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Nonparametric longitudinal allele-sharing model

Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent sib pairs, modelling the phenotypic quadratic differences as the dependent variable. Factors...

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Autores principales: Kulle, Bettina, Köhler, Karola, Rosenberger, Albert, Loesgen, Sabine, Bickeböller, Heike
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866525/
https://www.ncbi.nlm.nih.gov/pubmed/14975153
http://dx.doi.org/10.1186/1471-2156-4-S1-S85
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author Kulle, Bettina
Köhler, Karola
Rosenberger, Albert
Loesgen, Sabine
Bickeböller, Heike
author_facet Kulle, Bettina
Köhler, Karola
Rosenberger, Albert
Loesgen, Sabine
Bickeböller, Heike
author_sort Kulle, Bettina
collection PubMed
description Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent sib pairs, modelling the phenotypic quadratic differences as the dependent variable. Factors are the number of alleles shared identically by descent (IBD) and the age categories at which the dependent variable is measured, allowing for dependence due to age. To identify a linked marker a rank statistic tests the influence of IBD group on phenotypic quadratic differences. No assumptions are made on normality or variances of the dependent variable. We apply our method to 71 sib pairs from the Framingham Heart Study data provided at the Genetic Analysis Workshop 13. For all 15 available markers on chromosome 17 we analyzed the influence on systolic blood pressure. In addition, different selection strategies to sample from the whole data are discussed.
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spelling pubmed-18665252007-05-11 Nonparametric longitudinal allele-sharing model Kulle, Bettina Köhler, Karola Rosenberger, Albert Loesgen, Sabine Bickeböller, Heike BMC Genet Proceedings Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent sib pairs, modelling the phenotypic quadratic differences as the dependent variable. Factors are the number of alleles shared identically by descent (IBD) and the age categories at which the dependent variable is measured, allowing for dependence due to age. To identify a linked marker a rank statistic tests the influence of IBD group on phenotypic quadratic differences. No assumptions are made on normality or variances of the dependent variable. We apply our method to 71 sib pairs from the Framingham Heart Study data provided at the Genetic Analysis Workshop 13. For all 15 available markers on chromosome 17 we analyzed the influence on systolic blood pressure. In addition, different selection strategies to sample from the whole data are discussed. BioMed Central 2003-12-31 /pmc/articles/PMC1866525/ /pubmed/14975153 http://dx.doi.org/10.1186/1471-2156-4-S1-S85 Text en Copyright © 2003 Kulle 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
Kulle, Bettina
Köhler, Karola
Rosenberger, Albert
Loesgen, Sabine
Bickeböller, Heike
Nonparametric longitudinal allele-sharing model
title Nonparametric longitudinal allele-sharing model
title_full Nonparametric longitudinal allele-sharing model
title_fullStr Nonparametric longitudinal allele-sharing model
title_full_unstemmed Nonparametric longitudinal allele-sharing model
title_short Nonparametric longitudinal allele-sharing model
title_sort nonparametric longitudinal allele-sharing model
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866525/
https://www.ncbi.nlm.nih.gov/pubmed/14975153
http://dx.doi.org/10.1186/1471-2156-4-S1-S85
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