Gene x gene interactions in genome wide association studies - Bhattacharya, K (2014)
Genome wide association studies (GWAS) have revolutionized our approach to mapping genetic determinants of complex human diseases. However, even with success from recent studies, we have typically been able to explain only a fraction of the trait heritability. GWAS are typically analysed by testing for the marginal effects of single variants. Consequently, it has been suggested that gene-gene interactions might contribute to the missing heritability of complex diseases. GWAS incorporating interaction effects have not been routinely applied because of statistical and computational challenges relating to the number of tests performed, genome-wide. To overcome this issue, I have developed novel methodology to allow rapid testing of pairwise interactions in GWAS of complex traits, implemented in the IntRapid software. Simulations demonstrated that the power of this approach was equivalent to computationally demanding exhaustive searches of the genome, but required only a fraction of the computing time. Application of IntRapid to GWAS of a range of complex human traits undertaken by the Wellcome Trust Case Control Consortium (WTCCC) identified several interaction effects at nominal significance, which warrant further investigation in independent studies. In an attempt to fine-map the identified interacting loci, I undertook imputation of the WTCCC genotype data up to the 1000 Genomes Project reference panel (Phase 1 integrated release, March 2012) in the neighbourhood of the lead SNPs. I modified the IntRapid software to take account of imputed genotypes, and identified stronger signals of interaction after imputation at the majority of loci, where the lead SNP often had moved by hundreds of kilobases. The X-chromosome is often overlooked in GWAS of complex human traits, primarily because of the difference in the distribution of genotypes in males and females. I have extended IntRapid to allow for interactions with the X chromosome by considering males and females separately, and combining effect estimates across the sexes in a fixed-effects meta-analysis. Application to genotype data from the WTCCC failed to identify any strong signals of association with the X-chromosome, despite known epidemiological differences between the sexes for the traits considered. The novel methods developed as part of this doctoral work enable a user friendly, computationally efficient and powerful way of implementing genome-wide gene-gene interaction studies. Further work would be required to allow for more complex interaction modelling and deal with the associated computational burden, particularly when using next-generation sequencing (NGS) data which includes a much larger set of SNPs. However, IntRapid is demonstrably efficient in exhaustively searching for pairwise interactions in GWAS of complex traits, potentially leading to novel insights into the genetic architecture and biology of human disease.