Construction and benchmarking of a multi-ethnic reference panel for the imputation of HLA class I and II alleles

Autoantigen-specific CD4+ T cells acquire an exhausted phenotype and persist in human antigen-specific autoimmune diseases

Pro-inflammatory autoantigen-specific CD4+ T helper (auto-Th) cells are central orchestrators of autoimmune diseases (AIDs). We aimed to characterize these cells in human AIDs with defined autoantigens by combining human leukocyte antigen (HLA)-tetramer-based and activation-based multidimensional ex vivo analyses. In aquaporin4-antibody-positive neuromyelitis optica spectrum disorder (AQP4-NMOSD) patients, auto-Th cells expressed CD154, but proliferative capacity and pro-inflammatory cytokines were strongly reduced. Instead, exhaustion-associated co-inhibitory receptors were expressed together with FOXP3, the canonical regulatory T cell (Treg) transcription factor. Auto-Th cells responded in vitro to checkpoint inhibition and provided potent B cell help. Cells with the same exhaustion-like (ThEx) phenotype were identified in soluble liver antigen (SLA)-antibody-autoimmune hepatitis and BP180-antibody-positive bullous pemphigoid, AIDs of the liver and skin, respectively. While originally described in cancer and chronic infection, our data point to T cell exhaustion as a common mechanism of adaptation to chronic (self-)stimulation across AID types and link exhausted CD4+ T cells to humoral autoimmune responses, with implications for therapeutic targeting.

Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data

In addition to the classical HLA genes, the major histocompatibility complex (MHC) harbors a high number of other polymorphic genes with less established roles in disease associations and transplantation matching. To facilitate studies of the non-classical and non-HLA genes in large patient and biobank cohorts, we trained imputation models for MICA, MICB, HLA-E, HLA-F and HLA-G alleles on genome SNP array data. We show, using both population-specific and multi-population 1000 Genomes references, that the alleles of these genes can be accurately imputed for screening and research purposes. The best imputation model for MICA, MICB, HLA-E, -F and -G achieved a mean accuracy of 99.3% (min, max: 98.6, 99.9). Furthermore, validation of the 1000 Genomes exome short-read sequencing-based allele calling against a clinical-grade reference data showed an average accuracy of 99.8%, testifying for the quality of the 1000 Genomes data as an imputation reference. We also fitted the models for Infinium Global Screening Array (GSA, Illumina, Inc.) and Axiom Precision Medicine Research Array (PMRA, Thermo Fisher Scientific Inc.) SNP content, with mean accuracies of 99.1% (97.2, 100) and 98.9% (97.4, 100), respectively.

Prediction of progression to type 1 diabetes with dynamic biomarkers and risk scores

Identifying biomarkers of functional β-cell loss is an important step in the risk stratification of type 1 diabetes. Genetic risk scores (GRS), generated by profiling an array of single nucleotide polymorphisms, are a widely used type 1 diabetes risk-prediction tool. Type 1 diabetes screening studies have relied on a combination of biochemical (autoantibody) and GRS screening methodologies for identifying individuals at high-risk of type 1 diabetes. A limitation of these screening tools is that the presence of autoantibodies marks the initiation of β-cell loss, and is therefore not the best biomarker of progression to early-stage type 1 diabetes. GRS, on the other hand, represents a static biomarker offering a single risk score over an individual's lifetime. In this Personal View, we explore the challenges and opportunities of static and dynamic biomarkers in the prediction of progression to type 1 diabetes. We discuss future directions wherein newer dynamic risk scores could be used to predict type 1 diabetes risk, assess the efficacy of new and emerging drugs to retard, or prevent type 1 diabetes, and possibly replace or further enhance the predictive ability offered by static biomarkers, such as GRS.

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.

Genome-wide association analysis reveals the associations of NPHP4, TYW1-AUTS2 and SEMA6D for Behçet's disease and HLA-B*46:01 for its intestinal involvement

BACKGROUND

Intestinal involvement in Behçet's disease (BD) is associated with poor prognosis and is more prevalent in East Asian than in Mediterranean populations. Identifying the genetic causes of intestinal BD is important for understanding the pathogenesis and for appropriate treatment of BD patients.

METHODS

We performed genome-wide association studies (GWAS) and imputation/replication genotyping of human leukocyte antigen (HLA) alleles for 1,689 Korean and Turkish patients with BD (including 379 patients with intestinal BD) and 2,327 healthy controls, followed by replication using 593 Japanese patients with BD (101 patients with intestinal BD) and 737 healthy controls. Stratified cross-phenotype analyses were performed for 1) overall BD, 2) intestinal BD, and 3) intestinal BD without association of overall BD.

RESULTS

We identified three novel genome-wide significant susceptibility loci including NPHP4 (rs74566205; P=1.36 × 10-8), TYW1-AUTS2 (rs60021986; P=1.14 × 10-9), and SEMA6D (rs4143322; P=5.54 × 10-9) for overall BD, and a new association with HLA-B*46:01 for intestinal BD (P=1.67 × 10-8) but not for BD without intestinal involvement. HLA peptide binding analysis revealed that Mycobacterial peptides, have a stronger binding affinity to HLA-B*46:01 compared to the known risk allele HLA-B*51:01.

CONCLUSIONS

HLA-B*46:01 is associated with the development of intestinal BD; NPHP4, TYW1-AUTS2, and SEMA6D are susceptibility loci for overall BD.

High-resolution HLA genotyping improves PIRCHE-II assessment of molecular mismatching in kidney transplantation

HLA matching in solid organ transplant is performed with the aim of assessing immunologic compatibility in order to avoid hyperacute rejection and assess the risk of future rejection events. Molecular mismatch algorithms are intended to improve granularity in histocompatibility assessment and risk stratification. PIRCHE-II uses HLA genotyping to predict indirectly presented mismatched donor HLA peptides, though most clinical validation studies rely on imputing high resolution (HR) genotypes from low resolution (LR) typing data. We hypothesized that use of bona fide HR typing could overcome limitations in imputation, improving accuracy and predictive ability for donor-specific antibody development and acute rejection. We performed a retrospective analysis of adult and pediatric kidney transplant donor/recipient pairs (N = 419) with HR typing and compared the use of imputed LR genotyping verses HR genotyping for PIRCHE-II analysis and outcomes. Imputation success was highly dependent on the reference population used, as using historic Caucasian reference populations resulted in 10 % of pairs with unsuccessful imputation while multiethnic reference populations improved successful imputation with only 1 % unable to be imputed. Comparing PIRCHE-II analysis with HR and LR genotyping produced notably different results, with 20 % of patients discrepantly classified to immunologic risk groups. These data emphasize the importance of using multiethnic reference panels when performing imputation and indicate HR HLA genotyping has clinically meaningful benefit for PIRCHE-II analysis compared to imputed LR typing.

