Comparison of HLA allelic imputation programs

Genomic landscape of cancer in racially and ethnically diverse populations

Cancer incidence and mortality rates can vary widely among different racial and ethnic groups, attributed to a complex interplay of genetic, environmental and social factors. Recently, substantial progress has been made in investigating hereditary genetic risk factors and in characterizing tumour genomes. However, most research has been conducted in individuals of European ancestries and, increasingly, in individuals of Asian ancestries. The study of germline and somatic genetics in cancer across racial and ethnic groups using omics technologies offers opportunities to identify similarities and differences in both heritable traits and the molecular features of cancer genomes. An improved understanding of population-specific cancer genomics, as well as translation of those findings across populations, will help reduce cancer disparities and ensure that personalized medicine and public health approaches are equitable across racial and ethnic groups.

Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology

The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their "reference-free" nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.

Association of Human Leukocyte Antigen Alleles with COVID-19 Severity and Mortality in a Spanish Population

Background and Objectives: The aim of the following cross-sectional study is to determine the association between human leukocyte antigen (HLA) alleles and outcomes in patients presenting to the emergency department (ED) with SARS-CoV-2 infection. Methods and Materials: Genotyping was made using the Axiom Human Genotyping SARS-CoV-2 Research Array. Statistical analysis was made with Fisher's exact test and multivariable logistic regression, adjusted for sex, age and clinical variables. Results: Of 190 patients, 11.1% were discharged from the ED; 57.9% were admitted to the COVID-19 ward, without intensive care unit (ICU) admission; 15.3% survived an ICU admission; and 15.8% died. After multivariable analysis, two HLA alleles protected against hospital admission (HLA-C*05:01, adjusted odds ratio [aOR] 0.2, 95% confidence interval [CI] 0.055-0.731; and HLA-DQB1*02:02, aOR 0.046, CI 0.002-0.871) and one was associated with higher risk for ICU admission or death (HLA-DQA1*05:01, aOR 2.517, CI 1.086-5.833). Conclusions: In this population, HLA-C*05:01 and HLA-DQB1*02:02 are associated with a protective effect against hospital admission and HLA-DQA1*05:01 is associated with higher risk of ICU admission or death in the multivariable analysis. This may help stratify risk in COVID-19 patients.

Cardiovascular Pharmacogenetics: From Discovery of Genetic Association to Clinical Adoption of Derived Test

Recent breakthroughs in human genetics and in information technologies have markedly expanded our understanding at the molecular level of the response to drugs, i.e., pharmacogenetics (PGx), across therapy areas. This review is restricted to PGx for cardiovascular (CV) drugs. First, we examined the PGx information in the labels approved by regulatory agencies in Europe, Japan, and North America and related recommendations from expert panels. Out of 221 marketed CV drugs, 36 had PGx information in their labels approved by one or more agencies. The level of annotations and recommendations varied markedly between agencies and expert panels. Clopidogrel is the only CV drug with consistent PGx recommendation (i.e., "actionable"). This situation prompted us to dissect the steps from discovery of a PGx association to clinical translation. We found 101 genome-wide association studies that investigated the response to CV drugs or drug classes. These studies reported significant associations for 48 PGx traits mapping to 306 genes. Six of these 306 genes are mentioned in the corresponding PGx labels or recommendations for CV drugs. Genomic analyses also highlighted the wide between-population differences in risk allele frequencies and the individual load of actionable PGx variants. Given the high attrition rate and the long road to clinical translation, additional work is warranted to identify and validate PGx variants for more CV drugs across diverse populations and to demonstrate the utility of PGx testing. To that end, pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond. SIGNIFICANCE STATEMENT: Despite spectacular breakthroughs in human molecular genetics and information technologies, consistent evidence supporting PGx testing in the cardiovascular area is limited to a few drugs. Additional work is warranted to discover and validate new PGx markers and demonstrate their utility. Pre-emptive PGx combining genomic profiling with electronic medical records opens unprecedented opportunities to improve healthcare, for CV diseases and beyond.

Lack of association between classical HLA genes and asymptomatic SARS-CoV-2 infection

Human genetic studies of critical COVID-19 pneumonia have revealed the essential role of type I interferon-dependent innate immunity to SARS-CoV-2 infection. Conversely, an association between the HLA-B∗15:01 allele and asymptomatic SARS-CoV-2 infection in unvaccinated individuals was recently reported, suggesting a contribution of pre-existing T cell-dependent adaptive immunity. We report a lack of association of classical HLA alleles, including HLA-B∗15:01, with pre-omicron asymptomatic SARS-CoV-2 infection in unvaccinated participants in a prospective population-based study in the United States (191 asymptomatic vs. 945 symptomatic COVID-19 cases). Moreover, we found no such association in the international COVID Human Genetic Effort cohort (206 asymptomatic vs. 574 mild or moderate COVID-19 cases and 1,625 severe or critical COVID-19 cases). Finally, in the Human Challenge Characterisation study, the three HLA-B∗15:01 individuals infected with SARS-CoV-2 developed symptoms. As with other acute primary infections studied, no classical HLA alleles favoring an asymptomatic course of SARS-CoV-2 infection were identified.

