File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained

TitleA unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained
Authors
Issue Date2010
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosgenetics.org/
Citation
Plos Genetics, 2010, v. 6 n. 12, p. 1-13 How to Cite?
AbstractAn increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and noncases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait. © 2010 So, Sham.
Persistent Identifierhttp://hdl.handle.net/10722/137515
ISSN
2014 Impact Factor: 7.528
2015 SCImago Journal Rankings: 6.308
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong Research Grants CouncilHKU 766906M
HKU 774707M
Croucher Foundation
University of Hong Kong
Funding Information:

The work was supported by the Hong Kong Research Grants Council General Research Fund grants HKU 766906M and HKU 774707M and by the University of Hong Kong Strategic Research Theme of Genomics. H-CS 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
Grants

 

DC FieldValueLanguage
dc.contributor.authorSo, HCen_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 Genetics, 2010, v. 6 n. 12, p. 1-13en_HK
dc.identifier.issn1553-7390en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137515-
dc.description.abstractAn increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and noncases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait. © 2010 So, Sham.en_HK
dc.languageengen_US
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosgenetics.org/en_HK
dc.relation.ispartofPLoS geneticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.meshBiostatistics-
dc.subject.meshGenetic Predisposition to Disease-
dc.subject.meshGenetic Testing - statistics and numerical data-
dc.subject.meshGenetic Variation-
dc.subject.meshModels, Statistical-
dc.titleA unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explaineden_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1553-7390&volume=6&issue=12, article no. e1001230&spage=&epage=&date=2010&atitle=A+unifying+framework+for+evaluating+the+predictive+power+of+genetic+variants+based+on+the+level+of+heritability+explained-
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.pgen.1001230en_HK
dc.identifier.pmid21151957-
dc.identifier.pmcidPMC2996330-
dc.identifier.scopuseid_2-s2.0-78650687187en_HK
dc.identifier.hkuros189820en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78650687187&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6en_HK
dc.identifier.issue12en_HK
dc.identifier.spage1en_HK
dc.identifier.epage13en_HK
dc.identifier.eissn1553-7404-
dc.identifier.isiWOS:000285578900008-
dc.publisher.placeUnited Statesen_HK
dc.relation.projectGenome-wide association study of schizophrenia-
dc.identifier.scopusauthoridSo, HC=37031934700en_HK
dc.identifier.scopusauthoridSham, PC=34573429300en_HK
dc.identifier.citeulike8481909-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats