Early onset lung cancer, cigarette smoking and the SNP309 of the murine double minute-2 (MDM2) gene

The polymorphism SNP309 (rs2279744) in the promoter region of the MDM2 gene has been shown to alter protein expression and may play a role in the susceptibility to lung cancer. The MDM2 protein is a key inhibitor of p53 and several mechanisms of MDM2/p53 interactions are presently known: modulating DNA-repair, cell-cycle control, cell growth and apoptosis

Genome-wide meta-analysis reveals common splice site acceptor variant in CHRNA4 associated with nicotine dependence

We conducted a 1000 Genomes-imputed genome-wide association study (GWAS) meta-analysis for nicotine dependence, defined by the Fagerstrom Test for Nicotine Dependence in 17 074 ever smokers from five European-ancestry samples. We followed up novel variants in 7469 ever smokers from five independent European-ancestry samples. We identified genome-wide significant association in the alpha-4 nicotinic receptor subunit (CHRNA4) gene on chromosome 20q13: lowest P = 8.0 x 10(-9) across all the samples for rs2273500-C (frequency = 0.15; odds ratio = 1.12 and 95% confidence interval = 1.08-1.17 for severe vs mild dependence). rs2273500-C, a splice site acceptor variant resulting in an alternate CHRNA4 transcript predicted to be targeted for nonsense-mediated decay, was associated with decreased CHRNA4 expression in physiologically normal human brains (lowest P = 7.3 x 10(-4)). Importantly, rs2273500-C was associated with increased lung cancer risk (N = 28 998, odds ratio = 1.06 and 95% confidence interval = 1.00-1.12), likely through its effect on smoking, as rs2273500-C was no longer associated with lung cancer after adjustment for smoking. Using criteria for smoking behavior that encompass more than the single 'cigarettes per day' item, we identified a common CHRNA4 variant with important regulatory properties that contributes to nicotine dependence and smoking-related consequences.Peer reviewe

Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

Application of genomewide SNP arrays for detection of simulated susceptibility loci

The prospect of SNP-based genomewide association analysis has been extensively discussed, but practical experiences remain limited. We performed an association study using a recently developed array of 11,555 SNPs distributed throughout the human genome. A total of 104 DNA samples were hybridized to these chips with an average call rate of 97% (range 85.3-98.6%). The resulting genomewide scans were applied to distinguish between carriers and noncarriers of 37 test variants, used as surrogates for monogenic disease traits. The test variants were not contained in the chip and had been determined by other methods. Without adjustment for multiple testing, the procedure detected 24% of the test variants, but the positive predictive value was low (2%). Adjustment for multiple testing eliminated most false-positive associations, but the share of true positive associations decreased to 10-12%. We also simulated fine-mapping of susceptibility loci by restricting testing to the immediate neighborhood of test variants (±5 Mb). This increased the proportion of correctly identified test variants to 22-27%. Simulation of a bigenic inheritance reduced the sensitivity to 1%. Similarly adverse effect had reduction of allelic penetrance. In summary, we demonstrate the feasibility and considerable specificity of SNP array-based association studies to detect variants underlying monogenic, highly penetrant traits. The outcome is affected by allelic frequencies of chip SNPs, by the ratio between simulated "cases" and "controls," and by the degree of linkage disequilibrium. A major improvement is expected from raising the density of the SNP array