Statistical genetics in infectious disease susceptibility
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Date
06/07/2013Item status
Restricted AccessEmbargo end date
31/12/2100Author
Baillie, John Kenneth
Metadata
Abstract
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promise and was ultimately withdrawn from the market in dismal failure.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.