HLA imputation, what is it good for?

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.

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.

Association study of human leukocyte antigen variants and idiopathic pulmonary fibrosis

Introduction

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial pneumonia marked by progressive lung fibrosis and a poor prognosis. Recent studies have highlighted the potential role of infection in the pathogenesis of IPF, and a prior association of the HLA-DQB1 gene with idiopathic fibrotic interstitial pneumonia (including IPF) has been reported. Owing to the important role that the human leukocyte antigen (HLA) region plays in the immune response, here we evaluated if HLA genetic variation was associated specifically with IPF risk.

Methods

We performed a meta-analysis of associations of the HLA region with IPF risk in individuals of European ancestry from seven independent case-control studies of IPF (comprising 5159 cases and 27 459 controls, including a prior study of fibrotic interstitial pneumonia). Single nucleotide polymorphisms, classical HLA alleles and amino acids were analysed and signals meeting a region-wide association threshold of p<4.5×10-4 and a posterior probability of replication >90% were considered significant. We sought to replicate the previously reported HLA-DQB1 association in the subset of studies independent of the original report.

Results

The meta-analysis of all seven studies identified four significant independent single nucleotide polymorphisms associated with IPF risk. However, none met the posterior probability for replication criterion. The HLA-DQB1 association was not replicated in the independent IPF studies.

Conclusion

Variation in the HLA region was not consistently associated with risk in studies of IPF. However, this does not preclude the possibility that other genomic regions linked to the immune response may be involved in the aetiology of IPF.

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.

Association study of human leukocyte antigen (HLA) variants and idiopathic pulmonary fibrosis

Introduction

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial pneumonia marked by progressive lung fibrosis and a poor prognosis. Recent studies have highlighted the potential role of infection in the pathogenesis of IPF and a prior association of the HLA-DQB1 gene with idiopathic fibrotic interstitial pneumonia (including IPF) has been reported. Due to the important role that the Human Leukocyte Antigen (HLA) region plays in the immune response, here we evaluated if HLA genetic variation was associated specifically with IPF risk.

Methods

We performed a meta-analysis of associations of the HLA region with IPF risk in individuals of European ancestry from seven independent case-control studies of IPF (comprising a total of 5,159 cases and 27,459 controls, including the prior study of fibrotic interstitial pneumonia). Single nucleotide polymorphisms, classical HLA alleles and amino acids were analysed and signals meeting a region-wide association threshold p<4.5×10-4 and a posterior probability of replication >90% were considered significant. We sought to replicate the previously reported HLA-DQB1 association in the subset of studies independent of the original report.

Results

The meta-analysis of all seven studies identified four significant independent single nucleotide polymorphisms associated with IPF risk. However, none met the posterior probability for replication criterion. The HLA-DQB1 association was not replicated in the independent IPF studies.

Conclusion

Variation in the HLA region was not consistently associated with risk in studies of IPF. However, this does not preclude the possibility that other genomic regions linked to the immune response may be involved in the aetiology of IPF.

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.

Relevance of the MHC region for breast cancer susceptibility in Asians

BACKGROUND

Human leukocyte antigen (HLA) genes play critical roles in immune surveillance, an important defence against tumors. Imputing HLA genotypes from existing single-nucleotide polymorphism datasets is low-cost and efficient. We investigate the relevance of the major histocompatibility complex region in breast cancer susceptibility, using imputed class I and II HLA alleles, in 25,484 women of Asian ancestry.

METHODS

A total of 12,901 breast cancer cases and 12,583 controls from 12 case-control studies were included in our pooled analysis. HLA imputation was performed using SNP2HLA on 10,886 quality-controlled variants within the 15-55 Mb region on chromosome 6. HLA alleles (n = 175) with info scores greater than 0.8 and frequencies greater than 0.01 were included (resolution at two-digit level: 71; four-digit level: 104). We studied the associations between HLA alleles and breast cancer risk using logistic regression, adjusting for population structure and age. Associations between HLA alleles and the risk of subtypes of breast cancer (ER-positive, ER-negative, HER2-positive, HER2-negative, early-stage, and late-stage) were examined.

RESULTS

We did not observe associations between any HLA allele and breast cancer risk at P < 5e-8; the smallest p value was observed for HLA-C*12:03 (OR = 1.29, P = 1.08e-3). Ninety-five percent of the effect sizes (OR) observed were between 0.90 and 1.23. Similar results were observed when different subtypes of breast cancer were studied (95% of ORs were between 0.85 and 1.18).

CONCLUSIONS

No imputed HLA allele was associated with breast cancer risk in our large Asian study. Direct measurement of HLA gene expressions may be required to further explore the associations between HLA genes and breast cancer risk.

Human Leukocyte Antigen (HLA) Testing in Pharmacogenomics

The genetic region on the short arm of chromosome 6 where the human leukocyte antigen (HLA) genes are located is the major histocompatibility complex. The genes in this region are highly polymorphic, and some loci have a high degree of homology with other genes and pseudogenes. Histocompatibility testing has traditionally been performed in the setting of transplantation and involves determining which specific alleles are present. Several HLA alleles have been associated with disease risk or increased risk of adverse drug reaction (ADR) when treated with certain medications. Testing for these applications differs from traditional histocompatibility in that the desired result is simply presence or absence of the allele of interest, rather than determining which allele is present. At present, the majority of HLA typing is done by molecular methods using commercially available kits. A subset of pharmacogenomics laboratories has developed their own methods, and in some cases, query single nucleotide variants associated with certain HLA alleles rather than directly testing for the allele. In this chapter, a brief introduction to the HLA system is provided, followed by an overview of a variety of testing technologies including those specifically used in pharmacogenomics, and the chapter concludes with details regarding specific HLA alleles associated with ADR.

