Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits

Due to the complexity of linkage disequilibrium (LD) and gene regulation, understanding the genetic basis of common complex traits remains a major challenge. We develop a Bayesian model (BayesRR-RC) implemented in a hybrid-parallel algorithm that scales to whole-genome sequence data on many hundreds of thousands of individuals, taking 22 seconds per iteration to estimate the inclusion probabilities and effect sizes of 8.4 million markers and 78 SNP-heritability parameters in the UK Biobank. We show in theory and simulation that BayesRR-RC provides robust variance component and enrichment estimates, improved marker discovery and effect estimates over mixed-linear model association approaches, and accurate genomic prediction. Of the genetic variation captured for height, body mass index, cardiovascular disease, and type-2 diabetes in the UK Biobank, only [≤] 10% is attributable to proximal regulatory regions within 10kb upstream of genes, while 12-25% is attributed to coding regions, 32-44% to intronic regions, and 22-28% to distal 10-500kb upstream regions. [≥] 60% of the variance contributed by these exonic, intronic and distal 10-500kb regions is underlain by many thousands of common variants, which on average have larger effect sizes than for other annotation groups. Up to 24% of all cis and coding regions of each chromosome are associated with each trait, with over 3,100 independent exonic and intronic regions and over 5,400 independent regulatory regions having [≥] 95% probability of contributing [≥] 0.001% to the genetic variance of these four traits. Thus, these quantitative and disease traits are truly complex. The BayesRR-RC prior gives robust model performance across the data analysed, providing an alternative to current approaches.