Divergent HLA variations and heterogeneous expression but recurrent HLA loss-of- heterozygosity and common HLA-B and TAP transcriptional silencing across advanced pediatric solid cancers

The human leukocyte antigen (HLA) system is a major factor controlling cancer immunosurveillance and response to immunotherapy, yet its status in pediatric cancers remains fragmentary. We determined high-confidence HLA genotypes in 576 children, adolescents and young adults with recurrent/refractory solid tumors from the MOSCATO-01 and MAPPYACTS trials, using normal and tumor whole exome and RNA sequencing data and benchmarked algorithms. There was no evidence for narrowed HLA allelic diversity but discordant homozygosity and allele frequencies across tumor types and subtypes, such as in embryonal and alveolar rhabdomyosarcoma, neuroblastoma MYCN and 11q subtypes, and high-grade glioma, and several alleles may represent protective or susceptibility factors to specific pediatric solid cancers. There was a paucity of somatic mutations in HLA and antigen processing and presentation (APP) genes in most tumors, except in cases with mismatch repair deficiency or genetic instability. The prevalence of loss-of-heterozygosity (LOH) ranged from 5.9 to 7.7% in HLA class I and 8.0 to 16.7% in HLA class II genes, but was widely increased in osteosarcoma and glioblastoma (~15-25%), and for DRB1-DQA1-DQB1 in Ewing sarcoma (~23-28%) and low-grade glioma (~33-50%). HLA class I and HLA-DR antigen expression was assessed in 194 tumors and 44 patient-derived xenografts (PDXs) by immunochemistry, and class I and APP transcript levels quantified in PDXs by RT-qPCR. We confirmed that HLA class I antigen expression is heterogeneous in advanced pediatric solid tumors, with class I loss commonly associated with the transcriptional downregulation of HLA-B and transporter associated with antigen processing (TAP) genes, whereas class II antigen expression is scarce on tumor cells and occurs on immune infiltrating cells. Patients with tumors expressing sufficient HLA class I and TAP levels such as some glioma, osteosarcoma, Ewing sarcoma and non-rhabdomyosarcoma soft-tissue sarcoma cases may more likely benefit from T cell-based approaches, whereas strategies to upregulate HLA expression, to expand the immunopeptidome, and to target TAP-independent epitopes or possibly LOH might provide novel therapeutic opportunities in others. The consequences of HLA class II expression by immune cells remain to be established. Immunogenetic profiling should be implemented in routine to inform immunotherapy trials for precision medicine of pediatric cancers.

HLA-DPA1*02:01~B1*01:01 is a risk haplotype for primary sclerosing cholangitis mediating activation of NKp44+ NK cells

OBJECTIVE

Primary sclerosing cholangitis (PSC) is characterised by bile duct strictures and progressive liver disease, eventually requiring liver transplantation. Although the pathogenesis of PSC remains incompletely understood, strong associations with HLA-class II haplotypes have been described. As specific HLA-DP molecules can bind the activating NK-cell receptor NKp44, we investigated the role of HLA-DP/NKp44-interactions in PSC.

DESIGN

Liver tissue, intrahepatic and peripheral blood lymphocytes of individuals with PSC and control individuals were characterised using flow cytometry, immunohistochemical and immunofluorescence analyses. HLA-DPA1 and HLA-DPB1 imputation and association analyses were performed in 3408 individuals with PSC and 34 213 controls. NK cell activation on NKp44/HLA-DP interactions was assessed in vitro using plate-bound HLA-DP molecules and HLA-DPB wildtype versus knock-out human cholangiocyte organoids.

RESULTS

NKp44+NK cells were enriched in livers, and intrahepatic bile ducts of individuals with PSC showed higher expression of HLA-DP. HLA-DP haplotype analysis revealed a highly elevated PSC risk for HLA-DPA1*02:01~B1*01:01 (OR 1.99, p=6.7×10-50). Primary NKp44+NK cells exhibited significantly higher degranulation in response to plate-bound HLA-DPA1*02:01-DPB1*01:01 compared with control HLA-DP molecules, which were inhibited by anti-NKp44-blocking. Human cholangiocyte organoids expressing HLA-DPA1*02:01-DPB1*01:01 after IFN-γ-exposure demonstrated significantly increased binding to NKp44-Fc constructs compared with unstimulated controls. Importantly, HLA-DPA1*02:01-DPB1*01:01-expressing organoids increased degranulation of NKp44+NK cells compared with HLA-DPB1-KO organoids.

CONCLUSION

Our studies identify a novel PSC risk haplotype HLA-DP A1*02:01~DPB1*01:01 and provide clinical and functional data implicating NKp44+NK cells that recognise HLA-DPA1*02:01-DPB1*01:01 expressed on cholangiocytes in PSC pathogenesis.

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.

HLA Genes: A Hallmark of Functional Genetic Variation and Complex Evolution

The major histocompatibility complex (MHC) with its highly polymorphic HLA genes represents one of the most intensely studied genomic regions in the genome. MHC proteins play a key role in antigen-specific immunity and are associated with a wide range of complex diseases. Despite decades of research and many advances in the field, the characterization and interpretation of its genetic and genomic variability remain challenging. Here an overview is provided of the MHC, the nature of its exceptional variability, and the complex evolutionary processes assumed to drive this variability. Highlighted are also recent advances in the field that promise to improve our understanding of the variability in the MHC and in antigen-specific immunity more generally.