Association of Genetic Variants of HLA-DQA1 with Bullous Pemphigoid Induced by Dipeptidyl Peptidase-4 Inhibitors

Bullous pemphigoid (BP) is the most common autoimmune blistering disorder. Several factors, including an antidiabetic (dipeptidyl peptidase-4 inhibitor [DPP-4i]), have been reported to trigger BP. To identify the genetic variants associated with BP, GWAS and HLA fine-mapping analyses were conducted. The 21 cases of noninflammatory BP induced by DPP-4i (i.e., DPP-4i-induced noninflammatory BP) and 737 controls (first cohort) and the 8 cases and 164 controls (second cohort) were included in the GWAS. Combining GWAS satisfied the genome-wide significant association of HLA-DQA1 (chromosome 6, rs3129763 [T/C]) with the risk of DPP-4i-induced noninflammatory BP (allele T carrier of 72.4% [21 of 29] in cases vs. 15.3% [138 of 901] in controls; dominant model, OR = 14, P = 1.8 × 10-9). HLA fine mapping revealed that HLA-DQA1∗05 with serine at position 75 of HLA-DQα1 (Ser75) had the most significant association with the combined cohort of DPP-4i-induced noninflammatory BP (79.3% [23 of 29] cases vs. 16.1% [145 of 901] controls; dominant model, OR = 21, P = 2.0 × 10-10). HLA-DQα1 Ser75 polymorphism was located inside the functional pocket of HLA-DQ molecules, suggesting the impact of HLA-DQα1 Ser75 on DPP-4i-induced noninflammatory BP.

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.

HLA in isolated REM sleep behavior disorder and Lewy body dementia

Background and Objectives

Isolated/idiopathic REM sleep behavior disorder (iRBD) and Lewy body dementia (LBD) are synucleinopathies that have partial genetic overlap with Parkinson's disease (PD). Previous studies have shown that neuroinflammation plays a substantial role in these disorders. In PD, specific residues of the human leukocyte antigen ( HLA ) were suggested to be associated with a protective effect. This study examined whether the HLA locus plays a similar role in iRBD, LBD and PD.

Methods

We performed HLA imputation on iRBD genotyping data (1,072 patients and 9,505 controls) and LBD whole-genome sequencing (2,604 patients and 4,032 controls) using the multi-ethnic HLA reference panel v2 from the Michigan Imputation Server. Using logistic regression, we tested the association of HLA alleles, amino acids and haplotypes with disease susceptibility. We included age, sex and the top 10 principal components as covariates. We also performed an omnibus test to examine which HLA residue positions explain the most variance.

Results

In iRBD, HLA-DRB1 *11:01 was the only allele passing FDR correction (OR=1.57, 95% CI=1.27-1.93, p =2.70e-05). We also discovered associations between iRBD and HLA-DRB1 70D (OR=1.26, 95%CI=1.12-1.41, p =8.76e-05), 70Q (OR=0.81, 95% CI=0.72-0.91, p =3.65e-04) and 71R (OR=1.21, 95% CI=1.08-1.35, p =1.35e-03). In HLA-DRB1 , position 71 ( p omnibus =0.00102) and 70 ( p omnibus =0.00125) were associated with iRBD. We found no association in LBD.

Discussion

This study identified an association between HLA-DRB1 11:01 and iRBD, distinct from the previously reported association in PD. Therefore, the HLA locus may play different roles across synucleinopathies. Additional studies are required better to understand HLA's role in iRBD and LBD.

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.

Accurate genome-wide genotyping from archival tissue to explore the contribution of common genetic variants to pre-cancer outcomes

PURPOSE

The contribution of common genetic variants to pre-cancer progression is understudied due to long follow-up time, rarity of poor outcomes and lack of available germline DNA collection. Alternatively, DNA from diagnostic archival tissue is available, but its somatic nature, limited quantity and suboptimal quality would require an accurate cost-effective genome-wide germline genotyping methodology.

EXPERIMENTAL DESIGN

Blood and tissue DNA from 10 individuals were used to benchmark the accuracy of Single Nucleotide Polymorphisms (SNP) genotypes, Polygenic Risk Scores (PRS) or HLA haplotypes using low-coverage whole-genome sequencing (lc-WGS) and genotype imputation. Tissue-derived PRS were further evaluated for 36 breast cancer patients (11.7 years median follow-up time) diagnosed with DCIS and used to model the risk of Breast Cancer Subsequent Events (BCSE).

RESULTS

Tissue-derived germline DNA profiling resulted in accurate genotypes at common SNPs (blood correlation r2 > 0.94) and across 22 disease-related polygenic risk scores (PRS, mean correlation r = 0.93). Imputed Class I and II HLA haplotypes were 96.7% and 82.5% concordant with clinical-grade blood HLA haplotypes, respectively. In DCIS patients, tissue-derived PRS was significantly associated with BCSE (HR = 2, 95% CI 1.2-3.8). The top and bottom decile patients had an estimated 28% and 5% chance of BCSE at 10 years, respectively.

CONCLUSIONS

Archival tissue DNA germline profiling using lc-WGS and imputation, represents a cost and resource-effective alternative in the retrospective design of long-term disease genetic studies. Initial results in breast cancer suggest that common risk variants contribute to pre-cancer progression.

Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics)

The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.

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.

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.

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.

