Genetic Epidemiology of Glioblastoma Multiforme: Confirmatory and New Findings from Analyses of Human Leukocyte Antigen Alleles and Motifs

Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
PLoS ONE (Impact Factor: 3.23). 09/2009; 4(9):e7157. DOI: 10.1371/journal.pone.0007157
Source: PubMed


Human leukocyte antigen (HLA) class I genes mediate cytotoxic T-lymphocyte responses and natural killer cell function. In a previous study, several HLA-B and HLA-C alleles and haplotypes were positively or negatively associated with the occurrence and prognosis of glioblastoma multiforme (GBM).
As an extension of the Upper Midwest Health Study, we have performed HLA genotyping for 149 GBM patients and 149 healthy control subjects from a non-metropolitan population consisting almost exclusively of European Americans. Conditional logistic regression models did not reproduce the association of HLA-B*07 or the B*07-Cw*07 haplotype with GBM. Nonetheless, HLA-A*32, which has previously been shown to predispose GBM patients to a favorable prognosis, was negatively associated with occurrence of GBM (odds ratio=0.41, p=0.04 by univariate analysis). Other alleles (A*29, A*30, A*31 and A*33) within the A19 serology group to which A*32 belongs showed inconsistent trends. Sequencing-based HLA-A genotyping established that A*3201 was the single A*32 allele underlying the observed association. Additional evaluation of HLA-A promoter and exon 1 sequences did not detect any unexpected single nucleotide polymorphisms that could suggest differential allelic expression. Further analyses restricted to female GBM cases and controls revealed a second association with a specific HLA-B sequence motif corresponding to Bw4-80Ile (odds ratio=2.71, p=0.02).
HLA-A allelic product encoded by A*3201 is likely to be functionally important to GBM. The novel, sex-specific association will require further confirmation in other representative study populations.

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Available from: James Tang, Oct 10, 2015
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    • "Throughout the paper, we used data from KIR genes as an illustration. Our method can be directly applied to present-absent data from other genes, such as Human Leukocyte Antigen (HLA) motifs determined by sequence specific oligonucleotide assays (Song et al., 2009). In addition, our simulation results illustrated the use of identified haplotypes and haplotype patterns can improve the accuracy of haplotype inference even in the absence of missing data, therefore our method can be used for haplotype inference in general situations. "
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    ABSTRACT: The majority of killer cell immunoglobin-like receptor (KIR) genes are detected as either present or absent using locus-specific genotyping technology. Ambiguity arises from the presence of a specific KIR gene since the exact copy number (one or two) of that gene is unknown. Therefore, haplotype inference for these genes is becoming more challenging due to such large portion of missing information. Meantime, many haplotypes and partial haplotype patterns have been previously identified due to tight linkage disequilibrium (LD) among these clustered genes thus can be incorporated to facilitate haplotype inference. In this paper, we developed a hidden Markov model (HMM) based method that can incorporate identified haplotypes or partial haplotype patterns for haplotype inference from present-absent data of clustered genes (e.g., KIR genes). We compared its performance with an expectation maximization (EM) based method previously developed in terms of haplotype assignments and haplotype frequency estimation through extensive simulations for KIR genes. The simulation results showed that the new HMM based method outperformed the previous method when some incorrect haplotypes were included as identified haplotypes and/or the standard deviation of haplotype frequencies were small. We also compared the performance of our method with two methods that do not use previously identified haplotypes and haplotype patterns, including an EM based method, HPALORE, and a HMM based method, MaCH. Our simulation results showed that the incorporation of identified haplotypes and partial haplotype patterns can improve accuracy for haplotype inference. The new software package HaploHMM is available and can be downloaded at
    Frontiers in Genetics 08/2014; 5:267. DOI:10.3389/fgene.2014.00267
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    • "Furthermore, the A*32 allele was found to be negatively associated with GBM risk in a study of 149 GBM patients (Song et al., 2009). "
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    ABSTRACT: Few studies have examined the relationship between human leukocyte antigen (HLA) polymorphisms and adult glioma, particularly at class II loci. We evaluated the association between selected HLA class II polymorphisms and adult glioma in a large, hospital-based case-control study, using unconditional logistic regression. DQB1 06 (OR=1.67, 95% CI=1.17-2.39) and DRB1 13 (OR=1.69, 95% CI=1.08-2.64) alleles were associated with an increased risk of glioma, while the DQB1 05 allele showed an inverse association (OR=0.63, 95% CI=0.43-0.93). These results, which were of borderline significance once controlled for the false discovery rate, suggest a potential role for the DQB1 06, DQB1 05, and DRB1 13 alleles in glioma susceptibility.
    Journal of neuroimmunology 12/2010; 233(1-2):185-91. DOI:10.1016/j.jneuroim.2010.11.005 · 2.47 Impact Factor
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    • "The statistical approach to association analysis followed the strategies established in other related work [28], [30], [31], [82], [83], except that a) enlarged sample size (improved statistical power) facilitated separate analyses of 563 SPs versus 221 SCs, b) 56 individuals (32 SPs and 24 SCs) representing couples with unlinked viruses were retained in analyses because HLA genotyping had already been completed before non-identity of the virus in suspected recipients (seroconverters) was established; c) 19 SPs from cohabiting couples with short (<9 mth) follow-up were also included; d) 18 patients (15 SPs and 3 SCs) with VL less than the lower limit of detection (400 copies/mL) were excluded; e) several univariate models were presented in order to facilitate meta-analysis; f) software packages in SAS was updated to version 9.2 (SAS Institute), and g) results sorted in SAS were used to produce graphs using GraphPad Prism version 5.0 ( Summary statistics in association analyses included: 1) proportional odds ratio (pOR) and 95% confidence intervals (CIs); 2) regression beta estimates, expressed as means and standard errors (SE), and 3) univariate and multivariable (adjusted) p values. "
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    ABSTRACT: During untreated, chronic HIV-1 infection, plasma viral load (VL) is a relatively stable quantitative trait that has clinical and epidemiological implications. Immunogenetic research has established various human genetic factors, especially human leukocyte antigen (HLA) variants, as independent determinants of VL set-point. To identify and clarify HLA alleles that are associated with either transient or durable immune control of HIV-1 infection, we evaluated the relationships of HLA class I and class II alleles with VL among 563 seroprevalent Zambians (SPs) who were seropositive at enrollment and 221 seroconverters (SCs) who became seropositive during quarterly follow-up visits. After statistical adjustments for non-genetic factors (sex and age), two unfavorable alleles (A*3601 and DRB1*0102) were independently associated with high VL in SPs (p<0.01) but not in SCs. In contrast, favorable HLA variants, mainly A*74, B*13, B*57 (or Cw*18), and one HLA-A and HLA-C combination (A*30+Cw*03), dominated in SCs; their independent associations with low VL were reflected in regression beta estimates that ranged from -0.47+/-0.23 to -0.92+/-0.32 log(10) in SCs (p<0.05). Except for Cw*18, all favorable variants had diminishing or vanishing association with VL in SPs (p<or=0.86). Overall, each of the three HLA class I genes had at least one allele that might contribute to effective immune control, especially during the early course of HIV-1 infection. These observations can provide a useful framework for ongoing analyses of viral mutations induced by protective immune responses.
    PLoS ONE 03/2010; 5(3):e9629. DOI:10.1371/journal.pone.0009629 · 3.23 Impact Factor
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