HLA-DP on Epithelial Cells Enables Tissue Damage by NKp44+ Natural Killer Cells in Ulcerative Colitis

BACKGROUND & AIMS

Ulcerative colitis (UC) is characterized by severe inflammation and destruction of the intestinal epithelium, and is associated with specific risk single nucleotide polymorphisms in HLA class II. Given the recently discovered interactions between subsets of HLA-DP molecules and the activating natural killer (NK) cell receptor NKp44, genetic associations of UC and HLA-DP haplotypes and their functional implications were investigated.

METHODS

HLA-DP haplotype and UC risk association analyses were performed (UC: n = 13,927; control: n = 26,764). Expression levels of HLA-DP on intestinal epithelial cells (IECs) in individuals with and without UC were quantified. Human intestinal 3-dimensional (3D) organoid cocultures with human NK cells were used to determine functional consequences of interactions between HLA-DP and NKp44.

RESULTS

These studies identified HLA-DPA1∗01:03-DPB1∗04:01 (HLA-DP401) as a risk haplotype and HLA-DPA1∗01:03-DPB1∗03:01 (HLA-DP301) as a protective haplotype for UC in European populations. HLA-DP expression was significantly higher on IECs of individuals with UC compared with controls. IECs in human intestinal 3D organoids derived from HLA-DP401pos individuals showed significantly stronger binding of NKp44 compared with HLA-DP301pos IECs. HLA-DP401pos IECs in organoids triggered increased degranulation and tumor necrosis factor production by NKp44+ NK cells in cocultures, resulting in enhanced epithelial cell death compared with HLA-DP301pos organoids. Blocking of HLA-DP401-NKp44 interactions (anti-NKp44) abrogated NK cell activity in cocultures.

CONCLUSIONS

We identified an UC risk HLA-DP haplotype that engages NKp44 and activates NKp44+ NK cells, mediating damage to intestinal epithelial cells in an HLA-DP haplotype-dependent manner. The molecular interaction between NKp44 and HLA-DP401 in UC can be targeted by therapeutic interventions to reduce NKp44+ NK cell-mediated destruction of the intestinal epithelium in UC.

Assessing HLA imputation accuracy in a West African population

The Human Leukocyte Antigen (HLA) region plays an important role in autoimmune and infectious diseases. HLA is a highly polymorphic region and thus difficult to impute. We, therefore, sought to evaluate HLA imputation accuracy, specifically in a West African population, since they are understudied and are known to harbor high genetic diversity. The study sets were selected from 315 Gambian individuals within the Gambian Genome Variation Project (GGVP) Whole Genome Sequence datasets. Two different arrays, Illumina Omni 2.5 and Human Hereditary and Health in Africa (H3Africa), were assessed for the appropriateness of their markers, and these were used to test several imputation panels and tools. The reference panels were chosen from the 1000 Genomes (1kg-All), 1000 Genomes African (1kg-Afr), 1000 Genomes Gambian (1kg-Gwd), H3Africa, and the HLA Multi-ethnic datasets. HLA-A, HLA-B, and HLA-C alleles were imputed using HIBAG, SNP2HLA, CookHLA, and Minimac4, and concordance rate was used as an assessment metric. The best performing tool was found to be HIBAG, with a concordance rate of 0.84, while the best performing reference panel was the H3Africa panel, with a concordance rate of 0.62. Minimac4 (0.75) was shown to increase HLA-B allele imputation accuracy compared to HIBAG (0.71), SNP2HLA (0.51) and CookHLA (0.17). The H3Africa and Illumina Omni 2.5 array performances were comparable, showing that genotyping arrays have less influence on HLA imputation in West African populations. The findings show that using a larger population-specific reference panel and the HIBAG tool improves the accuracy of HLA imputation in a West African population.

Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software ( https://github.com/immunogenomics/HLA_analyses_tutorial ). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.

Autoimmune alleles at the major histocompatibility locus modify melanoma susceptibility

Autoimmunity and cancer represent two different aspects of immune dysfunction. Autoimmunity is characterized by breakdowns in immune self-tolerance, while impaired immune surveillance can allow for tumorigenesis. The class I major histocompatibility complex (MHC-I), which displays derivatives of the cellular peptidome for immune surveillance by CD8+ T cells, serves as a common genetic link between these conditions. As melanoma-specific CD8+ T cells have been shown to target melanocyte-specific peptide antigens more often than melanoma-specific antigens, we investigated whether vitiligo- and psoriasis-predisposing MHC-I alleles conferred a melanoma-protective effect. In individuals with cutaneous melanoma from both The Cancer Genome Atlas (n = 451) and an independent validation set (n = 586), MHC-I autoimmune-allele carrier status was significantly associated with a later age of melanoma diagnosis. Furthermore, MHC-I autoimmune-allele carriers were significantly associated with decreased risk of developing melanoma in the Million Veteran Program (OR = 0.962, p = 0.024). Existing melanoma polygenic risk scores (PRSs) did not predict autoimmune-allele carrier status, suggesting these alleles provide orthogonal risk-relevant information. Mechanisms of autoimmune protection were neither associated with improved melanoma-driver mutation association nor improved gene-level conserved antigen presentation relative to common alleles. However, autoimmune alleles showed higher affinity relative to common alleles for particular windows of melanocyte-conserved antigens and loss of heterozygosity of autoimmune alleles caused the greatest reduction in presentation for several conserved antigens across individuals with loss of HLA alleles. Overall, this study presents evidence that MHC-I autoimmune-risk alleles modulate melanoma risk unaccounted for by current PRSs.

Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response

With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

HLA allele and haplotype frequencies in Kazakhstani Russians and their relationship with other populations

HLA class I and class II genotypes from 947 Kazakhstani individuals of Russian origin were analyzed for investigating their most likely origin. The results were compared with similar data from other Russians (East and West), and also Worldwide populations, using standard genetic distances, neighbor-joining dendrograms, correspondence and haplotype analysis. Of the five HLA loci analyzed (HLA-A, HLA-C, HLA-B, HLA-DRB1, and HLA-DQB1) genotyped, 216 HLA alleles were identified. The most frequent alleles were A*02:01 (26.5%), B*07:02 (11.1%), C*04:01 (13.5%) and C*06:02 (12.1%), DRB1*07:01 (13.8%) and DRB1*15:01 (12.2%), and DQB1*03:01 (19.7%). Significant linkage disequilibrium was noted between all HLA pairs. DRB1*15:01 ~ DQB1*06:02 (10.5%), B*07:02 ~ C*07:02 (10.0%), B*07:02 ~ DRB1*15:01 (6.3%), and A*01:01 ~ B*08:01 (4.5%) were the most frequent two-locus haplotypes identified. Subsequent analyses showed that Kazakhstani Russians were closely related to West Russia-residing populations (Northwest Slavic, Vologda, Chelyabinsk, Moscow), East Europeans (Belarus Brest, Ukraine, Poland) and Scandinavians (Swedish, Finns), but distinct from East Russia-residing populations (Tuvians, Siberians from Chukotka, Kamchatka, and Ulchi) and East Mediterraneans (Levantines, Turks, North Macedonians, Albanians), and East Asians (Koreans, Japanese, Taiwanese, Mongolians). These results are in accordance with historical data indicating that the Russians of central Asia originate mainly from European Russia during the migratory flow of 18th and 19th centuries.