Fine mapping of the HLA locus in Parkinson's disease in Europeans

We fine mapped the leukocyte antigen (HLA) region in 13,770 Parkinson’s disease (PD) patients, 20,214 proxy-cases, and 490,861 controls of European origin. Four HLA types were associated with PD after correction for multiple comparisons, HLA-DQA1*03:01, HLA-DQB1*03:02, HLA-DRB1*04:01, and HLA-DRB1*04:04. Haplotype analyses followed by amino acid analysis and conditional analyses suggested that the association is protective and primarily driven by three specific amino acid polymorphisms present in most HLA-DRB1*04 subtypes—11V, 13H, and 33H (OR = 0.87, 95% CI: 0.83–0.90, p < 8.23 × 10⁻⁹ for all three variants). No other effects were present after adjustment for these amino acids. Our results suggest that specific HLA-DRB1 variants are associated with reduced risk of PD, providing additional evidence for the role of the immune system in PD. Although effect size is small and has no diagnostic significance, understanding the mechanism underlying this association may lead to the identification of new targets for therapeutics development.

Class II Human Leukocyte Antigen Variants Associate With Risk of Pegaspargase Hypersensitivity

We conducted the first human leukocyte antigen (HLA) allele and genome-wide association study to identify loci associated with hypersensitivity reactions exclusively to the PEGylated preparation of asparaginase (pegaspargase) in racially diverse cohorts of pediatric leukemia patients: St Jude Children's Research Hospital's Total XVI (TXVI, n = 598) and Children's Oncology Group AALL0232 (n = 2,472) and AALL0434 (n = 1,189). Germline DNA was genotyped using arrays. Genetic variants not genotyped directly were imputed. HLA alleles were imputed using SNP2HLA or inferred using BWAkit. Analyses between genetic variants and hypersensitivity were performed in each cohort first using cohort-specific covariates and then combined using meta-analyses. Nongenetic risk factors included fewer intrathecal injections (P = 2.7 × 10-5 in TXVI) and male sex (P = 0.025 in AALL0232). HLA alleles DQB1*02:02, DRB1*07:01, and DQA1*02:01 had the strongest associations with pegaspargase hypersensitivity (P < 5.0 × 10-5 ) in patients with primarily European ancestry (EA), with the three alleles associating in a single haplotype. The top allele HLA-DQB1*02:02 was tagged by HLA-DQB1 rs1694129 in EAs (r2  = 0.96) and less so in non-EAs. All single nucleotide polymorphisms associated with pegaspargase hypersensitivity reaching genome-wide significance in EAs were in class II HLA loci, and were partially replicated in non-EAs, as is true for other HLA associations. The rs9958628 variant, in ARHGAP28 (previously linked to immune response in children) had the strongest genetic association (P = 8.9 × 10-9 ) in non-EAs. The HLA-DQB1*02:02-DRB1*07:01-DQA1*02:01 associated with hypersensitivity reactions to pegaspargase is the same haplotype associated with reactions to non-PEGylated asparaginase, even though the antigens differ between the two preparations.

Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice

Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.

Genetic susceptibility to multiple sclerosis: interactions between conserved extended haplotypes of the MHC and other susceptibility regions

BACKGROUND

To study the accumulation of MS-risk resulting from different combinations of MS-associated conserved-extended-haplotypes (CEHs) of the MHC and three non-MHC "risk-haplotypes" nearby genes EOMES, ZFP36L1, and CLEC16A. Many haplotypes are MS-associated despite having population-frequencies exceeding the percentage of genetically-susceptible individuals. The basis of this frequency-disparity requires explanation.

METHODS

The SNP-data from the WTCCC was phased at the MHC and three non-MHC susceptibility-regions. CEHs at the MHC were classified into five haplotype-groups: (HLA-DRB1*15:01 ~ DQB1*06:02 ~ a1)-containing (H +); extended-risk (ER); all-protective (AP); neutral (0); and the single-CEH (c1). MS-associations for different "risk-combinations" at the MHC and other non-MHC "risk-loci" and the appropriateness of additive and multiplicative risk-accumulation models were assessed.

RESULTS

Different combinations of "risk-haplotypes" produce a final MS-risk closer to additive rather than multiplicative risk-models but neither model was consistent. Thus, (H +)-haplotypes had greater impact when combined with (0)-haplotypes than with (H +)-haplotypes, whereas, (H +)-haplotypes had greater impact when combined with a (c1)-haplotypes than with (0)-haplotypes. Similarly, risk-genotypes (0,H +), (c1,H +), (H + ,H +) and (0,c1) were additive with risks from non-MHC risk-loci, whereas risk-genotypes (ER,H +) and (AP,c1) were unaffected.

CONCLUSIONS

Genetic-susceptibility to MS is essential for MS to develop but actually developing MS depends heavily upon both an individual's particular combination of "risk-haplotypes" and how these loci interact.

MiDAS-Meaningful Immunogenetic Data at Scale

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. However, association studies involving immunogenetic loci most commonly involve simple analyses of classical HLA allelic diversity, resulting in limitations regarding the interpretability and reproducibility of results. We here present MiDAS, a comprehensive R package for immunogenetic data transformation and statistical analysis. MiDAS recodes input data in the form of HLA alleles and KIR types into biologically meaningful variables, allowing HLA amino acid fine mapping, analyses of HLA evolutionary divergence as well as experimentally validated HLA-KIR interactions. Further, MiDAS enables comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS thus closes the gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to immune and disease biology. It is freely available under a MIT license.

