Ensembled best subset selection using summary statistics for polygenic risk prediction

Abstract

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L0L2penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

“…L0Learn [21] , [25] , [27] has gained widespread recognition in bioinformatics research, with successful applications in haplotype reconstruction [47] , [48] , polygenic score (PGS) [49] , [50] calculation, and significant signal selection [51] , et. al.…”

Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices

“…The dominance of European populations persists in existing GWAS [50][51][52][53] , not limited to lung cancer and smoking cessation. However, with the burgeoning emphasis on incorporating minority populations in GWAS analyses and the development of new PRS approaches and toolkits to enhance predictive power in diverse populations [7][8][9]34,[54][55][56] , we can iteratively refine PRS implementation in our trial to synchronize with the latest advancements.…”

“…To address this gap, polygenic risk scores (PRSs) emerge as a valuable approach to assess disease susceptibility among populations and pinpoint individuals at higher risk [2][3][4] . PRSs can be derived from large-scale genome-wide association studies (GWAS) to estimate individual disease risk and have shown promise in predicting health outcomes and promoting preventive healthcare [5][6][7][8][9] . Despite their potential, PRSs' implementation in primary care is limited, especially in diverse populations.…”