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Linkage and association analysis in pedigrees from different populations

Using the Genetic Analysis Workshop 14 simulated datasets we carried out nonparametric linkage analyses and applied a log-linear method for analysis of case-parent-triad data with stratification on parental mating type. We proposed and applied a random effect modelling approach to explore the impact...

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Detalles Bibliográficos
Autores principales: Beyene, Joseph, Yan, Jun, Greenwood, Celia MT
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866724/
https://www.ncbi.nlm.nih.gov/pubmed/16451671
http://dx.doi.org/10.1186/1471-2156-6-S1-S59
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author Beyene, Joseph
Yan, Jun
Greenwood, Celia MT
author_facet Beyene, Joseph
Yan, Jun
Greenwood, Celia MT
author_sort Beyene, Joseph
collection PubMed
description Using the Genetic Analysis Workshop 14 simulated datasets we carried out nonparametric linkage analyses and applied a log-linear method for analysis of case-parent-triad data with stratification on parental mating type. We proposed and applied a random effect modelling approach to explore the impact of population heterogeneity on tests of association between genetic markers and disease status. The estimated genetic effect may appear to be strongly significant in one population but nonsignificant in another population, leading to confusion about interpretation. However, when results are interpreted in the light of a random effects model, both studies may be making similar statements about a genetic effect that varies depending on environment and background.
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spelling pubmed-18667242007-05-11 Linkage and association analysis in pedigrees from different populations Beyene, Joseph Yan, Jun Greenwood, Celia MT BMC Genet Proceedings Using the Genetic Analysis Workshop 14 simulated datasets we carried out nonparametric linkage analyses and applied a log-linear method for analysis of case-parent-triad data with stratification on parental mating type. We proposed and applied a random effect modelling approach to explore the impact of population heterogeneity on tests of association between genetic markers and disease status. The estimated genetic effect may appear to be strongly significant in one population but nonsignificant in another population, leading to confusion about interpretation. However, when results are interpreted in the light of a random effects model, both studies may be making similar statements about a genetic effect that varies depending on environment and background. BioMed Central 2005-12-30 /pmc/articles/PMC1866724/ /pubmed/16451671 http://dx.doi.org/10.1186/1471-2156-6-S1-S59 Text en Copyright © 2005 Beyene et al; licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Beyene, Joseph
Yan, Jun
Greenwood, Celia MT
Linkage and association analysis in pedigrees from different populations
title Linkage and association analysis in pedigrees from different populations
title_full Linkage and association analysis in pedigrees from different populations
title_fullStr Linkage and association analysis in pedigrees from different populations
title_full_unstemmed Linkage and association analysis in pedigrees from different populations
title_short Linkage and association analysis in pedigrees from different populations
title_sort linkage and association analysis in pedigrees from different populations
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866724/
https://www.ncbi.nlm.nih.gov/pubmed/16451671
http://dx.doi.org/10.1186/1471-2156-6-S1-S59
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