New insights from GWAS on BMI-related growth traits in a longitudinal cohort of admixed children with Native American and European ancestry

Body-mass index (BMI) is a hallmark of adiposity. In contrast with adulthood, the genetic architecture of BMI during childhood is poorly understood. The few genome-wide association studies (GWAS) on children have been performed almost exclusively in Europeans and at single ages. We performed cross-sectional and longitudinal GWAS for BMI-related traits on 904 admixed children with mostly Mapuche Native American and European ancestries. We found regulatory variants of the immune gene HLA-DQB3 strongly associated with BMI at 1.5 - 2.5 years old. A variant in the sex-determining gene DMRT1 was associated with the age at adiposity rebound (Age-AR) in girls (P = 9.8 × 10 - 9 ). BMI was significantly higher in Mapuche than in Europeans between 5.5 and 16.5 years old. Finally, Age-AR was significantly lower (P = 0.004 ) by 1.94 years and BMI at AR was significantly higher (P = 0.04 ) by 1.2 kg/m 2 , in Mapuche children compared with Europeans.

Assessing HLA imputation accuracy in a West African population

The Human Leukocyte Antigen (HLA) region plays an important role in autoimmune and infectious diseases. HLA is a highly polymorphic region and thus difficult to impute. We therefore sought to evaluate HLA imputation accuracy, specifically in a West African population, since they are understudied and are known to harbor high genetic diversity. The study sets were selected from Gambian individuals within the Gambian Genome Variation Project (GGVP) Whole Genome Sequence datasets. Two different arrays, Illumina Omni 2.5 and Human Hereditary and Health in Africa (H3Africa), were assessed for the appropriateness of their markers, and these were used to test several imputation panels and tools. The reference panels were chosen from the 1000 Genomes dataset (1kg-All), 1000 Genomes African dataset (1kg-Afr), 1000 Genomes Gambian dataset (1kg-Gwd), H3Africa dataset and the HLA Multi-ethnic dataset. HLA-A, HLA-B and HLA-C alleles were imputed using HIBAG, SNP2HLA, CookHLA and Minimac4, and concordance rate was used as an assessment metric. Overall, the best performing tool was found to be HIBAG, with a concordance rate of 0.84, while the best performing reference panel was the H3Africa panel with a concordance rate of 0.62. Minimac4 (0.75) was shown to increase HLA-B allele imputation accuracy compared to HIBAG (0.71), SNP2HLA (0.51) and CookHLA (0.17). The H3Africa and Illumina Omni 2.5 array performances were comparable, showing that genotyping arrays have less influence on HLA imputation in West African populations. The findings show that using a larger population-specific reference panel and the HIBAG tool improves the accuracy of HLA imputation in West African populations.

Author Summary

For studies that associate a particular HLA type to a phenotypic trait for instance HIV susceptibility or control, genotype imputation remains the main method for acquiring a larger sample size. Genotype imputation, process of inferring unobserved genotypes, is a statistical technique and thus deals with probabilities. Also, the HLA region is highly variable and therefore difficult to impute. In view of this, it is important to assess HLA imputation accuracy especially in African populations. This is because the African genome has high diversity, and such studies have hardly been conducted in African populations. This work highlights that using HIBAG imputation tool and a larger population-specific reference panel increases HLA imputation accuracy in an African population.

A novel unconventional T cell population enriched in Crohn's disease

OBJECTIVE

One of the current hypotheses to explain the proinflammatory immune response in IBD is a dysregulated T cell reaction to yet unknown intestinal antigens. As such, it may be possible to identify disease-associated T cell clonotypes by analysing the peripheral and intestinal T-cell receptor (TCR) repertoire of patients with IBD and controls.

DESIGN

We performed bulk TCR repertoire profiling of both the TCR alpha and beta chains using high-throughput sequencing in peripheral blood samples of a total of 244 patients with IBD and healthy controls as well as from matched blood and intestinal tissue of 59 patients with IBD and disease controls. We further characterised specific T cell clonotypes via single-cell RNAseq.

RESULTS

We identified a group of clonotypes, characterised by semi-invariant TCR alpha chains, to be significantly enriched in the blood of patients with Crohn's disease (CD) and particularly expanded in the CD8+ T cell population. Single-cell RNAseq data showed an innate-like phenotype of these cells, with a comparable gene expression to unconventional T cells such as mucosal associated invariant T and natural killer T (NKT) cells, but with distinct TCRs.

CONCLUSIONS

We identified and characterised a subpopulation of unconventional Crohn-associated invariant T (CAIT) cells. Multiple evidence suggests these cells to be part of the NKT type II population. The potential implications of this population for CD or a subset thereof remain to be elucidated, and the immunophenotype and antigen reactivity of CAIT cells need further investigations in future studies.

Detailed stratified GWAS analysis for severe COVID-19 in four European populations

Given the highly variable clinical phenotype of Coronavirus disease 2019 (COVID-19), a deeper analysis of the host genetic contribution to severe COVID-19 is important to improve our understanding of underlying disease mechanisms. Here, we describe an extended genome-wide association meta-analysis of a well-characterized cohort of 3255 COVID-19 patients with respiratory failure and 12 488 population controls from Italy, Spain, Norway and Germany/Austria, including stratified analyses based on age, sex and disease severity, as well as targeted analyses of chromosome Y haplotypes, the human leukocyte antigen region and the SARS-CoV-2 peptidome. By inversion imputation, we traced a reported association at 17q21.31 to a ~0.9-Mb inversion polymorphism that creates two highly differentiated haplotypes and characterized the potential effects of the inversion in detail. Our data, together with the 5th release of summary statistics from the COVID-19 Host Genetics Initiative including non-Caucasian individuals, also identified a new locus at 19q13.33, including NAPSA, a gene which is expressed primarily in alveolar cells responsible for gas exchange in the lung.

