I work on the genomic analyses of immune-mediated diseases mainly using the GWAS approach to identifiy regions of the genome associated with diseases, and differences between diseases. My current focus are on fine-mapping and follow-up of GWAS-identified regions using statistical algorithms and functional genomic datasets, as well as genomic comparison between diseases.
My research aims to better understand the genetic component of autoimmune diseases. During my PhD I’ve been involved in handling data with up to 10 million genetic variants from more than 100,000 individuals. A lot of my time was spent in painstakingly quality controlling (QC) and normalizing data generated from multiple platforms in multiple centres over different periods of time. The goal was, first to identify genetic variants that predispose individuals to each disease, second to link localized signals in the genome to genes and biological pathways affected by each disease, and third to compare patterns of genetic markers between related diseases. I set up and optimized two major pipelines during this time, which was a pipeline to detect copy number variants (CNVs), and a pipeline to conduct imputation. I identified novel regions of the genome that confer risk to each disease and each independent region went through many efforts to pinpoint causal variants, genes and then pathways through Bayesian finemapping procedures, and functional genomic annotations (chromatin openness, eQTL, prediction algorithms, etc.). I further performed meta-analyses and pleiotropic analyses to identify regions of the genome affecting more than one disease, through co-localization tests for overlaps to compare local associations between diseases, as well as genetic correlation estimation to compare genome-wide patterns. I also have previous experience in using networks to predict gene function in bacterial pathogens, and some molecular biology experiments. I am currently interested in understanding pleiotropy, and how these overlapping, yet different genetics lead to different diseases.