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Article: Estimating the total number of susceptibility variants underlying complex diseases from genome-wide association studies

TitleEstimating the total number of susceptibility variants underlying complex diseases from genome-wide association studies
Authors
Issue Date2010
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
Citation
Plos One, 2010, v. 5 n. 11 How to Cite?
AbstractRecently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution. The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore the power was taken into account in fitting. Besides considering the most significant variants, we also tried to relax the significance threshold, allowing more markers to be fitted. The effects of false positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the ''winner's curse'' effect was corrected analytically. Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner's curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits. Finally we applied the methods to a few real disease examples (lipid traits, type 2 diabetes and Crohn's disease) and estimated that hundreds to nearly a thousand variants underlie these traits. © 2010 So et al.
Persistent Identifierhttp://hdl.handle.net/10722/137517
ISSN
2015 Impact Factor: 3.057
2015 SCImago Journal Rankings: 1.395
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grants CouncilHKU 766906M
HKU 774707M
University of Hong Kong Strategic Research Theme of Genomics
Croucher Foundation
Funding Information:

The work was supported by the Hong Kong Research Grants Council General Research Fund HKU 766906M and HKU 774707M and the University of Hong Kong Strategic Research Theme of Genomics. HCS was supported by a Croucher Foundation Scholarship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References
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DC FieldValueLanguage
dc.contributor.authorSo, HCen_HK
dc.contributor.authorYip, BHKen_HK
dc.contributor.authorSham, PCen_HK
dc.date.accessioned2011-08-26T14:26:53Z-
dc.date.available2011-08-26T14:26:53Z-
dc.date.issued2010en_HK
dc.identifier.citationPlos One, 2010, v. 5 n. 11en_HK
dc.identifier.issn1932-6203en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137517-
dc.description.abstractRecently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution. The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore the power was taken into account in fitting. Besides considering the most significant variants, we also tried to relax the significance threshold, allowing more markers to be fitted. The effects of false positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the ''winner's curse'' effect was corrected analytically. Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner's curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits. Finally we applied the methods to a few real disease examples (lipid traits, type 2 diabetes and Crohn's disease) and estimated that hundreds to nearly a thousand variants underlie these traits. © 2010 So et al.en_HK
dc.languageengen_US
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.actionen_HK
dc.relation.ispartofPLoS ONEen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.meshAlgorithms-
dc.subject.meshDiabetes Mellitus, Type 2 - genetics-
dc.subject.meshGenetic Predisposition to Disease - genetics-
dc.subject.meshGenome-Wide Association Study - methods-
dc.subject.meshPolymorphism, Single Nucleotide-
dc.titleEstimating the total number of susceptibility variants underlying complex diseases from genome-wide association studiesen_HK
dc.typeArticleen_HK
dc.identifier.emailSham, PC: pcsham@hku.hken_HK
dc.identifier.authoritySham, PC=rp00459en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1371/journal.pone.0013898en_HK
dc.identifier.pmid21103334-
dc.identifier.pmcidPMC2984437-
dc.identifier.scopuseid_2-s2.0-78649512740en_HK
dc.identifier.hkuros189825en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78649512740&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume5en_HK
dc.identifier.issue11en_HK
dc.identifier.spagee13898en_US
dc.identifier.epagee13898en_US
dc.identifier.isiWOS:000284327800002-
dc.publisher.placeUnited Statesen_HK
dc.relation.projectGenome-wide association study of schizophrenia-
dc.identifier.scopusauthoridSo, HC=37031934700en_HK
dc.identifier.scopusauthoridYip, BHK=16685586100en_HK
dc.identifier.scopusauthoridSham, PC=34573429300en_HK
dc.identifier.citeulike8691792-

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