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Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study

The relationship between elevated blood pressure and cardiovascular and cerebrovascular disease risk is well accepted. Both systolic and diastolic hypertension are associated with this risk increase, but systolic blood pressure appears to be a more important determinant of cardiovascular risk than d...

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Autores principales: James, Katherine, Weitzel, Lindsay-Rae B, Engelman, Corinne D, Zerbe, Gary, Norris, Jill M
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866523/
https://www.ncbi.nlm.nih.gov/pubmed/14975151
http://dx.doi.org/10.1186/1471-2156-4-S1-S83
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author James, Katherine
Weitzel, Lindsay-Rae B
Engelman, Corinne D
Zerbe, Gary
Norris, Jill M
author_facet James, Katherine
Weitzel, Lindsay-Rae B
Engelman, Corinne D
Zerbe, Gary
Norris, Jill M
author_sort James, Katherine
collection PubMed
description The relationship between elevated blood pressure and cardiovascular and cerebrovascular disease risk is well accepted. Both systolic and diastolic hypertension are associated with this risk increase, but systolic blood pressure appears to be a more important determinant of cardiovascular risk than diastolic blood pressure. Subjects for this study are derived from the Framingham Heart Study data set. Each subject had five records of clinical data of which systolic blood pressure, age, height, gender, weight, and hypertension treatment were selected to characterize the phenotype in this analysis. We modeled systolic blood pressure as a function of age using a mixed modeling methodology that enabled us to characterize the phenotype for each individual as the individual's deviation from the population average rate of change in systolic blood pressure for each year of age while controlling for gender, body mass index, and hypertension treatment. Significant (p = 0.00002) evidence for linkage was found between this normalized phenotype and a region on chromosome 1. Similar linkage results were obtained when we estimated the phenotype while excluding values obtained during hypertension treatment. The use of linear mixed models to define phenotypes is a methodology that allows for the adjustment of the main factor by covariates. Future work should be done in the area of combining this phenotype estimation directly with the linkage analysis so that the error in estimating the phenotype can be properly incorporated into the genetic analysis, which, at present, assumes that the phenotype is measured (or estimated) without error.
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spelling pubmed-18665232007-05-11 Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study James, Katherine Weitzel, Lindsay-Rae B Engelman, Corinne D Zerbe, Gary Norris, Jill M BMC Genet Proceedings The relationship between elevated blood pressure and cardiovascular and cerebrovascular disease risk is well accepted. Both systolic and diastolic hypertension are associated with this risk increase, but systolic blood pressure appears to be a more important determinant of cardiovascular risk than diastolic blood pressure. Subjects for this study are derived from the Framingham Heart Study data set. Each subject had five records of clinical data of which systolic blood pressure, age, height, gender, weight, and hypertension treatment were selected to characterize the phenotype in this analysis. We modeled systolic blood pressure as a function of age using a mixed modeling methodology that enabled us to characterize the phenotype for each individual as the individual's deviation from the population average rate of change in systolic blood pressure for each year of age while controlling for gender, body mass index, and hypertension treatment. Significant (p = 0.00002) evidence for linkage was found between this normalized phenotype and a region on chromosome 1. Similar linkage results were obtained when we estimated the phenotype while excluding values obtained during hypertension treatment. The use of linear mixed models to define phenotypes is a methodology that allows for the adjustment of the main factor by covariates. Future work should be done in the area of combining this phenotype estimation directly with the linkage analysis so that the error in estimating the phenotype can be properly incorporated into the genetic analysis, which, at present, assumes that the phenotype is measured (or estimated) without error. BioMed Central 2003-12-31 /pmc/articles/PMC1866523/ /pubmed/14975151 http://dx.doi.org/10.1186/1471-2156-4-S1-S83 Text en Copyright © 2003 James 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
James, Katherine
Weitzel, Lindsay-Rae B
Engelman, Corinne D
Zerbe, Gary
Norris, Jill M
Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study
title Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study
title_full Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study
title_fullStr Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study
title_full_unstemmed Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study
title_short Genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the Framingham Heart Study
title_sort genome scan linkage results for longitudinal systolic blood pressure phenotypes in subjects from the framingham heart study
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866523/
https://www.ncbi.nlm.nih.gov/pubmed/14975151
http://dx.doi.org/10.1186/1471-2156-4-S1-S83
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