In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales.
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ABSTRACT:
SUBMITTER: Chen J
PROVIDER: S-EPMC8138874 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true.In this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test.Our algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.
| S-EPMC3024871 | biostudies-other
Project description:The goals of the KIR component of the 17th International HLA and Immunogenetics Workshop (IHIW) were to encourage and educate researchers to begin analyzing KIR at allelic resolution, and to survey the nature and extent of KIR allelic diversity across human populations. To represent worldwide diversity, we analyzed 1269 individuals from ten populations, focusing on the most polymorphic KIR genes, which express receptors having three immunoglobulin (Ig)-like domains (KIR3DL1/S1, KIR3DL2 and KIR3DL3). We identified 13 novel alleles of KIR3DL1/S1, 13 of KIR3DL2 and 18 of KIR3DL3. Previously identified alleles, corresponding to 33 alleles of KIR3DL1/S1, 38 of KIR3DL2, and 43 of KIR3DL3, represented over 90% of the observed allele frequencies for these genes. In total we observed 37 KIR3DL1/S1 allotypes, 40 for KIR3DL2 and 44 for KIR3DL3. As KIR allotype diversity can affect NK cell function, this demonstrates potential for high functional diversity worldwide. Allelic variation further diversifies KIR haplotypes. We determined KIR3DL3???KIR3DL1/S1???KIR3DL2 haplotypes from five of the studied populations, and observed multiple population-specific haplotypes in each. This included 234 distinct haplotypes in European Americans, 191 in Ugandans, 35 in Papuans, 95 in Egyptians and 86 in Spanish populations. For another 35 populations, encompassing 642,105 individuals we focused on KIR3DL2 and identified another 375 novel alleles, with approximately half of them observed in more than one individual. The KIR allelic level data gathered from this project represents the most comprehensive summary of global KIR allelic diversity to date, and continued analysis will improve understanding of KIR allelic polymorphism in global populations. Further, the wealth of new data gathered in the course of this workshop component highlights the value of collaborative, community-based efforts in immunogenetics research, exemplified by the IHIW.
| S-EPMC6322681 | biostudies-literature
Project description:Killer cell immunoglobulin like receptor (KIR) genes and human leukocyte antigen (HLA) genes play important roles in innate and adaptive immunity. They are highly polymorphic and cannot be genotyped with standard variant calling pipelines. Compared with HLA genes, many KIR genes are similar to each other in sequences and may be absent in the chromosomes. Therefore, although many tools have been developed to genotype HLA genes using common sequencing data, none of them work for KIR genes. Even specialized KIR genotypers could not resolve all the KIR genes. Here we describe T1K, a novel computational method for the efficient and accurate inference of KIR or HLA alleles from RNA-seq, whole-genome sequencing, or whole-exome sequencing data. T1K jointly considers alleles across all genotyped genes, so it can reliably identify present genes and distinguish homologous genes, including the challenging KIR2DL5A/KIR2DL5B genes. This model also benefits HLA genotyping, where T1K achieves high accuracy in benchmarks. Moreover, T1K can call novel single-nucleotide variants and process single-cell data. Applying T1K to tumor single-cell RNA-seq data, we found that KIR2DL4 expression was enriched in tumor-specific CD8+ T cells. T1K may open the opportunity for HLA and KIR genotyping across various sequencing applications.
| S-EPMC10519407 | biostudies-literature
Project description:The killer-cell immunoglobulin-like receptor (KIR) proteins evolve to fight viruses and mediate the body's reaction to pregnancy. These roles provide selection pressure for variation at both the structural/haplotype and base/allele levels. At the same time, the genes have evolved relatively recently by tandem duplication and therefore exhibit very high sequence similarity over thousands of bases. These variation-homology patterns make it impossible to interpret KIR haplotypes from abundant short-read genome sequencing data at population scale using existing methods. Here, we developed an efficient computational approach for in silico KIR probe interpretation (KPI) to accurately interpret individual's KIR genes and haplotype-pairs from KIR sequencing reads. We designed synthetic 25-base sequence probes by analyzing previously reported haplotype sequences, and we developed a bioinformatics pipeline to interpret the probes in the context of 16 KIR genes and 16 haplotype structures. We demonstrated its accuracy on a synthetic data set as well as a real whole genome sequences from 748 individuals from The Genome of the Netherlands (GoNL). The GoNL predictions were compared with predictions from SNP-based predictions. Our results show 100% accuracy rate for the synthetic tests and a 99.6% family-consistency rate in the GoNL tests. Agreement with the SNP-based calls on KIR genes ranges from 72%-100% with a mean of 92%; most differences occur in genes KIR2DS2, KIR2DL2, KIR2DS3, and KIR2DL5 where KPI predicts presence and the SNP-based interpretation predicts absence. Overall, the evidence suggests that KPI's accuracy is 97% or greater for both KIR gene and haplotype-pair predictions, and the presence/absence genotyping leads to ambiguous haplotype-pair predictions with 16 reference KIR haplotype structures. KPI is free, open, and easily executable as a Nextflow workflow supported by a Docker environment at https://github.com/droeatumn/kpi.
