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

A multi-ethnic reference panel to impute HLA classical and non-classical class I alleles in admixed samples: Testing imputation accuracy in an admixed sample from Brazil

The MHC class I region contains crucial genes for the innate and adaptive immune response, playing a key role in susceptibility to many autoimmune and infectious diseases. Genome-wide association studies have identified numerous disease-associated SNPs within this region. However, these associations do not fully capture the immune-biological relevance of specific HLA alleles. HLA imputation techniques may leverage available SNP arrays by predicting allele genotypes based on the linkage disequilibrium between SNPs and specific HLA alleles. Successful imputation requires diverse and large reference panels, especially for admixed populations. This study employed a bioinformatics approach to call SNPs and HLA alleles in multi-ethnic samples from the 1000 genomes (1KG) dataset and admixed individuals from Brazil (SABE), utilising 30X whole-genome sequencing data. Using HIBAG, we created three reference panels: 1KG (n = 2504), SABE (n = 1171), and the full model (n = 3675) encompassing all samples. In extensive cross-validation of these reference panels, the multi-ethnic 1KG reference exhibited overall superior performance than the reference with only Brazilian samples. However, the best results were achieved with the full model. Additionally, we expanded the scope of imputation by developing reference panels for non-classical, MICA, MICB and HLA-H genes, previously unavailable for multi-ethnic populations. Validation in an independent Brazilian dataset showcased the superiority of our reference panels over the Michigan Imputation Server, particularly in predicting HLA-B alleles among Brazilians. Our investigations underscored the need to enhance or adapt reference panels to encompass the target population's genetic diversity, emphasising the significance of multiethnic references for accurate imputation across different populations.

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.

18th International HLA and Immunogenetics Workshop: Report on the SNP-HLA Reference Consortium (SHLARC) component

The SNP-HLA Reference Consortium (SHLARC), a component of the 18th International HLA and Immunogenetics Workshop, is aimed at collecting diverse and extensive human leukocyte antigen (HLA) data to create custom reference panels and enhance HLA imputation techniques. Genome-wide association studies (GWAS) have significantly contributed to identifying genetic associations with various diseases. The HLA genomic region has emerged as the top locus in GWAS, particularly in immune-related disorders. However, the limited information provided by single nucleotide polymorphisms (SNPs), the hallmark of GWAS, poses challenges, especially in the HLA region, where strong linkage disequilibrium (LD) spans several megabases. HLA imputation techniques have been developed using statistical inference in response to these challenges. These techniques enable the prediction of HLA alleles from genotyped GWAS SNPs. Here we present the SHLARC activities, a collaborative effort to create extensive, and multi-ethnic reference panels to enhance HLA imputation accuracy.

Imputation-Based HLA Typing with GWAS SNPs

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the haplotype structure within the major histocompatibility complex (MHC) region. These methods predict HLA classical alleles using dense SNP genotypes, commonly found on array-based platforms used in genome-wide association studies (GWAS). The analysis of HLA classical alleles can be conducted on current SNP datasets at no additional cost. Here, we describe the workflow of HIBAG, an imputation method with attribute bagging, to infer a sample's HLA classical alleles using SNP data. Two examples are offered to demonstrate the functionality using public HLA and SNP data from the latest release of the 1000 Genomes project: genotype imputation using pre-built classifiers in a GWAS, and model training to create a new prediction model. The GPU implementation facilitates model building, making it hundreds of times faster compared to the single-threaded implementation.

Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS.

Whole genome sequencing (WGS) data on non-European and admixed individuals remains scarce. Here, the authors analyse WGS data from 1,171 admixed elderly Brazilians from a census cohort, characterising population-specific genetic variation and exploring the clinical utility of this expanded dataset.

Frequency and origin of the c.2090T>G p.(Leu697Trp) MYO3A variant associated with autosomal dominant hearing loss

We recently described a novel missense variant [c.2090T>G:p.(Leu697Trp)] in the MYO3A gene, found in two Brazilian families with late-onset autosomal dominant nonsyndromic hearing loss (ADNSHL). Since then, with the objective of evaluating its contribution to ADNSHL in Brazil, the variant was screened in additional 101 pedigrees with probable ADNSHL without conclusive molecular diagnosis. The variant was found in three additional families, explaining 3/101 (~3%) of cases with ADNSHL in our Brazilian pedigree collection. In order to identify the origin of the variant, 21 individuals from the five families were genotyped with a high-density SNP array (~600 K SNPs- Axiom Human Origins; ThermoFisher). The identity by descent (IBD) approach revealed that many pairs of individuals from the different families have a kinship coefficient equivalent to that of second cousins, and all share a minimum haplotype of ~607 kb which includes the c.2090T>G variant suggesting it probably arose in a common ancestor. We inferred that the mutation occurred in a chromosomal segment of European ancestry and the time since the most common ancestor was estimated in 1100 years (CI = 775-1425). This variant was also reported in a Dutch family, which shares a 87,121 bp haplotype with the Brazilian samples, suggesting that Dutch colonists may have brought it to Northeastern Brazil in the 17th century. Therefore, the present study opens new avenues to investigate this variant not only in Brazilians but also in European families with ADNSHL.

