scholarly journals SweHLA: the high confidence HLA typing bio-resource drawn from 1 000 Swedish genomes

2019 ◽  
Author(s):  
Jessika Nordin ◽  
Adam Ameur ◽  
Kerstin Lindblad-Toh ◽  
Ulf Gyllensten ◽  
Jennifer R.S. Meadows

AbstractThere is a need to accurately call human leukocyte antigen (HLA) genes from existing short-read sequencing data, however there is no single solution that matches the gold standard of lab typing. Here we aimed to combine results from available software, minimising the biases of applied algorithm and HLA reference. The result is a robust HLA population resource for the published 1 000 Swedish genomes, and a framework for future HLA interrogation. HLA 2-field alleles were called using four imputation and inference methods for the classical eight genes (class I: HLA-A, -B, -C; class II: HLA-DPA1, -DPB1, -DQA1, -DQB1, -DRB1). A high confidence population set (SweHLA) was determined using an n-1 concordance rule for class I (four software) and class II (three software) alleles. Results were compared across populations and individual programs benchmarked to SweHLA. Per allele, 875 to 988 of the 1 000 samples were genotyped in SweHLA; 920 samples had at least seven loci. While a small fraction of reference alleles were common to all software (class I=1.9% and class II=4.1%), this did not affect the overall call rate. Gene-level concordance was high compared to European populations (>0.83%), with COX and PGF the dominant SweHLA haplotypes. We noted that 15/18 discordant alleles (delta allele frequency > 2) were previously reported as disease-associated. These differences could in part explain across-study genetic replication failures, reinforcing the need to use multiple software. SweHLA demonstrates a way to use existing NGS data to generate a population resource agnostic to individual HLA software biases.

2019 ◽  
Vol 28 (5) ◽  
pp. 627-635 ◽  
Author(s):  
Jessika Nordin ◽  
Adam Ameur ◽  
Kerstin Lindblad-Toh ◽  
Ulf Gyllensten ◽  
Jennifer R. S. Meadows

AbstractThere is a need to accurately call human leukocyte antigen (HLA) genes from existing short-read sequencing data, however there is no single solution that matches the gold standard of Sanger sequenced lab typing. Here we aimed to combine results from available software programs, minimizing the biases of applied algorithm and HLA reference. The result is a robust HLA population resource for the published 1000 Swedish genomes, and a framework for future HLA interrogation. HLA 2nd-field alleles were called using four imputation and inference methods for the classical eight genes (class I: HLA-A, HLA-B, HLA-C; class II: HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1). A high confidence population set (SweHLA) was determined using an n−1 concordance rule for class I (four software) and class II (three software) alleles. Results were compared across populations and individual programs benchmarked to SweHLA. Per gene, 875 to 988 of the 1000 samples were genotyped in SweHLA; 920 samples had at least seven loci called. While a small fraction of reference alleles were common to all software (class I = 1.9% and class II = 4.1%), this did not affect the overall call rate. Gene-level concordance was high compared to European populations (>0.83%), with COX and PGF the dominant SweHLA haplotypes. We noted that 15/18 discordant alleles (delta allele frequency >2) were previously reported as disease-associated. These differences could in part explain across-study genetic replication failures, reinforcing the need to use multiple software solutions. SweHLA demonstrates a way to use existing NGS data to generate a population resource agnostic to individual HLA software biases.


Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 907-907
Author(s):  
Stefan O. Ciurea ◽  
Rima M. Saliba ◽  
Gabriela Rondon ◽  
Poliana A. Patah ◽  
Fleur Aung ◽  
...  

Abstract Abstract 907 Most candidates for hematopoietic stem cell transplantation lack a human leukocyte antigen (HLA)-identical sibling donor; however, many patients may have a related donor with whom they are mismatched at one antigen/allele. It is not known whether such a match is preferable to a matched unrelated donor (MUD). We hypothesized that, in transplantation using related donors, adding a single HLA antigen/allele mismatch, identified through high resolution HLA typing at HLA-A, -B, -C, -DRB1 and -DQB1, would be associated with worse outcomes than transplantation using matched unrelated donors. Patients and Methods: To test this hypothesis, we analyzed outcomes (survival, relapse, non-relapse mortality) of 367 patients who received transplants from either a 10/10 MUD (n=318) or a one-antigen/allele mismatched related donor (MRD) by 7/8 HLA typing (n=49) treated during the same period of time (1995-2009) at our institution. All patients had intermediate/high-resolution HLA typing at all 5 loci either prospectively or retrospectively, if treated after or before year 2002. Of the 49 patients treated with mismatched related donors, 28 patients (57%) had one antigen/allele mismatched at HLA class I or II loci (or 9/10), 18 patients (37%) had 2 alleles mismatched (or 8/10), and 3 patients (6%) had 3 alleles mismatched (or 7/10). From the 28 patients with a one-allele mismatch, 24 had class I mismatches at either HLA-A or -B loci, and 4 had class II mismatches at either HLA-DR or -DQ loci. Characteristics between the MUD group and 9/10 MRD group were similar [median age 53 vs. 47 years (p=0.08); AML/MDS diagnosis 84% vs. 82% (p=0.5); active disease at transplant 59% vs. 57% (p=0.9); myeloablatie conditioning 63% vs. 75% (p=0.2); bone marrow stem cells 58% vs. 70% (p=0.2); pentostatin use 14% vs. 11% (p=0.4); median year of transplant 2006 vs. 2004, respectively] except more patients in the MUD group received ATG (96% vs. 68%, p=0.02). Results: Outcomes at 3-years were analyzed for the 28 consecutive patients who had received a transplant from a 9/10 MRD based on 5-loci (including -DQB1) HLA typing. Graft failure was more common in patients treated from 9/10 related donors than from MUD. The incidences of primary and secondary graft failure for the 9/10 MRD were 7% and 14%, respectively, whereas none of the MUD transplant recipients had either primary or secondary graft failure (p= 0.02). Cumulative incidence of progression was 40% vs. 25% (p=0.02, HR 1.9, CI 1.1–3.9), non-relapse mortality 40% vs. 26% (p=0.05, HR 1.9, CI 1.0–3.6) and grade II-IV a GVHD was 27% vs. 38% (p=0.4, HR 0.7, CI 0.3–2.5) for the two groups, respectively. Median survival was 6 months for the 9/10 MRD vs. 18 months for the MUD group. The overall survival and progression-free survival rates were 19% and 45% (p=0.007, HR 1.8, CI 1.2–2.9) and 19% vs. 42% (p=0.006, HR1.8, CI 1.2–2.9), respectively. Outcomes for 9/10 MRD transplant patients with class I mismatches (n=24) were significantly worse than outcomes in those with MUD transplants (n=318). The 2-year actuarial OS rate was 27% for the 9/10 MRD and 48% for the MUD transplant group (HR 1.9; 95% CI 1.1 – 3.1; p=0.01). Conclusion: These results indicate that transplant outcomes for patients treated from a one-antigen/allele mismatch related donor are significantly worse than from a MUD, primarily due to increased non-relapse mortality. Patients receiving transplants form a 9/10 related donors, at least with a class I mismatch, should be treated on investigational protocols. Disclosures: No relevant conflicts of interest to declare.


