Nicholas Nesbit
Jacquelyn Cai
Phil H. Lee
aCenter for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
bDepartment of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
cStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
More InformationCorresponding author: E-mail address: PLEE0@mgh.harvard.edu (Phil H. Lee)
Received Date: 2020-11-04
Accepted Date:2021-01-25
Rev Recd Date:2021-01-24
Available Online: 2021-02-26 Publish Date:2021-03-20
Abstract
Abstract
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.Keywords: Pathway analysis,
Set-based association analysis,
Gene-set enrichment analysis,
Genome-wide association study,
Multilocus association analysis
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