Abstract
Two of the major approaches for linkage analysis with quantitative traits in humans include variance components and Haseman-Elston regression. Previously, these have been 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) 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.
Disciplines
Genetics | Statistical Methodology | Statistical Models | Statistical Theory
Suggested Citation
Chen, Wei-Min; Broman, Karl W.; and Liang, Kung-Yee, "Unification of Variance Components and Haseman-Elston Regression for Quantitative Trait Linkage Analysis" (October 2003). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 14.
https://biostats.bepress.com/jhubiostat/paper14
Included in
Genetics Commons, Statistical Methodology Commons, Statistical Models Commons, Statistical Theory Commons