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Article: Variance components linkage analysis with repeated measurements

TitleVariance components linkage analysis with repeated measurements
Authors
KeywordsCost-effective design
Linkage analysis
Power calculation
Quantitative trait
Repeated measures
Variance component
Issue Date2009
PublisherS Karger AG. The Journal's web site is located at http://www.karger.com/HHE
Citation
Human Heredity, 2009, v. 67 n. 4, p. 237-247 How to Cite?
AbstractBackground: When subjects are measured multiple times, linkage analysis needs to appropriately model these repeated measures. A number of methods have been proposed to model repeated measures in linkage analysis. Here, we focus on assessing the impact of repeated measures on the power and cost of a linkage study. Methods: We describe three alternative extensions of the variance components approach to accommodate repeated measures in a quantitative trait linkage study. We explicitly relate power and cost through the number of measures for different designs. Based on these models, we derive general formulas for optimal number of repeated measures for a given power or cost and use analytical calculations and simulations to compare power for different numbers of repeated measures across several scenarios. We give rigorous proof for the results under the balanced design. Results: Repeated measures substantially improve power and the proportional increase in LOD score depends mostly on measurement error and total heritability but not much on marker map, the number of alleles per marker or family structure. When measurement error takes up 20% of the trait variability and 4 measures/subject are taken, the proportional increase in LOD score ranges from 38% for traits with heritability of ~20% to 63% for traits with heritability of ~80%. An R package is provided to determine optimal number of repeated measures for given measurement error and cost. Variance component and regression based implementations of our methods are included in the MERLIN package to facilitate their use in practical studies. © 2009 S. Karger AG, Basel.
Persistent Identifierhttp://hdl.handle.net/10722/59734
ISSN
2015 Impact Factor: 1.539
2015 SCImago Journal Rankings: 0.942
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLiang, Len_HK
dc.contributor.authorChen, WMen_HK
dc.contributor.authorSham, PCen_HK
dc.contributor.authorAbecasis, GRen_HK
dc.date.accessioned2010-05-31T03:56:19Z-
dc.date.available2010-05-31T03:56:19Z-
dc.date.issued2009en_HK
dc.identifier.citationHuman Heredity, 2009, v. 67 n. 4, p. 237-247en_HK
dc.identifier.issn0001-5652en_HK
dc.identifier.urihttp://hdl.handle.net/10722/59734-
dc.description.abstractBackground: When subjects are measured multiple times, linkage analysis needs to appropriately model these repeated measures. A number of methods have been proposed to model repeated measures in linkage analysis. Here, we focus on assessing the impact of repeated measures on the power and cost of a linkage study. Methods: We describe three alternative extensions of the variance components approach to accommodate repeated measures in a quantitative trait linkage study. We explicitly relate power and cost through the number of measures for different designs. Based on these models, we derive general formulas for optimal number of repeated measures for a given power or cost and use analytical calculations and simulations to compare power for different numbers of repeated measures across several scenarios. We give rigorous proof for the results under the balanced design. Results: Repeated measures substantially improve power and the proportional increase in LOD score depends mostly on measurement error and total heritability but not much on marker map, the number of alleles per marker or family structure. When measurement error takes up 20% of the trait variability and 4 measures/subject are taken, the proportional increase in LOD score ranges from 38% for traits with heritability of ~20% to 63% for traits with heritability of ~80%. An R package is provided to determine optimal number of repeated measures for given measurement error and cost. Variance component and regression based implementations of our methods are included in the MERLIN package to facilitate their use in practical studies. © 2009 S. Karger AG, Basel.en_HK
dc.languageengen_HK
dc.publisherS Karger AG. The Journal's web site is located at http://www.karger.com/HHEen_HK
dc.relation.ispartofHuman Heredityen_HK
dc.rightsHuman Heredity. Copyright © S Karger AG.en_HK
dc.subjectCost-effective designen_HK
dc.subjectLinkage analysisen_HK
dc.subjectPower calculationen_HK
dc.subjectQuantitative traiten_HK
dc.subjectRepeated measuresen_HK
dc.subjectVariance componenten_HK
dc.titleVariance components linkage analysis with repeated measurementsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0001-5652&volume=67&spage=237&epage=247&date=2009&atitle=Variance+Components+Linkage+Analysis+with+Repeated+Measurementsen_HK
dc.identifier.emailSham, PC: pcsham@hku.hken_HK
dc.identifier.authoritySham, PC=rp00459en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1159/000194977en_HK
dc.identifier.pmid19172083-
dc.identifier.scopuseid_2-s2.0-58549113036en_HK
dc.identifier.hkuros158158en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-58549113036&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume67en_HK
dc.identifier.issue4en_HK
dc.identifier.spage237en_HK
dc.identifier.epage247en_HK
dc.identifier.isiWOS:000264864500003-
dc.publisher.placeSwitzerlanden_HK
dc.identifier.scopusauthoridLiang, L=7202069545en_HK
dc.identifier.scopusauthoridChen, WM=25947330100en_HK
dc.identifier.scopusauthoridSham, PC=34573429300en_HK
dc.identifier.scopusauthoridAbecasis, GR=6604013253en_HK

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