Dataset Information

Analysis of 60 reported glioma risk SNPs replicates published GWAS findings but fails to replicate associations from published candidate-gene studies.

ABSTRACT: Genomewide association studies (GWAS) and candidate-gene studies have implicated single-nucleotide polymorphisms (SNPs) in at least 45 different genes as putative glioma risk factors. Attempts to validate these associations have yielded variable results and few genetic risk factors have been consistently replicated. We conducted a case-control study of Caucasian glioma cases and controls from the University of California San Francisco (810 cases, 512 controls) and the Mayo Clinic (852 cases, 789 controls) in an attempt to replicate previously reported genetic risk factors for glioma. Sixty SNPs selected from the literature (eight from GWAS and 52 from candidate-gene studies) were successfully genotyped on an Illumina custom genotyping panel. Eight SNPs in/near seven different genes (TERT, EGFR, CCDC26, CDKN2A, PHLDB1, RTEL1, TP53) were significantly associated with glioma risk in the combined dataset (P < 0.05), with all associations in the same direction as in previous reports. Several SNP associations showed considerable differences across histologic subtype. All eight successfully replicated associations were first identified by GWAS, although none of the putative risk SNPs from candidate-gene studies was associated in the full case-control sample (all P values > 0.05). Although several confirmed associations are located near genes long known to be involved in gliomagenesis (e.g., EGFR, CDKN2A, TP53), these associations were first discovered by the GWAS approach and are in noncoding regions. These results highlight that the deficiencies of the candidate-gene approach lay in selecting both appropriate genes and relevant SNPs within these genes.

SUBMITTER: Walsh KM

PROVIDER: S-EPMC3670948 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of 60 reported glioma risk SNPs replicates published GWAS findings but fails to replicate associations from published candidate-gene studies.

Walsh Kyle M KM Anderson Erik E Hansen Helen M HM Decker Paul A PA Kosel Matt L ML Kollmeyer Thomas T Rice Terri T Zheng Shichun S Xiao Yuanyuan Y Chang Jeffrey S JS McCoy Lucie S LS Bracci Paige M PM Wiemels Joe L JL Pico Alexander R AR Smirnov Ivan I Lachance Daniel H DH Sicotte Hugues H Eckel-Passow Jeanette E JE Wiencke John K JK Jenkins Robert B RB Wrensch Margaret R MR

Genetic epidemiology 20121231 2

Genomewide association studies (GWAS) and candidate-gene studies have implicated single-nucleotide polymorphisms (SNPs) in at least 45 different genes as putative glioma risk factors. Attempts to validate these associations have yielded variable results and few genetic risk factors have been consistently replicated. We conducted a case-control study of Caucasian glioma cases and controls from the University of California San Francisco (810 cases, 512 controls) and the Mayo Clinic (852 cases, 789 ...[more]

PMID: 23280628

Similar Datasets

Project description:BACKGROUND: Detecting epistatic interactions associated with complex and common diseases can help to improve prevention, diagnosis and treatment of these diseases. With the development of genome-wide association studies (GWAS), designing powerful and robust computational method for identifying epistatic interactions associated with common diseases becomes a great challenge to bioinformatics society, because the study of epistatic interactions often deals with the large size of the genotyped data and the huge amount of combinations of all the possible genetic factors. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases and introduce a lot of false positives. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity. RESULTS: We propose a new Markov Blanket-based method, DASSO-MB (Detection of ASSOciations using Markov Blanket) to detect epistatic interactions in case-control GWAS. Markov blanket of a target variable T can completely shield T from all other variables. Thus, we can guarantee that the SNP set detected by DASSO-MB has a strong association with diseases and contains fewest false positives. Furthermore, DASSO-MB uses a heuristic search strategy by calculating the association between variables to avoid the time-consuming training process as in other machine-learning methods. We apply our algorithm to simulated datasets and a real case-control dataset. We compare DASSO-MB to other commonly-used methods and show that our method significantly outperforms other methods and is capable of finding SNPs strongly associated with diseases. CONCLUSIONS: Our study shows that DASSO-MB can identify a minimal set of causal SNPs associated with diseases, which contains less false positives compared to other existing methods. Given the huge size of genomic dataset produced by GWAS, this is critical in saving the potential costs of biological experiments and being an efficient guideline for pathogenesis research.

Dataset Information

Analysis of 60 reported glioma risk SNPs replicates published GWAS findings but fails to replicate associations from published candidate-gene studies.

Publications

Analysis of 60 reported glioma risk SNPs replicates published GWAS findings but fails to replicate associations from published candidate-gene studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets