A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies

TL;DR

This paper introduces a new efficient multivariate algorithm based on CAR scores for simultaneous SNP selection in high-dimensional genome-wide association studies, outperforming existing methods in identifying causal variants.

Contribution

The paper presents a novel computationally efficient procedure for shrinkage estimation of CAR scores, improving SNP selection accuracy in high-dimensional GWAS.

Findings

CAR score-based algorithm outperforms competing methods in causal SNP detection

The approach demonstrates higher accuracy in SNP ranking and recovery

An R package implementation is provided for practical use

Abstract

Background: Identification of causal SNPs in most genome wide association studies relies on approaches that consider each SNP individually. However, there is a strong correlation structure among SNPs that need to be taken into account. Hence, increasingly modern computationally expensive regression methods are employed for SNP selection that consider all markers simultaneously and thus incorporate dependencies among SNPs. Results: We develop a novel multivariate algorithm for large scale SNP selection using CAR score regression, a promising new approach for prioritizing biomarkers. Specifically, we propose a computationally efficient procedure for shrinkage estimation of CAR scores from high-dimensional data. Subsequently, we conduct a comprehensive comparison study including five advanced regression approaches (boosting, lasso, NEG, MCP, and CAR score) and a univariate approach (marginal correlation) to determine the effectiveness in finding true causal SNPs. Conclusions: Simultaneous SNP selection is a challenging task. We demonstrate that our CAR score-based algorithm consistently outperforms all competing approaches, both uni- and multivariate, in terms of correctly recovered causal SNPs and SNP ranking. An R package implementing the approach as well as R code to reproduce the complete study presented here is available from http://strimmerlab.org/software/care/ .