Non-invasive prenatal testing of beta-hemoglobinopathies using next generation sequencing, in-silico sequence size selection, and haplotyping.

AIM: To develop a non-invasive prenatal test for beta-hemoglobinopathies based on analyzing maternal plasma by using next generation sequencing. METHODS: We applied next generation sequencing (NGS) of maternal plasma to the non-invasive prenatal testing (NIPT) of autosomal recessive diseases, sickle cell disease and beta-thalassemia. Using the Illumina MiSeq, we sequenced plasma libraries obtained via a Twist Bioscience probe capture panel covering 4 Kb of chromosome 11, including the beta-globin (HBB) gene and >450 genomic single-nucleotide polymorphisms (SNPs) used to estimate the fetal fraction (FF). The FF is estimated by counting paternally transmitted allelic sequence reads present in the plasma but absent in the mother. We inferred fetal beta-globin genotypes by comparing the observed mutation (Mut) and reference (Ref) read ratios to those expected for the three possible fetal genotypes (Mut/Mut; Mut/Ref; Ref/Ref), based on the FF. RESULTS: We bioinformatically enriched the FF by excluding reads over a specified length via in-silico size selection (ISS), favoring the shorter fetal reads, which increased fetal genotype prediction accuracy. Finally, we determined the parental HBB haplotypes, which allowed us to use the read ratios observed at linked SNPs to help predict the fetal genotype at the mutation site(s). We determined HBB haplotypes via Oxford Nanopore MinION sequencing of a 2.2 kb amplicon and aligned these sequences using Soft Genetics' NextGENe LR software. CONCLUSION: The combined use of ISS and HBB haplotypes enabled us to correctly predict fetal genotypes in cases where the prediction based on variant read ratios alone was incorrect.

PyPop: a mature open-source software pipeline for population genomics.

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations. Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop.

Complete HLA genotyping of type 1 diabetes patients and controls from Mali reveals both expected and novel disease associations.

HLA genotyping was performed on 99 type 1 diabetes (T1D) patients and 200 controls from Mali. Next-generation sequencing of the classical HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, -DQA1, -DQB1, -DPA1, and -DPB1 loci revealed strong T1D association for all loci except HLA-C and -DPA1. Class II association is stronger than class I association, with most observed associations predisposing or protective as expected based on previous studies. For example, HLA-DRB1*03:01, HLA-DRB1*09:01, and HLA-DRB1*04:05 predispose for T1D, whereas HLA-DRB1*15:03 is protective. HLA-DPB1*04:02 (OR = 12.73, p = 2.92 × 10-05 ) and HLA-B*27:05 (OR = 21.36, p = 3.72 × 10-05 ) appear highly predisposing, although previous studies involving multiple populations have reported HLA-DPB1*04:02 as T1D-protective and HLA-B*27:05 as neutral. This result may reflect the linkage disequilibrium between alleles on the extended HLA-A*24:02~HLA-B*27:05~HLA-C*02:02~HLA-DRB1*04:05~HLA-DRB4*01:03~HLA-DQB1*02:02~HLA-DQA1*02:01~HLA-DPB1*04:02~HLA-DPA1*01:03 haplotype in this population rather than an effect of either allele itself. Individual amino acid (AA) analyses are consistent with most T1D association attributable to HLA class II rather than class I in this data set. AA-level analyses reveal previously undescribed differences of the HLA-C locus from the HLA-A and HLA-B loci, with more polymorphic positions, spanning a larger portion of the gene. This may reflect additional mechanisms for HLA-C to influence T1D risk, for example, through expression differences or through its role as the dominant ligand for killer cell immunoglobulin-like receptors (KIR). Comparison of these data to those from larger studies and on other populations may facilitate T1D prediction and help elucidate elusive mechanisms of how HLA contributes to T1D risk and autoimmunity.

The genotype list string code syntax for exchanging nomenclature-level genotyping results in clinical and research data management and analysis systems.

The nomenclatures used to describe HLA and killer-cell immunoglobulin-like receptor (KIR) alleles distinguish unique nucleotide and peptide sequences, and patterns of expression, but are insufficient for describing genotyping results, as description of ambiguities and relations across loci require terminology beyond allele names. The genotype list (GL) String grammar describes genotyping results for genetic systems with defined nomenclatures, like HLA and KIR, documenting what is known and unknown about a given genotyping result. However, the accuracy of a GL String is dependent on the reference database version under which it was generated. Here, we describe the GL string code (GLSC) system, which associates each GL String with meta-data describing the specific reference context in which the GL String was created, and in which it should be interpreted. GLSC is a defined syntax for exchanging GL Strings in the context of a specific gene-family namespace, allele-name code-system, and pertinent reference database version. GLSC allows HLA and KIR genotyping data to be transmitted, parsed and interpreted in the appropriate context, in an unambiguous manner, on modern data-systems, including Health Level 7 Fast Healthcare Interoperability Resource systems. Technical specification for GLSC can be found at https://glstring.org.

