HLA Genotypes - samples
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ABSTRACT:
PROVIDER: EGAD00001009965 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Several HLA allelic variants have been associated with protection from, or susceptibility to infectious and autoimmune diseases. Here, we examined whether specific HLA alleles would be associated with different Mtb infection outcomes. We found that DQA1*03:01, DPB1*04:02, and DRB4*01:01 were signficantly more frequent in inividuals with active TB (susceptibility alleles). Furthermore, individuals who express any of the three susceptibility alleles were associated with lower magnitude of responses against Mtb antigens. We investigated the gene expression changes induced in PBMCs by Mtb lysate and a peptide pool (MTB300) in individuals with or without expression of the susceptibility alleles.
2023-03-29 | GSE207214 | GEO
Project description:HLA-DRB1 alleles have been associated with several autoimmune diseases. In anti-citrullinated protein antibody positive rheumatoid arthritis (ACPA-positive RA), HLA-DRB1 shared epitope (SE) alleles are the major genetic risk factors. In order to investigate whether expression of different alleles of major histocompatibility complex (MHC) Class II genes influence functions of immune cells, we investigated transcriptomic profiles of a variety of immune cells from healthy individuals carrying different HLA-DRB1 alleles. Sequencing libraries from peripheral blood mononuclear cells, CD4+ T cells, CD8+ T cells, and CD14+ monocytes of 32 genetically pre-selected healthy female individuals were generated, sequenced and reads were aligned to the standard reference. For the MHC region, reads were mapped to available MHC reference haplotypes and AltHapAlignR was used to estimate gene expression. Using this method, HLA-DRB and HLA-DQ were found to be differentially expressed in different immune cells of healthy individuals as well as in whole blood samples of RA patients carrying HLA-DRB1 SE-positive versus SE-negative alleles. In contrast, no genes outside the MHC region were differentially expressed between individuals carrying HLA-DRB1 SE-positive and SE-negative alleles. Existing methods for HLA-DR allele-specific protein expression were evaluated but were not mature enough to provide appropriate complementary information at the protein level. Altogether, our findings suggest that immune effects associated with different allelic forms of HLA-DR and HLA-DQ may be associated not only with differences in the structure of these proteins, but also with differences in their expression levels.
2020-12-22 | GSE163605 | GEO
Project description:Leprosy is a chronic granulomatous disease caused by infection with Mycobacterium leprae. Genetic association studies indicated that leprosy risk is strongly associated with variation within the major histocompatibility complex (MHC) region, but the full number of variants in this region has yet to be elucidated. To identify further susceptibility loci or loss of function variants for this disease, we performed fine-mapping analysis of the MHC region using a Han Chinese reference panel (n= 10,689 patients, 29,948 genetic markers) in the data sets from our previous leprosy studies. Then, a fixed-effect meta-analysis was carried out separately for Chinese (case=2,901, control=3,801) and North Chinese (case=1,983, control=2,635) participants. The meta-analysis of Chinese participants identified 10 HLA-type or amino acid variants with lower than the genome-wide significant susceptibility signal. Next, gene-by-gene step-wise conditional analysis was performed in the combined dataset of these cohorts. Finally, we identified four new independent susceptibility loci (HLA-DQA1, HLA-C, rs3129063, and rs58327373) and confirmed one previously reported locus (HLA-DRB1) that significantly associated with leprosy in the Chinese Han population. Thus the results of this study increase knowledge about leprosy risk variants and illustrate the value of HLA imputation for fine mapping of causal variants in the MHC.
2018-09-30 | GSE119367 | GEO
Project description:EXCEED samples imputed to HRC reference panel using Michigan Imputation server
| EGAD00010001685 | EGA
Project description:HLA-C expresion varies widely across the different HLA-C alleles. MicroRNA binding can partly explain the differences in HLA-C allele expression however other contributing factors still remain undetermined. Here we use two common HLA-C alleles, HLA-C*05:01 and HLA-C*07:02, to explore differences in expression levels. Using functional, structural and peptide repertoire comparisons we demonstrate that HLA-C expression levels are not only modulated at the RNA level but also at the protein level. This dataset contains RAW data and database search results for HLA-C*05:01 and HLA-C*07:02 from the 721.221 cell line.
2017-07-03 | PXD006455 | Pride
Project description:Based on the prior knowledge of involvement of the human leukocyte antigen locus in Han Chinese patients with carbamazepine-induced Stevens-Johnson syndrome, we imputed the HLA types for each case and control subject from the array of SNPs in the Human610-Quad GWAS platform (NCBI Build 36.1). We then performed a genome-wide association test which included the imputed HLA alleles as markers.
| EGAS00000000036 | EGA
Project description:The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across publicly available as well as ten newly generated high quality HLA peptidomics datasets, we show that we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. This fully unsupervised approach uncovers new motifs for several alleles without known ligands and significantly improves neo-epitope predictions in three melanoma patients.
2017-08-18 | PXD005231 | Pride
Project description:HLA-I molecules bind short peptides and present them to CD8+ T cells for TCR recognition. The length of HLA-I ligands typically ranges from 8 to 12 amino acids, but high variability is observed between different alleles. Here we used recent HLA peptidomics data to analyze in an unbiased way peptide length distributions over 85 different HLA-I alleles. Our results revealed clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to unravel some of the molecular basis of peptide length distributions and predict peptide length distributions based on HLA-I sequences only. We further took advantage of our collection of curated HLA peptidomics studies to investigate multiple specificity in HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distributions and multiple specificity significantly improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on ten newly generated HLA peptidomics datasets from meningioma samples.
2019-07-31 | PXD009925 | Pride
Project description:LC-MS/MS-based identification of HLA-peptides is poised to provide a deep understanding of the rules underlying antigen presentation. However, a key obstacle limiting the utility of MS data is the ambiguity arising from the co-expression of multiple HLA alleles. Here, we introduce a strategy for profiling the HLA ligandome one allele at a time. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing a novel spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of novel binding motifs, and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a scalable strategy for systematically learning the rules of endogenous antigen presentation.
2017-02-21 | GSE93315 | GEO