Description and first insights on a large genomic biobank of lung transplantation.

The main limitation to long-term lung transplant (LT) survival is chronic lung allograft dysfunction (CLAD), which leads to irreversible lung damage and significant mortality. Individual factors can impact CLAD, but no large genetic investigation has been conducted to date. We established the multicentric Genetic COhort in Lung Transplantation (GenCOLT) biobank from a rich and homogeneous sub-part of COLT cohort. GenCOLT collected DNA, high-quality GWAS (genome-wide association study) genotyping and robust HLA data for donors and recipients to supplement COLT clinical data. GenCOLT closely mirrors the global COLT cohort without significant variations in variables like demographics, initial disease and survival rates (P > 0.05). The GenCOLT donors were 45 years-old on average, 44% women, and primarily died of stroke (54%). The recipients were 48 years-old at transplantation on average, 45% women, and the main underlying disease was chronic obstructive pulmonary disease (45%). The mean follow-up time was 67 months and survival at 5 years was 57.3% for the CLAD subgroup and 97.4% for the non-CLAD subgroup. After stringent quality controls, GenCOLT gathered more than 7.3 million SNP and HLA genotypes for 387 LT pairs, including 91% pairs composed of donor and recipient of European ancestry. Overall, GenCOLT is an accurate snapshot of LT clinical practice in France and Belgium between 2009 and 2018. It currently represents one of the largest genetic biobanks dedicated to LT with data available simultaneously for donors and recipients. This unique cohort will empower to run comprehensive GWAS investigations of CLAD and other LT outcomes.

A multi-ethnic reference panel to impute HLA classical and non-classical class I alleles in admixed samples: Testing imputation accuracy in an admixed sample from Brazil.

The MHC class I region contains crucial genes for the innate and adaptive immune response, playing a key role in susceptibility to many autoimmune and infectious diseases. Genome-wide association studies have identified numerous disease-associated SNPs within this region. However, these associations do not fully capture the immune-biological relevance of specific HLA alleles. HLA imputation techniques may leverage available SNP arrays by predicting allele genotypes based on the linkage disequilibrium between SNPs and specific HLA alleles. Successful imputation requires diverse and large reference panels, especially for admixed populations. This study employed a bioinformatics approach to call SNPs and HLA alleles in multi-ethnic samples from the 1000 genomes (1KG) dataset and admixed individuals from Brazil (SABE), utilising 30X whole-genome sequencing data. Using HIBAG, we created three reference panels: 1KG (n = 2504), SABE (n = 1171), and the full model (n = 3675) encompassing all samples. In extensive cross-validation of these reference panels, the multi-ethnic 1KG reference exhibited overall superior performance than the reference with only Brazilian samples. However, the best results were achieved with the full model. Additionally, we expanded the scope of imputation by developing reference panels for non-classical, MICA, MICB and HLA-H genes, previously unavailable for multi-ethnic populations. Validation in an independent Brazilian dataset showcased the superiority of our reference panels over the Michigan Imputation Server, particularly in predicting HLA-B alleles among Brazilians. Our investigations underscored the need to enhance or adapt reference panels to encompass the target population's genetic diversity, emphasising the significance of multiethnic references for accurate imputation across different populations.

Disentangling Multiple Sclerosis heterogeneity in the French territory among genetic and environmental factors via Bayesian heritability analysis.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data.

BACKGROUND AND PURPOSE: Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS. METHODS: For this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects. RESULTS: The projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model-based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, "peer-to-peer," and marketing distribution. CONCLUSIONS: This review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

Investigating the metabolite signature of an altered oral microbiota as a discriminant factor for multiple sclerosis: a pilot study.

