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 indepth 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.