Admixture mapping reveals evidence of differential multiple sclerosis risk by genetic ancestry

Multiple sclerosis (MS) is an autoimmune disease with high prevalence among populations of northern European ancestry. Past studies have shown that exposure to ultraviolet radiation could explain the difference in MS prevalence across the globe. In this study, we investigate whether the difference in MS prevalence could be explained by European genetic risk factors. We characterized the ancestry of MS-associated alleles using RFMix, a conditional random field parameterized by random forests, to estimate their local ancestry in the largest assembled admixed population to date, with 3,692 African Americans, 4,915 Asian Americans, and 3,777 Hispanics. The majority of MS-associated human leukocyte antigen (HLA) alleles, including the prominent HLA-DRB1*15:01 risk allele, exhibited cosmopolitan ancestry. Ancestry-specific MS-associated HLA alleles were also identified. Analysis of the HLA-DRB1*15:01 risk allele in African Americans revealed that alleles on the European haplotype conferred three times the disease risk compared to those on the African haplotype. Furthermore, we found evidence that the European and African HLA-DRB1*15:01 alleles exhibit single nucleotide polymorphism (SNP) differences in regions encoding the HLA-DRB1 antigen-binding heterodimer. Additional evidence for increased risk of MS conferred by the European haplotype were found for HLA-B*07:02 and HLA-A*03:01 in African Americans. Most of the 200 non-HLA MS SNPs previously established in European populations were not significantly associated with MS in admixed populations, nor were they ancestrally more European in cases compared to controls. Lastly, a genome-wide search of association between European ancestry and MS revealed a region of interest close to the ZNF596 gene on chromosome 8 in Hispanics; cases had a significantly higher proportion of European ancestry compared to controls. In conclusion, our study established that the genetic ancestry of MS-associated alleles is complex and implicated that difference in MS prevalence could be explained by the ancestry of MS-associated alleles.

Characterization of a novel MYO3A missense mutation associated with a dominant form of late onset hearing loss

Whole-exome sequencing of samples from affected members of two unrelated families with late-onset non-syndromic hearing loss revealed a novel mutation (c.2090 T > G; NM_017433) in MYO3A. The mutation was confirmed in 36 affected individuals, showing autosomal dominant inheritance. The mutation alters a single residue (L697W or p.Leu697Trp) in the motor domain of the stereocilia protein MYO3A, leading to a reduction in ATPase activity, motility, and an increase in actin affinity. MYO3A-L697W showed reduced filopodial actin protrusion initiation in COS7 cells, and a predominant tipward accumulation at filopodia and stereocilia when coexpressed with wild-type MYO3A and espin-1, an actin-regulatory MYO3A cargo. The combined higher actin affinity and duty ratio of the mutant myosin cause increased retention time at stereocilia tips, resulting in the displacement of the wild-type MYO3A protein, which may impact cargo transport, stereocilia length, and mechanotransduction. The dominant negative effect of the altered myosin function explains the dominant inheritance of deafness.

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.

HLA typing using genome wide data reveals susceptibility types for infections in a psychiatric disease enriched sample

BACKGROUND

The infections Toxoplasma gondii (T. gondii), cytomegalovirus, and Herpes Simplex Virus Type 1 (HSV1) are common persistent infections that have been associated with schizophrenia and bipolar disorder. The major histocompatibility complex (MHC, termed HLA in humans) region has been implicated in these infections and these mental illnesses. The interplay of MHC genetics, mental illness, and infection has not been systematically examined in previous research.

METHODS

In a cohort of 1636 individuals, we used genome-wide association data to impute 7 HLA types (A, B, C, DRB1, DQA1, DQB1, DPB1), and combined this data with serology data for these infections. We used regression analysis to assess the association between HLA alleles, infections (individually and collectively), and mental disorder status (schizophrenia, bipolar disorder, controls).

