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Combining controls can improve power in two-stage association studies.

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cam.issuedOnline2018-10-03
dc.contributor.authorLiley, James
dc.contributor.orcidLiley, James [0000-0002-0049-8238]
dc.date.accessioned2019-01-12T00:32:04Z
dc.date.available2019-01-12T00:32:04Z
dc.date.issued2018-10-03
dc.description.abstractBACKGROUND: High dimensional case control studies are ubiquitous in the biological sciences, particularly genomics. To maximise power while constraining cost and to minimise type-1 error rates, researchers typically seek to replicate findings in a second experiment on independent cohorts before proceeding with further analyses. This can be an expensive procedure, particularly when control samples are difficult to recruit or ascertain; for example in inter-disease comparisons, or studies on degenerative diseases. RESULTS: This paper presents a method in which control (or case) samples from the discovery cohort are re-used in a replication study. The theoretical implications of this method are discussed and simulated genome-wide association study (GWAS) tests are used to compare performance against the standard approach in a range of circumstances. Using similar methods, a procedure is proposed for 'partial replication' using a new independent cohort consisting of only controls. This methods can be used to provide some validation of findings when a full replication procedure is not possible. The new method has differing sensitivity to confounding in study cohorts compared to the standard procedure, which must be considered in its application. Type-1 error rates in these scenarios are analytically and empirically derived, and an online tool for comparing power and error rates is provided. CONCLUSIONS: In several common study designs, a shared-control method allows a substantial improvement in power while retaining type-1 error rate control. Although careful consideration must be made of all necessary assumptions, this method can enable more efficient use of data in GWAS and other applications.
dc.description.sponsorshipThis work was mostly performed while JL was funded by the NIHR Cambridge Biomedical Research Centre and on the Wellcome Trust PhD programme in Mathematical Genomics and Medicine at the University of Cambridge. During its completion, JL was funded by the Wellcome Trust (107881). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.identifier.doi10.17863/CAM.35234
dc.identifier.eissn1471-2156
dc.identifier.issn1471-2156
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/287921
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.publisher.urlhttp://dx.doi.org/10.1186/s12863-018-0675-y
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCase-control study
dc.subjectGWAS
dc.subjectReplication
dc.subjectCase-Control Studies
dc.subjectComputer Simulation
dc.subjectGenome-Wide Association Study
dc.subjectHumans
dc.subjectModels, Genetic
dc.subjectModels, Statistical
dc.subjectPolymorphism, Single Nucleotide
dc.titleCombining controls can improve power in two-stage association studies.
dc.typeArticle
dcterms.dateAccepted2018-09-16
prism.publicationNameBMC Genet
pubs.funder-project-idWellcome Trust (107881/Z/15/Z)
rioxxterms.licenseref.startdate2018-09-13
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1186/s12863-018-0675-y

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