Abstract: Most complex disorders have binary end-points defined by the affection status of an individual. However, these clinical end-point traits are typically determined on the basis of values of a set of heritable quantitative characters. These precursor variables are continuous in nature and contain more information on inter-individual variability than binary clinical end-points. Thus, it has been argued that analyzing quantitative precursors, including mapping genes controlling them, may be statistically a more powerful strategy for deciphering the architecture of a complex disease.

We (Ghosh and Majumder 2000) proposed a non-parametric regression method for linkage based on kernel smoothing using independent sib-pairs which estimates the empirical local relationship of the underlying variables. In this talk, we discuss some modifications of this method to incorporate more phenotypic information and sibship data and show that it is more powerful than the Haseman-Elston class of linear regression.

However, a single quantitative trait is ideally not a good surrogate for a binary clinical end-point. Thus, it may be more meaningful to use a correlated multivariate phenotype for identifying genes for a complex trait. In this talk, we propose, along the lines of Sham et al. (2002), a linear regression formulation in which the traditional response variable, that is, some function of the multivariate phenotype and the explanatory variable, that is, the genetic similarity between sib-pairs are interchanged.

Applications of the proposed non-parametric method as well as the multivariate phenotype approach are illustrated using data on alcoholism related phenotypes from the Collaborative Study On the Genetics Of Alcoholism project.