A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma
Fig 1
LOCUS model overview and study workflow.
(a) Inputs to LOCUS are an n × p design matrix X of p SNPs, and an n × q outcome matrix y of q molecular traits, e.g., gene, protein, lipid, metabolite or methylation levels, for n individuals. The model accounts for all the SNPs and molecular traits jointly. (b) Graphical model representation of LOCUS. The effect between a SNP s and a trait t is modelled by βst, and γst is a latent variable taking value unity if they are associated, and zero otherwise. The parameter ωs controls the pleiotropic level of each SNP, i.e., the number of traits with which it is associated. The parameter σ represents the typical size of effects, and the parameter τt is a precision parameter that relates to the residual variability of each trait t. (c) Outputs of LOCUS are posterior probabilities of associations, pr(γst = 1 ∣ y), for each SNP and each trait (p × q panel), and posterior means for the pleiotropy propensity of each SNP, E(ωs ∣ y) (Manhattan plot). (d) Workflow of the pQTL study. The MS and SomaLogic pQTL data are analyzed in parallel. LOCUS is applied on the Ottawa data for discovery, and 83% of the 18 and 118 pQTL associations discovered with the MS and SomaLogic data replicate in the independent study DiOGenes. The possible relevance of the validated pQTLs for disease endpoints is explored via analyses of clinical parameters from the Ottawa and DiOGenes cohorts. Further support is obtained by evaluating the overlap with eQTLs, epigenomic marks and GWAS risk loci.