Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1.
ABSTRACT To identify risk variants for lung cancer, we conducted a multistage genome-wide association study. In the discovery phase, we analyzed 315,450 tagging SNPs in 1,154 current and former (ever) smoking cases of European ancestry and 1,137 frequency-matched, ever-smoking controls from Houston, Texas. For replication, we evaluated the ten SNPs most significantly associated with lung cancer in an additional 711 cases and 632 controls from Texas and 2,013 cases and 3,062 controls from the UK. Two SNPs, rs1051730 and rs8034191, mapping to a region of strong linkage disequilibrium within 15q25.1 containing PSMA4 and the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5, were significantly associated with risk in both replication sets. Combined analysis yielded odds ratios of 1.32 (P < 1 x 10(-17)) for both SNPs. Haplotype analysis was consistent with there being a single risk variant in this region. We conclude that variation in a region of 15q25.1 containing nicotinic acetylcholine receptors genes contributes to lung cancer risk.
SourceAvailable from: Francesco Stingo[Show abstract] [Hide abstract]
ABSTRACT: Complex diseases, such as cancer, arise from complex etiologies consisting of multiple single-nucleotide polymorphisms (SNPs), each contributing a small amount to the overall risk of disease. Thus, many researchers have gone beyond single-SNPs analysis methods, focusing instead on groups of SNPs, for example by analysing haplotypes. More recently, pathway-based methods have been proposed that use prior biological knowledge on gene function to achieve a more powerful analysis of genome-wide association studies (GWAS) data. In this paper we propose a novel Bayesian modeling framework to identify molecular biomarkers for disease prediction. Our method combines pathway-based approaches with multiple SNP analyses of a specified region of interest. The model's development is motivated by SNP data from a lung cancer study. In our approach we define gene-level scores based on SNP allele frequencies and use a linear modeling setting to study the scores association to the observed phenotype. The basic idea behind the definition of gene-level scores is to weigh the SNPs within the gene according to their rarity, based on genotype frequencies expected under the Hardy-Weinberg equilibrium law. This results in scores giving more importance to the unusually low frequencies, i.e. to SNPs that might indicate peculiar genetic differences between subjects belonging to different groups. An additional feature of our approach is that we incorporate information on SNP-to-SNP associations into the model. In particular, we use network priors that model the linkage disequilibrium between SNPs. For posterior inference, we design a stochastic search method that identifies significant biomarkers (genes and SNPs) for disease prediction. We assess performances on simulated data and compare results to existing approaches. We then show the ability of the proposed methodology to detect relevant genes and associated SNPs in a lung cancer dataset.Statistics and its interface 03/2015; 8(2):137-151. · 0.46 Impact Factor
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ABSTRACT: In the airway tract acetylcholine (ACh) is known to be the mediator of the parasympathetic nervous system. However ACh is also synthesized by a large variety of non-neuronal cells. Strongest expression is documented in neuroendocrine and in epithelial cells (ciliated, basal and secretory elements). Growing evidence suggests that a cell-type specific Ach expression and release do exist and act with local autoparacrine loop in the non-neuronal airway compartment. Here we review the molecular mechanism by which Ach is involved in regulating various aspects of innate mucosal defense, including mucociliary clearance, regulation of macrophage activation as well as in promoting epithelial cells proliferation and conferring susceptibility to lung carcinoma onset. Importantly this non-neuronal cholinergic machinery is differently regulated than the neuronal one and could be specifically therapeutically targeted.08/2013; 2(4):284-94. DOI:10.3978/j.issn.2218-6751.2013.06.01
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ABSTRACT: Many studies have examined the association between the CHRNA3 rs1051730 polymorphism gene polymorphisms and lung cancer risk in various populations, but their results have been inconsistent. The PubMed was searched for case-control studies published up to Jan 01, 2015. Data were extracted and pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. In this meta-analysis, we assessed four published studies involving comprising 2,890 lung cancer cases and 2,521 controls of the association between CHRNA3 rs1051730 polymorphism and lung cancer risk. For the T allele carriers (C/T + T/T) and the homozygote T/T, the pooled ORs for all studies combined 2,890 cases and 2,521 controls were 1.93 (95% CI =1.48-2.53, P=0.34 for heterogeneity) and 1.63 (95% CI =1.27-1.99, P=0.46 for heterogeneity), when compared with the homozygous wild-type genotype (C/C). There was no observable publication bias for both polymorphisms. These results from the meta-analysis suggest that CHRNA3 rs1051730 polymorphism contributes to risk of lung cancer among Asian population.02/2015; 4(1):104-8. DOI:10.3978/j.issn.2218-6751.2015.01.09