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dc.contributor.authorZhang, Han
dc.contributor.authorShi, Jianxin
dc.contributor.authorLiang, Faming
dc.contributor.authorWheeler, William
dc.contributor.authorStolzenberg-Solomon, Rachael
dc.contributor.authorYu, Kai
dc.date.accessioned2016-02-25T12:29:22Z
dc.date.available2016-02-25T12:29:22Z
dc.date.issued2013-09-11
dc.identifier.citationZhang H, Shi J, Liang F, Wheeler W, Stolzenberg-Solomon R, et al. (2013) A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies. European Journal of Human Genetics 22: 696–702. Available: http://dx.doi.org/10.1038/ejhg.2013.201.
dc.identifier.issn1018-4813
dc.identifier.issn1476-5438
dc.identifier.pmid24022295
dc.identifier.doi10.1038/ejhg.2013.201
dc.identifier.urihttp://hdl.handle.net/10754/597265
dc.description.abstractAs increasing evidence suggests that multiple correlated genetic variants could jointly influence the outcome, a multilocus test that aggregates association evidence across multiple genetic markers in a considered gene or a genomic region may be more powerful than a single-marker test for detecting susceptibility loci. We propose a multilocus test, AdaJoint, which adopts a variable selection procedure to identify a subset of genetic markers that jointly show the strongest association signal, and defines the test statistic based on the selected genetic markers. The P-value from the AdaJoint test is evaluated by a computationally efficient algorithm that effectively adjusts for multiple-comparison, and is hundreds of times faster than the standard permutation method. Simulation studies demonstrate that AdaJoint has the most robust performance among several commonly used multilocus tests. We perform multilocus analysis of over 26,000 genes/regions on two genome-wide association studies of pancreatic cancer. Compared with its competitors, AdaJoint identifies a much stronger association between the gene CLPTM1L and pancreatic cancer risk (6.0 × 10(-8)), with the signal optimally captured by two correlated single-nucleotide polymorphisms (SNPs). Finally, we show AdaJoint as a powerful tool for mapping cis-regulating methylation quantitative trait loci on normal breast tissues, and find many CpG sites whose methylation levels are jointly regulated by multiple SNPs nearby.
dc.description.sponsorshipWe thank three anonymous referees for their helpful comments. This study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD. (http://biowulf.nih.gov). The work of H Zhang, J Shi, R Stolzenberg-Solomon and K Yu were supported by the Intramural Program of the National Institutes of Health and the National Cancer Institute. The work of F Liang was supported in part by the National Science Foundation (DMS-0607755, CMMI-0926803); and the award (KUS-C1-016-04) made by the King Abdullah University of Science and Technology.
dc.publisherSpringer Nature
dc.subjectcis-regulating meQTLs mapping
dc.subjectgenome-wide association study
dc.subjectmultilocus test
dc.subjectmultiple comparisons
dc.subjectpathway analysis
dc.subjectvariable selection
dc.subject.meshPolymorphism, Single Nucleotide
dc.subject.meshQuantitative Trait Loci
dc.subject.meshGenome-Wide Association Study
dc.subject.meshGenetic Association Studies
dc.titleA fast multilocus test with adaptive SNP selection for large-scale genetic-association studies
dc.typeArticle
dc.identifier.journalEuropean Journal of Human Genetics
dc.identifier.pmcidPMC3992564
dc.contributor.institutionBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX, USA and.
dc.contributor.institutionInformation Management Services, Inc., Silver Spring, MD, USA.
kaust.grant.numberKUS-C1-016-04


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