SweHLA: the high confidence HLA typing bio-resource drawn from 1000 Swedish genomes

The Impact of Patterns in Linkage Disequilibrium and Sequencing Quality on the Imprint of Balancing Selection

Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions.

Optimal population-specific HLA imputation with dimension reduction

Human genomics has quickly evolved, powering genome-wide association studies (GWASs). SNP-based GWASs cannot capture the intense polymorphism of HLA genes, highly associated with disease susceptibility. There are methods to statistically impute HLA genotypes from SNP-genotypes data, but lack of diversity in reference panels hinders their performance. We evaluated the accuracy of the 1000 Genomes data as a reference panel for imputing HLA from admixed individuals of African and European ancestries, focusing on (a) the full dataset, (b) 10 replications from 6 populations, and (c) 19 conditions for the custom reference panels. The full dataset outperformed smaller models, with a good F1-score of 0.66 for HLA-B. However, custom models outperformed the multiethnic or population models of similar size (F1-scores up to 0.53, against up to 0.42). We demonstrated the importance of using genetically specific models for imputing populations, which are currently underrepresented in public datasets, opening the door to HLA imputation for every genetic population.

Simultaneous detection of DNA variation and methylation at HLA class II locus and immune gene promoters using targeted SureSelect Methyl-Sequencing

The Human Leukocyte Antigen (HLA) locus associates with a variety of complex diseases, particularly autoimmune and inflammatory conditions. The HLA-DR15 haplotype, for example, confers the major risk for developing Multiple Sclerosis in Caucasians, pinpointing an important role in the etiology of this chronic inflammatory disease of the central nervous system. In addition to the protein-coding variants that shape the functional HLA-antigen-T cell interaction, recent studies suggest that the levels of HLA molecule expression, that are epigenetically controlled, also play a role in disease development. However, deciphering the exact molecular mechanisms of the HLA association has been hampered by the tremendous genetic complexity of the locus and a lack of robust approaches to investigate it. Here, we developed a method to specifically enrich the genomic DNA from the HLA class II locus (chr6:32,426,802-34,167,129) and proximal promoters of 2,157 immune-relevant genes, utilizing the Agilent RNA-based SureSelect Methyl-Seq Capture related method, followed by sequencing to detect genetic and epigenetic variation. We demonstrated successful simultaneous detection of the genetic variation and quantification of DNA methylation levels in HLA locus. Moreover, by the detection of differentially methylated positions in promoters of immune-related genes, we identified relevant pathways following stimulation of cells. Taken together, we present a method that can be utilized to study the interplay between genetic variance and epigenetic regulation in the HLA class II region, potentially, in a wide disease context.

Associations between HLA class II alleles and IgE sensitization to allergens in the Qatar Biobank cohort

Background

Allergic disorders are the consequence of IgE sensitization to allergens. Population studies have shown that certain human leukocyte antigen (HLA) alleles are associated with increased or decreased risk of developing allergy.

Objective

We aimed to characterize the relationship between HLA class II allelic diversity and IgE sensitization in an understudied Arab population.

Methods

We explored associations between IgE sensitization to 7 allergen mixes and mesquite (comprising 41 food or aeroallergens) and 45 common classical HLA class II alleles in a well-defined cohort of 797 individuals representing the general adult population of Qatari nationals and long-term residents. To do so, we performed HLA calling from whole genome sequencing data at 2-field resolution using 2 independent algorithms. We then applied 3 different regression models to assess either each allergen mix independently, in the context of IgE sensitization to other allergens tested, or polysensitization.

Results

More than half (n = 447) of the study participants showed IgE sensitization to at least 1 allergen, most of them (n = 400) to aeroallergens (Phadiatop). We identified statistically significant negative and positive associations with 24 HLA class II alleles. These have been reported to confer risk or protection from variety of diseases; however, only a few have previously been associated with allergy in other populations.

Conclusions

Our study reveals several new risk and protective genetic markers for allergen-specific IgE sensitization. This is a first and essential step toward a better understanding of the origins of allergic diseases in this understudied population.