Analysis of the Origin of Emiratis as Inferred from a Family Study Based on HLA-A, -C, -B, -DRB1, and -DQB1 Genes.

In this study, we investigated HLA class I and class II allele and haplotype frequencies in Emiratis and compared them to those of Asian, Mediterranean, and Sub-Saharan African populations. METHODS: Two-hundred unrelated Emirati parents of patients selected for bone marrow transplantation were genotyped for HLA class I (A, B, C) and class II (DRB1, DQB1) genes using reverse sequence specific oligonucleotide bead-based multiplexing. HLA haplotypes were assigned with certainty by segregation (pedigree) analysis, and haplotype frequencies were obtained by direct counting. HLA class I and class II frequencies in Emiratis were compared to data from other populations using standard genetic distances (SGD), Neighbor-Joining (NJ) phylogenetic dendrograms, and correspondence analysis. RESULTS: The studied HLA loci were in Hardy-Weinberg Equilibrium. We identified 17 HLA-A, 28 HLA-B, 14 HLA-C, 13 HLA-DRB1, and 5 HLA-DQB1 alleles, of which HLA-A*02 (22.2%), -B*51 (19.5%), -C*07 (20.0%), -DRB1*03 (22.2%), and -DQB1*02 (32.8%) were the most frequent allele lineages. DRB1*03~DQB1*02 (21.2%), DRB1*16~DQB1*05 (17.3%), B*35~C*04 (11.7%), B*08~DRB1*03 (9.7%), A*02~B*51 (7.5%), and A*26~C*07~B*08~DRB1*03~DQB1*02 (4.2%) were the most frequent two- and five-locus HLA haplotypes. Correspondence analysis and dendrograms showed that Emiratis were clustered with the Arabian Peninsula populations (Saudis, Omanis and Kuwaitis), West Mediterranean populations (North Africans, Iberians) and Pakistanis, but were distant from East Mediterranean (Turks, Albanians, Greek), Levantine (Syrians, Palestinians, Lebanese), Iranian, Iraqi Kurdish, and Sub-Saharan populations. CONCLUSIONS: Emiratis were closely related to Arabian Peninsula populations, West Mediterranean populations and Pakistanis. However, the contribution of East Mediterranean, Levantine Arab, Iranian, and Sub-Saharan populations to the Emiratis' gene pool appears to be minor.

Genotype List String 1.1: Extending the Genotype List String grammar for describing HLA and Killer-cell Immunoglobulin-like Receptor genotypes.

The Genotype List (GL) String grammar for reporting HLA and Killer-cell Immunoglobulin-like Receptor (KIR) genotypes in a text string was described in 2013. Since this initial description, GL Strings have been used to describe HLA and KIR genotypes for more than 40 million subjects, allowing these data to be recorded, stored and transmitted in an easily parsed, text-based format. After a decade of working with HLA and KIR data in GL String format, with advances in HLA and KIR genotyping technologies that have fostered the generation of full-gene sequence data, the need for an extension of the GL String system has become clear. Here, we introduce the new GL String delimiter "?," which addresses the need to describe ambiguity in assigning a gene sequence to gene paralogs. GL Strings that do not include a "?" delimiter continue to be interpreted as originally described. This extension represents version 1.1 of the GL String grammar.

The immunogenetics of viral antigen response is associated with subtype-specific glioma risk and survival.

