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Exploration of SNP-set Interactions in Genome-Wide Association Studies Open Access


Other title
Logic regression
SNP-SNP interaction
Type of item
Degree grantor
University of Alberta
Author or creator
Alam, Shomoita
Supervisor and department
Yasui, Yutaka (School of Public Health)
Examining committee member and department
Yuan, Yan (School of Public Health)
Dinu, Irina (School of Public Health)
Yasui, Yutaka (School of Public Health)
Department of Public Health Sciences
Date accepted
Graduation date
Master of Science
Degree level
Advance in biotechnologies has enabled genome-wide association studies (GWAS) that scan the entire human genome for understanding genetic contributions to the risk of a certain disease as well as to variation in treatment efficacy and side effects. In GWAS, the association between each single-nucleotide polymorphism (SNP) and a phenotype is assessed statistically, typically analyzing one single SNP at a time, ignoring potential SNP-SNP interactions. Such individual-SNP analysis approaches have extracted small fractions of expected genetic contributions to disease risks: this has been recognized as “the missing heritability problem” of GWAS. Biologically, it is highly unlikely that a single SNP alone would determine disease risk, especially for complex chronic diseases. We therefore tested whether biological interactions among multiple SNPs determine disease risks and whether it can explain the missing heritability problem. The methodologies proposed in this work take into account the interaction between selected SNP-sets using two methods: (1) method based on logic regression that incorporates two specific forms of interaction; and (2) method based on SNP-pair analysis which is an exploration of genotypes that are only observed in cases with a sufficient frequency and with no control having the same specific genotypes. Both methods could identify many previously-found and novel susceptibility genes for the datasets we tested on, although validation studies are required to avoid spurious findings. While our results do not provide a satisfactory solution to the ``missing heritability” problem, they show the importance of considering SNP interactions and their exploration in considering genetic contributions of disease etiology, prevention and treatment.
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