Nowadays with the newest technologies, the entire genome can be explored to uncover genetic variants which may be linked to diseases. This requires bioinformatics tools which are adequate for studies which are at the border between computing, statistics and biology. My thesis work focused on the bioinformatical analysis of genomic data from the GRIV AIDS cohort and from the IHAC (International HIV Acquisition Consortium) project. I first laid the foundation for imputation work by developing the SUBHAP software. Our team showed that the HLA region was essential in non-progression and viral charge control. This led me to study the non progressor non elite phenotype. Thus, I uncovered a variant of the CXCR6 gene which is, apart from HLA, the only result identified with a GWAS approach so far and which has been reproduced. The imputation of data from the IHAC project (10000 infected patients and 15000 control subjects) was also performed and the first associations are now being studied.
