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

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.

Genetic risk for hospitalization of African American patients with severe mental illness reveals HLA loci

Background

Mood disorders such as major depressive and bipolar disorders, along with posttraumatic stress disorder (PTSD), schizophrenia (SCZ), and other psychotic disorders, constitute serious mental illnesses (SMI) and often lead to inpatient psychiatric care for adults. Risk factors associated with increased hospitalization rate in SMI (H-SMI) are largely unknown but likely involve a combination of genetic, environmental, and socio-behavioral factors. We performed a genome-wide association study in an African American cohort to identify possible genes associated with hospitalization due to SMI (H-SMI).

Methods

Patients hospitalized for psychiatric disorders (H-SMI; n=690) were compared with demographically matched controls (n=4467). Quality control and imputation of genome-wide data were performed following the Psychiatric Genetic Consortium (PGC)-PTSD guidelines. Imputation of the Human Leukocyte Antigen (HLA) locus was performed using the HIBAG package.

Results

Genome-wide association analysis revealed a genome-wide significant association at 6p22.1 locus in the ubiquitin D (UBD/FAT10) gene (rs362514, p=9.43x10-9) and around the HLA locus. Heritability of H-SMI (14.6%) was comparable to other psychiatric disorders (4% to 45%). We observed a nominally significant association with 2 HLA alleles: HLA-A*23:01 (OR=1.04, p=2.3x10-3) and HLA-C*06:02 (OR=1.04, p=1.5x10-3). Two other genes (VSP13D and TSPAN9), possibly associated with immune response, were found to be associated with H-SMI using gene-based analyses.

Conclusion

We observed a strong association between H-SMI and a locus that has been consistently and strongly associated with SCZ in multiple studies (6p21.32-p22.1), possibly indicating an involvement of the immune system and the immune response in the development of severe transdiagnostic SMI.

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.

Imputation-Based HLA Typing with GWAS SNPs

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the haplotype structure within the major histocompatibility complex (MHC) region. These methods predict HLA classical alleles using dense SNP genotypes, commonly found on array-based platforms used in genome-wide association studies (GWAS). The analysis of HLA classical alleles can be conducted on current SNP datasets at no additional cost. Here, we describe the workflow of HIBAG, an imputation method with attribute bagging, to infer a sample's HLA classical alleles using SNP data. Two examples are offered to demonstrate the functionality using public HLA and SNP data from the latest release of the 1000 Genomes project: genotype imputation using pre-built classifiers in a GWAS, and model training to create a new prediction model. The GPU implementation facilitates model building, making it hundreds of times faster compared to the single-threaded implementation.

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.

Medical Applications of Molecular Biotechnologies in the Context of Hashimoto's Thyroiditis

Hashimoto's thyroiditis (HT) is a gender autoimmune disease that is manifested by chronic inflammation of the thyroid. Clinical trial studies (CTSs) use molecular biotechnologies (MB) to approach HT appearance. The aims of this study were to analyze the applications of MB in CTSs carried out in HT populations (HT-CTSs). Further, to evaluate the role of MB in the context of the hygiene hypothesis (HH). From 75 HT-CTSs found at clinicaltrials.gov web place, forty-five were considered for this investigation. Finally, six HT-CTSs were reported as molecular HT-CTSs (mHT-CTSs) because these were planning to utilize MB. Two of mHT-CTSs were programmed on the French population to isolate DNA viral sequences. Blood, urine, and thyroid tissue biospecimens were analyzed to pick out the parvo and polyoma viruses. Two mHT-CTSs carried out in China aimed to identify oral and fecal microbiotas by measuring PCR sequencing of the 16S rRNA gene. Two mHT-CTSs were programmed in the USA and Greece, respectively, for interception of DNA polymorphisms to associate with genetic susceptibility to HT. In conclusion, MB are mainly employed in HT-CTSs for infective pathogenesis and genetic fingerprinting of HT. Furthermore, MB do not provide evidence of HH; however, they are useful for providing direct evidence of the presence of viruses.

Distributions of the HLA-A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1 alleles and haplotype frequencies of 1763 stem cell donors in the Colombian Bone Marrow Registry typed by next-generation sequencing

The HLA compatibility continues to be the main limitation when finding compatible donors, especially if an identical match is not found within the patient's family group. The creation of bone marrow registries allowed a therapeutic option by identifying 10/10 compatible unrelated donors (URD). However, the availability and frequency of haplotypes and HLA alleles are different among ethnic groups and geographical areas, increasing the difficulty of finding identical matches in international registries. In this study, the HLA-A, -B, -C, -DRB1, and -DQB1 loci of 1763 donors registered in the Colombian Bone Marrow Registry were typed by next-generation sequencing. A total of 52 HLA-A, 111 HLA-B, 41 HLA-C, 47 HLA-DRB1, and 20 HLA-DQB1 alleles were identified. The 3 most frequent alleles for each loci were A*24:02g (20,8%), A*02:01g (16,1%), A*01:01g (7.06%); B*35:43g (7.69%), B*40:02g (7.18%), B*44:03g (6.07%); C*04:01g (15.40%), C*01:02g (10.49%), C*07:02g (10.44%); DRB1*04:07g (11.03%), DRB1*07:01g (9.78%), DRB1*08:02g (6.72%); DQB1*03:02g (20.96%), DQB1*03:01g (17.78%) and DQB1*02:01g (16.05%). A total of 497 HLA-A-C-B-DRB1-DQB1 haplotypes were observed with a frequency greater than or equal to 0.05% (> 0.05%); the haplotypes with the highest frequency were A*24:02g~B*35:43g~C*01:02g~DQB1*03:02g~DRB1*04:07g (3.34%), A*29:02g~B*44:03g~C*16:01g~DQB1*02:01g~DRB1*07:01g (2.04%), and A*01:01g~B*08:01g~C*07:01g~DQB1*02:01g~DRB1*03:01g (1.83%). This data will allow the new Colombian Bone Marrow Donor Registry to assess the genetic heterogeneity of the Colombian population and serve as a tool of interest for future searches of unrelated donors in the country.

