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.

Spatiotemporal and genetic regulation of A-to-I editing throughout human brain development

“…The most significant colocalization occurred between a variant for sleeping disorders and an editing site in the first non-coding exon of C16orf72 , previously linked to cognition, learning, and sleeping difficulties. 74 , 75 This editing site occurs in a non-coding regulatory region that has been shown to modulate mRNA stability and protein abundance. 76 To further dissect such mechanisms and their implications for brain maturation and disease risk, it will be critical for future work to greatly increase sample sizes.…”