scholarly journals Practical Use of Methods for Imputation of HLA Alleles from SNP Genotype Data

2016 ◽  
Author(s):  
Allan Motyer ◽  
Damjan Vukcevic ◽  
Alexander Dilthey ◽  
Peter Donnelly ◽  
Gil McVean ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Imputation Accuracy ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Genotype Data ◽  
Hla Alleles ◽  
Imputation Methods ◽  
Hla Genes ◽  
Disease Associations

AbstractThe human leukocyte antigen (HLA) genes play an essential role in immune function. Typing of HLA alleles is critical for transplantation and is informative for many disease associations. The high cost of accurate lab-based HLA typing has precluded its use in large-scale disease-association studies. The development of statistical methods to type alleles using linkage disequilibrium with nearby SNPs, called HLA imputation, has allowed large cohorts of individuals to be typed accurately, so that massive numbers of affected individuals and controls may be studied. This has resulted in many important findings. Several HLA imputation methods have been widely used, however their relative performance has not been adequately addressed. We have conducted a comprehensive study to evaluate the most widely used HLA imputation methods. We assembled a multi-ethnic panel of 10,561 individuals with SNP genotype data and lab-based typing of alleles at 11 HLA genes at two-field resolution, and used it to train and validate each method. Use of this panel leads to imputation accuracy far superior to what is currently publicly available. We present a highly-accurate new imputation method, HLA*IMP:03. We address the question of optimal use of HLA imputations in tests of genetic association, showing that it is usually not necessary to apply a probability threshold to achieve maximal power. We also investigated the effect on accuracy of SNP density and population stratification at the continental level and show that neither of these are a significant concern.

scholarly journals Accuracy of programs for the determination of HLA alleles from NGS data

2017 ◽  
Author(s):  
Antti Larjo ◽  
Robert Eveleigh ◽  
Elina Kilpeläinen ◽  
Tony Kwan ◽  
Tomi Pastinen ◽  
...  
Keyword(s):  
Human Leukocyte ◽  
Hla Typing ◽  
Ensemble Prediction ◽  
Hla Alleles ◽  
Leukocyte Antigen ◽  
Peptide Antigens ◽  
Hla Genes ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

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.

scholarly journals HLA Allele Imputation with Multitask Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Calvin Chi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Imputation Accuracy ◽  
Human Leukocyte ◽  
Genotype Imputation ◽  
Genotype Data ◽  
Hla Alleles ◽  
Learning Framework ◽  
Polymorphic Gene ◽  
Low Dimensional

The Human leukocyte antigen (HLA) system is a highly polymorphic gene complex encoding the major histocompatibility complex proteins in humans. HLA alleles are of strong epidemiological interest for their large effect sizes in associations with autoimmune diseases, infectious diseases, severe drug reactions, and transplant medicine. Since HLA genotyping can be time-consuming and cost-prohibitive, methods to impute HLA alleles from SNP genotype data have been developed, including HLA Genotype Imputation with Attribute Bagging (HIBAG), HLA*IMP:02, and SNP2HLA. However, limitations of these imputation programs include imputation accuracy, computational runtime, and ability to impute HLA allele haplotypes. We present a deep learning framework for HLA allele imputation using a multitask convolutional neural network (CNN) architecture. In this approach, we use phased SNP genotype data flanking ±250 kb from each HLA locus to simultaneously impute HLA allele haplotyes across loci HLA-A, -B, -C, -DQA1, -DQB1, -DPA1, -DPB1, and -DRB1. We start by tokenizing phased genotype sequences into k-mers that serve as input to the model. The CNN architecture starts with a shared embedding layer for learning low-dimensional representations of k-mers, shared convolutional layers for detecting genotype motifs, and branches off into separate densely-connected layers for imputing each HLA loci. We present evidence that the CNN used information from known tag SNPs to impute HLA alleles, and demonstrate the architecture is robust against a selection of hyperparameters. On the T1DGC dataset, our model achieved 97.6% imputation accuracy, which was superior to SNP2HLA's performance and comparable to HIBAG's performance. However, unlike HIBAG, our method can impute an entire HLA haplotype sequence instead of imputing one locus at a time. Additionally, by separating the training and inference steps, our imputation program provides user flexibility to reduce usage time.

