Epistasis in complex human traits - Bell, J; Bell, Jordana Tzenova (2006)
Epistasis (gene-gene interaction) is a universal component of common complex genetic traits. The identification and characterization of epistatic interactions are crucial to a full understanding of the complex genetic mechanisms that underlie human disease. The aim of this thesis is to examine epistasis in non-parametric linkage analysis of human complex traits, with an emphasis on the affected sibling pair (ASP) study design. Following an overview of approaches that model and detect epistasis in linkage analysis of human complex traits, I present an extension of a two-locus nonparametric linkage method in ASPs. The new two-locus approach, Merloc, jointly models pair-wise interactions between susceptibility loci in different types of affected relative pairs and estimates of the most likely underlying genetic model for a pairwise interaction, implemented to genome-wide applications. To test the performance of the approach, Merloc was compared to two multilocus non-parametric conditional linkage approaches. Power and type I error rates under null, single-locus, and twolocus genetic models of epistasis and heterogeneity indicated that Merloc outperformed the other methods. The method was applied to type 2 diabetes data to assess the evidence for epistasis between two susceptibility loci. Significant evidence for epistasis was obtained supporting previous findings from conditional interaction analysis. A search through the space of parametric two-locus models indicated that nine two-locus models best approximated the pair-wise interaction. Genome-wide strategies to detect epistasis were also examined in this thesis and the simultaneous search for genome-wide interactions was explored in detail. Two-dimensional (2D) linkage scans were performed using Merloc in three complex traits, essential hypertension, autism, and type 2 diabetes. Several peaks were detected in the two-dimensional likelihood surfaces with genome-wide suggestive evidence for linkage. Extensive simulations were used to examine the distribution of the test statistic under the null hypothesis in the context of two-dimensional linkage scans. Finally, two main extensions of this approach were considered - linkage approaches to examine more than two loci, and extending the method in this study to include a test of association.