Abstract of Thesis presented at COPPE/UFRJ as a partial fulfillment of the requirements for the degree of Doctor of Science (D.Sc.)

A Methodology to Discover Genetic Markers in Association Studies

Margarita Ramona Ruiz Olazar

May/2013

Advisor:

Eugenius Kaszkurewicz

Department:

Eletrical Engineering

      This work presents a methodology to discover genetic markers (SNPs) in GWAS covering from fundamental aspect of data quality control until the identification of the haplotypes that suggest risk of developing of the disease under study. The presented methodology is based on workflow technologies to take advantage of the flexible characteristics offered by the workflow engine to model and manage resources and reduce the time needed to perform the complex analysis involved in the fundamental steps in a GWAS, starting from raw data. A algorithm for test interaction SNP-SNP was developed, called MIGA-2L, that is based on mútual information in combination with a genetic algorithm that runs on masks of groups of SNPs to optimize the search. The methodology was tested on 82 different epistatic models of simulated datasets and also on five WTCCC dataset (Diabetes Type 1, Diabetes Type 2, Coronary artery disease, Hypertension and Bipolar disorder) from United Kingdom. A comparative analysis of the algorithm MIGA-2L was performed with the program Plink which is typically used in this type of association studies, these experiment were ran on a cluster SGI Altix ICE 8400 using the dataset mentioned above. Showing these results with computational as epidemiologic performance measures. The results obtained confirm that this methodology can be a useful computational tool to perform genome-wide case-control studies on real datasets.