Views on GWAS statistical analysis

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dc.contributor.authorCao, X.
dc.contributor.authorXing, L.
dc.contributor.authorHe, H.
dc.contributor.authorZhang, Xuekui
dc.date.accessioned2021-08-18T18:16:28Z
dc.date.available2021-08-18T18:16:28Z
dc.date.copyright2020en_US
dc.date.issued2020
dc.description.abstractGenome-wide association study (GWAS) is a popular approach to investigate relationships between genetic information and diseases. A number of associations are tested in a study and the results are often corrected using multiple adjustment methods. It is observed that GWAS studies suffer adequate statistical power for reliability. Hence, we document known models for reliability assessment using improved statistical power in GWAS analysis.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThe authors acknowledge the Natural Sciences and Engineering Research Council Discovery Grants RGPIN-2017-04722 and the Canada Research Chair Grant 950-231363 (X.Z.)en_US
dc.identifier.citationCao, X., Xing, L., He, H., Zhang, X. (2020). Views on GWAS statistical analysis. Bioinformatics, 16(5). https://doi.org/10.6026/97320630016393en_US
dc.identifier.urihttps://doi.org/10.6026/97320630016393
dc.identifier.urihttp://hdl.handle.net/1828/13273
dc.language.isoenen_US
dc.publisherBioinformationen_US
dc.subjectgenome-wide assocation studies
dc.subjectsingle nucleotide polymorphisms
dc.subjectstatistical power
dc.subjectmultiple testing adjustment
dc.subjectlinkage disequilibrium
dc.subjectsupervised learning
dc.subjectunsupervised learning
dc.subject.departmentDepartment of Mathematics and Statistics
dc.titleViews on GWAS statistical analysisen_US
dc.typeArticleen_US

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