Glioma is a highly fatal cancer with prognostically significant molecular subtypes and few known risk factors. Multiple studies have implicated infections in glioma susceptibility, but evidence remains inconsistent. Genetic variants in the human leukocyte antigen (HLA) region modulate host response to infection and have been linked to glioma risk. In this study, we leveraged genetic predictors of antibody response to 12 viral antigens to investigate the relationship with glioma risk and survival. Genetic reactivity scores (GRSs) for each antigen were derived from genome-wide-significant (p < 5 × 10-8) variants associated with immunoglobulin G antibody response in the UK Biobank cohort. We conducted parallel analyses of glioma risk and survival for each GRS and HLA alleles imputed at two-field resolution by using data from 3,418 glioma-affected individuals subtyped by somatic mutations and 8,156 controls. Genetic reactivity scores to Epstein-Barr virus (EBV) ZEBRA and EBNA antigens and Merkel cell polyomavirus (MCV) VP1 antigen were associated with glioma risk and survival (Bonferroni-corrected p < 0.01). GRSZEBRA and GRSMCV were associated in opposite directions with risk of IDH wild-type gliomas (ORZEBRA = 0.91, p = 0.0099/ORMCV = 1.11, p = 0.0054). GRSEBNA was associated with both increased risk for IDH mutated gliomas (OR = 1.09, p = 0.040) and improved survival (HR = 0.86, p = 0.010). HLA-DQA1∗03:01 was significantly associated with decreased risk of glioma overall (OR = 0.85, p = 3.96 × 10-4) after multiple testing adjustment. This systematic investigation of the role of genetic determinants of viral antigen reactivity in glioma risk and survival provides insight into complex immunogenomic mechanisms of glioma pathogenesis. These results may inform applications of antiviral-based therapies in glioma treatment.

HLA-DRB1 and -DQB1 Alleles, Haplotypes and Genotypes in Emirati Patients with Type 1 Diabetes Underscores the Benefits of Evaluating Understudied Populations.

Background: HLA class II (DR and DQ) alleles and antigens have historically shown strong genetic predisposition to type 1 diabetes (T1D). This study evaluated the association of DRB1 and DQB1 alleles, genotypes, and haplotypes with T1D in United Arab Emirates. Materials and Methods: Study subjects comprised 149 patients with T1D, and 147 normoglycemic control subjects. Cases and controls were Emiratis and were HLA-DRB1 and -DQB1 genotyped using sequence-based typing. Statistical analysis was performed using Bridging Immunogenomic Data-Analysis Workflow Gaps R package. Results: In total, 15 DRB1 and 9 DQB1 alleles were identified in the study subjects, of which the association of DRB1*03:01, DRB1*04:02, DRB1*11:01, DRB1*16:02, and DQB1*02:01, DQB1*03:02, DQB1*03:01, and DQB1*06:01 with altered risk of T1D persisted after correcting for multiple comparisons. Two-locus haplotype analysis identified DRB1*03:01∼DQB1*02:01 [0.44 vs. 0.18, OR (95% CI) = 3.44 (2.33-5.1), Pc = 3.48 × 10-10]; DRB1*04:02∼DQB1*03:02 [0.077 vs. 0.014, OR = 6.06 (2.03-24.37), Pc = 2.3 × 10-3] and DRB1*04:05∼DQB1*03:02 [0.060 vs. 0.010, OR = 6.24 (1.79-33.34), Pc = 0.011] as positively associated, and DRB1*16:02∼DQB1*05:02 [0.024 vs. 0.075, OR = 0.3 (0.11-0.74), Pc = 0.041] as negatively associated with T1D, after applying Bonferroni correction. Furthermore, the highest T1D risk was observed for DR3/DR4 [0.104 vs. 0.006, OR = 25.03 (8.23-97.2), Pc = 2.6 × 10-10], followed by DR3/DR3 [0.094 vs. 0.010, OR = 8.72 (3.17-25.32), Pc = 3.18 × 10-8] diplotypes. Conclusion: While DRB1 and DQB1 alleles and haplotypes associated with T1D in Emiratis showed similarities to Caucasian and non-Caucasian populations, several alleles and haplotypes associated with T1D in European, African, and Asian populations, were not observed. This underscores the contribution of ethnic diversity and possible diverse associations between DRB1 and DQB1 and T1D across different populations.

Current HLA Investigations on SARS-CoV-2 and Perspectives.

The rapid, global spread of the SARS-CoV-2 virus during the current pandemic has triggered numerous efforts in clinical and research settings to better understand the host genetics' interactions and the severity of COVID-19. Due to the established major role played by MHC/HLA polymorphism in infectious disease course and susceptibility, immunologists and geneticists have teamed up to investigate its contribution to the SARS-CoV-2 infection and COVID-19 progression. A major goal of the Covid-19|HLA & Immunogenetics Consortium is to support and unify these efforts. Here, we present a review of HLA immunogenomics studies in the SARS-CoV-2 pandemic and reflect on the role of various HLA data, their limitation and future perspectives.

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.

