A statistical method for predicting classical HLA alleles from SNP data

Efficient HLA imputation from sequential SNPs data by transformer

Human leukocyte antigen (HLA) genes are associated with a variety of diseases, yet the direct typing of HLA alleles is both time-consuming and costly. Consequently, various imputation methods leveraging sequential single nucleotide polymorphisms (SNPs) data have been proposed, employing either statistical or deep learning models, such as the convolutional neural network (CNN)-based model, DEEP*HLA. However, these methods exhibit limited imputation efficiency for infrequent alleles and necessitate a large size of reference dataset. In this context, we have developed a Transformer-based model to HLA allele imputation, named "HLA Reliable IMpuatioN by Transformer (HLARIMNT)" designed to exploit the sequential nature of SNPs data. We evaluated HLARIMNT's performance using two distinct reference panels; Pan-Asian reference panel (n = 530) and Type 1 Diabetes genetics Consortium (T1DGC) reference panel (n = 5225), alongside a combined panel (n = 1060). HLARIMNT demonstrated superior accuracy to DEEP*HLA across several indices, particularly for infrequent alleles. Furthermore, we explored the impact of varying training data sizes on imputation accuracy, finding that HLARIMNT consistently outperformed across all data size. These findings suggest that Transformer-based models can efficiently impute not only HLA types but potentially other gene types from sequential SNPs data.

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.

Blood donor biobank and HLA imputation as a resource for HLA homozygous cells for therapeutic and research use

Background

Allogeneic therapeutic cells may be rejected if they express HLA alleles not found in the recipient. As finding cell donors with a full HLA match to a recipient requires vast donor pools, the use of HLA homozygous cells has been suggested as an alternative. HLA homozygous cells should be well tolerated by those who carry at least one copy of donor HLA alleles. HLA-A-B homozygotes could be valuable for HLA-matched thrombocyte products. We evaluated the feasibility of blood donor biobank and HLA imputation for the identification of potential cell donors homozygous for HLA alleles.

Methods

We imputed HLA-A, -B, -C, -DRB1, -DQA1, -DQB1 and -DPB1 alleles from genotypes of 20,737 Finnish blood donors in the Blood Service Biobank. We confirmed homozygosity by sequencing HLA alleles in 30 samples and by examining 36,161 MHC-located polymorphic DNA markers.

Results

Three hundred and seventeen individuals (1.5%), representing 41 different haplotypes, were found to be homozygous for HLA-A, -B, -C, -DRB1, -DQA1 and -DQB1 alleles. Ten most frequent haplotypes homozygous for HLA-A to -DQB1 were HLA-compatible with 49.5%, and three most frequent homozygotes to 30.4% of the Finnish population. Ten most frequent HLA-A-B homozygotes were compatible with 75.3%, and three most frequent haplotypes to 42.6% of the Finnish population. HLA homozygotes had a low level of heterozygosity in MHC-located DNA markers, in particular in HLA haplotypes enriched in Finland.

Conclusions

The present study shows that HLA imputation in a blood donor biobank of reasonable size can be used to identify HLA homozygous blood donors suitable for cell therapy, HLA-typed thrombocytes and research. The homozygotes were HLA-compatible with a large fraction of the Finnish population. Regular blood donors reported to have positive attitude to research donation appear a good option for these purposes. Differences in population frequencies of HLA haplotypes emphasize the need for population-specific collections of HLA homozygous samples.

Recent Advances of Human Leukocyte Antigen (HLA) Typing Technology Based on High-Throughput Sequencing

The major histocompatibility complex (MHC) in humans is a genetic region consisting of cell surface proteins located on the short arm of chromosome 6. This is also known as the human leukocyte antigen (HLA) region. The HLA region consists of genes that exhibit complex genetic polymorphisms, and are extensively involved in immune responses. Each individual has a unique set of HLAs. Donor-recipient HLA allele matching is an important factor for organ transplantation. Therefore, an established rapid and accurate HLA typing technology is instrumental to preventing graft-verses-host disease (GVHD) in organ recipients. As of recent, high-throughput sequencing has allowed for an increase read length and higher accuracy and throughput, thus achieving complete and high-resolution full-length typing. With more advanced nanotechnology used in high-throughput sequencing, HLA typing is more widely used in third-generation single-molecule sequencing. This review article summarizes some of the most widely used sequencing typing platforms and evaluates the latest developments in HLA typing kits and their clinical applications.

A genome-wide association study of outcome from traumatic brain injury

BACKGROUND

Factors such as age, pre-injury health, and injury severity, account for less than 35% of outcome variability in traumatic brain injury (TBI). While some residual outcome variability may be attributable to genetic factors, published candidate gene association studies have often been underpowered and subject to publication bias.

METHODS

We performed the first genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in TBI. The study population consisted of 5268 patients from prospective European and US studies, who attended hospital within 24 h of TBI, and satisfied local protocols for computed tomography.

FINDINGS

The estimated heritability of TBI outcome was 0·26. GWAS revealed no genetic variants with genome-wide significance (p < 5 × 10-8), but identified 83 variants in 13 independent loci which met a lower pre-specified sub-genomic statistical threshold (p < 10-5). Similarly, none of the genes tested in TWAS met tissue-wide significance. An exploratory analysis of 75 published candidate variants associated with 28 genes revealed one replicable variant (rs1800450 in the MBL2 gene) which retained significance after correction for multiple comparison (p = 5·24 × 10-4).

INTERPRETATION

While multiple novel loci reached less stringent thresholds, none achieved genome-wide significance. The overall heritability estimate, however, is consistent with the hypothesis that common genetic variation substantially contributes to inter-individual variability in TBI outcome. The meta-analytic approach to the GWAS and the availability of summary data allows for a continuous extension with additional cohorts as data becomes available.

