The frequency of DRB1*1454 in South German Caucasians

HLA-HD: An accurate HLA typing algorithm for next-generation sequencing data

The accurate typing of human leukocyte antigen (HLA) alleles is critical for a variety of medical applications, such as genomic studies of multifactorial diseases, including immune system and inflammation-related disorders, and donor selection in organ transplantation and regenerative medicine. Here, we developed a new algorithm for determining HLA alleles using next-generation sequencing (NGS) results. The method consists of constructing an extensive dictionary of HLA alleles, precise mapping of the NGS reads, and calculating a score based on weighted read counts to select the most suitable pair of alleles. The developed algorithm compares the score of all allele pairs, taking into account variation not only in the domain for antigen presentation (G-DOMAIN), but also outside this domain. Using this method, HLA alleles could be determined with 6-digit precision. We showed that our method was more accurate than other NGS-based methods and revealed limitations of the conventional HLA typing technologies. Furthermore, we determined the complete genomic sequence of an HLA-A-like-pseudogene when we assembled NGS reads that had caused arguable typing, and found its identity with HLA-Y*02:01. The accuracy of the HLA-A allele typing was improved after the HLA-Y*02:01 sequence was included in the HLA allele dictionary.

Performance of HLA allele prediction methods in African Americans for class II genes HLA-DRB1, -DQB1, and -DPB1

Background

The expense of human leukocyte antigen (HLA) allele genotyping has motivated the development of imputation methods that use dense single nucleotide polymorphism (SNP) genotype data and the region’s haplotype structure, but the performance of these methods in admixed populations (such as African Americans) has not been adequately evaluated. We compared genotype-based—derived from both genome-wide genotyping and targeted sequencing—imputation results to existing allele data for HLA–DRB1, −DQB1, and –DPB1.

Results

In European Americans, the newly-developed HLA Genotype Imputation with Attribute Bagging (HIBAG) method outperformed HLA*IMP:02. In African Americans, HLA*IMP:02 performed marginally better than HIBAG pre-built models, but HIBAG models constructed using a portion of our African American sample with both SNP genotyping and four-digit HLA class II allele typing had consistently higher accuracy than HLA*IMP:02. However, HIBAG was significantly less accurate in individuals heterozygous for local ancestry (p ≤0.04). Accuracy improved in models with equal numbers of African and European chromosomes. Variants added by targeted sequencing and SNP imputation further improved both imputation accuracy and the proportion of high quality calls.

Conclusion

Combining the HIBAG approach with local ancestry and dense variant data can produce highly-accurate HLA class II allele imputation in African Americans.

Strategies to work with HLA data in human populations for histocompatibility, clinical transplantation, epidemiology and population genetics: HLA-NET methodological recommendations

HLA-NET (a European COST Action) aims at networking researchers working in bone marrow transplantation, epidemiology and population genetics to improve the molecular characterization of the HLA genetic diversity of human populations, with an expected strong impact on both public health and fundamental research. Such improvements involve finding consensual strategies to characterize human populations and samples and report HLA molecular typings and ambiguities; proposing user-friendly access to databases and computer tools and defining minimal requirements related to ethical aspects. The overall outcome is the provision of population genetic characterizations and comparisons in a standard way by all interested laboratories. This article reports the recommendations of four working groups (WG1-4) of the HLA-NET network at the mid-term of its activities. WG1 (Population definitions and sampling strategies for population genetics’ analyses) recommends avoiding outdated racial classifications and population names (e.g. ‘Caucasian’) and using instead geographic and/or cultural (e.g. linguistic) criteria to describe human populations (e.g. ‘pan-European’). A standard ‘HLA-NET POPULATION DATA QUESTIONNAIRE’ has been finalized and is available for the whole HLA community. WG2 (HLA typing standards for population genetics analyses) recommends retaining maximal information when reporting HLA typing results. Rather than using the National Marrow Donor Program coding system, all ambiguities should be provided by listing all allele pairs required to explain each genotype, according to the formats proposed in ‘HLA-NET GUIDELINES FOR REPORTING HLA TYPINGS’. The group also suggests taking into account a preliminary list of alleles defined by polymorphisms outside the peptide-binding sites that may affect population genetic statistics because of significant frequencies. WG3 (Bioinformatic strategies for HLA population data storage and analysis) recommends the use of programs capable of dealing with ambiguous data, such as the ‘gene[rate]’ computer tools to estimate frequencies, test for Hardy–Weinberg equilibrium and selective neutrality on data containing any number and kind of ambiguities. WG4 (Ethical issues) proposes to adopt thorough general principles for any HLA population study to ensure that it conforms to (inter)national legislation or recommendations/guidelines. All HLA-NET guidelines and tools are available through its website http://hla-net.eu.

A community standard for immunogenomic data reporting and analysis: proposal for a STrengthening the REporting of Immunogenomic Studies statement

Modern high-throughput HLA and KIR typing technologies are generating a wealth of immunogenomic data with the potential to revolutionize the fields of histocompatibility and immune-related disease association and population genetic research, much as SNP-based approaches have revolutionized association research. The STrengthening the REporting of Genetic Association studies (STREGA) statement provides community-based data reporting and analysis standards for genomic disease-association studies, identifying specific areas in which adoption of reporting guidelines can improve the consistent interpretation of genetic studies. While aspects of STREGA can be applied to immunogenomic studies, HLA and KIR research requires additional consideration, as the high levels of polymorphism associated with immunogenomic data pose unique methodological and computational challenges to the synthesis of information across datasets. Here, we outline the principle challenges to consistency in immunogenomic studies, and propose that an immunogenomic-specific analog to the STREGA statement, a STrengthening the REporting of Immunogenomic Studies (STREIS) statement, be developed as part of the 16th International HLA and Immunogenetics Workshop. We propose that STREIS extends at least four of the 22 elements of the STREGA statement to specifically address issues pertinent to immunogenomic data: HLA and KIR nomenclature, data-validation, ambiguity resolution, and the analysis of highly polymorphic genetic systems. As with the STREGA guidelines, the intent behind STREIS is not to dictate the design of immunogenomic studies, but to ensure consistent and transparent reporting of research, facilitating the synthesis of HLA and KIR data across studies.