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Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures

BACKGROUND: Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively....

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Autores principales: Rao, Shaoqi, Li, Lin, Li, Xia, Moser, Kathy L, Guo, Zheng, Shen, Gongqing, Cannata, Ruth, Zirzow, Erich, Topol, Eric J, Wang, Qing
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866459/
https://www.ncbi.nlm.nih.gov/pubmed/14975092
http://dx.doi.org/10.1186/1471-2156-4-S1-S24
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author Rao, Shaoqi
Li, Lin
Li, Xia
Moser, Kathy L
Guo, Zheng
Shen, Gongqing
Cannata, Ruth
Zirzow, Erich
Topol, Eric J
Wang, Qing
author_facet Rao, Shaoqi
Li, Lin
Li, Xia
Moser, Kathy L
Guo, Zheng
Shen, Gongqing
Cannata, Ruth
Zirzow, Erich
Topol, Eric J
Wang, Qing
author_sort Rao, Shaoqi
collection PubMed
description BACKGROUND: Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures. RESULTS: We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life. CONCLUSION: We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes.
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spelling pubmed-18664592007-05-11 Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures Rao, Shaoqi Li, Lin Li, Xia Moser, Kathy L Guo, Zheng Shen, Gongqing Cannata, Ruth Zirzow, Erich Topol, Eric J Wang, Qing BMC Genet Proceedings BACKGROUND: Longitudinal data often have multiple (repeated) measures recorded along a time trajectory. For example, the two cohorts from the Framingham Heart Study (GAW13 Problem 1) contain 21 and 5 repeated measures for hypertension phenotypes as well as epidemiological risk factors, respectively. Direct modelling of a large number of serially and biologically correlated traits in the context of linkage analysis can be prohibitively complex. Alternatively, we may consider using univariate transformation for linkage analysis of longitudinal repeated measures. RESULTS: We evaluated the utility of three conventional summary measures (mean, slope, and principal components) for genetic linkage analysis of longitudinal phenotypes by analyzing the chromosome 10 data of the Framingham Heart Study. Except for the temporal slope, all of the summary methods and the multivariate analysis identified the previously reported region, marker GATA64A09, for systolic blood pressure or high blood pressure. Further analysis revealed that this region may harbor gene(s) affecting human blood pressure at multiple stages of life. CONCLUSION: We conclude that mean and principal components are feasible alternatives for genetic linkage analysis of longitudinal phenotypes, but the slope might have a separate genetic basis from that of the original longitudinal phenotypes. BioMed Central 2003-12-31 /pmc/articles/PMC1866459/ /pubmed/14975092 http://dx.doi.org/10.1186/1471-2156-4-S1-S24 Text en Copyright © 2003 Rao 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
Rao, Shaoqi
Li, Lin
Li, Xia
Moser, Kathy L
Guo, Zheng
Shen, Gongqing
Cannata, Ruth
Zirzow, Erich
Topol, Eric J
Wang, Qing
Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures
title Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures
title_full Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures
title_fullStr Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures
title_full_unstemmed Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures
title_short Genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures
title_sort genetic linkage analysis of longitudinal hypertension phenotypes using three summary measures
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866459/
https://www.ncbi.nlm.nih.gov/pubmed/14975092
http://dx.doi.org/10.1186/1471-2156-4-S1-S24
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