scholarly journals Immunity and mental illness: findings from a Danish population-based immunogenetic study of seven psychiatric and neurodevelopmental disorders

2019 ◽  
Vol 27 (9) ◽  
pp. 1445-1455 ◽  
Author(s):  
Ron Nudel ◽  
Michael E. Benros ◽  
Morten Dybdahl Krebs ◽  
Rosa Lundbye Allesøe ◽  
Camilla Koldbæk Lemvigh ◽  
...  
Keyword(s):  
Intellectual Disability ◽  
Protective Effect ◽  
Neurodevelopmental Disorders ◽  
Association Studies ◽  
Human Leukocyte ◽  
Population Based ◽  
Autism Spectrum ◽  
Small Samples ◽  
Hla Association ◽  
Hla Genes

AbstractHuman leukocyte antigen (HLA) genes encode proteins with important roles in the regulation of the immune system. Many studies have also implicated HLA genes in psychiatric and neurodevelopmental disorders. However, these studies usually focus on one disorder and/or on one HLA candidate gene, often with small samples. Here, we access a large dataset of 65,534 genotyped individuals consisting of controls (N = 19,645) and cases having one or more of autism spectrum disorder (N = 12,331), attention deficit hyperactivity disorder (N = 14,397), schizophrenia (N = 2401), bipolar disorder (N = 1391), depression (N = 18,511), anorexia (N = 2551) or intellectual disability (N = 3175). We imputed participants’ HLA alleles to investigate the involvement of HLA genes in these disorders using regression models. We found a pronounced protective effect of DPB1*1501 on susceptibility to autism (p = 0.0094, OR = 0.72) and intellectual disability (p = 0.00099, OR = 0.41), with an increased protective effect on a comorbid diagnosis of both disorders (p = 0.003, OR = 0.29). We also identified a risk allele for intellectual disability, B*5701 (p = 0.00016, OR = 1.33). Associations with both alleles survived FDR correction and a permutation procedure. We did not find significant evidence for replication of previously-reported associations for autism or schizophrenia. Our results support an implication of HLA genes in autism and intellectual disability, which requires replication by other studies. Our study also highlights the importance of large sample sizes in HLA association studies.

scholarly journals A large-scale investigation into the role of classical HLA loci in multiple types of severe infections, with a focus on overlaps with autoimmune and mental disorders

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Ron Nudel ◽  
Rosa Lundbye Allesøe ◽  
Wesley K. Thompson ◽  
Thomas Werge ◽  
Simon Rasmussen ◽  
...  
Keyword(s):  
Autoimmune Disease ◽  
Psychiatric Disorders ◽  
Large Scale ◽  
Viral Infections ◽  
Human Leukocyte ◽  
Severe Infection ◽  
Small Sample ◽  
Disease Etiology ◽  
Hla Alleles ◽  
Severe Infections

Abstract Background Infections are a major disease burden worldwide. While they are caused by external pathogens, host genetics also plays a part in susceptibility to infections. Past studies have reported diverse associations between human leukocyte antigen (HLA) alleles and infections, but many were limited by small sample sizes and/or focused on only one infection. Methods We performed an immunogenetic association study examining 13 categories of severe infection (bacterial, viral, central nervous system, gastrointestinal, genital, hepatitis, otitis, pregnancy-related, respiratory, sepsis, skin infection, urological and other infections), as well as a phenotype for having any infection, and seven classical HLA loci (HLA-A, B, C, DPB1, DQA1, DQB1 and DRB1). Additionally, we examined associations between infections and specific alleles highlighted in our previous studies of psychiatric disorders and autoimmune disease, as these conditions are known to be linked to infections. Results Associations between HLA loci and infections were generally not strong. Highlighted associations included associations between DQB1*0302 and DQB1*0604 and viral infections (P = 0.002835 and P = 0.014332, respectively), DQB1*0503 and sepsis (P = 0.006053), and DQA1*0301 with “other” infections (a category which includes infections not included in our main categories e.g. protozoan infections) (P = 0.000369). Some HLA alleles implicated in autoimmune diseases showed association with susceptibility to infections, but the latter associations were generally weaker, or with opposite trends (in the case of HLA-C alleles, but not with alleles of HLA class II genes). HLA alleles associated with psychiatric disorders did not show association with susceptibility to infections. Conclusions Our results suggest that classical HLA alleles do not play a large role in the etiology of severe infections. The discordant association trends with autoimmune disease for some alleles could contribute to mechanistic theories of disease etiology.

scholarly journals Familial Pemphigus Vulgaris Occured in a Father and Son as the First Confirmed Cases

2016 ◽  
Vol 2016 ◽  
pp. 1-4
Author(s):  
Ali Haydar Eskiocak ◽  
Birgul Ozkesici ◽  
Soner Uzun
Keyword(s):  
Genetic Predisposition ◽  
Human Leukocyte ◽  
Pemphigus Vulgaris ◽  
Hla Typing ◽  
Enzyme Linked Immunosorbent Assay ◽  
Leukocyte Antigen ◽  
Hla Genes ◽  
Mother And Daughter ◽  
First Occurrence ◽  
Father And Son

Pemphigus vulgaris (PV) is a chronic autoimmune bullous disease of the skin and mucous membranes. Although there is some evidence pointing towards a genetic predisposition by some human leukocyte antigen (HLA) genes, familial occurrence of PV is very rare. Most of the familial PV cases so far reported have been in mother and daughter and in siblings. PV in father and son, as presented here, has not been reported in the literature before, except an unconfirmed report. The diagnosis of PV was established by histologic, cytologic studies and enzyme linked immunosorbent assay (ELISA) in Case1and by ELISA and BIOCHIP indirect immunofluorescence test in Case2. The son was responsive to moderate doses of methylprednisolone, with the treatment continuing with tapered doses. The father was in a subclinic condition; consequently, only close follow-up was recommended. HLA typing studies revealed identical HLA alleles of HLA-DR4 (DRB1⁎04) and HLA-DQB1⁎03in both of our cases; this had been found to be associated with PV in prior studies. Familial occurrences of PV and related HLA genes indicate the importance of genetic predisposition. The first occurrence of confirmed familial PV in father and son is reported here.

HLA class II genotyping: two assay systems compared

1995 ◽  
Vol 41 (4) ◽  
pp. 553-556 ◽  
Author(s):  
J Thonnard ◽  
F Deldime ◽  
M Heusterspreute ◽  
B Delepaut ◽  
F Hanon ◽  
...  
Keyword(s):  
Large Scale ◽  
Clinical Laboratory ◽  
Blot Hybridization ◽  
Human Leukocyte ◽  
Hla Typing ◽  
High Rate ◽  
Polymorphism Analysis ◽  
Dot Blot ◽  
Pcr Products ◽  
Typing Methods

Abstract In the last few years, a variety of DNA-based human leukocyte antigen (HLA) typing methods have emerged, revealing the extreme polymorphism of HLA genes. This polymorphism makes it difficult for a clinical laboratory to establish the best HLA typing strategy. In this study we have compared two techniques for performing HLA-DRB typing: a commercial rapid assay based on the polymerase chain reaction (PCR) followed by reverse dot-blot hybridization of the PCR products (the Inno-LiPA assay), and a method based on PCR followed by restriction fragment length polymorphism analysis. We found that both methods provide reliable results with a high rate of concordance (97%) and that Inno-LiPA is convenient for large-scale routine typing. However, if a high-resolution allelic typing is required, each method lacks accuracy but using them in association improves the accuracy of the results.

scholarly journals A review of HLA and COVID-19 association studies

2020 ◽  
Vol 3 (2) ◽  
pp. 25-30
Author(s):  
Renata Zunec
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Human Leukocyte ◽  
Genetic System ◽  
Epidemiological Studies ◽  
Population Based ◽  
Case Control ◽  
Case Control Studies ◽  
First Case ◽  
Scale Population

