Originally published as Genetics Published Articles Ahead of Print on July 14, 2005.
Genetics, Vol. 171, 1365-1376, November 2005, Copyright © 2005
doi:10.1534/genetics.105.043828
Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees
Stuart Macgregor*,
,1,
Sara A. Knott*,
Ian White* and
Peter M. Visscher*,2
* Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom and
Biostatistics and Bioinformatics Unit, Cardiff University, Cardiff CF14 4XN, United Kingdom
1 Corresponding author: Biostatistics and Bioinformatics Unit, Cardiff University, 4th Floor, Heath Park Hospital, Cardiff, CF14 4XN, United Kingdom.
E-mail: macgregors{at}cf.ac.uk
There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.
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Copyright © 2005 by the Genetics Society of America.