In multiple sclerosis (MS), alterations of the gut microbiota lead to inflammation. However, the role of other microbiomes in the body in MS has not been fully elucidated. In a pilot case-controlled study, we carried out simultaneous characterization of faecal and oral microbiota and conducted an in-depth analysis of bacterial alterations associated with MS. Using 16S rRNA sequencing and metabolic inference tools, we compared the oral/faecal microbiota and bacterial metabolism pathways in French MS patients (n = 14) and healthy volunteers (HV, n = 21). A classification model based on metabolite flux balance was established and validated in an independent German cohort (MS n = 12, HV n = 38). Our analysis revealed decreases in diversity indices and oral/faecal compartmentalization, the depletion of commensal bacteria (Aggregatibacter and Streptococcus in saliva and Coprobacter and Roseburia in faeces) and enrichment of inflammation-associated bacteria in MS patients (Leptotrichia and Fusobacterium in saliva and Enterobacteriaceae and Actinomyces in faeces). Several microbial pathways were also altered (the polyamine pathway and remodelling of bacterial surface antigens and energetic metabolism) while flux balance analysis revealed associated alterations in metabolite production in MS (nitrogen and nucleoside). Based on this analysis, we identified a specific oral metabolite signature in MS patients, that could discriminate MS patients from HV and rheumatoid arthritis patients. This signature allowed us to create and validate a discrimination model on an independent cohort, which reached a specificity of 92%. Overall, the oral and faecal microbiomes were altered in MS patients. This pilot study highlights the need to study the oral microbiota and oral health implications in patients with autoimmune diseases on a larger scale and suggests that knowledge of the salivary microbiome could help guide the identification of new pathogenic mechanisms associated with the microbiota in MS patients.

Optimal population-specific HLA imputation with dimension reduction.

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.

18th International HLA and Immunogenetics Workshop: Report on the SNP-HLA Reference Consortium (SHLARC) component.

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.

Multiple sclerosis: Exploring the limits and implications of genetic and environmental susceptibility.

OBJECTIVE: To explore and describe the basis and implications of genetic and environmental susceptibility to multiple sclerosis (MS) using the Canadian population-based data. BACKGROUND: Certain parameters of MS-epidemiology are directly observable (e.g., the recurrence-risk of MS in siblings and twins, the proportion of women among MS patients, the population-prevalence of MS, and the time-dependent changes in the sex-ratio). By contrast, other parameters can only be inferred from the observed parameters (e.g., the proportion of the population that is "genetically susceptible", the proportion of women among susceptible individuals, the probability that a susceptible individual will experience an environment "sufficient" to cause MS, and if they do, the probability that they will develop the disease). DESIGN/METHODS: The "genetically susceptible" subset (G) of the population (Z) is defined to include everyone with any non-zero life-time chance of developing MS under some environmental conditions. The value for each observed and non-observed epidemiological parameter is assigned a "plausible" range. Using both a Cross-sectional Model and a Longitudinal Model, together with established parameter relationships, we explore, iteratively, trillions of potential parameter combinations and determine those combinations (i.e., solutions) that fall within the acceptable range for both the observed and non-observed parameters. RESULTS: Both Models and all analyses intersect and converge to demonstrate that probability of genetic-susceptibitly, P(G), is limited to only a fraction of the population {i.e., P(G) ≤ 0.52)} and an even smaller fraction of women {i.e., P(G│F) < 0.32)}. Consequently, most individuals (particularly women) have no chance whatsoever of developing MS, regardless of their environmental exposure. However, for any susceptible individual to develop MS, requires that they also experience a "sufficient" environment. We use the Canadian data to derive, separately, the exponential response-curves for men and women that relate the increasing likelihood of developing MS to an increasing probability that a susceptible individual experiences an environment "sufficient" to cause MS. As the probability of a "sufficient" exposure increases, we define, separately, the limiting probability of developing MS in men (c) and women (d). These Canadian data strongly suggest that: (c < d ≤ 1). If so, this observation establishes both that there must be a "truly" random factor involved in MS pathogenesis and that it is this difference, rather than any difference in genetic or environmental factors, which primarily accounts for the penetrance difference between women and men. CONCLUSIONS: The development of MS (in an individual) requires both that they have an appropriate genotype (which is uncommon in the population) and that they have an environmental exposure "sufficient" to cause MS given their genotype. Nevertheless, the two principal findings of this study are that: P(G) ≤ 0.52)} and: (c < d ≤ 1). Threfore, even when the necessary genetic and environmental factors, "sufficient" for MS pathogenesis, co-occur for an individual, they still may or may not develop MS. Consequently, disease pathogenesis, even in this circumstance, seems to involve an important element of chance. Moreover, the conclusion that the macroscopic process of disease development for MS includes a "truly" random element, if replicated (either for MS or for other complex diseases), provides empiric evidence that our universe is non-deterministic.

