Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors

Ge, Tian; Chen, Chia‐Yen; Ni, Yang; Feng, Yen‐Chen Anne; Smoller, Jordan W.

doi:10.1101/416859

Cited by 280 publications

(496 citation statements)

References 64 publications

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“…RSS requires MATLAB and therefore is less accessible to users. PRS‐CS (Ge, Chen, Ni, Feng, & Smoller, ) improves computational efficiency of Bayesian regression by using a continuous shrinkage prior distribution on marker effect sizes. The user must specify a global shrinkage parameter, ϕ, that reflects the proportion of causal variants, but the program can estimate ϕ from GWAS results.…”

Section: Polygenic Risk Scoresmentioning

confidence: 99%

Genetic Risk Scores

2019

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Genome‐wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well‐established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome‐wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.

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Section: Polygenic Risk Scoresmentioning

confidence: 99%

Genetic Risk Scores

2019

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“…Recently, a novel PRS method that is based upon millions of SNPs in the genome, called genome‐wide polygenic score (GPS), was proposed . Leveraging summary association statistics from previously published GWAS, linkage disequilibrium (LD) information in public data set, improved algorithm and large studies, GPS has been successfully applied to many common diseases .…”

Section: Introductionmentioning

confidence: 99%

Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

Shi

Lin

et al. 2019

The Prostate

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Background Several polygenic risk score (PRS) methods are available for measuring the cumulative effect of multiple risk‐associated single nucleotide polymorphisms (SNPs). Their performance in predicting risk at the individual level has not been well studied. Methods We compared the performance of three PRS methods for prostate cancer risk assessment in a clinical trial cohort, including genetic risk score (GRS), pruning and thresholding (P + T), and linkage disequilibrium prediction (LDpred). Performance was evaluated for score deciles (broad‐sense validity) and score values (narrow‐sense validity). Results A training process was required to identify the best P + T model (397 SNPs) and LDpred model (3 011 362 SNPs). In contrast, GRS was directly calculated based on 110 established risk‐associated SNPs. For broad‐sense validity in the testing population, higher deciles were significantly associated with higher observed risk; Ptrend was 7.40 × 10−11, 7.64 × 10−13, and 7.51 × 10−10 for GRS, P + T, and LDpred, respectively. For narrow‐sense validity, the calibration slope (1 is best) was 1.03, 0.77, and 0.87, and mean bias score (0 is best) was 0.09, 0.21, and 0.10 for GRS, P + T, and LDpred, respectively. Conclusions The performance of GRS was better than P + T and LDpred. Fewer and well‐established SNPs of GRS also make it more feasible and interpretable for genetic testing at the individual level.

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“…The BSLMM is capable of learning the underlying mediation architecture from the data, producing good performances across a wide range of scenarios. Our model assumptions are also akin to the notion of quasi‐sparsity that has become popular with continuous shrinkage priors (Ge et al ., ). Specifically, we assume a mixture of two normal components a priori for the j th mediator,

j = 1, 2, \dots p

\begin{matrix} true \\ right {(β_{m})}_{j} & \sim & left π_{m} N (0, σ_{m 1}^{2}) + (1 - π_{m}) N (0, σ_{m 0}^{2}) \\ right {(α_{a})}_{j} & \sim & left π_{a} N (0, σ_{m a 1}^{2}) + (1 - π_{a}) N (0, σ_{m a 0}^{2}), \end{matrix}

…”

Section: Bayesian Methods For Estimationmentioning

confidence: 97%

Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies

et al. 2019

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Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high‐throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of ‐omics data, joint analysis of molecular‐level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high‐dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high‐dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi‐Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.

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Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors

Cited by 280 publications

References 64 publications

Genetic Risk Scores

Genetic Risk Scores

Broad‐ and narrow‐sense validity performance of three polygenic risk score methods for prostate cancer risk assessment

Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies

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