Embracing study heterogeneity for finding genetic interactions in large-scale research consortia.
genetic interaction
heterogeneity in marginal effects
meta-analysis
privacy-preserving algorithm
two-stage testing procedure
Journal
Genetic epidemiology
ISSN: 1098-2272
Titre abrégé: Genet Epidemiol
Pays: United States
ID NLM: 8411723
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
13
08
2018
revised:
02
08
2019
accepted:
09
08
2019
pubmed:
5
10
2019
medline:
31
3
2020
entrez:
5
10
2019
Statut:
ppublish
Résumé
Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.
Identifiants
pubmed: 31583758
doi: 10.1002/gepi.22262
pmc: PMC6980207
mid: NIHMS1048840
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
52-66Subventions
Organisme : NIEHS NIH HHS
ID : P30 ES013508
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI130460
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM010098
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI116794
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012607
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG005859
Pays : United States
Informations de copyright
© 2019 Wiley Periodicals, Inc.
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