How genetic risk contributes to autoimmune liver disease

Genome-wide association studies (GWAS) for autoimmune hepatitis (AIH) and GWAS/genome-wide meta-analyses (GWMA) for primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) have been successful over the past decade, identifying about 100 susceptibility loci in the human genome, with strong associations with the HLA locus and many susceptibility variants outside the HLA locus with relatively low risk. However, identifying causative variants and genes and determining their effects on liver cells and their immunological microenvironment is far from trivial. Polygenic risk scores (PRSs) based on current genome-wide data have limited potential to predict individual disease risk. Interestingly, results of mediated expression score regression analysis provide evidence that a substantial portion of gene expression at susceptibility loci is mediated by genetic risk variants, in contrast to many other complex diseases. Genome- and transcriptome-wide comparisons between AIH, PBC, and PSC could help to better delineate the shared inherited component of autoimmune liver diseases (AILDs), and statistical fine-mapping, chromosome X-wide association testing, and genome-wide in silico drug screening approaches recently applied to GWMA data from PBC could potentially be successfully applied to AIH and PSC. Initial successes through single-cell RNA sequencing (scRNA-seq) experiments in PBC and PSC now raise high hopes for understanding the impact of genetic risk variants in the context of liver-resident immune cells and liver cell subpopulations, and for bridging the gap between genetics and disease.

Trans-ancestral fine-mapping of MHC reveals key amino acids associated with spontaneous clearance of hepatitis C in HLA-DQβ1

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-DQβ1Leu26 (p valueMeta = 1.24 × 10-14) located in pocket 4 was negatively associated with HCV spontaneous clearance and HLA-DQβ1Pro55 (p valueMeta = 8.23 × 10-11) located in the peptide binding region was positively associated, independently of HLA-DQβ1Leu26. 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-DQβ1Leu26 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-DQβ1 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.

Allele imputation for the killer cell immunoglobulin-like receptor KIR3DL1/S1

Highly polymorphic interaction of KIR3DL1 and KIR3DS1 with HLA class I ligands modulates the effector functions of natural killer (NK) cells and some T cells. This genetically determined diversity affects severity of infections, immune-mediated diseases, and some cancers, and impacts the course of immunotherapies, including transplantation. KIR3DL1 is an inhibitory receptor, and KIR3DS1 is an activating receptor encoded by the KIR3DL1/S1 gene that has more than 200 diverse and divergent alleles. Determination of KIR3DL1/S1 genotypes for medical application is hampered by complex sequence and structural variation, requiring targeted approaches to generate and analyze high-resolution allele data. To overcome these obstacles, we developed and optimized a model for imputing KIR3DL1/S1 alleles at high-resolution from whole-genome SNP data. We designed the model to represent a substantial component of human genetic diversity. Our Global imputation model is effective at genotyping KIR3DL1/S1 alleles with an accuracy ranging from 88% in Africans to 97% in East Asians, with mean specificity of 99% and sensitivity of 95% for alleles >1% frequency. We used the established algorithm of the HIBAG program, in a modification named Pulling Out Natural killer cell Genomics (PONG). Because HIBAG was designed to impute HLA alleles also from whole-genome SNP data, PONG allows combinatorial diversity of KIR3DL1/S1 with HLA-A and -B to be analyzed using complementary techniques on a single data source. The use of PONG thus negates the need for targeted sequencing data in very large-scale association studies where such methods might not be tractable.

Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine-mapping in the MHC and genomewide

Because transethnic analysis may facilitate prioritization of causal genetic variants, we performed a genomewide association study (GWAS) of psoriasis in South Asians (SAS), consisting of 2,590 cases and 1,720 controls. Comparison with our existing European-origin (EUR) GWAS showed that effect sizes of known psoriasis signals were highly correlated in SAS and EUR (Spearman ρ = 0.78; p < 2 × 10-14). Transethnic meta-analysis identified two non-MHC psoriasis loci (1p36.22 and 1q24.2) not previously identified in EUR, which may have regulatory roles. For these two loci, the transethnic GWAS provided higher genetic resolution and reduced the number of potential causal variants compared to using the EUR sample alone. We then explored multiple strategies to develop reference panels for accurately imputing MHC genotypes in both SAS and EUR populations and conducted a fine-mapping of MHC psoriasis associations in SAS and the largest such effort for EUR. HLA-C*06 was the top-ranking MHC locus in both populations but was even more prominent in SAS based on odds ratio, disease liability, model fit and predictive power. Transethnic modeling also substantially boosted the probability that the HLA-C*06 protein variant is causal. Secondary MHC signals included coding variants of HLA-C and HLA-B, but also potential regulatory variants of these two genes as well as HLA-A and several HLA class II genes, with effects on both chromatin accessibility and gene expression. This study highlights the shared genetic basis of psoriasis in SAS and EUR populations and the value of transethnic meta-analysis for discovery and fine-mapping of susceptibility loci.

HLA imputation and its application to genetic and molecular fine-mapping of the MHC region in autoimmune diseases

Variations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region (MHC) significantly affect the risk of various diseases, especially autoimmune diseases. Fine-mapping of causal variants in this region was challenging due to the difficulty in sequencing and its inapplicability to large cohorts. Thus, HLA imputation, a method to infer HLA types from regional single nucleotide polymorphisms, has been developed and has successfully contributed to MHC fine-mapping of various diseases. Different HLA imputation methods have been developed, each with its own advantages, and recent methods have been improved in terms of accuracy and computational performance. Additionally, advances in HLA reference panels by next-generation sequencing technologies have enabled higher resolution and a more reliable imputation, allowing a finer-grained evaluation of the association between sequence variations and disease risk. Risk-associated variants in the MHC region would affect disease susceptibility through complicated mechanisms including alterations in peripheral responses and central thymic selection of T cells. The cooperation of reliable HLA imputation methods, informative fine-mapping, and experimental validation of the functional significance of MHC variations would be essential for further understanding of the role of the MHC in the immunopathology of autoimmune diseases.