FUNDING

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

The utility of imputation for molecular mismatch analysis in solid organ transplantation

HLA genotyping has undergone a rapid progression in resolution since the development of DNA-based typing methods. Despite the advent of high-resolution next-generation sequencing, the bulk of solid organ genotyping is performed at intermediate resolution, which provides multiple possible two-field results for each classical HLA loci. As a result, several methodologies have been developed to impute the most likely allele-level (two-field) HLA genotype for the purposes of donor-recipient compatibility analysis. The advent of molecular mismatch analysis, however, has placed a new emphasis on the accuracy of imputation. While seminal molecular mismatch studies have relied on the imputation of intermediate resolution genotyping, several recent studies have performed analysis showing that imputation generates inaccuracies in epitope identification. While the clinical impact of these errors is not clear, it is important that these concerns do not preclude future progress in understanding the utility of molecular mismatch analysis in transplantation. In the future, advances in genotyping methods will result in routine two-field resolution that will abrogate these concerns. In the meantime, however, studies are needed in order to address the role of molecular mismatch in diverse patient populations and to carefully address the potential of molecular mismatch analysis in the context of imputation.

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.

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.

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10^-120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

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.

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.

Golub E, Aouizerat BE, Kuniholm MH, Millstein J. Association of HLA Genotype With T-Cell Activation in Human Immunodeficiency Virus (HIV) and HIV/Hepatitis C Virus-Coinfected Women

BACKGROUND

Global immune activation and HLA alleles are each associated with the pathogenesis of human immunodeficiency virus (HIV) and hepatitis C virus .

METHODS

We evaluated the relationship between 44 HLA class I and 28 class II alleles and percentages of activated CD8 (CD8+CD38+DR+) and CD4 (CD4+CD38+DR+) T cells in 586 women who were naive to highly active antiretroviral therapy. We used linear generalized estimating equation regression models, adjusting for race/ethnicity, age, HIV load, and hepatitis C virus infection and controlling for multiplicity using a false discovery rate threshold of 0.10.

RESULTS

Ten HLA alleles were associated with CD8 and/or CD4 T-cell activation. Lower percentages of activated CD8 and/or CD4 T cells were associated with protective alleles B*57:03 (CD8 T cells, -6.6% [P = .002]; CD4 T cells, -2.7% [P = .007]), C*18:01 (CD8 T cells, -6.6%; P < .0008) and DRB1*13:01 (CD4 T cells, -2.7%; P < .0004), and higher percentages were found with B*18:01 (CD8 T cells, 6.2%; P < .0003), a detrimental allele. Other alleles/allele groups associated with activation included C*12:03, group DQA1*01:00, DQB1*03:01, DQB1*03:02, DQB1*06:02, and DQB1*06:03.

CONCLUSION

These findings suggest that a person's HLA type may play a role in modulating T-cell activation independent of viral load and sheds light on the relationship between HLA, T-cell activation, immune control, and HIV pathogenesis.

A high-resolution HLA imputation system for the Taiwanese population: a study of the Taiwan Biobank

An imputation algorithm for human leukocyte antigen (HLA) is helpful for exploring novel disease associations. However, population-specific HLA imputation references are essential for achieving high imputation accuracy. In this study, a subset of 1012 individuals from the Taiwan Biobank (TWB) who underwent both whole-genome SNP array and NGS-based HLA typing were used to establish Taiwanese HLA imputation references. The HIBAG package was used to generate the imputation references for eight HLA loci at a two- and three-field resolution. Internal validation was carried out to evaluate the call threshold and accuracy for each HLA gene. HLA class II genes found to be associated with rheumatoid arthritis (RA) were validated in this study by the imputed HLA alleles. Our Taiwanese population-specific references achieved average HLA imputation accuracies of 98.11% for two-field and 98.08% for three-field resolution. The frequency distribution of imputed HLA alleles among 23,972 TWB subjects were comparable with PCR-based HLA alleles in general Taiwanese reported in the allele frequency net database. We replicated four common HLA alleles (HLA-DRB1*03:01, DRB1*04:05, DQA1*03:03, and DQB1*04:01) significantly associated with RA. The population-specific references provide an informative tool to investigate the associations of HLA variants and human diseases in large-scale population-based studies.

Amino acid residues in five separate HLA genes can explain most of the known associations between the MHC and primary biliary cholangitis

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease characterised by progressive destruction of intrahepatic bile ducts. The strongest genetic association is with HLA-DQA1*04:01, but at least three additional independent HLA haplotypes contribute to susceptibility. We used dense single nucleotide polymorphism (SNP) data in 2861 PBC cases and 8514 controls to impute classical HLA alleles and amino acid polymorphisms using state-of-the-art methodologies. We then demonstrated through stepwise regression that association in the HLA region can be largely explained by variation at five separate amino acid positions. Three-dimensional modelling of protein structures and calculation of electrostatic potentials for the implicated HLA alleles/amino acid substitutions demonstrated a correlation between the electrostatic potential of pocket P6 in HLA-DP molecules and the HLA-DPB1 alleles/amino acid substitutions conferring PBC susceptibility/protection, highlighting potential new avenues for future functional investigation.

Primary Biliary Cholangitis (PBC) is a chronic autoimmune liver disease that exhibits strong genetic associations, especially with variants in the human leukocyte antigen (HLA) gene region. Here we use dense single nucleotide polymorphism (SNP) data from the largest PBC study to date (2861 cases, 8514 controls) to investigate the likely underlying causes of this association, via performing imputation of HLA classical alleles and amino acids. We show that the HLA association can be largely explained by variation at five separate amino acid positions, one of which shows functional relevance to electrostatic potentials of HLA-DP molecules.

Fine mapping of MHC region in lung cancer highlights independent susceptibility loci by ethnicity

The basis for associations between lung cancer and major histocompatibility complex genes is not completely understood. Here the authors further consider genetic variation within the MHC region in lung cancer patients and identify independent associations within HLA genes that explain MHC lung cancer associations in Europeans and Asian populations.

