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Pathway
analysis
for genome-wide genetic variation data: Analytic
principles
, latest developments, and new opportunities
Silberstein, Micah ; Nesbit, Nicholas ; Cai, Jacquelyn ; Lee, Phil H.
Journal of
genetics
and genomics, 2021-03, Vol.48 (3), p.173-183
[同儕審閱期刊]
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題名:
Pathway
analysis
for genome-wide genetic variation data: Analytic
principles
, latest developments, and new opportunities
著者:
Silberstein, Micah
;
Nesbit, Nicholas
;
Cai, Jacquelyn
;
Lee, Phil H.
主題:
Algorithms
;
Gene-set enrichment
analysis
;
Genetic Predisposition to Disease
;
Genome-Wide Association Study
;
Humans
;
Multilocus association
analysis
;
Pathway
analysis
;
Set-based association
analysis
所屬期刊:
Journal of
genetics
and genomics, 2021-03, Vol.48 (3), p.173-183
描述:
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.
出版者:
China: Elsevier Ltd
語言:
英文
識別號:
ISSN: 1673-8527
DOI: 10.1016/j.jgg.2021.01.007
PMID: 33896739
資源來源:
Elsevier ScienceDirect Journals Complete
連結
View this record in MEDLINE/PubMed
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