Analysis of HLA Variants and Graves' Disease and Its Comorbidities Using a High Resolution Imputation System to Examine Electronic Medical Health Records

Hyperthyroidism is a prevalent endocrine disorder, and genetics play a major role in the development of thyroid-associated diseases. In particular, the inheritance of HLA has been demonstrated to induce the highest susceptibility to Graves' disease (GD). However, thus far, no studies have reported the contribution of HLA to the development of GD and the complications that follow. Thus, in the present study, to the best of our knowledge, for the first time, a powerful imputation method, HIBAG, was used to predict the HLA subtypes among populations with available genome-wide SNP array data from the China Medical University Hospital (CMUH). The disease status was extracted from the CMUH electronic medical records; a total of 2,998 subjects with GD were identified as the cases to be tested and 29,083 subjects without any diagnosis of thyroid disorders were randomly selected as the controls. A total of 12 HLA class I genotypes (HLA-A*02:07-*11:01, HLA-B*40:01-*46:01 and *46:01-*46:01, and HLA-C*01:02-*01:02, *01:02-*03:04, and *01:02-*07:02) and 17 HLA class II genotypes (HLA-DPA1*02:02-*02:02, HLA-DPB1*02:01-*05:01, *02:02-*05:01, and *04:01-*05:01, HLA-DQA1*03:02, HLA-DRB1*09:01-*15:01, and *09:01-*09:01) were found to be associated with GD in the Taiwanese population. Moreover, the HLA subtypes HLA-A*11:01, HLA-B*46:01, HLA-DPA1*01:03, and HLA-DPB1*05:01 were found to be associated with heart disease, stroke, diabetes, and hypertension among subjects with GD. Our data suggest that several HLA alleles are markedly associated with GD and its comorbidities, including heart disease, hypertension, and diabetes.

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.

Constructing germline research cohorts from the discarded reads of clinical tumor sequences

Background

Hundreds of thousands of cancer patients have had targeted (panel) tumor sequencing to identify clinically meaningful mutations. In addition to improving patient outcomes, this activity has led to significant discoveries in basic and translational domains. However, the targeted nature of clinical tumor sequencing has a limited scope, especially for germline genetics. In this work, we assess the utility of discarded, off-target reads from tumor-only panel sequencing for the recovery of genome-wide germline genotypes through imputation.

Methods

We developed a framework for inference of germline variants from tumor panel sequencing, including imputation, quality control, inference of genetic ancestry, germline polygenic risk scores, and HLA alleles. We benchmarked our framework on 833 individuals with tumor sequencing and matched germline SNP array data. We then applied our approach to a prospectively collected panel sequencing cohort of 25,889 tumors.

Results

We demonstrate high to moderate accuracy of each inferred feature relative to direct germline SNP array genotyping: individual common variants were imputed with a mean accuracy (correlation) of 0.86, genetic ancestry was inferred with a correlation of > 0.98, polygenic risk scores were inferred with a correlation of > 0.90, and individual HLA alleles were inferred with a correlation of > 0.80. We demonstrate a minimal influence on the accuracy of somatic copy number alterations and other tumor features. We showcase the feasibility and utility of our framework by analyzing 25,889 tumors and identifying the relationships between genetic ancestry, polygenic risk, and tumor characteristics that could not be studied with conventional on-target tumor data.

Conclusions

We conclude that targeted tumor sequencing can be leveraged to build rich germline research cohorts from existing data and make our analysis pipeline publicly available to facilitate this effort.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00999-4.

A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.

The polymorphism of HLA-A, -C, -B, -DRB3/4/5, -DRB1, -DQB1 loci in Zhejiang Han population, China using NGS technology

The distributions of HLA allele and haplotype are various in the populations. Currently, the data for HLA alleles and haplotypes at three fields resolution level in Chinese Han population is rare. Here, the HLA alleles and haplotypes of the 1734 cord blood samples from Zhejiang Han population, China were reported at three fields resolution. All samples were randomly collected from the Zhejiang Cord Blood Bank, China. HLA-A, -B, -C, -DRB1, -DQB1, -DRB3/4/5 loci was genotyped using next generation sequencing method. The genotypes of the samples were assigned using the HLA TypeStream Visual Software version 1.2.0. The frequency of alleles, haplotype estimation and linkage disequilibrium analysis were performed with the Arlequin software 3.5.2.2. It was found that the top three frequent alleles of HLA-A, -B, -C, -DRB1, -DQB1, -DRB3/4/5 loci were A*11:01:01 (25.81%), A*24:02:01 (16.70%), A*02:01:01 (10.61%); B*40:01:02 (15.97%), B*46:01:01 (11.48%), B*58:01:01 (7.96%); C*07:02:01 (19.03%), C*01:02:01 (17.65%), C*03:04:01 (10.41%); DRB1*09:01:02G (17.96%), DRB1*12:02:01 (9.57%), DRB1*08:03:02 (9.54%); DQB1*03:01:01G (21.05%), DQB1*03:03:02 (19.15%), DQB1*06:01:01G (12.08%); DRB4*01:03:01 (25.72%), DRB3*02:02:01 (20.27%), DRB5*01:01:01 (10.96%), respectively. A total of 1528 distinct A∼C∼B∼DRB3/4/5∼DRB1∼DQB1 haplotypes were estimated, and the top three most common haplotypes were A*33:03:01∼C*03:02:02∼B*58:01:01∼DRB3*02:02:01∼DRB1*03:01:01∼ DQB1*02:01:01 (4.02%), A*30:01:01∼C*06:02:01∼B*13:02:01∼DRB4*01:03:01∼ DRB1*07:01:01 ∼DQB1*02:02:01 (3.11%) and A*02:07:01∼C*01:02:01∼B*46:01:01 ∼DRB4*01:03:01∼DRB1*09:01:02G∼DQB1*03:03:02 (3.05%). Some alleles of different HLA loci were shown strong linkage disequilibrium. In conclusion, the data of allele and haplotype of HLA-A, -B, -C, -DRB1, -DQB1 and -DRB3/4/5 loci at three fields resolution level were obtained in Zhejiang Han population, thus contributing to analyze the HLA ploymorphism in the populations.