A comparison of genotyping arrays

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study's requirements.

Rapid genotype imputation from sequence with reference panels

Inexpensive genotyping methods are essential to modern genomics. Here we present QUILT, which performs diploid genotype imputation using low-coverage whole genome sequence data. QUILT employs Gibbs sampling to partition reads into maternal and paternal sets, facilitating rapid haploid imputation using large reference panels. We show this partitioning to be accurate over many megabases, enabling highly accurate imputation close to theoretical limits and outperforming existing methods. Moreover, QUILT can impute accurately using diverse technologies, including using long reads from Oxford Nanopore Technologies, and a novel form of low-cost barcoded Illumina sequencing called haplotagging, with the latter showing improved accuracy at low coverages. Relative to DNA genotyping microarrays, QUILT offers improved accuracy at reduced cost, particularly for diverse populations that are traditionally underserved in modern genomic analyses, with accuracy nearly doubling at rare SNPs. Finally, QUILT can accurately impute (4-digit) HLA types, the first such method from low-coverage sequence data.

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.

Accurate imputation of human leukocyte antigens with CookHLA

The recent development of imputation methods enabled the prediction of human leukocyte antigen (HLA) alleles from intergenic SNP data, allowing studies to fine-map HLA for immune phenotypes. Here we report an accurate HLA imputation method, CookHLA, which has superior imputation accuracy compared to previous methods. CookHLA differs from other approaches in that it locally embeds prediction markers into highly polymorphic exons to account for exonic variability, and in that it adaptively learns the genetic map within MHC from the data to facilitate imputation. Our benchmarking with real datasets shows that our method achieves high imputation accuracy in a wide range of scenarios, including situations where the reference panel is small or ethnically unmatched.

Associations between human leukocyte antigens and renal function

Human leukocyte antigens (HLA) have been associated with renal function, but previous studies report contradictory findings with little consensus on the exact nature or impact of this observation. This study included 401,307 white British subjects aged 39–73 when they were recruited by UK Biobank. Subjects’ HLA types were imputed using HLA*IMP:02 software. Regression analysis was used to compare 362 imputed HLA types with estimated glomerular filtration rate (eGFR) as a primary outcome and clinical indications as secondary outcome measures. 22 imputed HLA types were associated with increased eGFR (and therefore increased renal function). Decreased eGFR (decreased renal function) was associated with 11 imputed HLA types, seven of which were also associated with increased risk of end-stage renal disease and/or chronic kidney disease. Many of these HLA types are commonly inherited together in established haplotypes, for example: HLA-A*01:01, B*08:01, C*07:01, DRB1*03:01, DQB1*02:01. This haplotype has a population frequency of 9.5% in England and each allele was associated with decreased renal function. 33 imputed HLA types were associated with kidney function in white British subjects. Linkage disequilibrium in HLA heritance suggests that this is not random and particularly affects carriers of established haplotypes. This could have important applications for the diagnosis and treatment of renal disease and global population health.

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.

Human Leukocyte Antigen B*14:01 and B*35:01 Are Associated With Trimethoprim-Sulfamethoxazole Induced Liver Injury

BACKGROUND AND AIMS

Trimethoprim (TMP)-sulfamethoxazole (SMX) is an important cause of idiosyncratic drug-induced liver injury (DILI), but its genetic risk factors are not well understood. This study investigated the relationship between variants in the human leukocyte antigen (HLA) class 1 and 2 genes and well-characterized cases of TMP-SMX DILI.

APPROACH AND RESULTS

European American and African American persons with TMP-SMX DILI were compared with respective population controls. HLA sequencing was performed by Illumina MiSeq (Illumina, San Diego, CA) for cases. The HLA genotype imputation with attribute bagging program was used to impute HLA alleles for controls. The allele frequency difference between case patients and controls was tested by Fisher's exact tests for each ethnic group. For European Americans, multivariable logistic regression with Firth penalization was used to test the HLA allelic effect after adjusting for age and the top two principal components. Molecular docking was performed to assess HLA binding with TMP and SMX. The European American subset had 51 case patients and 12,156 controls, whereas the African American subset had 10 case patients and 5,439 controls. Four HLA alleles were significantly associated in the European American subset, with HLA-B*14:01 ranking at the top (odds ratio, 9.20; 95% confidence interval, 3.16, 22.35; P = 0.0003) after covariate adjustment. All carriers of HLA-B*14:01 with TMP-SMX DILI possessed HLA-C*08:02, another significant allele (P = 0.0026). This pattern was supported by HLA-B*14:01-HLA-C*08:02 haplotype association (P = 1.33 × 10^-5 ). For the African American patients, HLA-B*35:01 had 2.8-fold higher frequency in case patients than in controls, with 5 of 10 patients carrying this allele. Molecular docking showed cysteine at position 67 in HLA-B*14:01 and phenylalanine at position 67 in HLA-B*35:01 to be the predictive binding sites for SMX metabolites.

CONCLUSIONS

HLA-B*14:01 is associated with TMP-SMX DILI in European Americans, and HLA-B*35:01 may be a potential genetic risk factor for African Americans.

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.

