Statistical genetics in infectious disease susceptibility
Item statusRestricted Access
Embargo end date31/12/2100
Baillie, John Kenneth
Death from infectious disease is common heritable, and in many cases a consequence of the host response, rather than direct effects of the pathogen. Since the host response in sepsis is orchestrated by the transmission of a variety of signals, both intra-cellular and inter-cellular, with which we have at least some capacity to intervene, it follows that it should be possible to prevent death through pharmaceutical modulation of inflammatory cascades. So far, it is not. The best candidate therapy for sepsis, activated protein C, failed to live up to initial promise and was ultimately withdrawn from the market in dismal failure. The premise of the work presented here is that a different approach – to develop an understanding of the host response at a genomic level – may yield more tractable insights, specifically into the problem of host susceptibility to influenza, a heritable cause of death in otherwise healthy people and a significant global threat. Since the sequencing of the human genome, it has become possible to identify genomic loci underlying host susceptibility to disease using genome-wide association studies (GWAS), best exemplified by the Wellcome Trust Case Control Consortium. This new technology creates substantial new challenges. The genetic markers associated with a phenotype are rarely causative, frequently in poorly-understood intergenic regions, and tend to have small effect sizes, such that tens or even hundreds of thousands of subjects must be recruited to have sufficient power to detect them. It is therefore not straightforward to translate these genotype-phenotype associations into useful understanding of the role of genes and gene products in disease pathogenesis. Attempts to overcome these challenges in order to discover genomic loci underlying individual susceptibility to infection form the core of this thesis. Ultimately these efforts converge with the development of a new computational method to detect phenotype-associated loci from genome-wide association studies (GWAS) using co-expression at regulatory regions of the genome.