Lung cancer has several genetic associations identified within the major histocompatibility complex (MHC); although the basis for these associations remains elusive. Here, we analyze MHC genetic variation among 26,044 lung cancer patients and 20,836 controls densely genotyped across the MHC, using the Illumina Illumina OncoArray or Illumina 660W SNP microarray. We impute sequence variation in classical HLA genes, fine-map MHC associations for lung cancer risk with major histologies and compare results between ethnicities. Independent and novel associations within HLA genes are identified in Europeans including amino acids in the HLA-B*0801 peptide binding groove and an independent HLA-DQB1*06 loci group. In Asians, associations are driven by two independent HLA allele sets that both increase risk in HLA-DQB1*0401 and HLA-DRB1*0701; the latter better represented by the amino acid Ala-104. These results implicate several HLA–tumor peptide interactions as the major MHC factor modulating lung cancer susceptibility.

A One-Penny Imputed Genome from Next-Generation Reference Panels

Genotype imputation is commonly performed in genome-wide association studies because it greatly increases the number of markers that can be tested for association with a trait. In general, one should perform genotype imputation using the largest reference panel that is available because the number of accurately imputed variants increases with reference panel size. However, one impediment to using larger reference panels is the increased computational cost of imputation. We present a new genotype imputation method, Beagle 5.0, which greatly reduces the computational cost of imputation from large reference panels. We compare Beagle 5.0 with Beagle 4.1, Impute4, Minimac3, and Minimac4 using 1000 Genomes Project data, Haplotype Reference Consortium data, and simulated data for 10k, 100k, 1M, and 10M reference samples. All methods produce nearly identical accuracy, but Beagle 5.0 has the lowest computation time and the best scaling of computation time with increasing reference panel size. For 10k, 100k, 1M, and 10M reference samples and 1,000 phased target samples, Beagle 5.0's computation time is 3× (10k), 12× (100k), 43× (1M), and 533× (10M) faster than the fastest alternative method. Cost data from the Amazon Elastic Compute Cloud show that Beagle 5.0 can perform genome-wide imputation from 10M reference samples into 1,000 phased target samples at a cost of less than one US cent per sample.

Applications of Immunopharmacogenomics: Predicting, Preventing, and Understanding Immune-Mediated Adverse Drug Reactions

Adverse drug reactions (ADRs) are a significant health care burden. Immune-mediated adverse drug reactions (IM-ADRs) are responsible for one-fifth of ADRs but contribute a disproportionately high amount of that burden due to their severity. Variation in human leukocyte antigen ( HLA) genes has emerged as a potential preprescription screening strategy for the prevention of previously unpredictable IM-ADRs. Immunopharmacogenomics combines the disciplines of immunogenomics and pharmacogenomics and focuses on the effects of immune-specific variation on drug disposition and IM-ADRs. In this review, we present the latest evidence for HLA associations with IM-ADRs, ongoing research into biological mechanisms of IM-ADRs, and the translation of clinical actionable biomarkers for IM-ADRs, with a focus on T cell-mediated ADRs.

Genome-wide association studies of multiple sclerosis

Large-scale genetic studies of multiple sclerosis have identified over 230 risk effects across the human genome, making it a prototypical common disease with complex genetic architecture. Here, after a brief historical background on the discovery and definition of the disease, we summarise the last fifteen years of genetic discoveries and map out the challenges that remain to translate these findings into an aetiological framework and actionable clinical understanding.

Genotype Imputation from Large Reference Panels

Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.

Genetic Association in the HLA Region

The MHC/HLA region has been consistently associated with a large number of complex traits, including but not limited to, most immune-mediated ones. Efforts to pinpoint drivers of this commonly encountered association peak at the short arm of chromosome 6, however, have been challenging, owing to the high density of genes and the long and extended linkage disequilibrium that are characteristic of this region.The development of methods to impute classical HLA alleles and amino acids from SNP genotyping data has offered an important additional layer of information to the investigators seeking to fine map the signal in the region. As a result, imputation-aided association analyses are now typically employed to shed light on the relationship of this locus with disease susceptibility and response to drugs.In this chapter we discuss how the signal in the HLA region can be interrogated in practice, from performing the imputation to understanding its output and to incorporating it into downstream analysis. In addition, we recount some of the analytical approaches that are commonly used and suggest ways in which the findings from such imputation-aided analyses can be interpreted.

A genomic perspective on HLA evolution

Several decades of research have convincingly shown that classical human leukocyte antigen (HLA) loci bear signatures of natural selection. Despite this conclusion, many questions remain regarding the type of selective regime acting on these loci, the time frame at which selection acts, and the functional connections between genetic variability and natural selection. In this review, we argue that genomic datasets, in particular those generated by next-generation sequencing (NGS) at the population scale, are transforming our understanding of HLA evolution. We show that genomewide data can be used to perform robust and powerful tests for selection, capable of identifying both positive and balancing selection at HLA genes. Importantly, these tests have shown that natural selection can be identified at both recent and ancient timescales. We discuss how findings from genomewide association studies impact the evolutionary study of HLA genes, and how genomic data can be used to survey adaptive change involving interaction at multiple loci. We discuss the methodological developments which are necessary to correctly interpret genomic analyses involving the HLA region. These developments include adapting the NGS analysis framework so as to deal with the highly polymorphic HLA data, as well as developing tools and theory to search for signatures of selection, quantify differentiation, and measure admixture within the HLA region. Finally, we show that high throughput analysis of molecular phenotypes for HLA genes-namely transcription levels-is now a feasible approach and can add another dimension to the study of genetic variation.

Introduction: MHC/KIR and governance of specificity

The MHC controls specificity, to ensure that appropriate immune responses are mounted to invading pathogens whilst maintaining tolerance to the host. It encodes molecules that act as sentinels, providing a snapshot of the health of the interior and exterior of the cell for immune surveillance. To maintain the ability to respond appropriately to any disease requires a delicate balance of expression and function, and many subtleties of the system have been described at the gene, individual and population level. The main players are the highly polymorphic classical MHC class I and class II molecules, as well as some non-classical loci of both types. Transporter associated with antigen processing (TAP) peptide transporters, proteasome components and Tapasin, encoded within the MHC, are also involved in selection of peptide for presentation. The plethora of mechanisms microorganisms use to subvert immune recognition, through blocking these antigen processing and presentation pathways, attests to the importance of HLA in resistance to infection. There is continued interest in MHC genetics in its own right, as well as in relation to KIR, to transplantation, infection, autoimmunity and reproduction. Also of topical interest, cancer immunotherapy through checkpoint inhibition depends on highly specific recognition of cancer peptide antigen and continued expression of HLA molecules. Here, we briefly introduce some background to the MHC/KIR axis in man. This special issue of immunogenetics expands on these topics, in humans and other model species.

Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest

Four single nucleotide polymorphism (SNP)-based human leukocyte antigen (HLA) imputation methods (e-HLA, HIBAG, HLA*IMP:02 and MAGPrediction) were trained using 1000 Genomes SNP and HLA genotypes and assessed for their ability to accurately impute molecular HLA-A, -B, -C and -DRB1 genotypes in the Human Genome Diversity Project cell panel. Imputation concordance was high (>89%) across all methods for both HLA-A and HLA-C, but HLA-B and HLA-DRB1 proved generally difficult to impute. Overall, <27.8% of subjects were correctly imputed for all HLA loci by any method. Concordance across all loci was not enhanced via the application of confidence thresholds; reliance on confidence scores across methods only led to noticeable improvement (+3.2%) for HLA-DRB1. As the HLA complex is highly relevant to the study of human health and disease, a standardized assessment of SNP-based HLA imputation methods is crucial for advancing genomic research. Considerable room remains for the improvement of HLA-B and especially HLA-DRB1 imputation methods, and no imputation method is as accurate as molecular genotyping. The application of large, ancestrally diverse HLA and SNP reference data sets and multiple imputation methods has the potential to make SNP-based HLA imputation methods a tractable option for determining HLA genotypes.

Impact of genetic variations and transcriptional alterations of HLA class I genes on cervical cancer pathogenesis

In a novel attempt to understand the variations in DNA sequences underlying HLA class I alleles associated with HPV16-related CaCx, we determined the alleles by reconstructing SNP-based haplotypes from resequencing of the most polymorphic exons 2 and 3 of HLA-A, HLA-B and HLA-C. We also determined the impact of SNPs and transcriptional alterations of the genes on CaCx. A high density of SNPs was identified from resequencing. HLA expression was determined by real-time PCR. We identified that even a single associated HLA allele had many underlying SNP-based haplotypes. Out of the most frequent (≥5%) HLA class I alleles, HLA-B*40:06 and HLA-B*15:02 respectively imparted significant risk towards and protection from CaCx as well as HPV16 infection. Employing median-joining networks to detect clusters of sequence-variations for specific HLA alleles, we found the protective SNP-based signature, GAATTTA, in all SNP-based haplotypes of HLA-B*15:02 allele. The signature was derived from seven SNPs within HLA-B which were newly associated with the disease. Contrarily, similarly derived risk-signature, TTGCGCC, mapped only to 52% of SNP-based haplotypes of HLA-B*40:06 allele. This indicated that all SNP-based haplotypes underlying a particular associated HLA allele might or might not have a single signature of risk/protection. HLA-A, HLA-B and HLA-C expressions were downregulated among CaCx cases compared to asymptomatic infections and HPV-negative controls. HLA-A and HLA-B were repressed in both cases harbouring episomal and integrated HPV16, whereas HLA-C in only the latter. Novel genetic variations and differential downregulation-patterns of HLA class I have a significant bearing on HPV16-related CaCx pathogenesis.

Comparison of HLA allelic imputation programs

Imputation of human leukocyte antigen (HLA) alleles from SNP-level data is attractive due to importance of HLA alleles in human disease, widespread availability of genome-wide association study (GWAS) data, and expertise required for HLA sequencing. However, comprehensive evaluations of HLA imputations programs are limited. We compared HLA imputation results of HIBAG, SNP2HLA, and HLA*IMP:02 to sequenced HLA alleles in 3,265 samples from BioVU, a de-identified electronic health record database coupled to a DNA biorepository. We performed four-digit HLA sequencing for HLA-A, -B, -C, -DRB1, -DPB1, and -DQB1 using long-read 454 FLX sequencing. All samples were genotyped using both the Illumina HumanExome BeadChip platform and a GWAS platform. Call rates and concordance rates were compared by platform, frequency of allele, and race/ethnicity. Overall concordance rates were similar between programs in European Americans (EA) (0.975 [SNP2HLA]; 0.939 [HLA*IMP:02]; 0.976 [HIBAG]). SNP2HLA provided a significant advantage in terms of call rate and the number of alleles imputed. Concordance rates were lower overall for African Americans (AAs). These observations were consistent when accuracy was compared across HLA loci. All imputation programs performed similarly for low frequency HLA alleles. Higher concordance rates were observed when HLA alleles were imputed from GWAS platforms versus the HumanExome BeadChip, suggesting that high genomic coverage is preferred as input for HLA allelic imputation. These findings provide guidance on the best use of HLA imputation methods and elucidate their limitations.

High resolution HLA haplotyping by imputation for a British population bioresource

This study aimed to establish the occurrence and frequency of HLA alleles and haplotypes for a healthy British Caucasian population bioresource from Oxfordshire. We present the results of imputation from HLA SNP genotyping data using SNP2HLA for 5553 individuals from Oxford Biobank, defining one- and two-field alleles together with amino acid polymorphisms. We show that this achieves a high level of accuracy with validation using sequence-specific primer amplification PCR. We define six- and eight-locus HLA haplotypes for this population by Bayesian methods implemented using PHASE. We determine patterns of linkage disequilibrium and recombination for these individuals involving classical HLA loci and show how analysis within a haplotype block structure may be more tractable for imputed data. Our findings contribute to knowledge of HLA diversity in healthy populations and further validate future large-scale use of HLA imputation as an informative approach in population bioresources.

