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Using a Bayesian hierarchical model for identifying single nucleotide polymorphisms associated with childhood acute lymphoblastic leukemia risk in case-parent triads.

ABSTRACT: Childhood acute lymphoblastic leukemia (ALL) is a condition that arises from complex etiologies. The absence of consistent environmental risk factors and the presence of modest familial associations suggest ALL is a complex trait with an underlying genetic component. The identification of genetic factors associated with disease is complicated by complex genetic covariance structures and multiple testing issues. Both issues can be resolved with appropriate Bayesian variable selection methods. The present study was undertaken to extend our hierarchical Bayesian model for case-parent triads to incorporate single nucleotide polymorphisms (SNPs) and incorporate the biological grouping of SNPs within genes. Based on previous evidence that genetic variation in the folate metabolic pathway influences ALL risk, we evaluated 128 tagging SNPs in 16 folate metabolic genes among 118 ALL case-parent triads recruited from the Texas Children's Cancer Center (Houston, TX) between 2003 and 2010. We used stochastic search gene suggestion (SSGS) in hierarchical Bayesian models to evaluate the association between folate metabolic SNPs and ALL. Using Bayes factors among these variants in childhood ALL case-parent triads, two SNPs were identified with a Bayes factor greater than 1. There was evidence that the minor alleles of NOS3 rs3918186 (OR = 2.16; 95% CI: 1.51-3.15) and SLC19A1 rs1051266 (OR = 2.07; 95% CI: 1.25-3.46) were positively associated with childhood ALL. Our findings are suggestive of the role of inherited genetic variation in the folate metabolic pathway on childhood ALL risk, and they also suggest the utility of Bayesian variable selection methods in the context of case-parent triads for evaluating the role of SNPs on disease risk.

SUBMITTER: Cao Y

PROVIDER: S-EPMC3868670 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Project description:In this study we investigated whether polymorphisms in the folate pathway influenced the risk of childhood acute lymphoblastic leukemia (ALL) or the survival rate of the patients. For this we selected and genotyped 67 SNPs in 15 genes in the folate pathway in 543 children with ALL and 529 controls. The results were evaluated by gender adjusted logistic regression and by the Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) methods. Bayesian structure based odds ratios for the relevant variables and interactions were also calculated. Altogether 9 SNPs in 8 genes were associated with altered susceptibility to ALL. After correction for multiple testing, two associations remained significant. The genotype distribution of the MTHFD1 rs1076991 differed significantly between the ALL and control population. Analyzing the subtypes of the disease the GG genotype increased only the risk of B-cell ALL (p?=?3.52×10(-4); OR?=?2.00). The GG genotype of the rs3776455 SNP in the MTRR gene was associated with a significantly reduced risk to ALL (p?=?1.21×10(-3); OR?=?0.55), which resulted mainly from the reduced risk to B-cell and hyperdiploid-ALL. The TC genotype of the rs9909104 SNP in the SHMT1 gene was associated with a lower survival rate comparing it to the TT genotype (80.2% vs. 88.8%; p?=?0.01). The BN-BMLA confirmed the main findings of the frequentist-based analysis and showed structural interactional maps and the probabilities of the different structural association types of the relevant SNPs especially in the hyperdiploid-ALL, involving additional SNPs in genes like TYMS, DHFR and GGH. We also investigated the statistical interactions and redundancies using structural model properties. These results gave further evidence that polymorphisms in the folate pathway could influence the ALL risk and the effectiveness of the therapy. It was also shown that in gene association studies the BN-BMLA could be a useful supplementary to the traditional frequentist-based statistical method.

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Using a Bayesian hierarchical model for identifying single nucleotide polymorphisms associated with childhood acute lymphoblastic leukemia risk in case-parent triads.

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