|dc.description.abstract||Of central importance in the dissection of the components that govern complex traits is understanding
the architecture of natural genetic variation. Genetic interaction, or epistasis,
constitutes one aspect of this, but epistatic analysis has been largely avoided in genome wide
association studies because of statistical and computational difficulties. This thesis explores
both issues in the context of two-locus interactions.
Initially, through simulation and deterministic calculations it was demonstrated that not only
can epistasis maintain deleterious mutations at intermediate frequencies when under selection,
but that it may also have a role in the maintenance of additive variance. Based on the epistatic
patterns that are evolutionarily persistent, and the frequencies at which they are maintained, it
was shown that exhaustive two dimensional search strategies are the most powerful approaches
for uncovering both additive variance and the other genetic variance components that are co-precipitated.
However, while these simulations demonstrate encouraging statistical benefits, two dimensional
searches are often computationally prohibitive, particularly with the marker densities and sample
sizes that are typical of genome wide association studies. To address this issue different
software implementations were developed to parallelise the two dimensional triangular search
grid across various types of high performance computing hardware. Of these, particularly effective
was using the massively-multi-core architecture of consumer level graphics cards. While
the performance will continue to improve as hardware improves, at the time of testing the speed
was 2-3 orders of magnitude faster than CPU based software solutions that are in current use.
Not only does this software enable epistatic scans to be performed routinely at minimal cost,
but it is now feasible to empirically explore the false discovery rates introduced by the high
dimensionality of multiple testing. Through permutation analysis it was shown that the significance threshold for epistatic searches is a function of both marker density and population
sample size, and that because of the correlation structure that exists between tests the threshold
estimates currently used are overly stringent.
Although the relaxed threshold estimates constitute an improvement in the power of two dimensional
searches, detection is still most likely limited to relatively large genetic effects. Through
direct calculation it was shown that, in contrast to the additive case where the decay of estimated
genetic variance was proportional to falling linkage disequilibrium between causal variants and
observed markers, for epistasis this decay was exponential. One way to rescue poorly captured
causal variants is to parameterise association tests using haplotypes rather than single markers.
A novel statistical method that uses a regularised parameter selection procedure on two locus
haplotypes was developed, and through extensive simulations it can be shown that it delivers a
substantial gain in power over single marker based tests.
Ultimately, this thesis seeks to demonstrate that many of the obstacles in epistatic analysis
can be ameliorated, and with the current abundance of genomic data gathered by the scientific
community direct search may be a viable method to qualify the importance of epistasis.||en
|dc.contributor.sponsor||Biotechnology and Biological Sciences Research Council (BBSRC)||en
|dc.publisher||The University of Edinburgh||en
|dc.relation.hasversion||Hemani G, Theocharidis A, Wei W, Haley CS. EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards. Bioinformatics (2011) 27 (11): 1462-1465.||en
|dc.relation.hasversion||Hadjipavlou G, Hemani G, Leach R, Louro B, Nadaf J, Rowe S, de Koning DJ. Extensive QTL and association analyses of the QTLMAS 2009 Data. BMC Proceedings (2010) 4:S1 11.||en
|dc.subject||genome-wide association study||en
|dc.subject||high performance computing||en
|dc.title||Dissecting genetic interactions in complex traits||en
|dc.type||Thesis or Dissertation||en
|dc.type.qualificationname||PhD Doctor of Philosophy||en