Challenges for the standardized reporting of NGS HLA genotyping: Surveying gaps between clinical and research laboratories.

Next generation sequencing (NGS) is being applied for HLA typing in research and clinical settings. NGS HLA typing has made it feasible to sequence exons, introns and untranslated regions simultaneously, with significantly reduced labor and reagent cost per sample, rapid turnaround time, and improved HLA genotype accuracy. NGS technologies bring challenges for cost-effective computation, data processing and exchange of NGS-based HLA data. To address these challenges, guidelines and specifications such as Genotype List (GL) String, Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING), and Histoimmunogenetics Markup Language (HML) were proposed to streamline and standardize reporting of HLA genotypes. As part of the 17th International HLA and Immunogenetics Workshop (IHIW), we implemented standards and systems for HLA genotype reporting that included GL String, MIRING and HML, and found that misunderstanding or misinterpretations of these standards led to inconsistencies in the reporting of NGS HLA genotyping results. This may be due in part to a historical lack of centralized data reporting standards in the histocompatibility and immunogenetics community. We have worked with software and database developers, clinicians and scientists to address these issues in a collaborative fashion as part of the Data Standard Hackathons (DaSH) for NGS. Here we report several categories of challenges to the consistent exchange of NGS HLA genotyping data we have observed. We hope to address these challenges in future DaSH for NGS efforts.

Standard reference sequences for submission of HLA genotyping for the 18th International HLA and Immunogenetics Workshop.

The International human leukocyte antigen (HLA) and Immunogenetics Workshops (IHIWs) have fostered international collaborations of researchers and experts in the fields of HLA, histocompatibility and immunology. These IHIW collaborations have comprised many projects focused on achieving a variety of specific goals. The international and collaborative nature of these projects necessitates the collection and analysis of complex data generated in multiple laboratories, often using multiple methods of acquisition. Collection and storage of these data in a consistent way adds value to IHIW projects, which can be extended to future work. DNA-based genotyping data, especially HLA genotyping data, can be transmitted in the form of a Histoimmunogenetics Markup Language (HML) document. HML facilitates clear communication of a genotype and supporting metadata, such as, sequencing platform, laboratory assays, consensus sequence, and interpretation. Sequence information can be reported relative to known reference sequences, which add meaning and context to genotypes. Selecting the correct reference sequence for a given allele sequence is nuanced, and guidelines have emerged through collaborative community efforts such as Data Standards Hackathons. Here, we describe the guidelines established for the selection of reference sequences to be used in transmission of HLA (and MICA/MICB) genotyping data for the 18th IHIW.

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

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

HLA alleles and haplotypes observed in 263 US families.

The 17th International HLA and Immunogenetics Workshop (IHIW) conducted a project entitled "The Study of Haplotypes in Families by NGS HLA". We investigated the HLA haplotypes of 1017 subjects in 263 nuclear families sourced from five US clinical immunogenetics laboratories, primarily as part of the evaluation of related donor candidates for hematopoietic stem cell and solid organ transplantation. The parents in these families belonged to five broad groups - African (72 parents), Asian (115), European (210), Hispanic (118) and "Other" (11). High-resolution HLA genotypes were generated for each subject using next-generation sequencing (NGS) HLA typing systems. We identified the HLA haplotypes in each family using HaplObserve, software that builds haplotypes in families by reviewing HLA allele segregation from parents to children. We calculated haplotype frequencies within each broad group, by treating the parents in each family as unrelated individuals. We also calculated standard measures of global linkage disequilibrium (LD) and conditional asymmetric LD for each ethnic group, and used untruncated and two-field allele names to investigate LD patterns. Finally we demonstrated the utility of consensus DNA sequences in identifying novel variants, confirming them using HLA allele segregation at the DNA sequence level.

Tools for building, analyzing and evaluating HLA haplotypes from families.

The highly polymorphic classical human leukocyte antigen (HLA) genes display strong linkage disequilibrium (LD) that results in conserved multi-locus haplotypes. For unrelated individuals in defined populations, HLA haplotype frequencies can be estimated using the expectation-maximization (EM) method. Haplotypes can also be constructed using HLA allele segregation from nuclear families. It is straightforward to identify many HLA genotyping inconsistencies by visually reviewing HLA allele segregation in family members. It is also possible to identify potential crossover events when two or more children are available in a nuclear family. This process of visual inspection can be unwieldy, and we developed the "HaplObserve" program to standardize the process and automatically build haplotypes using family-based HLA allele segregation. HaplObserve facilitates systematically building haplotypes, and reporting potential crossover events. HLA Haplotype Validator (HLAHapV) is a program originally developed to impute chromosomal phase from genotype data using reference haplotype data. We updated and adapted HLAHapV to systematically compare observed and estimated haplotypes. We also used HLAHapV to identify haplotypes when uninformative HLA genotypes are present in families. Finally, we developed "pould", an R package that calculates haplotype frequencies, and estimates standard measures of global (locus-level) LD from both observed and estimated haplotypes.

