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Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks
dc.contributor.authorZigarelli, Angela M.
dc.contributor.authorVenera, Hanna M.
dc.contributor.authorReceveur, Brody A.
dc.contributor.authorWolf, Jack M.
dc.contributor.authorWestra, Jason
dc.contributor.authorTintle, Nathan L.
dc.date.accessioned2023-05-01T19:10:05Z
dc.date.available2024-06-01 15:10:04en
dc.date.available2023-05-01T19:10:05Z
dc.date.issued2023-05
dc.identifier.citationZigarelli, Angela M.; Venera, Hanna M.; Receveur, Brody A.; Wolf, Jack M.; Westra, Jason; Tintle, Nathan L. (2023). "Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks." Annals of Human Genetics 87(3): 125-136.
dc.identifier.issn0003-4800
dc.identifier.issn1469-1809
dc.identifier.urihttps://hdl.handle.net/2027.42/176253
dc.description.abstractAs biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.
dc.publisherSpringer
dc.publisherWiley Periodicals, Inc.
dc.subject.otherstatistical
dc.subject.othergenetic data banks
dc.subject.othergenetic markers
dc.subject.othergenetic privacy
dc.subject.othergenotype–phenotype associations
dc.titleMultimarker omnibus tests by leveraging individual marker summary statistics from large biobanks
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelGenetics
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/176253/1/ahg12495.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/176253/2/ahg12495_am.pdf
dc.identifier.doi10.1111/ahg.12495
dc.identifier.sourceAnnals of Human Genetics
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dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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