2008 ◽  
Vol 197 (3) ◽  
pp. 474-478 ◽  
Author(s):  
Beth D. Kirkpatrick ◽  
Rashidul Haque ◽  
Priya Duggal ◽  
Dinesh Mondal ◽  
Cathy Larsson ◽  
...  

2017 ◽  
Author(s):  
Antti Larjo ◽  
Robert Eveleigh ◽  
Elina Kilpeläinen ◽  
Tony Kwan ◽  
Tomi Pastinen ◽  
...  

AbstractThe human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing on the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as many HLA alleles are major genetic factors that confer susceptibility to autoimmune diseases and is important for the matching of HLA alleles in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next generation sequencing (NGS) data. As genome screens are becoming increasingly routine in research, we wanted to test how well HLA alleles can be deduced from genome screens not designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using the ImmunoSEQ platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from the 1000 Genomes collection. When used with the default settings none of the methods gave perfect results for all the genes and samples. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. Most of the algorithms did not perform very well for the exome-only data. The results indicate that the use of these algorithms for accurate HLA allele determination based on NGS data is not straightforward.


2018 ◽  
Author(s):  
Rose Orenbuch ◽  
Ioan Filip ◽  
Devon Comito ◽  
Jeffrey Shaman ◽  
Itsik Pe'er ◽  
...  

Human leukocyte antigen (HLA) locus makes up the major compatibility complex (MHC) and plays a critical role in host response to disease, including cancers and autoimmune disorders. In the clinical setting, HLA typing is necessary for determining tissue compatibility. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between the HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional splicing and bias due to amplification. Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two field precision for MHC class I genes, and over 99.7% for MHC class II. Importantly, arcasHLA takes as its input pre-aligned BAM files, and outputs three-field resolution for all HLA genes in less than 2 minutes. Finally, we discuss evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. arcasHLA is available at https://github.com/RabadanLab/arcasHLA.


2020 ◽  
Vol 94 (13) ◽  
Author(s):  
Austin Nguyen ◽  
Julianne K. David ◽  
Sean K. Maden ◽  
Mary A. Wood ◽  
Benjamin R. Weeder ◽  
...  

ABSTRACT Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen A [HLA-A], -B, and -C genes) may affect susceptibility to and severity of the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). We performed a comprehensive in silico analysis of viral peptide-MHC class I binding affinity across 145 HLA-A, -B, and -C genotypes for all SARS-CoV-2 peptides. We further explored the potential for cross-protective immunity conferred by prior exposure to four common human coronaviruses. The SARS-CoV-2 proteome was successfully sampled and was represented by a diversity of HLA alleles. However, we found that HLA-B*46:01 had the fewest predicted binding peptides for SARS-CoV-2, suggesting that individuals with this allele may be particularly vulnerable to COVID-19, as they were previously shown to be for SARS (M. Lin, H.-T. Tseng, J. A. Trejaut, H.-L. Lee, et al., BMC Med Genet 4:9, 2003, https://bmcmedgenet.biomedcentral.com/articles/10.1186/1471-2350-4-9). Conversely, we found that HLA-B*15:03 showed the greatest capacity to present highly conserved SARS-CoV-2 peptides that are shared among common human coronaviruses, suggesting that it could enable cross-protective T-cell-based immunity. Finally, we reported global distributions of HLA types with potential epidemiological ramifications in the setting of the current pandemic. IMPORTANCE Individual genetic variation may help to explain different immune responses to a virus across a population. In particular, understanding how variation in HLA may affect the course of COVID-19 could help identify individuals at higher risk from the disease. HLA typing can be fast and inexpensive. Pairing HLA typing with COVID-19 testing where feasible could improve assessment of severity of viral disease in the population. Following the development of a vaccine against SARS-CoV-2, the virus that causes COVID-19, individuals with high-risk HLA types could be prioritized for vaccination.


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