| Title: | Improved polygenic prediction by Bayesian multiple regression on summary statistics |
| Journal: | Nature Communications |
| Published: | 8 Nov 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31704910/ |
| DOI: | https://doi.org/10.1038/s41467-019-12653-0 |
| URL: | https://www.nature.com/articles/s41467-019-12653-0.pdf |
| Citations: | 371 (158 in last 2 years) as of 8 Aug 2024 |
Publication 3256
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Abstract
Accurate prediction of an individual's phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis (n ≈ 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.</p>
15 Authors
- Luke R. Lloyd-Jones
- Jian Zeng
- Julia Sidorenko
- Loïc Yengo
- Gerhard Moser
- Kathryn E. Kemper
- Huanwei Wang
- Zhili Zheng
- Reedik Magi
- Tõnu Esko
- Andres Metspalu
- Naomi R. Wray
- Michael E. Goddard
- Jian Yang
- Peter M. Visscher
Enabling scientific discoveries that improve human health