Journal
NATURE COMMUNICATIONS
Volume 10, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-019-12653-0
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Funding
- Australian Research Council [DP160102400, FT180100186]
- Australian National Health and Medical Research Council [1113400, 1078037, 1078901, 1080157]
- National Institute of Health [R21 ES025052, R01MH100141, R01 AG042568]
- Sylvia & Charles Viertel Charitable Foundation
- CQU
- National Health and Medical Research Council of Australia [1080157] Funding Source: NHMRC
- NATIONAL INSTITUTE ON AGING [R01AG042568] Funding Source: NIH RePORTER
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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 approximate to 700, 000) on height and BMI, we show that on average across traits and two independent data sets that SBayesR improves prediction R-2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding.
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