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Improved polygenic prediction by Bayesian multiple regression on summary statistics
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DOI：10.1038/s41467-019-12653-0.png)
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摘要
En 中文
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.
Keyword:
GENOME-WIDE ASSOCIATION
MODELING LINKAGE DISEQUILIBRIUM
COMPLEX TRAITS
MIXED-MODEL
MISSING HERITABILITY
RISK
VARIANTS
IMPUTATION
ACCURACY
POPULATION
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期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
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引用论文
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