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

Improving inference in large-scale genetic data linked to electronic medical record data requires the development of novel computationally efficient regression methods. Here, the authors develop a Bayesian approach for association analyses to improve SNP-heritability estimation, discovery, fine-mapping and genomic prediction. 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 <= 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.

Automated summary

The authors developed a novel Bayesian approach for association analysis in large-scale genetic data, improving SNP-heritability estimation, discovery, fine-mapping, and genomic prediction. They found that genetic variation for height, body mass index, cardiovascular disease, and type 2 diabetes is mainly attributed to coding regions, introns, and distal upstream regions, highlighting new insights into genetic factors associated with these traits.