High resolution HLA analysis reveals independent class I haplotypes and amino-acid motifs protective for multiple sclerosis.

We investigated association between HLA class I and class II alleles and haplotypes, and KIR loci and their HLA class I ligands, with multiple sclerosis (MS) in 412 European American MS patients and 419 ethnically matched controls, using next-generation sequencing. The DRB1*15:01~DQB1*06:02 haplotype was highly predisposing (odds ratio (OR) = 3.98; 95% confidence interval (CI) = 3-5.31; p-value (p) = 2.22E-16), as was DRB1*03:01~DQB1*02:01 (OR = 1.63; CI = 1.19-2.24; p = 1.41E-03). Hardy-Weinberg (HW) analysis in MS patients revealed a significant DRB1*03:01~DQB1*02:01 homozyote excess (15 observed; 8.6 expected; p = 0.016). The OR for this genotype (5.27; CI = 1.47-28.52; p = 0.0036) suggests a recessive MS risk model. Controls displayed no HW deviations. The C*03:04~B*40:01 haplotype (OR = 0.27; CI = 0.14-0.51; p = 6.76E-06) was highly protective for MS, especially in haplotypes with A*02:01 (OR = 0.15; CI = 0.04-0.45; p = 6.51E-05). By itself, A*02:01 is moderately protective, (OR = 0.69; CI = 0.54-0.87; p = 1.46E-03), and haplotypes of A*02:01 with the HLA-B Thr80 Bw4 variant (Bw4T) more so (OR = 0.53; CI = 0.35-0.78; p = 7.55E-04). Protective associations with the Bw4 KIR ligand resulted from linkage disequilibrium (LD) with DRB1*15:01, but the Bw4T variant was protective (OR = 0.64; CI = 0.49-0.82; p = 3.37-04) independent of LD with DRB1*15:01. The Bw4I variant was not associated with MS. Overall, we find specific class I HLA polymorphisms to be protective for MS, independent of the strong predisposition conferred by DRB1*15:01.

Correction: High resolution HLA analysis reveals independent class I haplotypes and amino-acid motifs protective for multiple sclerosis.

Since the publication of this article, the authors have found that the numbers of patients and controls were reversed. This study included 412 MS patients and 419 controls. This correction applies to the Abstract, the final paragraph of the Introduction, and the first paragraph of the Materials and Methods. This was entirely a reporting error and does not impact the Results or Conclusions.

Sequence-based HLA-A, B, C, DP, DQ, and DR typing of 496 adults from San Diego, California, USA.

DNA sequence-based typing at the HLA-A, -B, -C, -DPB1, -DQA1, -DQB1, and -DRB1 loci was performed on 496 healthy adult donors from San Diego, California, to characterize allele frequencies in support of studies of T cell responses to common allergens. Deviations from Hardy Weinberg proportions were detected at each locus except A and C. Several alleles were found in more than 15% of individuals, including the class II alleles DPB1∗02:01, DPB1∗04:01, DQA1∗01:02, DQA1∗05:01, DQB1∗03:01, and the class I allele A∗02:01. Genotype data will be available in the Allele Frequencies Net Database (AFND 3562).

Sequence-based HLA-A, B, C, DP, DQ, and DR typing of 714 adults from Colombo, Sri Lanka.

DNA sequence-based typing at the HLA-A, -B, -C, -DPB1, -DQA1, -DQB1, and -DRB1 loci was performed on 714 healthy adult blood bank donors from Colombo, Sri Lanka, to characterize allele frequencies in support of studies on T cell immunity against pathogens, including Dengue virus. Deviations from Hardy Weinberg proportions were not detected at any locus. Several alleles were found in >30% of individuals, including the class II alleles DPB1 * 04:01, DPB1 * 02:01, DQB1 * 06:01 and DRB1 * 07:01, and the class I alleles A * 33:03 and A * 24:02. Genotype data will be available in the Allele Frequencies Net Database.