Targeted analysis of genomic regions enriched in African ancestry reveals novel classical HLA alleles associated with asthma in Southwestern Europeans

Despite asthma has a considerable genetic component, an important proportion of genetic risks remain unknown, especially for non-European populations. Canary Islanders have the largest African genetic ancestry observed among Southwestern Europeans and the highest asthma prevalence in Spain. Here we examined broad chromosomal regions previously associated with an excess of African genetic ancestry in Canary Islanders, with the aim of identifying novel risk variants associated with asthma susceptibility. In a two-stage cases-control study, we revealed a variant within HLA-DQB1 significantly associated with asthma risk (rs1049213, meta-analysis p = 1.30 × 10-7, OR [95% CI] = 1.74 [1.41-2.13]) previously associated with asthma and broad allergic phenotype. Subsequent fine-mapping analyses of classical HLA alleles revealed a novel allele significantly associated with asthma protection (HLA-DQA1*01:02, meta-analysis p = 3.98 × 10-4, OR [95% CI] = 0.64 [0.50-0.82]) that had been linked to infectious and autoimmune diseases, and peanut allergy. HLA haplotype analyses revealed a novel haplotype DQA1*01:02-DQB1*06:04 conferring asthma protection (meta-analysis p = 4.71 × 10-4, OR [95% CI] = 0.47 [0.29- 0.73]).

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.

An immunogenetic view of COVID-19

Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.

The changing landscape of HLA typing: Understanding how and when HLA typing data can be used with confidence from bench to bedside

Human leukocyte antigen (HLA) genes are extraordinary for their extreme diversity and widespread impact on human health and disease. More than 30,000 HLA alleles have been officially named and more alleles continue to be discovered at a rapid pace. HLA typing systems which have been developed to detect HLA diversity have advanced rapidly and are revolutionizing our understanding of HLA's clinical importance. However, continuous improvements in knowledge and technology have created challenges for clinicians and scientists. This review explains how differences in HLA typing systems can impact the HLA types that are assigned. The consequences of differences in laboratory testing methods and reference databases are described. The challenges of using HLA types that are not equivalent are illustrated. A fundamental understanding of the continual expansion of our understanding of HLA diversity and limitations in some of the typing data is essential for using typing data appropriately in clinical and research settings.

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.

SNP-HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC-centric analyses in genomics

Genome‐wide associations studies have repeatedly identified the major histocompatibility complex genomic region (6p21.3) as key in immune pathologies. Researchers have also aimed to extend the biological interpretation of associations by focusing directly on human leukocyte antigen (HLA) polymorphisms and their combination as haplotypes. To circumvent the effort and high costs of HLA typing, statistical solutions have been developed to infer HLA alleles from single‐nucleotide polymorphism (SNP) genotyping data. Though HLA imputation methods have been developed, no unified effort has yet been undertaken to share large and diverse imputation models, or to improve methods. By training the HIBAG software on SNP + HLA data generated by the Consortium on Asthma among African‐ancestry Populations in the Americas (CAAPA) to create reference panels, we highlighted the importance of (a) the number of individuals in reference panels, with a twofold increase in accuracy (from 10 to 100 individuals) and (b) the number of SNPs, with a 1.5‐fold increase in accuracy (from 500 to 24,504 SNPs). Results showed improved accuracy with CAAPA compared to the African American models available in HIBAG, highlighting the need for precise population‐matching. The SNP‐HLA Reference Consortium is an international endeavor to gather data, enhance HLA imputation and broaden access to highly accurate imputation models for the immunogenomics community.

Genetics of Antiphospholipid Syndrome

PURPOSE OF REVIEW

Antiphospholipid syndrome (APS) is a rare heterogenous disorder associated with the presence of antiphospholipid antibodies and can present in a wide variety of clinical manifestations including thrombosis and pregnancy complications. Although the etiology of APS remains poorly understood, there is strong support for considering APS as a complex genetic disease in which multiple genetic risk factors, in conjunction with environmental factors, affect its onset, progression, and severity. Here, we provide a comprehensive review of the current knowledge of the genetic basis of APS, which remains in its infancy.

RECENT FINDINGS

Most genetic studies to date in APS were performed in small cohorts of patients. As a result, only few genetic associations reported are convincing. Several reports suggested genetic associations with HLA class II alleles in APS, and only two genetic loci outside of the HLA region (STAT4 and C1D) reached the threshold for genome-wide level of significance (P < 5 × 10^-8). In this review, we also shed light on the genetic differences among the diverse clinical subsets of APS and briefly discuss the role that DNA methylation changes might play in the pathophysiology of this disease. The genetic basis of APS remains poorly characterized. Larger collaborative multicenter studies using well-characterized patients are needed to comprehensively understand the role of genetic susceptibility in APS.

Imputation-Based HLA Typing with SNPs in GWAS Studies

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the extended haplotype structure within the major histocompatibility complex (MHC) to predict classical HLA alleles using dense SNP genotypes, such as those available on chip panels of genome-wide association study (GWAS). These methods enable HLA analyses of classical alleles on existing SNP datasets genotyped in GWAS studies at no extra cost. Here, I describe the workflow of HIBAG, an imputation method with attribute bagging, for obtaining a sample's HLA class I and II genotypes of two-field resolution using SNP data. Two examples are provided to illustrate with a publicly available HLA and SNP dataset: genotype imputation with pre-fit classifiers in GWAS, and model training to build a new classifier.