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Inherited Risk Enrichment Analysis ofgene sets using Genome-wide AssociationStudies for Coronary Artery Disease
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Genome-wide association studies (GWAS) has been in the heartof medical research for the last 5 years. These studies seek forcommon variants in the genome that are linked to risk for commoncomplex diseases (CCDs). Although GWAS has defined a numberof interesting genetic loci for a range of CCDs, the current GWASanalysis has limitation such as investigating the DNA variantsone-by-one focusing on the most significant DNA variants. As aconsequence, most risk variants for CCDs are, in my belief, stillhidden in the GWAS data. Herein, I use a method of GWASanalysis that considers risk-enrichment for groups of functionallyassociated genes defined by for example gene networks, believedto play a role in CCDs.In this method, a set of expression SNP (single nucleotidepolymorphism) was selected from genes which are known to berelated to coronary artery disease (CAD) in a way that a singleeSNP was chosen for each gene. Then using the data availablefrom the International HapMap Project and a GWAS data available,it is possible to find SNPs which are in strong linkage withthe initial set, which we call it expanded set. Depending on theassociation of the initial set to the CAD, expanded set can showan enrichment score greater or smaller compared to the null distributionset of SNPs with same properties of the expanded set.In conclusions, CCDs are not a consequence of isolated geneticvariants/genes in isolated pathways but instead sets of geneticvariants/genes acting in conjunction, cause CAD. Genetic riskenrichment analysis is a fairly simple and straightforward methodto determine to what extent a group of functionally associatedgenetic variants/genes are enriched for a given CCD. In addition,this analysis can perhaps help to decipher some of the 90-85% ofrisk variation in populations that remains unaccounted.

Place, publisher, year, edition, pages
2013.
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:kth:diva-143501OAI: oai:DiVA.org:kth-143501DiVA: diva2:706748
Educational program
Master of Science in Engineering - Biotechnology
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Examiners
Available from: 2015-04-20 Created: 2014-03-21 Last updated: 2015-04-20Bibliographically approved

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