PPM 03-0227-190008

HLA plays a pivotal role in the immune system by keeping self tolerance in check and presenting foreign antigens for targeted destruction. There are hundreds of known alleles for HLA genes in humans, and many have been associated with over 100 autoimmune diseases and cancers. Allelic variation affects HLA proteins structure and function, but also gene expression. There is ample evidence that expression levels associate with disease outcome, and in the GWAS catalogue, around 50% of reported loci are in intronic and regulatory regions. Hence, assessing expression of HLA genes is an integral part of understanding their role in infectious and autoimmune disease, and will open up venues for better diagnostics and therapies. Our goal in this project is to exploit QGP genomic and transcriptomic data for this purpose. Since the HLA genomic region is complex, special bioinformatics tools are needed to align and interpret short read sequences generated by NGS platforms to this region. We have already used WGS data to generate Class I & II HLA typing for 6000 Qatar Genome Project samples using such tailored methods, and will match those with 3000 RNAseq data being currently generated, to investigate expression. This dataset, together with phenotypic data from Qatar BioBank, will provide unprecedented resource to determine allele-specific expression, eQTLs, and phenotype associations. In addition, we intend to do long read sequencing for samples with altered expression to map out transcript isoforms. Results generated from this project will be submitted to public databases and published as a peer-reviewed journal article. This study will be the first on this scale and will greatly advance knowledge on the complex expression of HLA. It may enable scientists to predict the disease outcome in advance, particularly in autoimmune, infectious and GVH diseases.