Immunochip analysis identifies novel susceptibility loci in the human leukocyte antigen region for acquired thrombotic thrombocytopenic purpura

Essentials Genetic predisposition to acquired thrombotic thrombocytopenic purpura (aTTP) is mainly unknown. Genetic risk factors for aTTP were studied by Immunochip analysis and replication study. Human leukocyte antigen (HLA) variant rs6903608 conferred a 2.5-fold higher risk of developing aTTP. rs6903608 and HLA-DQB1*05:03 may explain most of the HLA association signal in aTTP. Click to hear Dr Cataland's presentation on acquired thrombotic thrombocytopenic purpura SUMMARY: Background Acquired thrombotic thrombocytopenic purpura (TTP) is a rare, life-threatening thrombotic microangiopathy associated with the development of autoantibodies against the von Willebrand factor-cleaving protease ADAMTS-13. Similarly to what has been found for other autoimmune disorders, there is evidence of a genetic contribution, including the association of the human leukocyte antigen (HLA) class II complex with disease risk. Objective To identify novel genetic risk factors in acquired TTP. Patients/Methods We undertook a case-control genetic association study in 190 European-origin TTP patients and 1255 Italian healthy controls by using the Illumina Immunochip. Replication analysis in 88 Italian cases and 456 controls was performed with single-nucleotide polymorphism (SNP) TaqMan assays. Results and conclusion We identified one common variant (rs6903608) located within the HLA class II locus that was independently associated with acquired TTP at genome-wide significance and conferred a 2.6-fold increased risk of developing a TTP episode (95% confidence interval [CI] 2.02-3.27, P = 1.64 × 10-14 ). We also found five non-HLA variants mapping to chromosomes 2, 6, 8 and X that were suggestively associated with the disease: rs9490550, rs115265285, rs5927472, rs7823314, and rs1334768 (nominal P-values ranging from 1.59 × 10-5 to 7.60 × 10-5 ). Replication analysis confirmed the association of HLA variant rs6903608 with acquired TTP (pooled P = 3.95 × 10-19 ). Imputation of classic HLA genes followed by stepwise conditional analysis revealed that the combination of rs6903608 and HLA-DQB1*05:03 may explain most of the HLA association signal in acquired TTP. Our results refined the association of the HLA class II locus with acquired TTP, confirming its importance in the etiology of this autoimmune disease.

High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs

Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30–250 CPU hours per sample) remain a significant challenge to practical application.

Determining an individual’s HLA type (the sequence of the exons of the HLA genes) is important in many areas of biomedical research. For example, HLA types shape immune epitope repertoires, which are relevant in cancer immunotherapy, and influence autoimmune and infectious disease risk. Whole-genome sequencing data, currently being generated for hundreds of thousands of individuals, contains the information necessary for HLA typing–but inferring accurate HLA types from these is a challenging problem. First, the HLA genes are the most polymorphic genes in the human genome; second, these genes and their variant alleles exhibit high degrees of sequence similarity (due to a shared evolutionary origin). This makes it difficult to establish which specific HLA gene a given observed sequencing read derives from. We show that this problem can be addressed using a Population Reference Graph (PRG): for each gene, the PRG contains not only the reference sequence but also variant alleles, thus enabling, using a novel sequence-to-graph mapping algorithm, the accurate mapping of reads to HLA genes. We also show that HLA*PRG, the algorithm implementing our approach, achieves–based on standard whole-genome sequencing data–accuracies comparable to those of specialized gold-standard methods. HLA*PRG is open source and freely available.

Human leucocyte antigen class I and II imputation in a multiracial population

Human leucocyte antigen (HLA) genes play a central role in response to pathogens and in autoimmunity. Research to understand the effects of HLA genes on health has been limited because HLA genotyping protocols are labour intensive and expensive. Recently, algorithms to impute HLA genotype data using genome-wide association study (GWAS) data have been published. However, imputation accuracy for most of these algorithms was based primarily on training data sets of European ancestry individuals. We considered performance of two HLA-dedicated imputation algorithms - SNP2HLA and HIBAG - in a multiracial population of n = 1587 women with HLA genotyping data by gold standard methods. We first compared accuracy - defined as the percentage of correctly predicted alleles - of HLA-B and HLA-C imputation using SNP2HLA and HIBAG using a breakdown of the data set into an 80% training group and a 20% testing group. Estimates of accuracy for HIBAG were either the same or better than those for SNP2HLA. We then conducted a more thorough test of HIBAG imputation accuracy using five independent 10-fold cross-validation procedures with delineation of ancestry groups using ancestry informative markers. Overall accuracy for HIBAG was 89%. Accuracy by HLA gene was 93% for HLA-A, 84% for HLA-B, 94% for HLA-C, 83% for HLA-DQA1, 91% for HLA-DQB1 and 88% for HLA-DRB1. Accuracy was highest in the African ancestry group (the largest group) and lowest in the Hispanic group (the smallest group). Despite suboptimal imputation accuracy for some HLA gene/ancestry group combinations, the HIBAG algorithm has the advantage of providing posterior estimates of accuracy which enable the investigator to analyse subsets of the population with high predicted (e.g. >95%) imputation accuracy.

Naked-eye fingerprinting of single nucleotide polymorphisms on psoriasis patients

We report a low-cost test, based on gold nanoparticles, for the colorimetric (naked-eye) fingerprinting of a panel of single nucleotide polymorphisms (SNPs), relevant for the personalized therapy of psoriasis. Such pharmacogenomic tests are not routinely performed on psoriasis patients, due to the high cost of standard technologies. We demonstrated high sensitivity and specificity of our colorimetric test by validating it on a cohort of 30 patients, through a double-blind comparison with two state-of-the-art instrumental techniques, namely reverse dot blotting and sequencing, finding 100% agreement. This test offers high parallelization capabilities and can be easily generalized to other SNPs of clinical relevance, finding broad utility in diagnostics and pharmacogenomics.

Autoimmune diseases - connecting risk alleles with molecular traits of the immune system

Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.