Brain molecular mechanisms in Rasmussen encephalitis

OBJECTIVE

This study was undertaken to identify molecular mechanisms in brain tissue of Rasmussen encephalitis (RE) when compared to people with non-RE epilepsy (PWE) and control cases using whole exome sequencing (WES), RNAseq, and proteomics.

METHODS

Frozen brain tissue (ages = 2-19 years) was obtained from control autopsy (n = 14), surgical PWE (n = 10), and surgical RE cases (n = 27). We evaluated WES variants in RE associated with epilepsy, seizures, RE, and human leukocyte antigens (HLAs). Differential expression was evaluated by RNAseq (adjusted p < .05) and label-free quantitative mass spectrometry (false discovery rate < 5%) in the three groups.

RESULTS

WES revealed no common pathogenic variants in RE, but several rare and likely deleterious variants of unknown significance (VUS; ANGPTL7/MTOR, SCN1A, FCGR3B, MTOR) and more common HLA VUS in >25% of RE cases (HLA-DRB1, HLA-DQA2), all with allele frequency < 5% in the general population. RNAseq in RE versus PWE (1516 altered transcripts) revealed significant activation of crosstalk between dendritic and natural killer cells (p = 7.94 × 10-6 , z = 2.65), in RE versus control (7466 transcripts) neuroinflammation signaling activation (p = 6.31 × 10-13 , z = 5.07), and in PWE versus control (945 transcripts) phagosome formation activation (p = 2.00 × 10-13 , z = 5.61). Proteomics detected fewer altered targets.

SIGNIFICANCE

In RE, we identified activated immune signaling pathways and immune cell type annotation enrichment that suggest roles of the innate and adaptive immune responses, as well as HLA variants that may increase vulnerability to RE. Follow-up studies could evaluate cell type density and subregional localization associated with top targets, clinical history (neuropathology, disease duration), and whether modulating crosstalk between dendritic and natural killer cells may limit disease progression.

Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions

Identifying the specific human leukocyte antigen (HLA) allele combination of an individual is crucial in organ donation, risk assessment of autoimmune and infectious diseases and cancer immunotherapy. However, due to the high genetic polymorphism in this region, HLA typing requires specialized methods. We investigated the performance of five next-generation sequencing (NGS) based HLA typing tools with a non-restricted license namely HLA*LA, Optitype, HISAT-genotype, Kourami and STC-Seq. This evaluation was done for the five HLA loci, HLA-A, -B, -C, -DRB1 and -DQB1 using whole-exome sequencing (WES) samples from 829 individuals. The robustness of the tools to lower depth of coverage (DOC) was evaluated by subsampling and HLA typing 230 WES samples at DOC ranging from 1X to 100X. The HLA typing accuracy was measured across four typing resolutions. Among these, we present two clinically-relevant typing resolutions (P group and pseudo-sequence), which specifically focus on the peptide binding region. On average, across the five HLA loci examined, HLA*LA was found to have the highest typing accuracy. For the individual loci, HLA-A, -B and -C, Optitype's typing accuracy was the highest and HLA*LA had the highest typing accuracy for HLA-DRB1 and -DQB1. The tools' robustness to lower DOC data varied widely and further depended on the specific HLA locus. For all Class I loci, Optitype had a typing accuracy above 95% (according to the modification of the amino acids in the functionally relevant portion of the HLA molecule) at 50X, but increasing the DOC beyond even 100X could still improve the typing accuracy of HISAT-genotype, Kourami, and STC-seq across all five HLA loci as well as HLA*LA's typing accuracy for HLA-DQB1. HLA typing is also used in studies of ancient DNA (aDNA), which is often based on sequencing data with lower quality and DOC. Interestingly, we found that Optitype's typing accuracy is not notably impaired by short read length or by DNA damage, which is typical of aDNA, as long as the DOC is sufficiently high.

Practical Implementation of Genetics: New Concepts in Immunogenomics to Predict, Prevent, and Diagnose Drug Hypersensitivity

Delayed drug hypersensitivities are CD8⁺ T cell-mediated reactions associated with up to 50% mortality. Human leukocyte antigen (HLA) alleles are known to predispose disease and are specific to drug, reaction, and patient ethnicity. Pretreatment screening is recommended for a handful of the strongest associations to identify and prevent drug use in high-risk patients. However, an incomplete predictive value implicates other HLA-imposed risk factors, and low carriage of many identified HLA-risk alleles combined with the high cost of sequence-based typing has limited economic viability for similar recommendation of screening across drugs and health care systems. For mitigation, an expanding armory of low-cost polymerase chain reaction-based screens is being developed, and HLA-imposed risk factors are being discovered. These include (1) polymorphic variants of metabolic and endoplasmic reticulum aminopeptidase enzymes toward multiallelic screening with increased predictivity; (2) regulation by immune checkpoint inhibitors, enabling detolerized animal models of human disease; and (3) immunodominant T cell receptors (TCR) on clonally expanded CD8⁺ T cells. For the latter, HLA risk-restricted TCR provides immunogenomic strategies and samples from a single patient to identify novel HLA-risk associations in underserved minority populations, tissue-relevant effector biomarkers toward earlier diagnosis and treatment, and HLA-TCR-presented immunogenic structures to aid future drug development.