Investigating a variance-components approach for linkage analysis in quantitative traits

Model-based linkage methods have had limited success in locating quantitative trait loci (QTLs) in complex traits since the underlying genetic mechanisms are not well known. As a result, robust or model-free approaches for detecting linkage have grown in popularity. We discuss a mixed effects model, which involves the estimation of genetic and non-genetic variance components, as well as recombination fractions. Using the Genometric Analysis Simulation Program (GASP), we first attempt to investigate the properties of this method on simple traits, which differ in terms of their variance components. To further understand its performance in a complex setting, we apply this method to simulated, familial data for an oligogenic disease with quantitative risk factors from the 10th Genetic Analysis Workshop (GAW10). We see that the ability of the variance-components approach to map QTLs depends on the amount of variability it contributes to the quantitative trait. As well, we find that the presence of the recombination fraction in the model results in consistent estimates of the variance components across the chromosome; however, it does not seem to improve the mapping ability of the model.Science, Faculty ofStatistics, Department ofGraduat