A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

Handle URI:
http://hdl.handle.net/10754/597265
Title:
A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies
Authors:
Zhang, Han; Shi, Jianxin; Liang, Faming; Wheeler, William; Stolzenberg-Solomon, Rachael; Yu, Kai
Abstract:
As 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.
Citation:
Zhang 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.
Publisher:
Nature Publishing Group
Journal:
European Journal of Human Genetics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
11-Sep-2013
DOI:
10.1038/ejhg.2013.201
PubMed ID:
24022295
PubMed Central ID:
PMC3992564
Type:
Article
ISSN:
1018-4813; 1476-5438
Sponsors:
We 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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Hanen
dc.contributor.authorShi, Jianxinen
dc.contributor.authorLiang, Famingen
dc.contributor.authorWheeler, Williamen
dc.contributor.authorStolzenberg-Solomon, Rachaelen
dc.contributor.authorYu, Kaien
dc.date.accessioned2016-02-25T12:29:22Zen
dc.date.available2016-02-25T12:29:22Zen
dc.date.issued2013-09-11en
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.en
dc.identifier.issn1018-4813en
dc.identifier.issn1476-5438en
dc.identifier.pmid24022295en
dc.identifier.doi10.1038/ejhg.2013.201en
dc.identifier.urihttp://hdl.handle.net/10754/597265en
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.en
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.en
dc.publisherNature Publishing Groupen
dc.subjectcis-regulating meQTLs mappingen
dc.subjectgenome-wide association studyen
dc.subjectmultilocus testen
dc.subjectmultiple comparisonsen
dc.subjectpathway analysisen
dc.subjectvariable selectionen
dc.subject.meshPolymorphism, Single Nucleotideen
dc.subject.meshQuantitative Trait Locien
dc.subject.meshGenome-Wide Association Studyen
dc.subject.meshGenetic Association Studiesen
dc.titleA fast multilocus test with adaptive SNP selection for large-scale genetic-association studiesen
dc.typeArticleen
dc.identifier.journalEuropean Journal of Human Geneticsen
dc.identifier.pmcidPMC3992564en
dc.contributor.institutionBiostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.en
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX, USA and.en
dc.contributor.institutionInformation Management Services, Inc., Silver Spring, MD, USA.en
kaust.grant.numberKUS-C1-016-04en

Related articles on PubMed

All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.