MULTIVARIATE GENETIC-ANALYSIS OF AN OLIGOGENIC DISEASE

Joint multivariate segregation and linkage analysis provides a method for simultaneously analyzing data on affection status, correlated phen otypic traits, environmental risk factors, and other covariates. The p ower of this approach for mapping disease susceptibility loci of small effect (oligogenes) was evaluated by analyzing the GAW9 Problem 2 dat a set. The program REGRESS, which assumes a pleiotropy model in which one locus influences both affection status (AF) and a quantitative tra it, was used to conduct joint segregation and linkage analysis of biva riate phenotypes, each comprising AF and one quantitative trait (Q2,Q3 ,Q4). A genome-wide search using markers spaced approximately 10 cM ap art was conducted and regions on chromosomes 1, 2, and 5 were identifi ed as demonstrating linkage with three respective bivariate phenotypes at the following markers: AF/QZ - D1G2; AF/Q3 - D2G10; and AF/Q4 - D5 G18. The effects of other loci were included in a general model by spe cifying the quantitative traits they influenced as covariates along wi th age, sex, and an environmental effect. Use of covariate and quantit ative trait data in each analysis resulted in respective chi(2) values with 1 df of 38.4, 65.4, and 22.0 to reject the no linkage hypothesis at <(theta)over cap> = 0, with respective equivalent lod scores of 8. 3, 14.2, and 4.8. Rejection at p < 0.0002 occurred using markers as fa r away as 20 cM. These loci were not detected when AF alone was analyz ed. (C) 1995 Wiley-Liss, Inc.