A statistically robust variance-components approach for quantitative trait linkage analysis.
Previously we showed (Wang, Guerra & Cohen 1998) that a statistically robust version of the Haseman & Elston (1972) sib-pair method greatly increased power to detect linkage in the presence of outliers. In this paper we report on M-estimation to accommodate outliers in the variance-components approach to linkage analysis developed by Amos (1994). Simulations show that in the presence of outliers the robust variance-components approach provides substantially greater power, more precise estimation of heritabilities, and better false-positive rates than the original Gaussian based approach. In the absence of outliers the performance of the robust variance-components approach is similar to that of the Gaussian based approach. For illustration we apply the method to two well characterized lipoprotein systems.
Wang
J
J
Center for Human Nutrition, University of Texas Southwestern Medical Center at Dallas 75235-9052, USA. wang@crcdec.swmed.edu
Guerra
R
R
Cohen
J
J
D000483:Alleles; D000704:Analysis of Variance; D001057:Apolipoproteins E / Q000235:genetics; D008078:Cholesterol, LDL / Q000235:genetics; D005192:Family Health; D005787:Gene Frequency; D008040:Genetic Linkage / Q000235:genetics*; D006801:Humans; D006938:Hyperlipoproteinemia Type II / Q000235:genetics; D008049:Lipase / Q000235:genetics; D008099:Liver / Q000201:enzymology; D008957:Models, Genetic; D009679:Nuclear Family; D011110:Polymorphism, Genetic; D019655:Quantitative Trait, Heritable; D013223:Statistics as Topic / Q000379:methods*
D001057:Apolipoproteins E; D008078:Cholesterol, LDL; D008049:Lipase