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Introduction: Myeloid cells trafficking from the periphery to the central nervous system are key players in multiple sclerosis (MS) through antigen presentation, cytokine secretion and repair processes. Methods: Combination of mass cytometry on blood cells from 60 MS patients at diagnosis and 29 healthy controls, along with single cell RNA sequencing on paired blood and cerebrospinal fluid (CSF) samples from 5 MS patients were used for myeloid cells detailing. Results: Myeloid compartment study demonstrated an enrichment of a peculiar classical monocyte population in 22% of MS patients at the time of diagnosis. Notably, this patients' subgroup exhibited a more aggressive disease phenotype two years post-diagnosis. This monocytic population, detected in both the CSF and blood, was characterized by CD206, CD209, CCR5 and CCR2 expression, and was found to be more frequent in MS patients carrying the HLA-DRB1*15:01 allele. Furthermore, pathways analysis predicted that these cells had antigen presentation capabilities coupled with pro-inflammatory phenotype. Discussion: Altogether, these results point toward the amplification of a specific and pathogenic myeloid cell subset in MS patients with genetic susceptibilities. Multiple sclerosis patients were included at diagnosis, for mass cytometry or single-cell RNA sequencing studies (scRNA-seq). Differential abundance analysis on cytof data indicated the exclusive blood enrichment in circulating myeloid cells co-expressing CD14, CCR5, CD206, and CD209 in a group of MS patients. Characterization of these patients demonstrated a significantly higher proportion of HLA-DRB1*15-01 patients allotype and a worsen outcome after 2 years follow up compared to other patients. scRNA-seq analysis confirmed the pathogenic potential of this monocyte subset through their definition as antigen presenting pro-inflammatory cells. [ABSTRACT FROM AUTHOR]

The SNP‐HLA Reference Consortium (SHLARC), a component of the 18th International HLA and Immunogenetics Workshop, is aimed at collecting diverse and extensive human leukocyte antigen (HLA) data to create custom reference panels and enhance HLA imputation techniques. Genome‐wide association studies (GWAS) have significantly contributed to identifying genetic associations with various diseases. The HLA genomic region has emerged as the top locus in GWAS, particularly in immune‐related disorders. However, the limited information provided by single nucleotide polymorphisms (SNPs), the hallmark of GWAS, poses challenges, especially in the HLA region, where strong linkage disequilibrium (LD) spans several megabases. HLA imputation techniques have been developed using statistical inference in response to these challenges. These techniques enable the prediction of HLA alleles from genotyped GWAS SNPs. Here we present the SHLARC activities, a collaborative effort to create extensive, and multi‐ethnic reference panels to enhance HLA imputation accuracy. [ABSTRACT FROM AUTHOR]

Human genomics has quickly evolved, powering genome‐wide association studies (GWASs). SNP‐based GWASs cannot capture the intense polymorphism of HLA genes, highly associated with disease susceptibility. There are methods to statistically impute HLA genotypes from SNP‐genotypes data, but lack of diversity in reference panels hinders their performance. We evaluated the accuracy of the 1000 Genomes data as a reference panel for imputing HLA from admixed individuals of African and European ancestries, focusing on (a) the full dataset, (b) 10 replications from 6 populations, and (c) 19 conditions for the custom reference panels. The full dataset outperformed smaller models, with a good F1‐score of 0.66 for HLA‐B. However, custom models outperformed the multiethnic or population models of similar size (F1‐scores up to 0.53, against up to 0.42). We demonstrated the importance of using genetically specific models for imputing populations, which are currently underrepresented in public datasets, opening the door to HLA imputation for every genetic population. [ABSTRACT FROM AUTHOR]

• We analyzed Multiple Sclerosis sources of variability in 403 French family trios. • 65 % of variability was explained by genetics, region of birth and their interaction. • A South-West to North-East risk gradient was observed. • The remaining 35 % of variability was explained by other environmental factors. This study aimed to investigate the factors contributing to the variability of Multiple Sclerosis (MS) among individuals born and residing in France. Geographical variation in MS prevalence was observed in France, but the role of genetic and environmental factors in explaining this heterogeneity has not been yet elucidated. We employed a heritability analysis on a cohort of 403 trios with an MS-affected proband in the French population. This sample was retrieved from REFGENSEP register of MS cases collected in 23 French hospital centers from 1992 to 2017. Our objective was to quantify the proportion of MS liability variability explained by genetic variability, sex, shared environment effects, region of birth and year of birth. We further considered gene x environment (GxE) interaction effects between genetic variability and region of birth. We have implemented a Bayesian liability threshold model to obtain posterior distributions for the parameters of interest adjusting for ascertainment bias. Our analysis revealed that GxE interaction effects between genetic variability and region of birth represent the primary significant explanatory factor for MS liability variability in French individuals (29 % [95 %CI: 5 %; 53 %]), suggesting that additive genetic effects are modified by environmental factors associated to the region of birth. The individual contributions of genetic variability and region of birth explained, respectively, ≈15 % and ≈16 % of MS variability, highlighting a significantly higher MS liability in individuals born in the Northern regions compared to the Southern region. Overall, the joint contribution of genetic variability, region of birth, and their interaction was then estimated to explain 65 % [95 %CI: 35 %; 92 %] of MS liability variability. The remaining proportion of MS variability is attributed to environmental exposures associated with the year of birth, shared within the same household, and specific to individuals. Overall, our analysis highlighted the interaction between genetic variability and environmental exposures linked to the region of birth as the main factor explaining MS variability within individuals born and residing in France. Among the environmental exposures prevalent in the Northern regions, and potentially interacting with genetic variability, lower vitamin D levels due to reduced sun exposure, higher obesity prevalence and higher pollution levels represent the main risk factors in influencing MS risk. These findings emphasize the importance of accounting for environmental factors linked to geographical location in the investigation of MS risk factors, as well as to further explore the influence of GxE interactions in modifying genetic risk. [ABSTRACT FROM AUTHOR]

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