Uddin, Mohammed (2009) A co-operative co-evolutionary genetic algorithm for haplotype pattern detection in case-control data. Masters thesis, Memorial University of Newfoundland.
- Accepted Version
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Genomic variations such as Single Nucleotide Polymorphisms (SNP) and their underlying haplotype patterns in case-control cohorts are used to identify genes associated with diseases. Complex diseases involve multiple genes which may be distributed over the genome. A popular technique for detecting such markers and patterns is the sliding window technique using statistical models. However, the statistical techniques used are computationally expensive, and derived patterns are typically restricted both in length and to consist of contiguous markers. In this thesis, we have developed a cooperative coevolutionary genetic algorithm (CCGA) that can compute both contiguous and non-contiguous marker haplotype patterns from case-control haplotype data; moreover, this algorithm can tolerate missing/ambiguous positions in haplotype data arising during haplotype phasing from genotypes. -- We have tested our algorithm on three case-control cohorts (the Ankylosing Spondilitis (AS) inflammatory arthritis cohorts from Alberta (AL) and Newfoundland (NF) populations (genotyped for the IL1 gene cluster on chromosome 2) and the Japanese Schizophrenia cohort (genotyped for the Netrin Gl gene on chromosome 1). The results obtained using our CCGA are in strong accordance with previously published results. Specifically, (1) in the AL spondylitis cohort, we have found significant haplotype patterns (p < 0.0005 and haplotype risk ratio ≥ 1.5) that confer susceptibility of four genes (ILIA, IL1B, IL1F7 and IL1F10) with AS, three of which (ILIA, IL1B, IL1F10) were confirmed by two independent studies; and (2) in the Japanese schizophrenia cohort, 7 SNPs (rs4481881, rs4307594, rs3924253, rs4132604, rsl373336, rsi444042, and rs96501) and their haplotypes showed significant (p < 0.0005 and haplotype risk ratio ≥ 1.5) association with schizophrenia, the most significant of which (rs4307594, rs3924253, and rsl373336) were confirmed by two independent studies.
|Item Type:||Thesis (Masters)|
|Additional Information:||Includes bibliographical references (leaves 92-101)|
|Department(s):||Science, Faculty of > Computer Science|
|Library of Congress Subject Heading:||Genetic algorithms; Genomics; Haploidy--Mathematical models; Medical genetics--Mathematical models|
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