Genetic risk variants for membranous nephropathy: extension of and association with other chronic kidney disease aetiologies

Background

Membranous nephropathy (MN) is a common cause of nephrotic syndrome in adults. Previous genome-wide association studies (GWAS) of 300 000 genotyped variants identified MN-associated loci at HLA-DQA1 and PLA2R1.

Methods

We used a combined approach of genotype imputation, GWAS, human leucocyte antigen (HLA) imputation and extension to other aetiologies of chronic kidney disease (CKD) to investigate genetic MN risk variants more comprehensively. GWAS using 9 million high-quality imputed genotypes and classical HLA alleles were conducted for 323 MN European-ancestry cases and 345 controls. Additionally, 4960 patients with different CKD aetiologies in the German Chronic Kidney Disease (GCKD) study were genotyped for risk variants at HLA-DQA1 and PLA2R1.

Results

In GWAS, lead variants in known loci [rs9272729, HLA-DQA1, odds ratio (OR) = 7.3 per risk allele, P = 5.9 × 10^-27 and rs17830558, PLA2R1, OR = 2.2, P = 1.9 × 10^-8] were significantly associated with MN. No novel signals emerged in GWAS of X-chromosomal variants or in sex-specific analyses. Classical HLA alleles (DRB1*0301-DQA1*0501-DQB1*0201 haplotype) were associated with MN but provided little additional information beyond rs9272729. Associations were replicated in 137 GCKD patients with MN (HLA-DQA1: P = 6.4 × 10^-24; PLA2R1: P = 5.0 × 10^-4). MN risk increased steeply for patients with high-risk genotype combinations (OR > 79). While genetic variation in PLA2R1 exclusively associated with MN across 19 CKD aetiologies, the HLA-DQA1 risk allele was also associated with lupus nephritis (P = 2.8 × 10^-6), type 1 diabetic nephropathy (P = 6.9 × 10^-5) and focal segmental glomerulosclerosis (P = 5.1 × 10^-5), but not with immunoglobulin A nephropathy.

Conclusions

PLA2R1 and HLA-DQA1 are the predominant risk loci for MN detected by GWAS. While HLA-DQA1 risk variants show an association with other CKD aetiologies, PLA2R1 variants are specific to MN.

Mapping the genetic diversity of HLA haplotypes in the Japanese populations

Japan has often been viewed as an Asian country that possesses a genetically homogenous community. The basis for partitioning the country into prefectures has largely been geographical, although cultural and linguistic differences still exist between some of the districts/prefectures, especially between Okinawa and the mainland prefectures. The Major Histocompatibility Complex (MHC) region has consistently emerged as the most polymorphic region in the human genome, harbouring numerous biologically important variants; nevertheless the presence of population-specific long haplotypes hinders the imputation of SNPs and classical HLA alleles. Here, we examined the extent of genetic variation at the MHC between eight Japanese populations sampled from Okinawa, and six other prefectures located in or close to the mainland of Japan, specifically focusing at the haplotypes observed within each population, and what the impact of any variation has on imputation. Our results indicated that Okinawa was genetically farther to the mainland Japanese than were Gujarati Indians from Tamil Indians, while the mainland Japanese from six prefectures were more homogeneous than between northern and southern Han Chinese. The distribution of haplotypes across Japan was similar, although imputation was most accurate for Okinawa and several mainland prefectures when population-specific panels were used as reference.

Identification and utilization of donor and recipient genetic variants to predict survival after HCT: are we ready for primetime?

Overall survival following hematopoietic cell transplantation (HCT) has improved over the past two decades through better patient selection and advances in HLA typing, supportive care, and infection prophylaxis. Nonetheless, mortality rates are still unsatisfactory and transplant-related mortality remains a major cause of death after unrelated allogeneic HCT. Since there are no known pre-HCT, non-HLA biologic predictors of survival following transplant, for over a decade, scientists have been investigating the role of non-HLA germline genetic variation in survival and treatment-related mortality after HCT. Variation in single nucleotide polymorphisms (SNPs) has the potential to impact chemotherapy, radiation, and immune responses, leading to different post-HCT survival outcomes. In this paper, we address the current knowledge of the contribution of genetic variation to survival following HCT and discuss study design and methodology for investigating HCT survival on a genomic scale.

HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set

Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy.

Class II HLA interactions modulate genetic risk for multiple sclerosis

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01-HLA-DRB1*15:01 and HLA-DQB1*03:01-HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles.

Imputation of KIR Types from SNP Variation Data

Large population studies of immune system genes are essential for characterizing their role in diseases, including autoimmune conditions. Of key interest are a group of genes encoding the killer cell immunoglobulin-like receptors (KIRs), which have known and hypothesized roles in autoimmune diseases, resistance to viruses, reproductive conditions, and cancer. These genes are highly polymorphic, which makes typing expensive and time consuming. Consequently, despite their importance, KIRs have been little studied in large cohorts. Statistical imputation methods developed for other complex loci (e.g., human leukocyte antigen [HLA]) on the basis of SNP data provide an inexpensive high-throughput alternative to direct laboratory typing of these loci and have enabled important findings and insights for many diseases. We present KIR^∗IMP, a method for imputation of KIR copy number. We show that KIR^∗IMP is highly accurate and thus allows the study of KIRs in large cohorts and enables detailed investigation of the role of KIRs in human disease.

Association Between HLA Haplotypes and Increased Serum Levels of IgG4 in Patients With Primary Sclerosing Cholangitis

Increased serum levels of IgG4 have been reported in 9%-15% of patients with primary sclerosing cholangitis (PSC); it is not clear whether this increase contributes to pathogenesis. We performed genetic analyses of the HLA complex in patients with PSC from Norway, Sweden, and from the United States. We found an association between levels of IgG4 above the upper reference limit and specific HLA haplotypes. These patients had a significantly lower frequency of the strongest PSC risk factor, HLA-B*08, than patients without increased IgG4, and significantly higher frequencies of HLA-B*07 and HLA-DRB1*15. HLA genotype therefore might affect the serum concentration of IgG4, and increased IgG4 might be a marker of a distinct phenotype of PSC.

