1. Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits.
- Author
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Patxot, Marion, Banos, Daniel Trejo, Kousathanas, Athanasios, Orliac, Etienne J., Ojavee, Sven E., Moser, Gerhard, Holloway, Alexander, Sidorenko, Julia, Kutalik, Zoltan, Mägi, Reedik, Visscher, Peter M., Rönnegård, Lars, and Robinson, Matthew R.
- Subjects
GENETIC variation ,ELECTRONIC health records ,CARDIOVASCULAR diseases ,BODY mass index - 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 ≤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. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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