| Title: | Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits |
| Journal: | Nature Communications |
| Published: | 30 Nov 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34848700/ |
| DOI: | https://doi.org/10.1038/s41467-021-27258-9 |
| URL: | https://www.nature.com/articles/s41467-021-27258-9.pdf |
| Citations: | 22 (11 in last 2 years) as of 8 Aug 2024 |
Publication 10747
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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.</p>
13 Authors
- Marion Patxot
- Daniel Trejo Banos
- Athanasios Kousathanas
- Etienne J. Orliac
- Sven E. Ojavee
- Gerhard Moser
- Alexander Holloway
- Julia Sidorenko
- Zoltan Kutalik
- Reedik Mägi
- Peter M. Visscher
- Lars Rönnegård
- Matthew R. Robinson
1 Application
| Application ID | Title |
|---|---|
| 35520 | Improving estimation and prediction of common complex disease risk |
Enabling scientific discoveries that improve human health