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

Citation

Robinson, Matthew (2021), Probabilistic inference of the genetic architecture of functional enrichment of complex traits, Dryad, Dataset, https://doi.org/10.5061/dryad.sqv9s4n51

Abstract

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.

Methods

Posterior estimates from a Bayesian mixture of regression model for height, body mass index, cardiovascular disease and type-2 diabetes as estimated in the UK Biobank.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Award: PCEGP3-181181