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

作　　者：Micah Silberstein Nicholas Nesbit Jacquelyn Cai Phil H.Lee 

机构地区：[1]Center for Genomic Medicine,Massachusetts General Hospital,Boston,MA 02114,USA [2]Department of Psychiatry,Harvard Medical School,Boston,MA 02115,USA [3]Stanley Center for Psychiatric Research,Broad Institute of MIT and Harvard,Cambridge,MA 02142,USA

出　　处：《Journal of Genetics and Genomics》2021年第3期173-183,共11页遗传学报（英文版）

基　　金：supported by National Institutes of Health R00 MH101367 and R01 MH119243(to P.H.Lee)。

摘　　要：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.

关 键 词：Pathway analysis Set-based association analysis Gene-set enrichment analysis Genome-wide association study Multilocus association analysis 

分 类 号：Q811.4[生物学—生物工程]