Whole-exome sequencing reveals common and rare variants in immunologic and neurological genes implicated in achalasia

Idiopathic achalasia (IA) is a severe motility disorder characterized by neuronal degeneration in the myenteric plexus, but the etiology remains largely unknown. We performed whole-exome sequencing (WES) in 100 IA-affected individuals and 313 non-IA control subjects and validated the results in 230 IA-affected individuals and 1,760 non-IA control subjects. Common missense variants rs1705003 (CUTA, GenBank: NC_000006.11:g.33385953A>G) and rs1126511 (HLA-DPB1, GenBank: NC_000006.11:g.33048466G>T) at 6p21.32 were reproducibly associated with increased risk of IA (rs1126511: OR = 1.83, p = 2.34 × 10^-9; rs1705003: OR = 2.37, p = 3.21 × 10^-7), meeting exome-wide significance. Both variants can affect the expression of their target genes at the transcript level. An array-based association analysis in 280 affected individuals and 1,121 control subjects determined the same signal at 6p21.32. Further conditional analyses supported that the two missense variants identified in WES-based association study were potential causal variants of IA. For rare variants, the top genes identified by gene-based analysis were significantly enriched in nerve and muscle phenotypic genes in the mouse. Moreover, the functional rare variants in these genes tended to cooccur in IA-affected individuals. In an independent cohort, we successfully validated three rare variants (CREB5, GenBank: NC_000007.13:g.28848865G>T; ESYT3, GenBank: NC_000003.11:g.138183253C>T; and LPIN1, GenBank: NC_000002.11:g.11925128A>G) which heightens the risk of developing IA. Our study identified and validated two common variants and three rare variants associated with IA in immunologic and neurological genes, providing new insight into the etiology of IA.

Transethnic analysis of the human leukocyte antigen region for ulcerative colitis reveals not only shared but also ethnicity-specific disease associations

Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gut. Genetic association studies have identified the highly variable human leukocyte antigen (HLA) region as the strongest susceptibility locus for IBD and specifically DRB1*01:03 as a determining factor for ulcerative colitis (UC). However, for most of the association signal such as delineation could not be made because of tight structures of linkage disequilibrium within the HLA. The aim of this study was therefore to further characterize the HLA signal using a transethnic approach. We performed a comprehensive fine mapping of single HLA alleles in UC in a cohort of 9272 individuals with African American, East Asian, Puerto Rican, Indian and Iranian descent and 40 691 previously analyzed Caucasians, additionally analyzing whole HLA haplotypes. We computationally characterized the binding of associated HLA alleles to human self-peptides and analyzed the physicochemical properties of the HLA proteins and predicted self-peptidomes. Highlighting alleles of the HLA-DRB1*15 group and their correlated HLA-DQ-DR haplotypes, we not only identified consistent associations (regarding effects directions/magnitudes) across different ethnicities but also identified population-specific signals (regarding differences in allele frequencies). We observed that DRB1*01:03 is mostly present in individuals of Western European descent and hardly present in non-Caucasian individuals. We found peptides predicted to bind to risk HLA alleles to be rich in positively charged amino acids. We conclude that the HLA plays an important role for UC susceptibility across different ethnicities. This research further implicates specific features of peptides that are predicted to bind risk and protective HLA proteins.

State-of-the-art genome inference in the human MHC

The Major Histocompatibility Complex (MHC) on the short arm of chromosome 6 is associated with more diseases than any other region of the genome; it encodes the antigen-presenting Human Leukocyte Antigen (HLA) proteins and is one of the key immunogenetic regions of the genome. Accurate genome inference and interpretation of MHC association signals have traditionally been hampered by the region's uniquely complex features, such as high levels of polymorphism; inter-gene sequence homologies; structural variation; and long-range haplotype structures. Recent algorithmic and technological advances have, however, significantly increased the accessibility of genetic variation in the MHC; these developments include (i) accurate SNP-based HLA type imputation; (ii) genome graph approaches for variation-aware genome inference from next-generation sequencing data; (iii) long-read-based diploid de novo assembly of the MHC; (iv) cost-effective targeted MHC sequencing methods. Applied to hundreds of thousands of samples over the last years, these technologies have already enabled significant biological discoveries, for example in the field of autoimmune disease genetics. Remaining challenges concern the development of integrated methods that leverage haplotype-resolved de novo assembly of the MHC for the development of improved MHC genotyping methods for short reads and the construction of improved reference panels for SNP-based imputation. Improved genome inference in the MHC can crucially contribute to an improved genetic and functional understanding of many immune-related phenotypes and diseases.

Genome-wide study of a Neolithic Wartberg grave community reveals distinct HLA variation and hunter-gatherer ancestry

The Wartberg culture (WBC, 3500-2800 BCE) dates to the Late Neolithic period, a time of important demographic and cultural transformations in western Europe. We performed genome-wide analyses of 42 individuals who were interred in a WBC collective burial in Niedertiefenbach, Germany (3300-3200 cal. BCE). The results showed that the farming population of Niedertiefenbach carried a surprisingly large hunter-gatherer ancestry component (34–58%). This component was most likely introduced during the cultural transformation that led to the WBC. In addition, the Niedertiefenbach individuals exhibited a distinct human leukocyte antigen gene pool, possibly reflecting an immune response that was geared towards detecting viral infections.

Alexander Immel et al. performed genome-wide analyses of 42 individuals from a collective burial in Niedertiefenbach, Germany from the Wartberg Culture. The authors find that this population had a large hunter-gatherer ancestry component and a distinct HLA pool, which indicates immune defenses against viral pathogens.