Human leukocyte antigen class II gene diversity tunes antibody repertoires to common pathogens

Allelic diversity of human leukocyte antigen (HLA) class II genes may help maintain humoral immunity against infectious diseases. In this study, we investigated germline genetic variation in classical HLA class II genes and employed a systematic, unbiased approach to explore the relative contribution of this genetic variation in the antibody repertoire to various common pathogens. We leveraged a well-defined cohort of 800 adults representing the general Arab population in which genetic material is shared because of the high frequency of consanguineous unions. By applying a high-throughput method for large-scale antibody profiling to this well-defined cohort, we were able to dissect the overall effect of zygosity for classical HLA class II genes, as well as the effects associated with specific HLA class II alleles, haplotypes and genotypes, on the antimicrobial antibody repertoire breadth and antibody specificity with unprecedented resolution. Our population genetic studies revealed that zygosity of the classical HLA class II genes is a strong predictor of antibody responses to common human pathogens, suggesting that classical HLA class II gene heterozygosity confers a selective advantage. Moreover, we demonstrated that multiple HLA class II alleles can have additive effects on the antibody repertoire to common pathogens. We also identified associations of HLA-DRB1 genotypes with specific antigens. Our findings suggest that HLA class II gene polymorphisms confer specific humoral immunity against common pathogens, which may have contributed to the genetic diversity of HLA class II loci during hominine evolution.

HLA-variants associated with azathioprine-induced pancreatitis in patients with Crohn's disease

The immunosuppressant drug azathioprine is associated with a 4% risk of acute pancreatitis in patients with inflammatory bowel disease (IBD). Studies have demonstrated an increased risk in carriers of HLA‐DQA1*02:01 and HLA‐DRB1*07:01. We investigated whether these human leukocyte antigen (HLA) types were associated with azathioprine‐induced pancreatitis also in Swedish patients with IBD, and whether the type of disease affected the association. Nineteen individuals with IBD who developed acute pancreatitis after initiation of azathioprine were genotyped and compared with a population control cohort (n = 4891) and a control group matched for disease (n = 81). HLA‐DQA1*02:01 and HLA‐DRB1*07:01 were in full linkage disequilibrium, and were significantly associated with acute pancreatitis both when cases were compared with population controls (OR 3.97 [95% CI 1.57–9.97], p = 0.0035) and matched controls (OR 3.55 [95% CI 1.23–10.98], p = 0.0275). In a disease‐specific analysis, the correlation was positive in patients with Crohn's disease versus matched controls (OR 9.27 [95% CI 1.86–46.19], p = 0.0066), but not in those with ulcerative colitis versus matched controls (OR 0.69 [95% CI 0.07–6.74], p = 0.749). In patients with Crohn's disease, we estimated the conditional risk of carriers of HLA‐DQA1*02:01‐HLA‐DRB1*07:01 to 7.3%, and the conditional risk of a non‐carrier to 2.2%. We conclude that HLA‐DQA1*02:01‐HLA‐DRB1*07:01 is a marker for increased risk of acute pancreatitis in individuals of Swedish genetic origin, treated with azathioprine for Crohn's disease.

Personalized HLA typing leads to the discovery of novel HLA alleles and tumor‐specific HLA variants

Accurate and full‐length typing of the HLA region is important in many clinical and research settings. With the advent of next generation sequencing (NGS), several HLA typing algorithms have been developed, including many that are applicable to whole exome sequencing (WES). However, most of these solutions operate by providing the closest‐matched HLA allele among the known alleles in IPD‐IMGT/HLA Database. These database‐matching approaches have demonstrated very high performance when typing well characterized HLA alleles. However, as they rely on the completeness of the HLA database, they are not optimal for detecting novel or less well characterized alleles. Furthermore, the database‐matching approaches are also not adequate in the context of cancer, where a comprehensive characterization of somatic HLA variation and expression patterns of a tumor's HLA locus may guide therapy and clinical outcome, because of the pivotal role HLA alleles play in tumor antigen recognition and immune escape. Here, we describe a personalized HLA typing approach applied to WES data that leverages the strengths of database‐matching approaches while simultaneously allowing for the discovery of novel HLA alleles and tumor‐specific HLA variants, through the systematic integration of germline and somatic variant calling. We applied this approach on WES from 10 metastatic melanoma patients and validated the HLA typing results using HLA targeted NGS sequencing from patients where at least one HLA germline candidate was detected on Class I HLA. Targeted NGS sequencing confirmed 100% performance for the 1st and 2nd fields. In total, five out of the six detected HLA germline variants were because of Class I ambiguities at the third or fourth fields, and their detection recovered the correct HLA allele genotype. The sixth germline variant let to the formal discovery of a novel Class I allele. Finally, we demonstrated a substantially improved somatic variant detection accuracy in HLA alleles with a 91% of success rate in simulated experiments. The approach described here may allow the field to genotype more accurately using WES data, leading to the discovery of novel HLA alleles and help characterize the relationship between somatic variation in the HLA region and immunosurveillance.

