Incorporating Quantitative Covariates into Simultaneous Localization of Two Linked Loci Using Affected Relative Pairs

Abstract

Many dichotomous traits for complex diseases often involve more than one locus and can be associated with quantitative biomarkers or environmental factors. Incorporating these quantitative variables into linkage analyses as well as localizing two linked disease loci simultaneously could therefore improve the efficiency in mapping genes. Previously, we proposed a robust multipoint Identity-by-Descent (IBD) approach to estimate a disease locus using affected sib pairs with incorporation of a quantitative covariate. In the present study, we extended this approach to simultaneously estimate two linked loci using different types of affected relative pairs (ARPs). We showed that efficiency is enhanced by localizing two disease loci simultaneously and by using relative pairs rather than using affected sib pairs alone after incorporating a quantitative covariate through parametric or non-parametric modeling. In addition, to being able to identify factors associated with the disease and to improve the efficiency in estimating disease loci, this extension also allows us to account for heterogeneity in risk ratios for different ARPs. The collaborative study on the genetics of alcoholism (COGA) data released for GAW14 was used to illustrate the application of this extended method. The quantitative variable “maximum number of drinks in a 24 hour period" was incorporated into the linkage mapping when searching for two linked disease loci simultaneously using affected relative pairs. This example illustrates that the efficiency in estimating disease loci was enhanced by incorporating a quantitative covariate, by using all relative pairs as well as by mapping two linked loci simultaneously.