New multivariate test for linkage, with application to pleiotropy: fuzzy Haseman-Elston.
We propose a new method of linkage analysis based on using the grade of membership scores resulting from fuzzy clustering procedures to define new dependent variables for the various Haseman-Elston approaches. For a single continuous trait with low heritability, the aim was to identify subgroups such that the grade of membership scores to these subgroups would provide more information for linkage than the original trait. For a multivariate trait, the goal was to provide a means of data reduction and data mining. Simulation studies using continuous traits with relatively low heritability (H=0.1, 0.2, and 0.3) showed that the new approach does not enhance power for a single trait. However, for a multivariate continuous trait (with three components), it is more powerful than the principal component method and more powerful than the joint linkage test proposed by Mangin et al. ([1998] Biometrics 54:88-99) when there is pleiotropy.
Kaabi
Belhassen
B
Department of Epidemiology and Biostatistics, Rammelkamp Center for Education and Research, MetroHealth Campus, Case Western Reserve University, Cleveland, Ohio, USA.
Elston
Robert C
RC
D016000:Cluster Analysis; D003198:Computer Simulation; D015870:Gene Expression; D008040:Genetic Linkage; D006801:Humans; D015999:Multivariate Analysis; D010375:Pedigree