How Ancestry Influences the Chances of Finding Unrelated Donors: An Investigation in Admixed Brazilians

A match of HLA loci between patients and donors is critical for successful hematopoietic stem cell transplantation. However, the extreme polymorphism of HLA loci - an outcome of millions of years of natural selection - reduces the chances that two individuals will carry identical combinations of multilocus HLA genotypes. Further, HLA variability is not homogeneously distributed throughout the world: African populations on average have greater variability than non-Africans, reducing the chances that two unrelated African individuals are HLA identical. Here, we explore how self-identification (often equated with "ethnicity" or "race") and genetic ancestry are related to the chances of finding HLA compatible donors in a large sample from Brazil, a highly admixed country. We query REDOME, Brazil's Bone Marrow Registry, and investigate how different criteria for identifying ancestry influence the chances of finding a match. We find that individuals who self-identify as "Black" and "Mixed" on average have lower chances of finding matches than those who self-identify as "White" (up to 57% reduction). We next show that an individual's African genetic ancestry, estimated using molecular markers and quantified as the proportion of an individual's genome that traces its ancestry to Africa, is strongly associated with reduced chances of finding a match (up to 60% reduction). Finally, we document that the strongest reduction in chances of finding a match is associated with having an MHC region of exclusively African ancestry (up to 75% reduction). We apply our findings to a specific condition, for which there is a clinical indication for transplantation: sickle-cell disease. We show that the increased African ancestry in patients with this disease leads to reduced chances of finding a match, when compared to the remainder of the sample, without the condition. Our results underscore the influence of ancestry on chances of finding compatible HLA matches, and indicate that efforts guided to increasing the African component of registries are necessary.

The landscape of host genetic factors involved in immune response to common viral infections

BACKGROUND

Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and therapeutic opportunities.

METHODS

We conducted a comprehensive study including genome-wide and transcriptome-wide association analyses to identify genetic loci associated with immunoglobulin G antibody response to 28 antigens for 16 viruses using serological data from 7924 European ancestry participants in the UK Biobank cohort.

RESULTS

Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. We identified specific amino acid (AA) residues that are associated with seroreactivity, the strongest associations presented in a range of AA positions within DRβ1 at positions 11, 13, 71, and 74 for Epstein-Barr virus (EBV), Varicella zoster virus (VZV), human herpesvirus 7, (HHV7), and Merkel cell polyomavirus (MCV). Genome-wide association analyses discovered 7 novel genetic loci outside the HLA associated with viral antibody response (P < 5.0 × 10-8), including FUT2 (19q13.33) for human polyomavirus BK (BKV), STING1 (5q31.2) for MCV, and CXCR5 (11q23.3) and TBKBP1 (17q21.32) for HHV7. Transcriptome-wide association analyses identified 114 genes associated with response to viral infection, 12 outside of the HLA region, including ECSCR: P = 5.0 × 10-15 (MCV), NTN5: P = 1.1 × 10-9 (BKV), and P2RY13: P = 1.1 × 10-8 EBV nuclear antigen. We also demonstrated pleiotropy between viral response genes and complex diseases, from autoimmune disorders to cancer to neurodegenerative and psychiatric conditions.

CONCLUSIONS

Our study confirms the importance of the HLA region in host response to viral infection and elucidates novel genetic determinants beyond the HLA that contribute to host-virus interaction.

The landscape of host genetic factors involved in immune response to common viral infections

INTRODUCTION

Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and therapeutic opportunities.

METHODS

We conducted a comprehensive study including genome-wide and transcriptome-wide association analyses to identify genetic loci associated with immunoglobulin G antibody response to 28 antigens for 16 viruses using serological data from 7924 European ancestry participants in the UK Biobank cohort.

RESULTS

Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. We identified specific amino acid (AA) residues that are associated with seroreactivity, the strongest associations presented in a range of AA positions within DRβ1 at positions 11, 13, 71, and 74 for Epstein-Barr Virus (EBV), Varicella Zoster Virus (VZV), Human Herpes virus 7, (HHV7) and Merkel cell polyomavirus (MCV). Genome-wide association analyses discovered 7 novel genetic loci outside the HLA associated with viral antibody response (P<5.×10-8), including FUT2 (19q13.33) for human polyomavirus BK (BKV), STING1 (5q31.2) for MCV, as well as CXCR5 (11q23.3) and TBKBP1 (17q21.32) for HHV7. Transcriptome-wide association analyses identified 114 genes associated with response to viral infection, 12 outside of the HLA region, including ECSCR: P=5.0×10-15 (MCV), NTN5: P=1.1×10-9 (BKV), and P2RY13: P=1.1×10-8 EBV nuclear antigen. We also demonstrated pleiotropy between viral response genes and complex diseases; from autoimmune disorders to cancer to neurodegenerative and psychiatric conditions.

CONCLUSIONS

Our study confirms the importance of the HLA region in host response to viral infection and elucidates novel genetic determinants beyond the HLA that contribute to host-virus interaction.

Increasing accuracy of HLA imputation by a population-specific reference panel in a FinnGen biobank cohort

The HLA genes, the most polymorphic genes in the human genome, constitute the strongest single genetic susceptibility factor for autoimmune diseases, transplantation alloimmunity and infections. HLA imputation via statistical inference of alleles based on single-nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with alleles is a powerful first-step screening tool. Due to different LD structures between populations, the accuracy of HLA imputation may benefit from matching the imputation reference with the study population. To evaluate the potential advantage of using population-specific reference in HLA imputation, we constructed an HLA reference panel consisting of 1150 Finns with 5365 major histocompatibility complex region SNPs consistent between genome builds. We evaluated the accuracy of the panel against a European panel in an independent test set of 213 Finnish subjects. We show that the Finnish panel yields a lower imputation error rate (1.24% versus 1.79%). More than 30% of imputation errors occurred in haplotypes enriched in Finland. The frequencies of imputed HLA alleles were highly correlated with clinical-grade HLA allele frequencies and allowed accurate replication of established HLA–disease associations in ∼102 000 biobank participants. The results show that a population-specific reference increases imputation accuracy in a relatively isolated population within Europe and can be successfully applied to biobank-scale genome data collections.

Psoriasis and Genetics

Psoriasis is a common inflammatory skin disease caused by the interplay between multiple genetic and environmental risk factors. This review summarises recent progress in elucidating the genetic basis of psoriasis, particularly through large genome-wide association studies. We illustrate the power of genetic analyses for disease stratification. Psoriasis can be stratified by phenotype (common plaque versus rare pustular variants), or by outcome (prognosis, comorbidities, response to treatment); recent progress has been made in delineating the genetic contribution in each of these areas. We also highlight how genetic data can directly inform the development of effective psoriasis treatments.