Power and robustness of linkage tests for quantitative traits in general pedigrees.
There are numerous statistical methods for quantitative trait linkage analysis in human studies. An ideal such method would have high power to detect genetic loci contributing to the trait, would be robust to non-normality in the phenotype distribution, would be appropriate for general pedigrees, would allow the incorporation of environmental covariates, and would be appropriate in the presence of selective sampling. We recently described a general framework for quantitative trait linkage analysis, based on generalized estimating equations, for which many current methods are special cases. This procedure is appropriate for general pedigrees and easily accommodates environmental covariates. In this report, we use computer simulations to investigate the power and robustness of a variety of linkage test statistics built upon our general framework. We also propose two novel test statistics that take account of higher moments of the phenotype distribution, in order to accommodate non-normality. These new linkage tests are shown to have high power and to be robust to non-normality. While we have not yet examined the performance of our procedures in the context of selective sampling via computer simulations, the proposed tests satisfy all of the other qualities of an ideal quantitative trait linkage analysis method.
Chen
Wei-Min
WM
Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA. wechen@umich.edu
Broman
Karl W
KW
Liang
Kung-Yee
KY
D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D008040:Genetic Linkage / Q000235:genetics*; D006801:Humans; D008297:Male; D010375:Pedigree; D040641:Quantitative Trait Loci / Q000235:genetics*; D035781:Siblings