A Robust Bayesian Method for Building Polygenic Risk Scores using Projected Summary Statistics and Bridge Prior
arxiv(2024)
摘要
Polygenic Risk Scores (PRS) developed from genome-wide association studies
(GWAS) are of increasing interest for various clinical and research
applications. Bayesian methods have been particularly popular for building PRS
in genome-wide scale because of their natural ability to regularize model and
borrow information in high-dimension. In this article, we present new
theoretical results, methods, and extensive numerical studies to advance
Bayesian methods for PRS applications. We conduct theoretical studies to
identify causes of convergence issues of some Bayesian methods when required
input GWAS summary-statistics and linkage disequilibrium (LD) (genetic
correlation) data are derived from distinct samples. We propose a remedy to the
problem by the projection of the summary-statistics data into the column space
of the genetic correlation matrix. We further implement a PRS development
algorithm under the Bayesian Bridge prior which can allow more flexible
specification of effect-size distribution than those allowed under popular
alternative methods. Finally, we conduct careful benchmarking studies of
alternative Bayesian methods using both simulation studies and real datasets,
where we carefully investigate both the effect of prior specification and
estimation strategies for LD parameters. These studies show that the proposed
algorithm, equipped with the projection approach, the flexible prior
specification, and an efficient numerical algorithm leads to the development of
the most robust PRS across a wide variety of scenarios.
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