This article is part of the supplement: Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors
Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure
1 ARC Epidemiology Unit, University of Manchester, Manchester, United Kingdom
2 Biostatistics Group, University of Manchester, Manchester, United Kingdom
3 Research and Development Genetics, AstraZeneca, Alderley Park, Macclesfield, United Kingdom
4 Centre for Integrated Genomic Medical Research, University of Manchester, Manchester, United Kingdom
BMC Genetics 2003, 4(Suppl 1):S26 doi:10.1186/1471-2156-4-S1-S26Published: 31 December 2003
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.
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.
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.