Immunogenetics of HLA-B: SNP, allele, and haplotype diversity in populations from different continents and ancestry backgrounds.

HLA-B is among the most variable gene in the human genome. This gene encodes a key molecule for antigen presentation to CD8+ T lymphocytes and NK cell modulation. Despite the myriad of studies evaluating its coding region (with an emphasis on exons 2 and 3), few studies evaluated introns and regulatory sequences in real population samples. Thus, HLA-B variability is probably underestimated. We applied a bioinformatics pipeline tailored for HLA genes on 5347 samples from 80 different populations, which includes more than 1000 admixed Brazilians, to evaluate the HLA-B variability (SNPs, indels, MNPs, alleles, and haplotypes) in exons, introns, and regulatory regions. We observed 610 variable sites throughout HLA-B; the most frequent variants are shared worldwide. However, the haplotype distribution is geographically structured. We detected 920 full-length haplotypes (exons, introns, and untranslated regions) encoding 239 different protein sequences. HLA-B gene diversity is higher in admixed populations and Europeans while lower in African ancestry individuals. Each HLA-B allele group is associated with specific promoter sequences. This HLA-B variation resource may improve HLA imputation accuracy and disease-association studies and provide evolutionary insights regarding HLA-B genetic diversity in human populations.

Patient-centric synthetic data generation, no reason to risk re-identification in biomedical data analysis.

While nearly all computational methods operate on pseudonymized personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients' trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients' privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an "avatar data", for each initial sensitive individual. This method, compared with 2 other synthetic data generation techniques (Synthpop, CT-GAN), is applied to real health data with a clinical trial and a cancer observational study to evaluate the protection it provides while retaining the original statistical information. Compared to Synthpop and CT-GAN, the Avatar method shows a similar level of signal maintenance while allowing to compute additional privacy metrics. In the light of distance-based privacy metrics, each individual produces an avatar simulation that is on average indistinguishable from 12 other generated avatar simulations for the clinical trial and 24 for the observational study. Data transformation using the Avatar method both preserves, the evaluation of the treatment's effectiveness with similar hazard ratios for the clinical trial (original HR = 0.49 [95% CI, 0.39-0.63] vs. avatar HR = 0.40 [95% CI, 0.31-0.52]) and the classification properties for the observational study (original AUC = 99.46 (s.e. 0.25) vs. avatar AUC = 99.84 (s.e. 0.12)). Once validated by privacy metrics, anonymous synthetic data enable the creation of value from sensitive pseudonymized data analyses by tackling the risk of a privacy breach.

KiT-GENIE, the French genetic biobank of kidney transplantation.

KiT-GENIE is a monocentric DNA biobank set up to consolidate the very rich and homogeneous DIVAT French cohort of kidney donors and recipients (D/R) in order to explore the molecular factors involved in kidney transplantation outcomes. We collected DNA samples for kidney transplantations performed in Nantes, and we leveraged GWAS genotyping data for securing high-quality genetic data with deep SNP and HLA annotations through imputations and for inferring D/R genetic ancestry. Overall, the biobank included 4217 individuals (n = 1945 D + 2,272 R, including 1969 D/R pairs), 7.4 M SNPs and over 200 clinical variables. KiT-GENIE represents an accurate snapshot of kidney transplantation clinical practice in Nantes between 2002 and 2018, with an enrichment in living kidney donors (17%) and recipients with focal segmental glomerulosclerosis (4%). Recipients were predominantly male (63%), of European ancestry (93%), with a mean age of 51yo and 86% experienced their first graft over the study period. D/R pairs were 93% from European ancestry, and 95% pairs exhibited at least one HLA allelic mismatch. The mean follow-up time was 6.7 years with a hindsight up to 25 years. Recipients experienced biopsy-proven rejection and graft loss for 16.6% and 21.3%, respectively. KiT-GENIE constitutes one of the largest kidney transplantation genetic cohorts worldwide to date. It includes homogeneous high-quality clinical and genetic data for donors and recipients, hence offering a unique opportunity to investigate immunogenetic and genetic factors, as well as donor-recipient interactions and mismatches involved in rejection, graft survival, primary disease recurrence and other comorbidities.

