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

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.

journal_name

Ann Hum Genet

journal_title

Annals of human genetics

authors

Wang T,Elston RC

doi

10.1111/j.1469-1809.2006.00303.x

subject

Has Abstract

pub_date

2007-01-01 00:00:00

pages

96-106

issue

Pt 1

eissn

0003-4800

issn

1469-1809

pii

AHG303

journal_volume

71

pub_type

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