Regression-based multivariate linkage analysis with an application to blood pressure and body mass index

T. Wang, Robert C. Elston

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Multivariate linkage analysis has been suggested for the analysis of correlated traits, such as blood pressure (BP) and body mass index (BMI), because it may offer greater power and provide clearer results than univariate analyses. Currently, the most commonly used multivariate linkage methods are extensions of the univariate variance component model. One concern about those methods is their inherent sensitivity to the assumption of multivariate normality which cannot be easily guaranteed in practice. Another problem possibly related to all multivariate linkage analysis methods is the difficulty in interpreting nominal p-values, because the asymptotic distribution of the test statistic has not been well characterized. Here we propose a regression-based multivariate linkage method in which a robust score statistic is used to detect linkage. The p-value of the statistic is evaluated by a simple and rapid simulation procedure. Theoretically, this method can be used for any number and type of traits and for general pedigree data. We apply this approach to a genome linkage analysis of blood pressure and body mass index data from the Beaver Dam Eye Study.

Original languageEnglish (US)
Pages (from-to)96-106
Number of pages11
JournalAnnals of Human Genetics
Volume71
Issue number1
DOIs
StatePublished - Jan 2007
Externally publishedYes

Keywords

  • Blood pressure
  • Body mass index
  • Haseman-Elston regression
  • Multivariate linkage analysis

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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