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Article: Matched Ascertainment of Informative Families for Complex Genetic Modelling

TitleMatched Ascertainment of Informative Families for Complex Genetic Modelling
Authors
KeywordsSegregation analysis
Mixed models
Variance components
Probit models
Issue Date2010
PublisherSpringer US
Citation
Behavior Genetics, 2010, v. 3, p. 404-414 How to Cite?
AbstractFamily data are used extensively in quantitative genetic studies to disentangle the genetic and environmental contributions to various diseases. Many family studies based their analysis on population-based registers containing a large number of individuals composed of small family units. For binary trait analyses, exact marginal likelihood is a common approach, but, due to the computational demand of the enormous data sets, it allows only a limited number of effects in the model. This makes it particularly difficult to perform joint estimation of variance components for a binary trait and the potential confounders. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascertained families match the full cohort with respect to some relevant statistics, such as the risk to relatives of an affected individual. The ascertainment-adjusted analysis, which we implement using a pseudo-likelihood approach, is shown to be efficient relative to the analysis of the whole cohort and robust to mis-specification of the random effect distribution.
Persistent Identifierhttp://hdl.handle.net/10722/124038
ISSN
2015 Impact Factor: 3.268
2015 SCImago Journal Rankings: 1.457
PubMed Central ID
ISI Accession Number ID
References

Breslow NE, Clayton D (1993) Approximate inference in generalized linear mixed models. J Am Stat Assoc 88:9–25 doi: 10.2307/2290687

Burton PR (2003) Correcting for nonrandom ascertainment in generalized linear mixed models (GLMMs), fitted using Gibbs sampling. Genet Epidemiol 24:24–35 doi: 10.1002/gepi.10206

Burton PR, Tiller KJ, Gurrin LC, Cookson WOCM, Musk AW, Palmer LJ (1999) Genetic variance components analysis for binary phenotypes using generalized linear mixed models (GLMMs) and Gibbs sampling. Genet Epidemiol 17:118–140 doi: 10.1002/(SICI)1098-2272(1999)17:2<118::AID-GEPI3>3.0.CO;2-V

de Andrade M, Amos CI (2000) Ascertainment issues in variance component models. Geneti Epidemiol 19:333–344 doi: 10.1002/1098-2272(200012)19:4<333::AID-GEPI5>3.0.CO;2-#

Epstein MP, Lin X, Boehnke M (2002) Ascertainment-adjusted parameter estimates revisited. Am J Hum Genet 70:886–895 doi: 10.1086/339517

Falconer DS (1965) The inheritance of liability to certain diseases estimated from the incidence among relatives. Ann Hum Genet 29:51–76 doi: 10.1111/j.1469-1809.1965.tb00500.x

Genz A (1992) Numerical computation of multivariate normal probabilities. J Comput Graph Stat 1:141–149 doi: 10.2307/1390838

Glidden D, Liang KY (2002) Ascertainment adjustment in complex diseases. Genet Epidemiol 23:201–208 doi: 10.1002/gepi.10204

Kalbfleisch JD, Lawless JF (1988) Likelihood analysis of multistate models for disease incidence and mortality. Stat Med 7:147–160 doi: 10.1002/sim.4780070116

Lu SE, Wang MC (2002) Cohort case–control design and analysis for clustered failure-time data. Biometrics 58:764–772 doi: 10.1111/j.0006-341X.2002.00764.x

Moger TA, Pawitan Y, Borgan O (2008) Case-cohort methods for survival data on families from routine registers. Stat Med 27:1062–1074 doi: 10.1002/sim.3004

Noh M, Lee Y, Pawitan Y (2005) Robust ascertainment-adjusted parameter estimation. Genet Epidemiol 29:68–75 doi: 10.1002/gepi.20078

Noh, M, Yip B, Lee Y, Pawitan Y (2006) Multicomponent variance estimation for binary traits in family-based studies. Genet Epidemiol 30:37–47 doi: 10.1002/gepi.20099

Pawitan Y, Reilly M, Nilson E, Cnattingius S, Lichtenstein P (2004) Estimation of genetic and environmental factors for binary traits using family data. Stat Med 23:449–465 doi: 10.1002/sim.1603

Svensson A, Pawitan Y, Cnattingius S, Reilly M, Lichtenstein P (2006) Familial aggregation of small-for-gestational-age births: the importance of fetal genetic effects. Am J Obstet Gynecol 194:475–9 doi: 10.1016/j.ajog.2005.08.019

Yip B, Björk C, Lichtenstein P, Hultman C, Pawitan Y (2008) Covariance components models for multivariate binary-traits in family data analysis. Stat Med 27:1086–1105 doi: 10.1002/sim.2996

Zeger SL, Karim MR (1991) Generalized linear models with random effects: a Gibbs sampling approach. J Am Stat Assoc 86:79–86 doi: 10.2307/2289717

 

DC FieldValueLanguage
dc.contributor.authorYip, H. Benjaminen_HK
dc.contributor.authorReilly, Marieen_HK
dc.contributor.authorCnattingius, Svenen_HK
dc.contributor.authorPawitan, Yudien_HK
dc.date.accessioned2010-10-19T04:35:09Z-
dc.date.available2010-10-19T04:35:09Z-
dc.date.issued2010en_HK
dc.identifier.citationBehavior Genetics, 2010, v. 3, p. 404-414en
dc.identifier.issn0001-8244en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124038-
dc.description.abstractFamily data are used extensively in quantitative genetic studies to disentangle the genetic and environmental contributions to various diseases. Many family studies based their analysis on population-based registers containing a large number of individuals composed of small family units. For binary trait analyses, exact marginal likelihood is a common approach, but, due to the computational demand of the enormous data sets, it allows only a limited number of effects in the model. This makes it particularly difficult to perform joint estimation of variance components for a binary trait and the potential confounders. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascertained families match the full cohort with respect to some relevant statistics, such as the risk to relatives of an affected individual. The ascertainment-adjusted analysis, which we implement using a pseudo-likelihood approach, is shown to be efficient relative to the analysis of the whole cohort and robust to mis-specification of the random effect distribution.en_HK
dc.languageengen_HK
dc.publisherSpringer USen_HK
dc.relation.ispartofBehavior Geneticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsSpringer Science+Business Media, LLCen_HK
dc.subjectSegregation analysisen_HK
dc.subjectMixed modelsen_HK
dc.subjectVariance componentsen_HK
dc.subjectProbit modelsen_HK
dc.titleMatched Ascertainment of Informative Families for Complex Genetic Modellingen_HK
dc.typeArticleen_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s10519-009-9322-8en_HK
dc.identifier.pmid20033275-
dc.identifier.pmcidPMC2953624-
dc.identifier.scopuseid_2-s2.0-77953287026-
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dc.identifier.volume40en_HK
dc.identifier.issue3en_HK
dc.identifier.spage404en_HK
dc.identifier.epage414en_HK
dc.identifier.eissn1573-3297en_HK
dc.identifier.isiWOS:000276603900013-
dc.description.otherSpringer Open Choice, 01 Dec 2010-
dc.identifier.citeulike6477994-

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