Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data

Abstract

With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development.

Keywords: biobank data analysis; electronic health records-EHR; genetic relatedness; mixed model approaches; phenome-wide association studies; saddlepoint approximation; unbalanced phenotypic distribution.

FIGURE 1

PheWAS computation time. The computation time is evaluated at CPU core of Intel i7-7700T 2.90GHz and then projected to a phenome-wide association studies including 100 balanced binary phenotypes and 10 million variants.

FIGURE 2

Flowchart of score test association analysis.