Association of Protective HLA-A With HLA-B∗27 Positive Ankylosing Spondylitis

Objectives

To further elucidate the role of the MHC in ankylosing spondylitis by typing 17 genes, searching for HLA-B^∗27 independent associations and assessing the impact of sex on this male biased disease.

Methods

High-confidence two-field resolution genotyping was performed on 310 cases and 2196 controls using an n-1 concordance method. Protein-coding variants were called from next-generation sequencing reads using up to four software programs and the consensus result recorded. Logistic regression tests were applied to the dataset as a whole, and also in stratified sets based on sex or HLA-B^∗27 status. The amino acids driving association were also examined.

Results

Twenty-five HLA protein-coding variants were significantly associated to disease in the population. Three novel protective associations were found in a HLA-B^∗27 positive population, HLA-A^∗24:02 (OR = 0.4, CI = 0.2–0.7), and HLA-A amino acids Leu95 and Gln156. We identified a key set of seven loci that were common to both sexes, and robust to change in sample size. Stratifying by sex uncovered three novel risk variants restricted to the female population (HLA-DQA1^∗04.01, -DQB1^∗04:02, -DRB1^∗08:01; OR = 2.4–3.1). We also uncovered a set of neutral variants in the female population, which in turn conferred strong effects in the male set, highlighting how population composition can lead to the masking of true associations.

Conclusion

Population stratification allowed for a nuanced investigation into the tightly linked MHC region, revealing novel HLA-B^∗27 signals as well as replicating previous HLA-B^∗27 dependent results. This dissection of signals may help to elucidate sex biased disease predisposition and clinical progression.

Treatment- and population-specific genetic risk factors for anti-drug antibodies against interferon-beta: a GWAS

BACKGROUND

Upon treatment with biopharmaceuticals, the immune system may produce anti-drug antibodies (ADA) that inhibit the therapy. Up to 40% of multiple sclerosis patients treated with interferon β (IFNβ) develop ADA, for which a genetic predisposition exists. Here, we present a genome-wide association study on ADA and predict the occurrence of antibodies in multiple sclerosis patients treated with different interferon β preparations.

METHODS

We analyzed a large sample of 2757 genotyped and imputed patients from two cohorts (Sweden and Germany), split between a discovery and a replication dataset. Binding ADA (bADA) levels were measured by capture-ELISA, neutralizing ADA (nADA) titers using a bioassay. Genome-wide association analyses were conducted stratified by cohort and treatment preparation, followed by fixed-effects meta-analysis.

RESULTS

Binding ADA levels and nADA titers were correlated and showed a significant heritability (47% and 50%, respectively). The risk factors differed strongly by treatment preparation: The top-associated and replicated variants for nADA presence were the HLA-associated variants rs77278603 in IFNβ-1a s.c.- (odds ratio (OR) = 3.55 (95% confidence interval = 2.81-4.48), p = 2.1 × 10-26) and rs28366299 in IFNβ-1b s.c.-treated patients (OR = 3.56 (2.69-4.72), p = 6.6 × 10-19). The rs77278603-correlated HLA haplotype DR15-DQ6 conferred risk specifically for IFNβ-1a s.c. (OR = 2.88 (2.29-3.61), p = 7.4 × 10-20) while DR3-DQ2 was protective (OR = 0.37 (0.27-0.52), p = 3.7 × 10-09). The haplotype DR4-DQ3 was the major risk haplotype for IFNβ-1b s.c. (OR = 7.35 (4.33-12.47), p = 1.5 × 10-13). These haplotypes exhibit large population-specific frequency differences. The best prediction models were achieved for ADA in IFNβ-1a s.c.-treated patients. Here, the prediction in the Swedish cohort showed AUC = 0.91 (0.85-0.95), sensitivity = 0.78, and specificity = 0.90; patients with the top 30% of genetic risk had, compared to patients in the bottom 30%, an OR = 73.9 (11.8-463.6, p = 4.4 × 10-6) of developing nADA. In the German cohort, the AUC of the same model was 0.83 (0.71-0.92), sensitivity = 0.80, specificity = 0.76, with an OR = 13.8 (3.0-63.3, p = 7.5 × 10-4).

CONCLUSIONS

We identified several HLA-associated genetic risk factors for ADA against interferon β, which were specific for treatment preparations and population backgrounds. Genetic prediction models could robustly identify patients at risk for developing ADA and might be used for personalized therapy recommendations and stratified ADA screening in clinical practice. These analyses serve as a roadmap for genetic characterizations of ADA against other biopharmaceutical compounds.