Développement de méthodes bio-informatiques pour la découverte de variants codants et non codants dans le cadre des traits sanguins
Cardiovascular health, immune function, hemostasis and the response to other illnesses depend on the abundance and specific features of blood cells. Over the years, a considerable effort has been made to find which genetic variants, genes and regulatory mechanisms are involved in the creation of these cells.  The inactivation of an allele, called a loss-of-function (LoF), is a type of coding variant we would like to associate with blood phenotypes. For obvious ethical reasons, these mutations cannot be artificially induced in human, so we fall back on natural occurrences and hope that large cohorts will provide enough samples to find statistically significant associations.  The inactivation of both alleles, called a knockout (KO), may have stronger consequences than a simple loss-of-function. We also hope to find naturally occurring knockouts thanks to the size of a large cohort. The combination of two different LoF variants is called a compound heterozygote knockout.  We are also interested in non-coding variants that affect the expression of genes that are involved in hematopoiesis. Some of these variants create or disrupt the binding sites of transcription factors (TF), the proteins that bind to specific DNA sequences and regulate gene expression. Transcription factors binding sites (TFBS) are found in gene promoters and cell type specific enhancers.  While some of these mutations can be benign or even beneficial, the presence of a LoF or KO may be too detrimental for the individual to survive. The results of this study are limited by survival bias.  Compared to a genome-wide association study, this study focuses on a smaller number of genetic variants to increase statistical power and give an interpretation to the statistically significant findings.  The Trans-Omics for Precision Medicine (TOPMed) program collects and ensures the quality of the 45,000 whole-genome sequences we used in this study, as well as the corresponding complete blood counts. Thanks to this raw data, we were able to find several known and novel associations between rare variants and blood phenotypes.
GWAS SNP meta-analysis Gene expression Hematopoiesis Transcription factor Promoter