Identification of Novel Genetic Risk Factors for Focal Segmental Glomerulosclerosis in Children: Results From the Chronic Kidney Disease in Children (CKiD) Cohort.

RATIONALE & OBJECTIVE: Focal segmental glomerulosclerosis (FSGS) is a major cause of pediatric nephrotic syndrome, and African Americans exhibit an increased risk for developing FSGS compared with other populations. Predisposing genetic factors have previously been described in adults. Here we performed genomic screening of primary FSGS in a pediatric African American population. STUDY DESIGN: Prospective cohort with case-control genetic association study design. SETTING & PARTICIPANTS: 140 African American children with chronic kidney disease from the Chronic Kidney Disease in Children (CKiD) cohort, including 32 cases with FSGS. PREDICTORS: Over 680,000 common single-nucleotide polymorphisms (SNPs) were tested for association. We also ran a pathway enrichment analysis and a human leucocyte antigen (HLA)-focused association study. OUTCOME: Primary biopsy-proven pediatric FSGS. ANALYTICAL APPROACH: Multivariate logistic regression models. RESULTS: The genome-wide association study revealed 169 SNPs from 14 independent loci significantly associated with FSGS (false discovery rate [FDR]<5%). We observed notable signals for genetic variants within the APOL1 (P=8.6×10-7; OR, 25.8 [95% CI, 7.1-94.0]), ALMS1 (P=1.3×10-7; 13.0% in FSGS cases vs 0% in controls), and FGFR4 (P=4.3×10-6; OR, 24.8 [95% CI, 6.3-97.7]) genes, all of which had previously been associated with adult FSGS, kidney function, or chronic kidney disease. We also highlighted novel, functionally relevant genes, including GRB2 (which encodes a slit diaphragm protein promoting podocyte structure through actin polymerization) and ITGB1 (which is linked to renal injuries). Our results suggest a major role for immune responses and antigen presentation in pediatric FSGS through (1) associations with SNPs in PTPRJ (or CD148, P=3.5×10-7), which plays a role in T-cell receptor signaling, (2) HLA-DRB1∗11:01 association (P=6.1×10-3; OR, 4.5 [95% CI, 1.5-13.0]), and (3) signaling pathway enrichment (P=1.3×10-6). LIMITATIONS: Sample size and no independent replication cohort with genomic data readily available. CONCLUSIONS: Our genetic study has identified functionally relevant risk factors and the importance of immune regulation for pediatric primary FSGS, which contributes to a better description of its molecular pathophysiological mechanisms. PLAIN-LANGUAGE SUMMARY: We assessed the genetic risk factors for primary focal segmental glomerulosclerosis (FSGS) by simultaneously testing over 680,000 genetic markers spread across the genome in 140 children, including 32 with FSGS lesions. Fourteen independent genetic regions were significantly associated with pediatric FSGS, including APOL1 and ALMS1-NAT8, which were previously found to be associated with FSGS and chronic kidney diseases in adults. Novel genes with relevant biological functions were also highlighted, such as GRB2 and FGFR4, which play a role in the kidney filtration barrier and in kidney cell differentiation, respectively. Finally, we revealed the importance of immune regulation in pediatric FSGS through associations involving cell surface proteins presenting antigens to the immune system and interacting with T-cell receptors.

Multiple sclerosis clinical decision support system based on projection to reference datasets.

OBJECTIVE: Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on-demand computational power to address the challenges of dynamically managing MS. METHODS: A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg-interferon beta-1a vs placebo on patients with relapsing-remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial. RESULTS: We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user-centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta-1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician-patient data display to facilitate communication. INTERPRETATION: The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.

Trans-ancestral fine-mapping of MHC reveals key amino acids associated with spontaneous clearance of hepatitis C in HLA-DQß1.