Human leukocyte antigen associations with renal function among ethnic minorities in the United Kingdom

Human leukocyte antigens (HLA) have been associated with renal function, but previous studies report contradictory findings. There has been a lack of research into how HLA affects renal function in Black, Asian and Minority Ethnic (BAME) people in the UK, despite BAME people being disproportionately affected by renal dysfunction. This study included >27 000 UK Biobank subjects of six ethnicities (>12 100 Irish, >5400 Indian, >4000 Black Caribbean, >3000 Black African, >1600 Pakistani, and >1400 Chinese) aged 39 to 73. Subjects' high-resolution HLA genotypes were imputed using HLA*IMP:02 software. Regression analysis was used to compare 108 imputed HLA alleles with two measures of estimated glomerular filtration rate (eGFR): one based on serum creatinine; one based on serum cystatin. Secondary analysis compared CKD stage 2 subjects to healthy controls. Nine imputed HLA alleles were associated with eGFR (adjusted P < .05). Six associations were based on creatinine in Black African subjects: HLA-B*53:01 (beta = -2.628, adjusted P = 4.69 × 10^-4 ); C*04:01 (beta = -1.667, adjusted P = .0269); DPA1*02:01 (beta = -1.569, adjusted P = .0182); and DPA1*02:02 (beta = -1.716, adjusted P = .0251) were linked to decreased renal function, while DRB1*03:01 (beta = 3.200, adjusted P = 3.99 × 10^-3 ) and DPA1*01:03 (beta = 2.276, adjusted P = 2.31 × 10^-5 ) were linked to increased renal function. Two of these (HLA-B*53:01 and C*04:01) are commonly inherited together. In Irish subjects, HLA-DRB1*04:01 (beta = 1.075, adjusted P = .0138) was linked to increased eGFR (based on cystatin); in Indian subjects, HLA-DRB1*03:01 (beta = -1.72, adjusted P = 4.78 × 10^-3 ) and DQB1*02:01 (beta = -1.755, adjusted P = 2.26 × 10^-3 )were associated with decreased eGFR (based on cystatin). No associations were found in the other three ethnic groups. Nine HLA alleles appear to be associated with kidney function in BAME people in the UK. This could have applications for the diagnosis and treatment of renal disease and could help reduce health inequalities in the UK.

In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of natural killer (NK) and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for four-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

Association Between Single-Nucleotide Polymorphisms in HLA Alleles and Human Immunodeficiency Virus Type 1 Viral Load in Demographically Diverse, Antiretroviral Therapy-Naive Participants From the Strategic Timing of AntiRetroviral Treatment Trial

The impact of variation in host genetics on replication of human immunodeficiency virus type 1 (HIV-1) in demographically diverse populations remains uncertain. In the current study, we performed a genome-wide screen for associations of single-nucleotide polymorphisms (SNPs) to viral load (VL) in antiretroviral therapy-naive participants (n = 2440) with varying demographics from the Strategic Timing of AntiRetroviral Treatment (START) trial. Associations were assessed using genotypic data generated by a customized SNP array, imputed HLA alleles, and multiple linear regression. Genome-wide significant associations between SNPs and VL were observed in the major histocompatibility complex class I region (MHC I), with effect sizes ranging between 0.14 and 0.39 log10 VL (copies/mL). Supporting the SNP findings, we identified several HLA alleles significantly associated with VL, extending prior observations that the (MHC I) is a major host determinant of HIV-1 control with shared genetic variants across diverse populations and underscoring the limitations of genome-wide association studies as being merely a screening tool.

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.

HLA Class II Allele Analyses Implicate Common Genetic Components in Type 1 and Non-Insulin-Treated Type 2 Diabetes

CONTEXT

Common genetic susceptibility may underlie the frequently observed co-occurrence of type 1 and type 2 diabetes in families. Given the role of HLA class II genes in the pathophysiology of type 1 diabetes, the aim of the present study was to test the association of high density imputed human leukocyte antigen (HLA) genotypes with type 2 diabetes.

OBJECTIVES AND DESIGN

Three cohorts (Ntotal = 10 413) from Leipzig, Germany were included in this study: LIFE-Adult (N = 4649), LIFE-Heart (N = 4815) and the Sorbs (N = 949) cohort. Detailed metabolic phenotyping and genome-wide single nucleotide polymorphism (SNP) data were available for all subjects. Using 1000 Genome imputation data, HLA genotypes were imputed on 4-digit level and association tests for type 2 diabetes, and related metabolic traits were conducted.

RESULTS

In a meta-analysis including all 3 cohorts, the absence of HLA-DRB5 was associated with increased risk of type 2 diabetes (P = 0.001). In contrast, HLA-DQB*06:02 and HLA-DQA*01:02 had a protective effect on type 2 diabetes (P = 0.005 and 0.003, respectively). Both alleles are part of the well-established type 1 diabetes protective haplotype DRB1*15:01~DQA1*01:02~DQB1*06:02, which was also associated with reduced risk of type 2 diabetes (OR 0.84; P = 0.005). On the contrary, the DRB1*07:01~DQA1*02:01~DQB1*03:03 was identified as a risk haplotype in non-insulin-treated diabetes (OR 1.37; P = 0.002).

CONCLUSIONS

Genetic variation in the HLA class II locus exerts risk and protective effects on non-insulin-treated type 2 diabetes. Our data suggest that the genetic architecture of type 1 diabetes and type 2 diabetes might share common components on the HLA class II locus.

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.

