Constructing Genetic Risk Scores: Robust Bayesian Approach through Projected Summary Statistics and Flexible Shrinkage

• 成果类型: Article; Early Access
• 署名作者: Dun, Yuzheng; Chatterjee, Nilanjan; Jin, Jin; Nishimura, Akihiko
• 署名单位: Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Medicine; University of Pennsylvania; Pennsylvania Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2663587
• 发表日期: 2026-06-27
• 关键词: Conjugate gradient sampler; High-dimensional Regression; polygenic score; Shrinkage prior; Statistical genetics; breast-cancer; large-scale; regression; prediction; disease; susceptibility

摘要

Polygenic risk scores (PRS) developed from genome-wide association studies (GWAS) can be used for risk stratification by quantifying the genetic contribution to disease, and many clinical applications have been proposed. Bayesian methods are popular for building PRS because of their natural ability to regularize models and incorporate external information. In this article, we present new theoretical results, methods, and extensive numerical studies to advance Bayesian methods for PRS applications. We identify a potential risk, under a common Bayesian PRS framework, of posterior impropriety when integrating the required GWAS summary statistics and linkage disequilibrium (LD) data from distinct sources. As a principled remedy, we propose a projection of the summary statistics that ensures compatibility between the two sources and in turn a proper behavior of the posterior. We further introduce a new PRS method, with accompanying software, under the less-explored Bayesian bridge prior to more flexibly model varying sparsity levels in effect-size distributions. We extensively benchmark it against alternative Bayesian methods using synthetic and real datasets, quantifying the impact of prior specification and LD estimation strategy. Our proposed PRS-Bridge, equipped with the projection technique and flexible prior, demonstrates the most consistent and generally superior performance across a variety of scenarios. for this article are available online, including a standardized description of the materials available for reproducing the work.