Mapping genotype to phenotype for linkage analysis

We model functions that use genetic information as input and trait informat ion as output to understand genetic linkage in complex diseases. Using simu lated data from GAW11, we have applied categorical classification methods a nd neural network analysis. We use sharing at selected markers as input, an d the classification of the sib pair (for example, affected-affected or aff ected-unaffected) as output. In addition, our methods include environmental risk factors as predictors of phenotype. Categorical and neural network me thods each led to results consistent with findings from other methods such as the logistic regression method of Rice et al. [this issue]. Post-analysi s comparison with the GAW11 answers showed that these methods are capable o f detecting correct signals in a single replicate. One advantage of our met hods is that they allow analysis of the entire genome at once, so that inte ractions among multiple trait-influencing loci may be detected. Furthermore , these methods can use a variety of sib pairs rather than affected pairs o nly. (C) 1999 Wiley-Liss, Inc.