Genetic Discrimination Between LADA and Childhood-Onset Type 1 Diabetes Within the MHC

OBJECTIVE

The MHC region harbors the strongest loci for latent autoimmune diabetes in adults (LADA); however, the strength of association is likely attenuated compared with that for childhood-onset type 1 diabetes. In this study, we recapitulate independent effects in the MHC class I region in a population with type 1 diabetes and then determine whether such conditioning in LADA yields potential genetic discriminators between the two subtypes within this region.

RESEARCH DESIGN AND METHODS

Chromosome 6 was imputed using SNP2HLA, with conditional analysis performed in type 1 diabetes case subjects (n = 1,985) and control subjects (n = 2,219). The same approach was applied to a LADA cohort (n = 1,428) using population-based control subjects (n = 2,850) and in a separate replication cohort (656 type 1 diabetes case, 823 LADA case, and 3,218 control subjects).

RESULTS

The strongest associations in the MHC class II region (rs3957146, β [SE] = 1.44 [0.05]), as well as the independent effect of MHC class I genes, on type 1 diabetes risk, particularly HLA-B*39 (β [SE] = 1.36 [0.17]), were confirmed. The conditional analysis in LADA versus control subjects showed significant association in the MHC class II region (rs3957146, β [SE] = 1.14 [0.06]); however, we did not observe significant independent effects of MHC class I alleles in LADA.

CONCLUSIONS

In LADA, the independent effects of MHC class I observed in type 1 diabetes were not observed after conditioning on the leading MHC class II associations, suggesting that the MHC class I association may be a genetic discriminator between LADA and childhood-onset type 1 diabetes.

HLA class I alleles are associated with clinic-based migraine and increased risks of chronic migraine and medication overuse

OBJECTIVE

We aimed to evaluate associations of human leukocyte antigen variants with migraine or headache in hospital and population-based settings.

METHODS

The case-control study population, aged 30-70, included 605 clinic-based migraine patients in a medical center and 8449 population-based participants in Taiwan Biobank (TWB). Clinic-based cases were ascertained by neurologists. Participants in Taiwan Biobank were interviewed by a structured questionnaire including headache and migraine history; among them, 2394 had headache or migraine history while 6055 were free of headache and served as controls. All subjects were genotyped by Axiom Genome-Wide Single Nucleotide Polymorphism Arrays and imputed for eight classical human leukocyte antigen genes. Human leukocyte antigen frequencies were compared between clinic-based and self-reported patients and controls. We utilized likelihood ratio tests to examine human leukocyte antigen-disease associations and logistic regressions to estimate the effect of human leukocyte antigen alleles on migraine.

RESULTS

Human leukocyte antigen-B and C showed significant associations with clinic-based migraine (q-value < 0.05). Human leukocyte antigen-B*39:01, human leukocyte antigen-B*51:01, human leukocyte antigen-B*58:01 and human leukocyte antigen-C*03:02 were significantly associated with migraine, with age and sex-adjusted odds ratios (95% CIs) of 1.80 (1.28-2.53), 1.50 (1.15-1.97), 1.36 (1.14-1.62) and 1.36 (1.14-1.62), correspondingly. Clinic-based migraineurs carrying human leukocyte antigen-B*58:01 or human leukocyte antigen-C*03:02 had 1.63 (1.11-2.39) -fold likelihood to have chronic migraine with medication-overuse headache compared to episodic migraine. However, no human leukocyte antigen genes were associated with self-reported headache or migraine in the community.

CONCLUSIONS

Human leukocyte antigen class I genetic variants are positively associated with risk of clinic-based migraine but not self-reported migraine or headache and may contribute to migraine chronification and medication overuse.

Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis

Rheumatoid arthritis (RA) risk has a large genetic component (~60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.

A Rapid Allele-Specific Assay for HLA-A*32:01 to Identify Patients at Risk for Vancomycin-Induced Drug Reaction with Eosinophilia and Systemic Symptoms

Human leukocyte antigen (HLA) alleles have been implicated as risk factors for immune-mediated adverse drug reactions. The authors recently reported a strong association between HLA-A*32:01 and vancomycin-induced drug reaction with eosinophilia and systemic symptoms. Identification of individuals with the risk allele before or shortly after the initiation of vancomycin therapy is of great clinical importance to prevent morbidity and mortality, and improve drug safety and antibiotic treatment options. A prerequisite to the success of pharmacogenetic screening tests is the development of simple, robust, cost-effective single HLA allele test that can be implemented in routine diagnostic laboratories. In this study, the authors developed a simple, real-time allele-specific PCR for typing the HLA-A*32:01 allele. Four-hundred and fifty-eight DNA samples including 30 HLA-A*32:01-positive samples were typed by allele-specific PCR. Compared with American Society for Histocompatibility and Immunogenetics-accredited, sequence-based, high-resolution, full-allelic HLA typing, this assay demonstrates 100% accuracy, 100% sensitivity (95% CI, 88.43% to 100%), and 100% specificity (95% CI, 99.14% to 100%). The lowest limit of detection of this assay using PowerUp SYBR Green is 10 ng of template DNA. The assay demonstrates a sensitivity and specificity to differentiate the HLA-A*32:01 allele from closely related non-HLA-A*32 alleles and may be used in clinical settings to identify individuals with the risk allele before or during the course of vancomycin therapy.

