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

We develop a Bayesian model (BayesRR-RC) that provides robust
SNP-heritability estimation, an alternative to marker discovery, and
accurate genomic prediction, taking 22 seconds per iteration to estimate
8.4 million SNP-effects and 78 SNP-heritability parameters in the UK
Biobank. We find that only $\leq$ 10\% of the genetic variation captured
for height, body mass index, cardiovascular disease, and type 2 diabetes
is attributable to proximal regulatory regions within 10kb upstream of
genes, while 12-25% is attributed to coding regions, 32-44% to introns,
and 22-28% to distal 10-500kb upstream regions. 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.
Our open-source software (GMRM) provides a scalable alternative to current
approaches for biobank data.