that locus reveal a putative causal variant in a cardiac muscle specific regulatory region activated during cardiomyocyte differentiation that binds to the ACTN2 gene, a crucial structural protein inside the cardiac sarcolemma (Hi-C interaction p-value = 0.00002). Genomeediting in human embryonic stem cell-derived cardiomyocytes confirms the influence of the identified regulatory region in the expression of ACTN2. Our findings extend our understanding of biological mechanisms underlying heart failure.

BackgroundProteomic profiling may allow identification of plasma proteins that associate with subsequent changesin kidney function, elucidating biologic processes underlying the development and progression of CKD.MethodsWe quantified the association between 4877 plasma proteins and a composite outcome of ESKD or decline in eGFR by ≥50% among 9406 participants in the Atherosclerosis Risk in Communities (ARIC) Study (visit 3; mean age, 60 years) who were followed for a median of 14.4 years. We performed separate analyses for these proteins in a subset of 4378 participants (visit 5), who were followed at a later time point, for a median of 4.4 years. For validation, we evaluated proteins with significant associations (false discovery rate <5%) in both time periods in 3249 participants in the Chronic Renal Insufficiency Cohort (CRIC) and 703 participants in the African American Study of Kidney Disease and Hypertension (AASK). We also compared the genetic determinants of protein levels with those from a meta-analysis genome-wide association study of eGFR.ResultsIn models adjusted for multiple covariates, including baseline eGFR and albuminuria, we identified 13 distinct proteins that were significantly associated with the composite end point in both time periods, including TNF receptor superfamily members 1A and 1B, trefoil factor 3, and β-trace protein. Of these proteins, 12 were also significantly associated in CRIC, and nine were significantly associated in AASK. Higher levels of each protein associated with higher risk of 50% eGFR decline or ESKD. We found genetic evidence for a causal role for one protein, lectin mannose-binding 2 protein (LMAN2).ConclusionsLarge-scale proteomic analysis identified both known and novel proteomic risk factors for eGFR decline.