Asparaginase-induced hepatotoxicity: rapid development of cholestasis and hepatic steatosis

BACKGROUND

L-Asparaginase is a bacterial enzyme used in the treatment of acute lymphoblastic leukemia. In the ongoing U.S. Drug-Induced Liver Injury Network (DILIN) prospective study, standard and pegylated asparaginase were the most frequent cause of liver injury with jaundice among anti-cancer agents (8 of 40: 20%). The unique features of this hepatotoxicity are described.

METHODS

Eight cases from 5 DILIN centers were reviewed for clinical course, laboratory values, imaging, and histopathology.

RESULTS

Seven females, aged 29-59 years, and one 8-year-old boy, all with leukemia, developed jaundice within 9-21 days (median 15 days) of starting asparaginase or pegaspargase, during the first (n = 6) or second (n = 2) cycle. Prominent symptoms were jaundice (n = 8), fatigue (6), abdominal pain (6) but rarely pruritus (1). Initial median ALT level was 284 U/L (range 83-1076), Alk P 159 U/L (64-452), and bilirubin 4.4 mg/dL (3.7-8.4). Bilirubin levels rose thereafter in all patients to median peak of 17.5 mg/dL (11.7-25.7), INR rose to 1.1-1.7 and serum albumin fell to 1.5-2.6 g/dL. Hepatic imaging revealed fatty liver in all patients. Liver biopsy showed steatosis but minimal hepatocyte necrosis. One patient restarted on pegaspargase re-developed less severe injury.

CONCLUSION

Asparaginase is a common cause of antineoplastic-induced liver injury with jaundice, typically with short latency, marked steatosis, and prolonged jaundice, which can lead to delays in antileukemic therapy. The cause of injury is likely direct inhibition of hepatic protein synthesis caused by asparagine depletion.

HLA-A*32:01 is strongly associated with vancomycin-induced drug reaction with eosinophilia and systemic symptoms

BACKGROUND

Vancomycin is a prevalent cause of the severe hypersensitivity syndrome drug reaction with eosinophilia and systemic symptoms (DRESS), which leads to significant morbidity and mortality and commonly occurs in the setting of combination antibiotic therapy, affecting future treatment choices. Variations in HLA class I in particular have been associated with serious T cell-mediated adverse drug reactions, which has led to preventive screening strategies for some drugs.

OBJECTIVE

We sought to determine whether variation in the HLA region is associated with vancomycin-induced DRESS.

METHODS

Probable vancomycin-induced DRESS cases were matched 1:2 with tolerant control subjects based on sex, race, and age by using BioVU, Vanderbilt's deidentified electronic health record database. Associations between DRESS and carriage of HLA class I and II alleles were assessed by means of conditional logistic regression. An extended sample set from BioVU was used to conduct a time-to-event analysis of those exposed to vancomycin with and without the identified HLA risk allele.

RESULTS

Twenty-three subjects met the inclusion criteria for vancomycin-associated DRESS. Nineteen (82.6%) of 23 cases carried HLA-A*32:01 compared with 0 (0%) of 46 of the matched vancomycin-tolerant control subjects (P = 1 × 10-8) and 6.3% of the BioVU population (n = 54,249, P = 2 × 10-16). Time-to-event analysis of DRESS development during vancomycin treatment among the HLA-A*32:01-positive group indicated that 19.2% had DRESS and did so within 4 weeks.

CONCLUSIONS

HLA-A*32:01 is strongly associated with vancomycin-induced DRESS in a population of predominantly European ancestry. HLA-A*32:01 testing could improve antibiotic safety, help implicate vancomycin as the causal drug, and preserve future treatment options with coadministered antibiotics.

Contribution of non-HLA incompatibility between donor and recipient to kidney allograft survival: genome-wide analysis in a prospective cohort

BACKGROUND

The introduction of HLA matching of donors and recipients was a breakthrough in kidney transplantation. However, half of all transplanted kidneys still fail within 15 years after transplantation. Epidemiological data suggest a fundamental role of non-HLA alloimmunity.

METHODS

We genotyped 477 pairs of deceased donors and first kidney transplant recipients with stable graft function at three months that were transplanted between Dec 1, 2005, and April 30, 2015. Genome-wide genetic mismatches in non-synonymous single nucleotide polymorphisms (nsSNPs) were calculated to identify incompatibilities in transmembrane and secreted proteins. We estimated the association between nsSNP mismatch and graft loss in a Cox proportional hazard model, adjusting for HLA mismatch and clinical covariates. Customised peptide arrays were generated to screen for antibodies against genotype-derived mismatched epitopes in 25 patients with biopsy-confirmed chronic antibody-mediated rejection.

FINDINGS

59 268 nsSNPs affecting a transmembrane or secreted protein were analysed. The median number of nsSNP mismatches in immune-accessible transmembrane and secreted proteins between donors and recipients was 1892 (IQR 1850-1936). The degree of nsSNP mismatch was independently associated with graft loss in a multivariable model adjusted for HLA eplet mismatch (HLA-A, HLA-B, HLA-C, HLA-DP, HLA-DQ, and HLA-DR). Each increase by a unit of one IQR had an HR of 1·68 (95% CI 1·17-2·41, p=0·005). 5-year death censored graft survival was 98% in the quartile with the lowest mismatch, 91% in the second quartile, 89% in the third quartile, and 82% in the highest quartile (p=0·003, log-rank test). Customised peptide arrays verified a donor-specific alloimmune response to genetically predicted mismatched epitopes.

