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dc.contributor.author
Gumpinger, Anja C.
dc.contributor.author
Rieck, Bastian Alexander
dc.contributor.author
International Headache Genetics Consortium
dc.contributor.author
Borgwardt, Karsten
dc.date.accessioned
2021-04-12T06:39:45Z
dc.date.available
2020-07-03T19:29:48Z
dc.date.available
2020-07-06T14:30:51Z
dc.date.available
2021-04-12T06:39:45Z
dc.date.issued
2021-01-01
dc.identifier.issn
1367-4803
dc.identifier.issn
1460-2059
dc.identifier.other
10.1093/bioinformatics/btaa581
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/424355
dc.identifier.doi
10.3929/ethz-b-000424355
dc.description.abstract
Motivation Correlating genetic loci with a disease phenotype is a common approach to improve our understanding of the genetics underlying complex diseases. Standard analyses mostly ignore two aspects, namely genetic heterogeneity and interactions between loci. Genetic heterogeneity, the phenomenon that different genetic markers lead to the same phenotype, promises to increase statistical power by aggregating low-signal variants. Incorporating interactions between loci results in a computational and statistical bottleneck due to the vast amount of candidate interactions. Results We propose a novel method SiNIMin that addresses these two aspects by finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple A. thaliana phenotypes, and for a study of rare variants in migraine patients. Availability Code available at https://github.com/BorgwardtLab/SiNIMin.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Oxford University Press
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Network-guided search for genetic heterogeneity between gene pairs
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-06-23
ethz.journal.title
Bioinformatics
ethz.journal.volume
37
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Bioinformatics
ethz.pages.start
57
en_US
ethz.pages.end
65
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Significant Pattern Mining
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Oxford
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09486 - Borgwardt, Karsten M. / Borgwardt, Karsten M.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09486 - Borgwardt, Karsten M. / Borgwardt, Karsten M.
ethz.grant.agreementno
155913
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
SNSF Starting Grants
ethz.date.deposited
2020-07-03T19:30:03Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-04-12T06:39:54Z
ethz.rosetta.lastUpdated
2021-04-12T06:39:54Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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