| S-EPMC7727328 | biostudies-literature
Project description:This SuperSeries is composed of the following subset Series: GSE27869: Gene expression profiles of 400 siRNA knocked down on HUVEC GSE27870: The effect of TNFa on endothelial cells Refer to individual Series
2011-11-29 | E-GEOD-27871 | biostudies-arrayexpress
Project description:BackgroundCervical cancer is the fourth most common cancer in women, and we recently reported human leukocyte antigen (HLA) alleles showing strong associations with cervical neoplasia risk and protection. HLA ligands are recognized by killer immunoglobulin-like receptors (KIRs) expressed on a range of immune cell subsets, governing their proinflammatory activity. We hypothesized that the inheritance of particular HLA-KIR combinations would increase cervical neoplasia risk.MethodsHere, we used HLA and KIR dosages imputed from single-nucleotide polymorphism genotype data from 2143 cervical neoplasia cases and 13858 healthy controls of European decent.ResultsThe following 4 novel HLA alleles were identified in association with cervical neoplasia, owing to their linkage disequilibrium with known cervical neoplasia-associated HLA-DRB1 alleles: HLA-DRB3*9901 (odds ratio [OR], 1.24; P = 2.49 × 10-9), HLA-DRB5*0101 (OR, 1.29; P = 2.26 × 10-8), HLA-DRB5*9901 (OR, 0.77; P = 1.90 × 10-9), and HLA-DRB3*0301 (OR, 0.63; P = 4.06 × 10-5). We also found that homozygosity of HLA-C1 group alleles is a protective factor for human papillomavirus type 16 (HPV16)-related cervical neoplasia (C1/C1; OR, 0.79; P = .005). This protective association was restricted to carriers of either KIR2DL2 (OR, 0.67; P = .00045) or KIR2DS2 (OR, 0.69; P = .0006).ConclusionsOur findings suggest that HLA-C1 group alleles play a role in protecting against HPV16-related cervical neoplasia, mainly through a KIR-mediated mechanism.
| S-EPMC6217726 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2011-11-29 | GSE27871 | GEO
Project description:Within-host genetic diversity and large transmission bottlenecks confound phylodynamic inference of epidemiological dynamics. Conventional phylodynamic approaches assume that nodes in a time-scaled pathogen phylogeny correspond closely to the time of transmission between hosts that are ancestral to the sample. However, when hosts harbor diverse pathogen populations, node times can substantially pre-date infection times. Imperfect bottlenecks can cause lineages sampled in different individuals to coalesce in unexpected patterns. To address realistic violations of standard phylodynamic assumptions we developed a new inference approach based on a multi-scale coalescent model, accounting for nonlinear epidemiological dynamics, heterogeneous sampling through time, non-negligible genetic diversity of pathogens within hosts, and imperfect transmission bottlenecks. We apply this method to HIV-1 and Ebola virus (EBOV) outbreak sequence data, illustrating how and when conventional phylodynamic inference may give misleading results. Within-host diversity of HIV-1 causes substantial upwards bias in the number of infected hosts using conventional coalescent models, but estimates using the multi-scale model have greater consistency with reported number of diagnoses through time. In contrast, we find that within-host diversity of EBOV has little influence on estimated numbers of infected hosts or reproduction numbers, and estimates are highly consistent with the reported number of diagnoses through time. The multi-scale coalescent also enables estimation of within-host effective population size using single sequences from a random sample of patients. We find within-host population genetic diversity of HIV-1 p17 to be 2Nμ=0.012 (95% CI 0.0066-0.023), which is lower than estimates based on HIV envelope serial sequencing of individual patients.
| S-EPMC5400386 | biostudies-literature
Project description:The 17th International HLA and Immunogenetics Workshop (IHIW) organizers conducted a Pilot Study (PS) in which 13 laboratories (15 groups) participated to assess the performance of the various sequencing library preparation protocols, NGS platforms and software in use prior to the workshop. The organizers sent 50 cell lines to each of the 15 groups, scored the 15 independently generated sets of NGS HLA genotyping data, and generated "consensus" HLA genotypes for each of the 50 cell lines. Proficiency Testing (PT) was subsequently organized using four sets of 24 cell lines, selected from 48 of 50 PS cell lines, to validate the quality of NGS HLA typing data from the 34 participating IHIW laboratories. Completion of the PT program with a minimum score of 95% concordance at the HLA-A, HLA-B, HLA-C, HLA-DRB1 and HLA-DQB1 loci satisfied the requirements to submit NGS HLA typing data for the 17th IHIW projects. Together, these PS and PT efforts constituted the 17th IHIW Quality Control project. Overall PT concordance rates for HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1, HLA-DRB3, HLA-DRB4 and HLA-DRB5 were 98.1%, 97.0% and 98.1%, 99.0%, 98.6%, 98.8%, 97.6%, 96.0%, 99.1%, 90.0% and 91.7%, respectively. Across all loci, the majority of the discordance was due to allele dropout. The high cost of NGS HLA genotyping per experiment likely prevented the retyping of initially failed HLA loci. Despite the high HLA genotype concordance rates of the software, there remains room for improvement in the assembly of more accurate consensus DNA sequences by NGS HLA genotyping software.
| S-EPMC6446570 | biostudies-literature
Project description:BackgroundClassic Kaposi's Sarcoma (cKS) is a rare vascular tumor associated with Human Herpesvirus 8 (KSHV) infection, nevertheless not all KSHV-infected individuals have cKS.ObjectiveWe investigated whether particular KIR/HLA receptor/ligand genotypes would be preferentially present in KSHV-infected and uninfected individuals who have or have not developed cKS.MethodsKIR/HLA genotypes were analyzed by molecular genotyping in 50 KSHV-infected individuals who did or did not have cKS and in 33 age-and sex-matched KSHV seronegative individuals.ResultsThere was no association of individual KIR, HLA or receptor ligand combinations with KSHV infection. However, activating KIR and KIR/HLA genotypes were significantly more frequent in cKS cases, specifically KIR3DS1, KIR2DS1, and KIR2DS1 with its HLA-C2 ligand.ConclusionA nonspecific inflammatory response triggered by activation of NK cells upon KIR-HLA interaction could be associated with the pathogenesis of KS.
| S-EPMC3379936 | biostudies-literature