Spontaneous clearance of acute hepatitis C virus (HCV) infection is associated with single nucleotide polymorphisms (SNPs) on the MHC class II. We fine-mapped the MHC region in European (n = 1,600; 594 HCV clearance/1,006 HCV persistence) and African (n = 1,869; 340 HCV clearance/1,529 HCV persistence) ancestry individuals and evaluated HCV peptide binding affinity of classical alleles. In both populations, HLA-DQß1Leu26 (p valueMeta = 1.24 × 10-14) located in pocket 4 was negatively associated with HCV spontaneous clearance and HLA-DQß1Pro55 (p valueMeta = 8.23 × 10-11) located in the peptide binding region was positively associated, independently of HLA-DQß1Leu26. These two amino acids are not in linkage disequilibrium (r2 < 0.1) and explain the SNPs and classical allele associations represented by rs2647011, rs9274711, HLA-DQB1∗03:01, and HLA-DRB1∗01:01. Additionally, HCV persistence classical alleles tagged by HLA-DQß1Leu26 had fewer HCV binding epitopes and lower predicted binding affinities compared to clearance alleles (geometric mean of combined IC50 nM of persistence versus clearance; 2,321 nM versus 761.7 nM, p value = 1.35 × 10-38). In summary, MHC class II fine-mapping revealed key amino acids in HLA-DQß1 explaining allelic and SNP associations with HCV outcomes. This mechanistic advance in understanding of natural recovery and immunogenetics of HCV might set the stage for much needed enhancement and design of vaccine to promote spontaneous clearance of HCV infection.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation.

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

HLA-EPI: A new EPIsode in exploring donor/recipient epitopic compatibilities.

The HLA system plays a pivotal role both in transplantation and immunology. While classical HLA genotypes matching is made at the allelic level, recent progresses were developed to explore antibody-antigen recognition by studying epitopes. Donor to recipient matching at the epitopic level is becoming a trending topic in the transplantation research field because anti-HLA antibodies are epitope-specific rather than allele-specific. Indeed, different HLA alleles often share common epitopes. We present the HLA-Epi tool (hla.univ-nantes.fr) to study an HLA genotype at the epitope level. Using the international HLA epitope registry (Epregistry.com.br) as a reference, we developed HLA-Epi to easily determine epitopic and allelic compatibility levels between several HLA genotypes. The epitope database covers the most common HLA alleles (N = 2976 HLA alleles), representing more than 99% of the total observed frequency of HLA alleles. The freely accessible web tool HLA-Epi calculates an epitopic mismatch load between different sets of potential recipient-donor pairs at different resolution levels. We have characterized the epitopic mismatches distribution in a cohort of more than 10,000 kidney transplanted pairs from European ancestry, which showed low number of epitopic mismatches: 56.9 incompatibilities on average. HLA-Epi allows the exploration of epitope pairing matching to better understand epitopes contribution to immune responses regulation, particularly during transplantation. This free and ready-to-use bioinformatics tool not only addresses limitations of other related tools, but also offers a cost-efficient and reproducible strategy to analyze HLA epitopes as an alternative to HLA allele compatibility. In the future, this could improve sensitization prevention for allograft allocation decisions and reduce the risk of alloreactivity.

Approaching Genetics Through the MHC Lens: Tools and Methods for HLA Research.

The current SARS-CoV-2 pandemic era launched an immediate and broad response of the research community with studies both about the virus and host genetics. Research in genetics investigated HLA association with COVID-19 based on in silico, population, and individual data. However, they were conducted with variable scale and success; convincing results were mostly obtained with broader whole-genome association studies. Here, we propose a technical review of HLA analysis, including basic HLA knowledge as well as available tools and advice. We notably describe recent algorithms to infer and call HLA genotypes from GWAS SNPs and NGS data, respectively, which opens the possibility to investigate HLA from large datasets without a specific initial focus on this region. We thus hope this overview will empower geneticists who were unfamiliar with HLA to run MHC-focused analyses following the footsteps of the Covid-19|HLA & Immunogenetics Consortium.

Population-specific diversity of the immunoglobulin constant heavy G chain (IGHG) genes.

Human immunoglobulin G (IgG) molecules, IgG1, IgG2 and IgG3, exhibit substantial inter-individual variation in their constant heavy chain regions, as discovered by serological methods. This polymorphism is encoded by the IGHG1, IGHG2, and IGHG3 genes and may influence antibody function. We sequenced the coding fragments of these genes in 95 European Americans, 94 African Americans, and 94 Black South Africans. Striking differences were observed between the population groups, including extremely low amino acid sequence variation in IGHG1 among South Africans, and higher IGHG2 and IGHG3 diversity in individuals of African descent compared to individuals of European descent. Molecular definition of the loci illustrates a greater level of allelic polymorphism than previously described, including the presence of common IGHG2 and IGHG3 variants that were indistinguishable serologically. Comparison of our data with the 1000 Genome Project sequences indicates overall agreement between the datasets, although some inaccuracies in the 1000 Genomes Project are likely. These data represent the most comprehensive analysis of IGHG polymorphisms across major populations, which can now be applied to deciphering their functional impact.