Enrichment for Northern European-derived multiple sclerosis risk alleles in Sardinia

Background:

The list of genomic loci associated with multiple sclerosis (MS) susceptibility outside the major histocompatibility complex (MHC) in patients of Northern European (NE) ancestry has increased to 103. Despite the extraordinarily high MS prevalence in the isolated Sardinian population, the contribution of genetic risk factors to MS in Sardinia is largely not understood.

Objective:

The objective of this paper is to examine the relevance of non-MHC MS susceptibility variants in Sardinia.

Methods:

We examined a log-additive MS-specific genetic burden score (MSGB) using 110 NE-derived risk alleles in a dataset of 75 Sardinian cases, 346 Sardinian controls and 177 cases and 1967 controls from the United States (US).

Results:

Sardinian cases demonstrate a heavier non-MHC MSGB load than Sardinian controls and US cases ( p = 2E-06, p = 1E-06, respectively). Furthermore, Sardinian controls carry a heavier burden than US controls ( p = 2E-14). Our results confirm the limited ability of the 110-SNP MSGB to predict disease status in Sardinia (AUROC = 0.629).

Conclusions:

Risk alleles discovered in samples of NE ancestry are relevant to MS in Sardinia. Our results suggest a general enrichment of MS susceptibility alleles in Sardinians, encouraging the pursuit of further studies of MS in this population.

Deciphering associations for lung cancer risk through imputation and analysis of 12,316 cases and 16,831 controls

Recent genome-wide association studies have identified common variants at multiple loci influencing lung cancer risk. To decipher the genetic basis of the association signals at 3q28, 5p15.33, 6p21.33, 9p21 and 12p13.33, we performed a meta-analysis of data from five genome-wide association studies in populations of European ancestry totalling 12 316 lung cancer cases and 16 831 controls using imputation to recover untyped genotypes. For four of the regions, it was possible to refine the association signal identifying a smaller region of interest likely to harbour the functional variant. Our analysis did not provide evidence that any of the associations at the loci being a consequence of synthetic associations rather than linkage disequilibrium with a common risk variant at these risk loci.

High-accuracy imputation for HLA class I and II genes based on high-resolution SNP data of population-specific references

Statistical imputation of classical human leukocyte antigen (HLA) alleles is becoming an indispensable tool for fine-mappings of disease association signals from case-control genome-wide association studies. However, most currently available HLA imputation tools are based on European reference populations and are not suitable for direct application to non-European populations. Among the HLA imputation tools, The HIBAG R package is a flexible HLA imputation tool that is equipped with a wide range of population-based classifiers; moreover, HIBAG R enables individual researchers to build custom classifiers. Here, two data sets, each comprising data from healthy Japanese individuals of difference sample sizes, were used to build custom classifiers. HLA imputation accuracy in five HLA classes (HLA-A, HLA-B, HLA-DRB1, HLA-DQB1 and HLA-DPB1) increased from the 82.5-98.8% obtained with the original HIBAG references to 95.2-99.5% with our custom classifiers. A call threshold (CT) of 0.4 is recommended for our Japanese classifiers; in contrast, HIBAG references recommend a CT of 0.5. Finally, our classifiers could be used to identify the risk haplotypes for Japanese narcolepsy with cataplexy, HLA-DRB1*15:01 and HLA-DQB1*06:02, with 100% and 99.7% accuracy, respectively; therefore, these classifiers can be used to supplement the current lack of HLA genotyping data in widely available genome-wide association study data sets.

Genome-wide comparative analysis of atopic dermatitis and psoriasis gives insight into opposing genetic mechanisms

Atopic dermatitis and psoriasis are the two most common immune-mediated inflammatory disorders affecting the skin. Genome-wide studies demonstrate a high degree of genetic overlap, but these diseases have mutually exclusive clinical phenotypes and opposing immune mechanisms. Despite their prevalence, atopic dermatitis and psoriasis very rarely co-occur within one individual. By utilizing genome-wide association study and ImmunoChip data from >19,000 individuals and methodologies developed from meta-analysis, we have identified opposing risk alleles at shared loci as well as independent disease-specific loci within the epidermal differentiation complex (chromosome 1q21.3), the Th2 locus control region (chromosome 5q31.1), and the major histocompatibility complex (chromosome 6p21-22). We further identified previously unreported pleiotropic alleles with opposing effects on atopic dermatitis and psoriasis risk in PRKRA and ANXA6/TNIP1. In contrast, there was no evidence for shared loci with effects operating in the same direction on both diseases. Our results show that atopic dermatitis and psoriasis have distinct genetic mechanisms with opposing effects in shared pathways influencing epidermal differentiation and immune response. The statistical analysis methods developed in the conduct of this study have produced additional insight from previously published data sets. The approach is likely to be applicable to the investigation of the genetic basis of other complex traits with overlapping and distinct clinical features.

The higher frequency of IgA deficiency among Swedish twins is not explained by HLA haplotypes

Serum immunoglobulin A (IgA) concentrations were determined in 12 600 adult Swedish twins, applying a high-throughput reverse-phase protein microarray technique. The prevalence of IgA deficiency (IgAD) was found to be 1:241 in monozygotic (MZ) twins and 1:198 in dizygotic (DZ) twins. Hence, the prevalence in twins is markedly elevated as compared with the normal Swedish adult population (1:600). The twins did not show a difference in the frequency of HLA haplotypes in comparison with almost 40 000 healthy Swedish controls. As expected, the risk-conveying HLA alleles A*01, B*08 and DRB1*01 were overrepresented among the IgAD twins and were also associated with significantly lower mean serum IgA concentrations in the twin cohort. In contrast, significantly higher mean IgA concentrations were found among individuals carrying the protective HLA alleles B*07 and DRB1*15. Exome sequencing data from two MZ twin pairs discordant for the deficiency showed no differences between the siblings. Model fitting analyses derived a heritability of 35% and indicate that genetic influences are modestly important for IgAD. The probandwise concordance rates for IgAD were found to be 31% for MZ and 13% for DZ twins.

