| Title: | Fine mapping and accurate prediction of complex traits using Bayesian Variable Selection models applied to biobank-size data |
| Journal: | European Journal of Human Genetics |
| Published: | 19 Jul 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35853950/ |
| DOI: | https://doi.org/10.1038/s41431-022-01135-5 |
| URL: | https://www.nature.com/articles/s41431-022-01135-5.pdf |
Publication 10362
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Abstract
Modern GWAS studies use an enormous sample size and ultra-high density SNP genotypes. These conditions reduce the mapping resolution of marginal association tests-the method most often used in GWAS. Multi-locus Bayesian Variable Selection (BVS) offers a one-stop solution for powerful and precise mapping of risk variants and polygenic risk score (PRS) prediction. We show (with an extensive simulation) that multi-locus BVS methods can achieve high power with a low false discovery rate and a much better mapping resolution than marginal association tests. We demonstrate the performance of BVS for mapping and PRS prediction using data from blood biomarkers from the UK-Biobank (~300,000 samples and ~5.5 million SNPs). The article is accompanied by open-source R-software that implement the methods used in the study and scales to biobank-sized data.</p>
5 Authors
- Gustavo de los Campos
- Alexander Grueneberg
- Scott Funkhouser
- Paulino Pérez-Rodríguez
- Anirban Samaddar
1 Application
| Application ID | Title |
|---|---|
| 15326 | Compressed Sensing and high-dimensional statistical methods in complex trait genomics |
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