Polygenic prediction via Bayesian regression and continuous shrinkage priors
By Tian Ge, Chia-Yen Chen, Yang Ni, Y. Feng, Jordan W. Smoller. Nature Communications, 2018
1,656 citations recorded by the source corpus.
Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods. Polygenic risk scores (PRS) have the potential to predict complex diseases and traits from genetic data. Here, Ge et al. develop PRS-CS which uses a Bayesian regression framework, continuous shrinkage (CS) priors and an external LD reference panel for polygenic prediction of binary and quantitative traits from GWAS summary statistics.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/cbe7de4b539d450440269c8328eb938588f6229a
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In Genetics
- Gonçalo R. Abecasis
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Citations in the corpus
- LDpred2: better, faster, stronger (2020, neutral): “2019) and likely contributed to discrepancies in reported prediction accuracies (Choi and O’Reilly 2019; Ge et al. 2019).”
- The power of genetic diversity in genome-wide association studies of lipids (2021, methodology): “For each ancestry-specific or multi-ancestry GWAS, we created multiple PRS weights, either genome-wide with PRS-CS 33 or using pruning and thresholding to select independent variants.”
- PRSice-2: Polygenic Risk Score software for biobank-scale data (2019, neutral): “Consistent with previous findings [15, 24, 25], PRSice-2 has comparable predictive power to lassosum and LDpred, typically generating PRSs with predictive power higher than those of LDpred but not as high as lassosum.”