An Immunochip-based interrogation of scleroderma susceptibility variants identifies a novel association at DNASE1L3

Introduction

The aim of the study was to interrogate the genetic architecture and autoimmune pleiotropy of scleroderma susceptibility in the Australian population.

Methods

We genotyped individuals from a well-characterized cohort of Australian scleroderma patients with the Immunochip, a custom array enriched for single nucleotide polymorphisms (SNPs) at immune loci. Controls were taken from the 1958 British Birth Cohort. After data cleaning and adjusting for population stratification the final dataset consisted of 486 cases, 4,458 controls and 146,525 SNPs. Association analyses were conducted using logistic regression in PLINK. A replication study was performed using 833 cases and 1,938 controls.

Results

A total of eight loci with suggestive association (P <10^-4.5) were identified, of which five showed significant association in the replication cohort (HLA-DRB1, DNASE1L3, STAT4, TNP03-IRF5 and VCAM1). The most notable findings were at the DNASE1L3 locus, previously associated with systemic lupus erythematosus, and VCAM1, a locus not previously associated with human disease. This study identified a likely functional variant influencing scleroderma susceptibility at the DNASE1L3 locus; a missense polymorphism rs35677470 in DNASE1L3, with an odds ratio of 2.35 (P = 2.3 × 10⁻¹⁰) in anti-centromere antibody (ACA) positive cases.

Conclusions

This pilot study has confirmed previously reported scleroderma associations, revealed further genetic overlap between scleroderma and systemic lupus erythematosus, and identified a putative novel scleroderma susceptibility locus.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0438-8) contains supplementary material, which is available to authorized users.

Fine-mapping of HLA associations with chronic lymphocytic leukemia in US populations

Chronic lymphocytic leukemia (CLL) displays remarkable ethnic predisposition for whites, with relative sparing of African-American and Asian populations. In addition, CLL displays among the highest familial predispositions of all hematologic malignancies, yet the genetic basis for these differences is not clearly defined. The highly polymorphic HLA genes of the major histocompatibility complex play a central role in immune surveillance and confer risk for autoimmune and infectious diseases and several different cancers, the role for which in the development of CLL has not been extensively investigated. The National Marrow Donor Program/Be The Match has collected HLA typing from CLL patients in need of allogeneic hematopoietic stem cell transplant and has recruited millions of volunteers to potentially donate hematopoietic stem cells. HLA genotypes for 3491 US white, 397 African-American, and 90 Hispanic CLL patients were compared with 50 000 controls per population from the donor registry. We identified several HLA alleles associated with CLL susceptibility in each population, reconfirming predisposing roles of HLA-A*02:01 and HLA-DRB4*01:01 in whites. Associations for haplotype DRB4*01:01∼DRB1*07:01∼DQB1*03:03 were replicated across all 3 populations. These findings provide a comprehensive assessment of the role of HLA in the development of severe CLL.

Periodontitis and Porphyromonas gingivalis in patients with rheumatoid arthritis

OBJECTIVE

To examine the degree to which shared risk factors explain the relationship of periodontitis (PD) to rheumatoid arthritis (RA) and to determine the associations of PD and Porphyromonas gingivalis with pathologic and clinical features of RA.

METHODS

Patients with RA (n = 287) and patients with osteoarthritis as disease controls (n = 330) underwent a standardized periodontal examination. The HLA-DRB1 status of all participants was imputed using single-nucleotide polymorphisms from the extended major histocompatibility complex. Circulating anti-P gingivalis antibodies were measured using an enzyme-linked immunosorbent assay, and subgingival plaque was assessed for the presence of P gingivalis using polymerase chain reaction (PCR). Associations of PD with RA were examined using multivariable regression.

RESULTS

Presence of PD was more common in patients with RA and patients with anti-citrullinated protein antibody (ACPA)-positive RA (n = 240; determined using the anti-cyclic citrullinated peptide 2 [anti-CCP-2] test) than in controls (35% and 37%, respectively, versus 26%; P = 0.022 and P = 0.006, respectively). There were no differences between RA patients and controls in the levels of anti-P gingivalis or the frequency of P gingivalis positivity by PCR. The anti-P gingivalis findings showed a weak, but statistically significant, association with the findings for both anti-CCP-2 (r = 0.14, P = 0.022) and rheumatoid factor (RF) (r = 0.19, P = 0.001). Presence of PD was associated with increased swollen joint counts (P = 0.004), greater disease activity according to the 28-joint Disease Activity Score using C-reactive protein level (P = 0.045), and higher total Sharp scores of radiographic damage (P = 0.015), as well as with the presence and levels of anti-CCP-2 (P = 0.011) and RF (P < 0.001). The expression levels of select ACPAs (including antibodies to citrullinated filaggrin) were higher in patients with subgingival P gingivalis and in those with higher levels of anti-P gingivalis antibodies, irrespective of smoking status. Associations of PD with established seropositive RA were independent of all covariates examined, including evidence of P gingivalis infection.

CONCLUSION

Both PD and P gingivalis appear to shape the autoreactivity of RA. In addition, these results demonstrate an independent relationship between PD and established seropositive RA.

Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications

High-throughput DNA sequencing technology has transformed genetic research and is starting to make an impact on clinical practice. However, analyzing high-throughput sequencing data remains challenging, particularly in clinical settings where accuracy and turnaround times are critical. We present a new approach to this problem, implemented in a software package called Platypus. Platypus achieves high sensitivity and specificity for SNPs, indels and complex polymorphisms by using local de novo assembly to generate candidate variants, followed by local realignment and probabilistic haplotype estimation. It is an order of magnitude faster than existing tools and generates calls from raw aligned read data without preprocessing. We demonstrate the performance of Platypus in clinically relevant experimental designs by comparing with SAMtools and GATK on whole-genome and exome-capture data, by identifying de novo variation in 15 parent-offspring trios with high sensitivity and specificity, and by estimating human leukocyte antigen genotypes directly from variant calls.