title: Adding further power to the Haseman and Elston method for detecting linkage in larger sibships: weighting sums and differences.
abstract: Haseman and Elston (H-E) proposed a robust test to detect linkage between a quantitative trait and a genetic marker. In their method the squared sib-pair trait difference is regressed on the estimated proportion of alleles at a locus shared identical by descent by sib pairs. This method has recently been improved by changing the dependent variable from the squared difference to the mean-corrected product of the sib-pair trait values, a significantly positive regression indicating linkage. Because situations arise in which the original test is more powerful, a further improvement of the H-E method occurs when the dependent variable is changed to a weighted average of the squared sib-pair trait difference and the squared sib-pair mean-corrected trait sum. Here we propose an optimal method of performing this weighting for larger sibships, allowing for the correlation between pairs within a sibship. The optimal weights are inversely proportional to the residual variances obtained from the two different regressions based on the squared sib-pair trait differences and the squared sib-pair mean-corrected trait sums, respectively, allowing for correlations among sib pairs. The proposed method is compared with the existing extension of the H-E approach for larger sibships. Control of the type I error probabilities for sibships of any size can be improved by using a generalized estimating equation approach and the robust sandwich estimate of the variance, or a Monte-Carlo permutation test.
authors.affiliation: Department of Epidemiology, University of Texas, M.D. Anderson Cancer Center, Houston, Tex. 77030, USA. sshete@mdanderson.org
authors.forename: Sanjay
authors.initials: S
authors.lastname: Shete
authors.forename: Kevin B
authors.initials: KB
authors.lastname: Jacobs
authors.forename: Robert C
authors.initials: RC
authors.lastname: Elston
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008040:Genetic Linkage; D006801:Humans