Pathway analysis for genome-wide genetic variation data: Analytic principles, latest developments, and new opportunities

Pathway analysis, also known as gene-set enrichment analysis, is a multilocus analytic strategy that integrates a priori, biological knowledge into the statistical analysis of high-throughput genetics data. Originally developed for the studies of gene expression data, it has become a powerful analytic procedure for in-depth mining of genome-wide genetic variation data. Astonishing discoveries were made in the past years, uncovering genes and biological mechanisms underlying common and complex disorders. However, as massive amounts of diverse functional genomics data accrue, there is a pressing need for newer generations of pathway analysis methods that can utilize multiple layers of high-throughput genomics data. In this review, we provide an intellectual foundation of this powerful analytic strategy, as well as an update of the state-of-the-art in recent method developments. The goal of this review is threefold: (1) introduce the motivation and basic steps of pathway analysis for genome-wide genetic variation data; (2) review the merits and the shortcomings of classic and newly emerging integrative pathway analysis tools; and (3) discuss remaining challenges and future directions for further method developments. Copyright (C) 2021, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Limited and Science Press. All rights reserved.

Automated summary

Pathway analysis, also known as gene-set enrichment analysis, integrates biological knowledge into statistical analysis of high-throughput genetics data. It has led to discoveries of genes and biological mechanisms underlying common and complex disorders. Challenges include utilizing multiple layers of high-throughput genomics data and improving existing pathway analysis methods.