The Kit-FIG project successfully provided major results published in peer review papers publication (Vince et al., Gen Ep, 2020; Vince et al., JACI, 2020; Geffard et al., Bioinf., 2020; Sayadi et al., AICCSA, 2020; Sayadi et al., AINA, 2021), scientific presentation in conferences (8 oral presentations and 7 posters at European Federation of immunogenetics, American Society of Human Genetics, MCAA) and general public outreach (Nantes Utopiales, Researchers’ night).
Specifically, KiT-FIG ensured major advances in HLA imputation. Though HLA imputation methods exist, no unified effort has yet been undertaken to share large and diverse imputation models, or to improve methods. By generating unique reference panels, we highlighted the importance of (a) the number of individuals in reference panels, with a twofold increase in accuracy (from 10 to 100 individuals) and (b) the number of SNPs, with a 1.5‐fold increase in accuracy (from 500 to 24,504 SNPs). Building on these results, we created the SNP‐HLA Reference Consortium (SHLARC) to gather data, enhance HLA imputation and broaden access to highly accurate imputation models for the immunogenomics community. This consortium is funded by a grant obtained from the University of Nantes (NExT project, 400k€, PI: Nicolas Vince) and gather 32 teams across 16 countries.
To go beyond HLA allelic data and provide better biologically relevant in silico functional immunogenomic parameters, we built Easy-HLA (hla.univ-nantes.fr) a web-based software suite designed to facilitate analysis and gain knowledge from HLA typing. Easy-HLA implements a computational and statistical method of HLA haplotypes inference based on published reference populations containing over 600,000 haplotypes. Easy-HLA is freely accessible to all, and we already count 300 users worldwide so far.
With our specific bioinformatic tools we studied the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) as a practical example. We ran the first HLA-centric association study with asthma and specific asthma related phenotypes in this large cohort. We showed that HLA-DRB1*09:01 was associated with increased tIgE levels (P=8.5x10-4 weighted effect size 0.51 [0.15-0.87]). Our study emphasizes that by leveraging powerful computational machine-learning methods, specific/extreme phenotypes, and population diversity, we can explore HLA gene polymorphisms in depth and reveal the full extent of complex disease associations.
