Quantitative trait linkage analysis by generalized estimating equations: unification of variance components and Haseman-Elston regression.
Two of the major approaches for linkage analysis with quantitative traits in humans include variance components and Haseman-Elston regression. Previously, these were viewed as quite separate methods. We describe a general model, fit by use of generalized estimating equations (GEE), for which the variance components and Haseman-Elston methods (including many of the extensions to the original Haseman-Elston method) are special cases, corresponding to different choices for a working covariance matrix. We also show that the regression-based test of Sham et al. ([2002] Am. J. Hum. Genet. 71:238-253) is equivalent to a robust score statistic derived from our GEE approach. These results have several important implications. First, this work provides new insight regarding the connection between these methods. Second, asymptotic approximations for power and sample size allow clear comparisons regarding the relative efficiency of the different methods. Third, our general framework suggests important extensions to the Haseman-Elston approach which make more complete use of the data in extended pedigrees and allow a natural incorporation of environmental and other covariates.
Chen
Wei-Min
WM
Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, USA.
Broman
Karl W
KW
Liang
Kung-Yee
KY
D000465:Algorithms; D002874:Chromosome Mapping; D002877:Chromosomes, Human / Q000235:genetics; D008040:Genetic Linkage; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010375:Pedigree; D010641:Phenotype; D019655:Quantitative Trait, Heritable