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is reported to vary across different populations in the prevalence of infection, in the death rate of patients, in the severity of symptoms and in the drug response of patients. Among host genetic factors that can influence all these attributes human leukocyte antigen (HLA) genetic system stands out as one of the leading candidates. Case-control studies, large-scale population-based studies, as well as experimental bioinformatics studies are of utmost importance to confirm HLA susceptibility spectrum of COVID-19. This review presents the results of the first case-control and epidemiological studies performed in several populations, early after the pandemic breakout. The results are pointing to several susceptible and protective HLA alleles and haplotypes associations with COVID-19, some of which might be of interest for the future studies in Croatia, due to its common presence in the population. However, further multiple investigations from around the world, as numerous as possible, are needed to confirm or deteriorate these preliminary results.

scholarly journals arcasHLA: high resolution HLA typing from RNA seq

2018 ◽  
Author(s):  
Rose Orenbuch ◽  
Ioan Filip ◽  
Devon Comito ◽  
Jeffrey Shaman ◽  
Itsik Pe'er ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Mhc Class Ii ◽  
Critical Role ◽  
Human Leukocyte ◽  
Hla Typing ◽  
Sequencing Data ◽  
Leukocyte Antigen ◽  
Hla Genes ◽  
Biological Dataset ◽  
High Level

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.

scholarly journals DDA: A Novel Network-Based Scoring Method to Identify Disease-Disease Associations

2015 ◽  
Vol 9 ◽  
pp. BBI.S35237 ◽  
Author(s):  
Apichat Suratanee ◽  
Kitiporn Plaimas
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Disease Diagnosis ◽  
Scoring Method ◽  
Protein Protein Interaction ◽  
Disease Associations ◽  
Statistical Relationships ◽  
Complex Relationships

Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease-disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein-protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease-disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.

scholarly journals Trans-ethnic analysis of the human leukocyte antigen region for ulcerative colitis reveals shared but also ethnicity-specific disease associations

2020 ◽  
Author(s):  
Frauke Degenhardt ◽  
Gabriele Mayr ◽  
Mareike Wendorff ◽  
Gabrielle Boucher ◽  
Eva Ellinghaus ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Human Leukocyte Antigen ◽  
Association Studies ◽  
Chemical Properties ◽  
Genetic Association Studies ◽  
Human Leukocyte ◽  
Chronic Inflammatory Disease ◽  
Hla Alleles ◽  
Leukocyte Antigen ◽  
Hla Dq

Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gut. Genetic association studies have identified the highly variable human leukocyte antigen (HLA) region as the strongest susceptibility locus for IBD, and specifically DRB1*01:03 as a determining factor for ulcerative colitis (UC). However, for most of the association signal such a delineation could not be made due to tight structures of linkage disequilibrium within the HLA. The aim of this study was therefore to further characterize the HLA signal using a trans-ethnic approach. We performed a comprehensive fine mapping of single HLA alleles in UC in a cohort of 9,272 individuals with African American, East Asian, Puerto Rican, Indian and Iranian descent and 40,691 previously analyzed Caucasians, additionally analyzing whole HLA haplotypes. We computationally characterized the binding of associated HLA alleles to human self-peptides and analysed the physico-chemical properties of the HLA proteins and predicted self-peptidomes. Highlighting alleles of the HLA-DRB1*15 group and their correlated HLA-DQ-DR haplotypes, we identified consistent associations across different ethnicities but also identified population-specific signals. We observed that DRB1*01:03 is mostly present in individuals of Western European descent and hardly present in non-Caucasian individuals. We found peptides predicted to bind to risk HLA alleles to be rich in positively charged amino acids such. We conclude that the HLA plays an important role for UC susceptibility across different ethnicities. This research further implicates specific features of peptides that are predicted to bind risk and protective HLA proteins.

Sign in / Sign up

Export Citation Format

Share Document