INTERPRETATION

Genetic mismatch of non-HLA haplotypes coding for transmembrane or secreted proteins is associated with an increased risk of functional graft loss independently of HLA incompatibility. As in HLA alloimmunity, donor-specific alloantibodies can be identified against genotype derived non-HLA epitopes.

FUNDING

Austrian Science Fund, WWTF (Vienna Science and Technology Fund), and Ministry of Health of the Czech Republic.

Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population

To perform detailed fine-mapping of the major-histocompatibility-complex region, we conducted next-generation sequencing (NGS)-based typing of the 33 human leukocyte antigen (HLA) genes in 1,120 individuals of Japanese ancestry, providing a high-resolution allele catalog and linkage-disequilibrium structure of both classical and nonclassical HLA genes. Together with population-specific deep-whole-genome-sequencing data (n = 1,276), we conducted NGS-based HLA, single-nucleotide-variant and indel imputation of large-scale genome-wide-association-study data from 166,190 Japanese individuals. A phenome-wide association study assessing 106 clinical phenotypes identified abundant, significant genotype-phenotype associations across 52 phenotypes. Fine-mapping highlighted multiple association patterns conferring independent risks from classical HLA genes. Region-wide heritability estimates and genetic-correlation network analysis elucidated the polygenic architecture shared across the phenotypes.

Next-Generation Sequencing Based HLA Typing: Deciphering Immunogenetic Aspects of Sarcoidosis

Unraveling of the HLA-related immunogenetic basis of several immune disorders is complex due to the extensive HLA polymorphism and strong linkage-disequilibrium between HLA loci. A lack of in phase sequence information, a relative deficiency of high resolution genotyping including non-coding regions and ambiguous haplotype assignment make it difficult to compare findings across association studies and to attribute a causal role to specific HLA alleles/haplotypes in disease susceptibility and modification of disease phenotypes. Earlier, historical antibody and DNA-based methods of HLA typing, primarily of low resolution at antigen/alellic group levels, yielded "indicative" findings which were partially improved by high-resolution DNA-based typing. Only recently, next-generation sequencing (NGS) approaches based on deep-sequencing of the complete HLA genes combined with bioinformatics tools began to provide the access to complete information at an allelic level. Analyzing HLA with NGS approaches, therefore, promises to provide further insight in the etiopathogenesis of several immune disorders in which HLA associations have been implicated. These range from coeliac disease and rheumatological conditions to even more complex disorders, such as type-1 diabetes, systemic lupus erythematosus and sarcoidosis. A systemic disease of unknown etiology, sarcoidosis has previously been associated with numerous HLA variants and also other gene polymorphisms, often in linkage with the HLA region. To date, the biological significance of these associations has only partially been defined. Therefore, more precise assignments of HLA alleles/haplotypes using NGS approaches could help to elucidate the exact role of HLA variation in the multifaceted etiopathogenesis of sarcoidosis, including epigenetic mechanisms. NGS-based HLA analyses may be also relevant for defining variable clinical phenotypes and for predicting the disease course or the response to current/plausible novel therapies.

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.

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.

What has GWAS done for HLA and disease associations?

The major histocompatibility complex (MHC) is located in chromosome 6p21 and contains crucial regulators of immune response, including human leucocyte antigen (HLA) genes, alongside other genes with nonimmunological roles. More recently, a repertoire of noncoding RNA genes, including expressed pseudogenes, has also been identified. The MHC is the most gene dense and most polymorphic part of the human genome. The region exhibits haplotype-specific linkage disequilibrium patterns, contains the strongest cis- and trans-eQTLs/meQTLs in the genome and is known as a hot spot for disease associations. Another layer of complexity is provided to the region by the extreme structural variation and copy number variations. While the HLA-B gene has the highest number of alleles, the HLA-DR/DQ subregion is structurally most variable and shows the highest number of disease associations. Reliance on a single reference sequence has complicated the design, execution and analysis of GWAS for the MHC region and not infrequently, the MHC region has even been excluded from the analysis of GWAS data. Here, we contrast features of the MHC region with the rest of the genome and highlight its complexities, including its functional polymorphisms beyond those determined by single nucleotide polymorphisms or single amino acid residues. One of the several issues with customary GWAS analysis is that it does not address this additional layer of polymorphisms unique to the MHC region. We highlight alternative approaches that may assist with the analysis of GWAS data from the MHC region and unravel associations with all functional polymorphisms beyond single SNPs. We suggest that despite already showing the highest number of disease associations, the true extent of the involvement of the MHC region in disease genetics may not have been uncovered.

Benefit of Preemptive Pharmacogenetic Information on Clinical Outcome

The development of new knowledge around the genetic determinants of variable drug action has naturally raised the question of how this new knowledge can be used to improve the outcome of drug therapy. Two broad approaches have been taken: a point-of-care approach in which genotyping for specific variant(s) is undertaken at the time of drug prescription, and a preemptive approach in which multiple genetic variants are typed in an individual patient and the information archived for later use when a drug with a "pharmacogenetic story" is prescribed. This review addresses the current state of implementation, the rationale for these approaches, and barriers that must be overcome. Benefits to pharmacogenetic testing are only now being defined and will be discussed.