File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)

Conference Paper: Evaluating variance in liability explained by individual genetic variants and relationship to individualized risk prediction

TitleEvaluating variance in liability explained by individual genetic variants and relationship to individualized risk prediction
Authors
KeywordsBiology
Genetics
Issue Date2009
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/35841
Citation
The 18th Annual Meeting of the International Genetic Epidemiology Society (IGES 2009), Honolulu, HI, 10-20 October 2009. In Genetic Epidemiology, 2009, v. 33 n. 8, p. 809, abstract no. 195 How to Cite?
AbstractAn increasing number of susceptibility genes have been identified for complex diseases in recent years. However, how much the candidate genes discovered to date could explain the total genetic component of a disease is unknown. We developed a statistical framework to address this problem focusing on dichotomous disease traits and applied it to real examples of complex diseases. The genes were mainly selected based on results from meta-analyses of association studies. The total variance contributed by known candidate genes for each disease is generally not high, implying that a substantial part of heritability for most complex diseases remains unexplained. We also extended our model to deal with multi-allelic loci, haplotypes as well as gene-gene and gene-environmental interactions. In addition, we derived methods for calculating the variance explained for continuous predictor variables. We further found that the variance explained is closely related to the ability of risk prediction for individuals. We developed a methodology to quantify the absolute disease risk from liability measures. Specificity and sensitivity could be calculated for every cutoff of the absolute disease risk, hence the receiver operating characteristic curves and areas under the curve may be computed. Finally, we developed an approach to incorporate family history of the individual into known genetic factors when predicting disease risks.
Persistent Identifierhttp://hdl.handle.net/10722/126820
ISSN
2015 Impact Factor: 2.553
2015 SCImago Journal Rankings: 2.101

 

DC FieldValueLanguage
dc.contributor.authorSo, HCen_HK
dc.contributor.authorCherny, SSen_HK
dc.contributor.authorSham, PCen_HK
dc.date.accessioned2010-10-31T12:50:25Z-
dc.date.available2010-10-31T12:50:25Z-
dc.date.issued2009en_HK
dc.identifier.citationThe 18th Annual Meeting of the International Genetic Epidemiology Society (IGES 2009), Honolulu, HI, 10-20 October 2009. In Genetic Epidemiology, 2009, v. 33 n. 8, p. 809, abstract no. 195en_HK
dc.identifier.issn0741-0395-
dc.identifier.urihttp://hdl.handle.net/10722/126820-
dc.description.abstractAn increasing number of susceptibility genes have been identified for complex diseases in recent years. However, how much the candidate genes discovered to date could explain the total genetic component of a disease is unknown. We developed a statistical framework to address this problem focusing on dichotomous disease traits and applied it to real examples of complex diseases. The genes were mainly selected based on results from meta-analyses of association studies. The total variance contributed by known candidate genes for each disease is generally not high, implying that a substantial part of heritability for most complex diseases remains unexplained. We also extended our model to deal with multi-allelic loci, haplotypes as well as gene-gene and gene-environmental interactions. In addition, we derived methods for calculating the variance explained for continuous predictor variables. We further found that the variance explained is closely related to the ability of risk prediction for individuals. We developed a methodology to quantify the absolute disease risk from liability measures. Specificity and sensitivity could be calculated for every cutoff of the absolute disease risk, hence the receiver operating characteristic curves and areas under the curve may be computed. Finally, we developed an approach to incorporate family history of the individual into known genetic factors when predicting disease risks.-
dc.languageengen_HK
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/35841-
dc.relation.ispartofGenetic Epidemiology-
dc.rightsGenetic Epidemiology. Copyright © John Wiley & Sons, Inc.-
dc.subjectBiology-
dc.subjectGenetics-
dc.titleEvaluating variance in liability explained by individual genetic variants and relationship to individualized risk predictionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0741-0395&volume=33&issue=8&spage=809&epage=809, abstract no. 195&date=2009&atitle=Evaluating+variance+in+liability+explained+by+individual+genetic+variants+and+relationship+to+individualized+risk+prediction-
dc.identifier.emailCherny, SS: cherny@hku.hken_HK
dc.identifier.emailSham, PC: pcsham@HKUCC.hku.hken_HK
dc.identifier.authorityCherny, SS=rp00232en_HK
dc.identifier.authoritySham, PC=rp00459en_HK
dc.identifier.doi10.1002/gepi.20463-
dc.identifier.hkuros174594en_HK
dc.identifier.volume33-
dc.identifier.issue8-
dc.identifier.spage809-
dc.identifier.epage809-
dc.description.otherThe 18th Annual Meeting of the International Genetic Epidemiology Society (IGES 2009), Honolulu, HI, 10-20 October 2009. In Genetic Epidemiology, 2009, v. 33 n. 8, p. 809, abstract no. 195-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats