A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits

Ferreira, Manuel A. R., Visscher, Peter M., Martin, Nicholas G. and Duffy, David L. (2006) A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits. European Journal of Human Genetics, 14 8: 953-962. doi:10.1038/sj.ejhg.5201646


Author Ferreira, Manuel A. R.
Visscher, Peter M.
Martin, Nicholas G.
Duffy, David L.
Title A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits
Journal name European Journal of Human Genetics   Check publisher's open access policy
ISSN 1018-4813
Publication date 2006-08
Sub-type Article (original research)
DOI 10.1038/sj.ejhg.5201646
Volume 14
Issue 8
Start page 953
End page 962
Total pages 10
Editor G-J. B. van Ommen
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Collection year 2006
Language eng
Subject C1
321011 Medical Genetics
730107 Inherited diseases (incl. gene therapy)
Abstract Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.
Keyword Genetics & Heredity
Linkage
Multiple Traits
Pleiotropic
Multivariate
Empirical
Power
Quantitative Traits
General Pedigrees
Genetic-linkage
Asthma
Multivariate
Association
Genome
Phenotype
Families
Tests
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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