RESULTS

After Bonferroni correction for multiple comparisons, HLA C∗07:01 was associated with increased HSV1 infection among mentally healthy controls (OR 3.4, p = 0.0007) but not in the schizophrenia or bipolar groups (P > 0.05). For the multiple infection outcome, HLA B∗ 38:01 and HLA C∗12:03 were protective in the healthy controls (OR ≈ 0.4) but did not have a statistically-significant effect in the schizophrenia or bipolar groups. T. gondii had several nominally-significant positive associations, including the haplotypes HLA DRB∗03:01 ∼ HLA DQA∗05:01 ∼ HLA DQB∗02:01 and HLA B∗08:01 ∼ HLA C∗07:01.

CONCLUSIONS

We identified HLA types that showed strong and significant associations with neurotropic infections. Since some of these associations depended on mental illness status, the engagement of HLA-related pathways may be altered in schizophrenia due to immunogenetic differences or exposure history.

Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate

The analysis of genomic data (~400,000 autosomal SNPs) enabled the reliable estimation of inbreeding levels in a sample of 541 individuals sampled from a highly admixed Brazilian population isolate (an African-derived quilombo in the State of São Paulo). To achieve this, different methods were applied to the joint information of two sets of markers (one complete and another excluding loci in patent linkage disequilibrium). This strategy allowed the detection and exclusion of markers that biased the estimation of the average population inbreeding coefficient (Wright's fixation index FIS), which value was eventually estimated as around 1% using any of the methods we applied. Quilombo demographic inferences were made by analyzing the structure of runs of homozygosity (ROH), which were adapted to cope with a highly admixed population with a complex foundation history. Our results suggest that the amount of ROH <2Mb of admixed populations should be somehow proportional to the genetic contribution from each parental population.

A genomic perspective on HLA evolution

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

Imputation-Based HLA Typing with SNPs in GWAS Studies

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the extended haplotype structure within the major histocompatibility complex (MHC) to predict classical HLA alleles using dense SNP genotypes, such as those available on chip panels of genome-wide association study (GWAS). These methods enable HLA analyses of classical alleles on existing SNP datasets genotyped in GWAS studies at no extra cost. Here, I describe the workflow of HIBAG, an imputation method with attribute bagging, for obtaining a sample's HLA class I and II genotypes of two-field resolution using SNP data. Two examples are provided to illustrate with a publicly available HLA and SNP dataset: genotype imputation with pre-fit classifiers in GWAS, and model training to build a new classifier.

Comparison of HLA allelic imputation programs

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

Increasing The Genetic Admixture of Available Lines of Human Pluripotent Stem Cells

Human pluripotent stem cells (hPSCs) may significantly improve drug development pipeline, serving as an in vitro system for the identification of novel leads, and for testing drug toxicity. Furthermore, these cells may be used to address the issue of differential drug response, a phenomenon greatly influenced by genetic factors. This application depends on the availability of hPSC lines from populations with diverse ancestries. So far, it has been reported that most lines of hPSCs derived worldwide are of European or East Asian ancestries. We have established 23 lines of hPSCs from Brazilian individuals, and we report the analysis of their genomic ancestry. We show that embryo-derived PSCs are mostly of European descent, while induced PSCs derived from participants of a national-wide Brazilian cohort study present high levels of admixed European, African and Native American genomic ancestry. Additionally, we use high density SNP data and estimate local ancestries, particularly those of CYP genes loci. Such information will be of key importance when interpreting variation among cell lines with respect to cellular phenotypes of interest. The availability of genetically admixed lines of hPSCs will be of relevance when setting up future in vitro studies of drug response.

A web resource for mining HLA associations with adverse drug reactions: HLA-ADR

Human leukocyte antigens (HLA) are an important family of genes involved in the immune system. Their primary function is to allow the host immune system to be able to distinguish between self and non-self peptides—e.g. derived from invading pathogens. However, these genes have also been implicated in immune-mediated adverse drug reactions (ADRs), presenting a problem to patients, clinicians and pharmaceutical companies. We have previously developed the Allele Frequency Net Database (AFND) that captures the allelic and haplotype frequencies for these HLA genes across many healthy populations from around the world. Here, we report the development and release of the HLA-ADR database that captures data from publications where HLA alleles and haplotypes have been associated with ADRs (e.g. Stevens–Johnson Syndrome/toxic epidermal necrolysis and drug-induced liver injury). HLA-ADR was created by using data obtained through systematic review of the literature and semi-automated literature mining. The database also draws on data already present in AFND allowing users to compare and analyze allele frequencies in both ADR patients and healthy populations. The HLA-ADR database provides clinicians and researchers with a centralized resource from which to investigate immune-mediated ADRs.

Database URL: http://www.allelefrequencies.net/hla-adr/.