Detection of epistasis in genome-wide association studies

In the last decade, single nucleotide polymorphisms (SNPs) have been used as the basis for genomewide association studies (GWAS); large-scale studies examining hundreds of thousands of SNPs across a large number of individuals for a given condition. Analysis of GWAS typically involves examining each of these markers individually to determine whether they are associated with the condition of interest. While such studies have been successfully able to detected novel associations between genetic variations and certain conditions, few GWAS have been able to fully determine all genetic factors that influence complex traits, traits caused by a combination of multiple genetic and environmental factors. This problem of “missing heritability” illustrates the limited predictive power and biological understanding obtained from GWAS using current analysis techniques. While many hypotheses exist as to why missing heritability is observed, a commonly-held belief is that the univariate analysis typically conducted in GWAS whereby SNPs are examined one by one is unlikely to match the complexity of most biological systems. Biologist have long recognised the importance of genetic interactions, commonly referred to as epistasis. From studies of model organisms, epistasis has been shown to play a strong role in influencing many traits with a genetic basis. Furthermore, the existence of physical interactions between molecules involved in gene-regulation and biochemical and metabolic systems has been well documented. It has been suggested that such biological interactions are likely to be reflected by interaction of genetic loci. Thus, further study of genetic interactions may help to detect variants that cannot be detected from a purely univariate approach. This thesis contributes novel methods that overcome several statistical and computational difficulties that arise from exhaustively analysing all pairwise interactions between SNPs in large-scale GWAS and validates these approaches across several GWAS. After exploring the range of definitions for interaction, in both a biological and statistical sense, and describing the statistical approaches that are currently used to detect these in association studies, we introduce a novel family of exact, unconditional statistics designed specifically for the analysis of GWAS. Within this family of statistics, we focus on developing a test for interaction with several advantageous properties compared to current statistical methods. We then explore computational approaches for fast tabulation of genotype frequencies, required for all commonly used interaction statistics, and develop a highly parallelized framework for i conducting interaction analysis on the IBM Blue-Gene supercomputer. A comparison with other current methods indicates that the developed interaction framework is faster than the current state-of-the-art. The novel statistics and computational framework developed in this work are then applied to an interaction analysis of five independent celiac studies. We detect numerous interaction effects between SNPs that are statistically significant and replicate across multiple studies under a wide range of criteria. We show that the detected interactions contribute novel signal which is not captured by known risk factors. Finally, we conduct an comparison and characterisation of all statistical tests examined in this work by exploring their performance across twelve GWAS using empirically-driven signals that are typically not represented in simulation studies.