Cargando…

Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure

BACKGROUND: The design of appropriate strategies to analyze and interpret linkage results for complex human diseases constitutes a challenge. Parameters such as power, definition of phenotype, and replicability have to be taken into account in order to reach meaningful conclusions. Incorporating dat...

Descripción completa

Detalles Bibliográficos
Autores principales: Shephard, Neil, Falcaro, Milena, Zeggini, Eleftheria, Chapman, Philip, Hinks, Anne, Barton, Anne, Worthington, Jane, Pickles, Andrew, John, Sally
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2003
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866461/
https://www.ncbi.nlm.nih.gov/pubmed/14975094
http://dx.doi.org/10.1186/1471-2156-4-S1-S26
_version_ 1782133274574848000
author Shephard, Neil
Falcaro, Milena
Zeggini, Eleftheria
Chapman, Philip
Hinks, Anne
Barton, Anne
Worthington, Jane
Pickles, Andrew
John, Sally
author_facet Shephard, Neil
Falcaro, Milena
Zeggini, Eleftheria
Chapman, Philip
Hinks, Anne
Barton, Anne
Worthington, Jane
Pickles, Andrew
John, Sally
author_sort Shephard, Neil
collection PubMed
description BACKGROUND: The design of appropriate strategies to analyze and interpret linkage results for complex human diseases constitutes a challenge. Parameters such as power, definition of phenotype, and replicability have to be taken into account in order to reach meaningful conclusions. Incorporating data on repeated phenotypic measures may increase the power to detect linkage but requires sophisticated analysis methods. Using the simulated Genetic Analysis Workshop 13 data set, we have estimated a variety of systolic blood pressure (SBP) phenotypic measures and examined their performance with respect to consistency among replicates and to true and false positive linkage signals. RESULTS: The whole-genome scan conducted on a dichotomous hypertension phenotype indicated the involvement of few true loci with nominal significance and gave rise to a high rate of false positives. Analysis of a cross-sectional quantitative SBP measure performed better, although genome-wide significance was again not reached. Additional phenotypic measures were derived from the longitudinal data using random effects modelling for censored data with varying levels of covariate adjustment. These models provided evidence for significant linkage to most genes influencing SBP and produced few false positive results. Overall, replicability of results was poor for loci, representing weak effects. CONCLUSION: Longitudinally derived phenotypes performed better than cross-sectional measures in linkage analyses. Bearing in mind the sample design and size of these data, linkage results that fail to replicate should not be dismissed; instead, different lines of evidence derived from complementary analysis methods should be combined to prioritize follow up.
format Text
id pubmed-1866461
institution National Center for Biotechnology Information
language English
publishDate 2003
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-18664612007-05-11 Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure Shephard, Neil Falcaro, Milena Zeggini, Eleftheria Chapman, Philip Hinks, Anne Barton, Anne Worthington, Jane Pickles, Andrew John, Sally BMC Genet Proceedings BACKGROUND: The design of appropriate strategies to analyze and interpret linkage results for complex human diseases constitutes a challenge. Parameters such as power, definition of phenotype, and replicability have to be taken into account in order to reach meaningful conclusions. Incorporating data on repeated phenotypic measures may increase the power to detect linkage but requires sophisticated analysis methods. Using the simulated Genetic Analysis Workshop 13 data set, we have estimated a variety of systolic blood pressure (SBP) phenotypic measures and examined their performance with respect to consistency among replicates and to true and false positive linkage signals. RESULTS: The whole-genome scan conducted on a dichotomous hypertension phenotype indicated the involvement of few true loci with nominal significance and gave rise to a high rate of false positives. Analysis of a cross-sectional quantitative SBP measure performed better, although genome-wide significance was again not reached. Additional phenotypic measures were derived from the longitudinal data using random effects modelling for censored data with varying levels of covariate adjustment. These models provided evidence for significant linkage to most genes influencing SBP and produced few false positive results. Overall, replicability of results was poor for loci, representing weak effects. CONCLUSION: Longitudinally derived phenotypes performed better than cross-sectional measures in linkage analyses. Bearing in mind the sample design and size of these data, linkage results that fail to replicate should not be dismissed; instead, different lines of evidence derived from complementary analysis methods should be combined to prioritize follow up. BioMed Central 2003-12-31 /pmc/articles/PMC1866461/ /pubmed/14975094 http://dx.doi.org/10.1186/1471-2156-4-S1-S26 Text en Copyright © 2003 Shephard 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
Shephard, Neil
Falcaro, Milena
Zeggini, Eleftheria
Chapman, Philip
Hinks, Anne
Barton, Anne
Worthington, Jane
Pickles, Andrew
John, Sally
Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
title Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
title_full Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
title_fullStr Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
title_full_unstemmed Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
title_short Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
title_sort linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866461/
https://www.ncbi.nlm.nih.gov/pubmed/14975094
http://dx.doi.org/10.1186/1471-2156-4-S1-S26
work_keys_str_mv AT shephardneil linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT falcaromilena linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT zegginieleftheria linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT chapmanphilip linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT hinksanne linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT bartonanne linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT worthingtonjane linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT picklesandrew linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure
AT johnsally linkageanalysisofcrosssectionalandlongitudinallyderivedphenotypicmeasurestoidentifylociinfluencingbloodpressure