Current HLA Investigations on SARS-CoV-2 and Perspectives.

The rapid, global spread of the SARS-CoV-2 virus during the current pandemic has triggered numerous efforts in clinical and research settings to better understand the host genetics' interactions and the severity of COVID-19. Due to the established major role played by MHC/HLA polymorphism in infectious disease course and susceptibility, immunologists and geneticists have teamed up to investigate its contribution to the SARS-CoV-2 infection and COVID-19 progression. A major goal of the Covid-19|HLA & Immunogenetics Consortium is to support and unify these efforts. Here, we present a review of HLA immunogenomics studies in the SARS-CoV-2 pandemic and reflect on the role of various HLA data, their limitation and future perspectives.

Genetic susceptibility to multiple sclerosis in African Americans.

OBJECTIVE: To explore the nature of genetic-susceptibility to multiple sclerosis (MS) in African-Americans. BACKGROUND: Recently, the number of genetic-associations with MS has exploded although the MS-associations of specific haplotypes within the major histocompatibility complex (MHC) have been known for decades. For example, the haplotypes HLA-DRB1*15:01~HLA-DQB1*06:02, and HLA-DRB1*03:01~ HLA-DQB1*02:01 have odds ratios (ORs) for an MS-association orders of magnitude stronger than many of these newly-discovered associations. Nevertheless, all these haplotypes are part of much larger conserved extended haplotypes (CEHs), which span both the Class I and Class II MHC regions. African-Americans are at greater risk of developing MS compared to a native Africans but at lesser risk compared to Europeans. It is the purpose of this manuscript to explore the relationship between MS-susceptibility and the CEH make-up of our African-American cohort. DESIGN/METHODS: The African-American (AA) cohort consisted of 1,305 patients with MS and 1,155 controls, who self-identified as being African-American. For comparison, we used the 18,492 controls and 11,144 MS-cases from the predominantly European Wellcome Trust Case Control Consortium (WTCCC) and the 28,557 phased native Africans from the multinational "Be the Match" registry. The WTCCC and the African-Americans were phased at each of five HLA loci (HLA-A, HLA-C, HLA-B, HLA-DRB1 and HLA-DQB1) and the at 11 SNPs (10 of which were in non-coding regions) surrounding the Class II region of the DRB1 gene using previously-published probabilistic phasing algorithms. RESULTS: Of the 32 most frequent CEHs, 18 (56%) occurred either more frequently or exclusively in Africans) whereas 9 (28%) occurred more frequently or exclusively in Europeans. The remaining 5 CEHs occurred in neither control group although, likely, these were African in origin. Eight of these CEHs carried the DRB1*15:03~DQB1*06:02~a36 haplotype and three carried the DRB1*15:01~DQB1*06:02~a1 haplotype. In African Americans, a single-copy of the European CEH (03:01_07:02_07:02_15:01_06:02_a1) was associated with considerable MS-risk (OR = 3.30; p = 0.0001)-similar to that observed in the WTCCC (OR = 3.25; p<10-168). By contrast, the MS-risk for the European CEH (02:01_07:02_07:02_15:01_06:02_a1) was less (OR = 1.49; ns)-again, similar to the WTCCC (OR = 2.2; p<10-38). Moreover, four African haplotypes were "protective" relative to a neutral reference, to three European CEHs, and also to the five other African CEHs. CONCLUSIONS: The common CEHs in African Americans are divisible into those that are either African or European in origin, which are derived without modification from their source population. European CEHs, linked to MS-risk, in general, had similar impacts in African-Americans as they did in Europeans. By contrast, African CEHs had mixed MS-risks. For a few, the MS-risk exceeded that in a neutral-reference group whereas, for many others, these CEHs were "protective"-perhaps providing a partial rationale for the lower MS-risk in African-Americans compared to European-Americans.