Cross-Trait Prediction Accuracy of High-Dimensional Ridge-Type Estimators in Genome-Wide Association Studies

• Activity Number: 176 - Statistical Genetics III – Predictive Modeling, GxE Interaction, and Causal Inference
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Bingxin Zhao* and Hongtu Zhu
• Companies: University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill
• Keywords: GWAS; Summary statistics; Marginal screening; Ridge estimator; Best linear unbiased prediction (BLUP); Prediction accuracy

Abstract

In many fields of genetics and genomics, univariate model serves as a workhorse tool to screen massive features, while many of them could be signals. Genome-wide association studies (GWAS) epitomize this type of application: mass-univariate models are fitted separately on millions of genetic variants, and a large number of them have small but nonzero contributions to human complex traits. We study the cross-trait prediction accuracy of marginal estimator in this situation. We model GWAS in a general dense high-dimensional framework and compare marginal estimator to a class of ridge-type conditional estimators, including the popular best linear unbiased prediction (BLUP) in genetics. We show that the relative out-of-sample performance of these estimators highly depends on the dimension/sample size ratio, and reveal that marginal estimator can easily become near-optimal within this class as dimension increases, even though it is an extremely over-regularized special case. In practice, our analysis delivers useful messages for genome-wide polygenic risk prediction and the computational cost and accuracy tradeoff in dense high-dimensions.