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Article: Software for generating liability distributions for pedigrees conditional on their observed disease states and covariates

TitleSoftware for generating liability distributions for pedigrees conditional on their observed disease states and covariates
Authors
KeywordsAge of onset
Complex
Disease
Family history
Family history score
Gibbs sampler
Multifactorial
Prediction
Right censoring
Risk
Risk factor
Issue Date2010
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/35841
Citation
Genetic Epidemiology, 2010, v. 34 n. 2, p. 159-170 How to Cite?
AbstractFor many multifactorial diseases, aetiology is poorly understood. A major research aim is the identification of disease predictors (environmental, biological, and genetic markers). In order to achieve this, a two-stage approach is proposed. The initial or synthesis stage combines observed pedigree data with previous genetic epidemiological research findings, to produce estimates of pedigree members' disease risk and predictions of their disease liability. A further analysis stage uses the latter as inputs to look for associations with potential disease markers. The incorporation of previous research findings into an analysis should lead to power gains. It also allows separate predictions for environmental and genetic liabilities to be generated. This should increase power for detecting disease predictors that are environmental or genetic in nature. Finally, the approach brings pragmatic benefits in terms of data reduction and synthesis, improving comprehensibility, and facilitating the use of existing statistical genetics tools. In this article we present a statistical model and Gibbs sampling approach to generate liability predictions for multifactorial disease for the synthesis stage. We have implemented the approach in a software program. We apply this program to a specimen disease pedigree, and discuss the results produced, comparing its results with those generated under a more naïve model. We also detail simulation studies that validate the software's operation. © 2009 Wiley-Liss, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/137506
ISSN
2015 Impact Factor: 2.553
2015 SCImago Journal Rankings: 2.101
ISI Accession Number ID
Funding AgencyGrant Number
National Eye InstituteEY12562
University of Hong Kong Genomics Strategic Research Theme
Funding Information:

Contract grant sponsor: National Eye Institute; Contract grant number: EY12562.

References

 

DC FieldValueLanguage
dc.contributor.authorCampbell, DDen_HK
dc.contributor.authorSham, PCen_HK
dc.contributor.authorKnight, Jen_HK
dc.contributor.authorWickham, Hen_HK
dc.contributor.authorLandau, Sen_HK
dc.date.accessioned2011-08-26T14:26:46Z-
dc.date.available2011-08-26T14:26:46Z-
dc.date.issued2010en_HK
dc.identifier.citationGenetic Epidemiology, 2010, v. 34 n. 2, p. 159-170en_HK
dc.identifier.issn0741-0395en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137506-
dc.description.abstractFor many multifactorial diseases, aetiology is poorly understood. A major research aim is the identification of disease predictors (environmental, biological, and genetic markers). In order to achieve this, a two-stage approach is proposed. The initial or synthesis stage combines observed pedigree data with previous genetic epidemiological research findings, to produce estimates of pedigree members' disease risk and predictions of their disease liability. A further analysis stage uses the latter as inputs to look for associations with potential disease markers. The incorporation of previous research findings into an analysis should lead to power gains. It also allows separate predictions for environmental and genetic liabilities to be generated. This should increase power for detecting disease predictors that are environmental or genetic in nature. Finally, the approach brings pragmatic benefits in terms of data reduction and synthesis, improving comprehensibility, and facilitating the use of existing statistical genetics tools. In this article we present a statistical model and Gibbs sampling approach to generate liability predictions for multifactorial disease for the synthesis stage. We have implemented the approach in a software program. We apply this program to a specimen disease pedigree, and discuss the results produced, comparing its results with those generated under a more naïve model. We also detail simulation studies that validate the software's operation. © 2009 Wiley-Liss, Inc.en_HK
dc.languageengen_US
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/35841en_HK
dc.relation.ispartofGenetic Epidemiologyen_HK
dc.rightsGenetic Epidemiology. Copyright © John Wiley & Sons, Inc.-
dc.subjectAge of onseten_HK
dc.subjectComplexen_HK
dc.subjectDiseaseen_HK
dc.subjectFamily historyen_HK
dc.subjectFamily history scoreen_HK
dc.subjectGibbs sampleren_HK
dc.subjectMultifactorialen_HK
dc.subjectPredictionen_HK
dc.subjectRight censoringen_HK
dc.subjectRisken_HK
dc.subjectRisk factoren_HK
dc.subject.meshGenetic Predisposition to Disease - genetics-
dc.subject.meshModels, Genetic-
dc.subject.meshPedigree-
dc.subject.meshSoftware-
dc.subject.meshDepression/genetics-
dc.titleSoftware for generating liability distributions for pedigrees conditional on their observed disease states and covariatesen_HK
dc.typeArticleen_HK
dc.identifier.emailSham, PC: pcsham@hku.hken_HK
dc.identifier.authoritySham, PC=rp00459en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/gepi.20446en_HK
dc.identifier.pmid19771574-
dc.identifier.scopuseid_2-s2.0-76649113727en_HK
dc.identifier.hkuros189609en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-76649113727&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume34en_HK
dc.identifier.issue2en_HK
dc.identifier.spage159en_HK
dc.identifier.epage170en_HK
dc.identifier.isiWOS:000274376800008-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCampbell, DD=16041366500en_HK
dc.identifier.scopusauthoridSham, PC=34573429300en_HK
dc.identifier.scopusauthoridKnight, J=13002769800en_HK
dc.identifier.scopusauthoridWickham, H=6701762103en_HK
dc.identifier.scopusauthoridLandau, S=7101776972en_HK

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