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We conducted a 1000 Genomes–imputed genome-wide association study (GWAS) meta-analysis for nicotine dependence, defined by the Fagerström 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 × 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 × 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.

The T-cell protein tyrosine phosphatase (TCPTP) pathway consists of signaling events mediated by TCPTP. Mutations and genetic variants of some genes in the TCPTP pathway are associated with lung cancer risk and survival. In the present study, we first investigated associations of 5,162 single nucleotide polymorphisms (SNPs) in 43 genes of this TCPTP pathway with lung cancer risk by using summary data of six published genome-wide association studies (GWAS) of 12,160 cases and 16,838 controls. We identified 11 independent SNPs in eight genes after correction for multiple comparisons by a false discovery rate <0.20. Then, we performed in silico functional analyses for these 11 SNPs by eQTL analysis, two of which, PTPN2 SNPs rs2847297 and rs2847282, were chosen as tagSNPs. We further included two additional GWAS datasets of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls), and the overall effects of these two SNPs among all eight GWAS studies remained significant (OR = 0.95, 95% CI = 0.92-0.98, and P = 0.004 for rs2847297; OR = 0.95, 95% CI = 0.92-0.99, and P = 0.009 for rs2847282). In conclusion, the PTPN2 rs2847297 and rs2847282 may be potential susceptible loci for lung cancer risk.

Lung cancer etiology is multifactorial, and growing evidence has indicated that long non-coding RNAs (lncRNAs) are important players in lung carcinogenesis. We performed a large-scale meta-analysis of 690,564 SNPs in 15,531 autosomal lncRNAs by using datasets from six previously published genome-wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium in populations of European ancestry. Previously unreported significant SNPs (P value < 1 × 10 -7 ) were further validated in two additional independent lung cancer GWAS datasets from Harvard University and deCODE. In the final meta-analysis of all eight GWAS datasets with 17,153 cases and 239,337 controls, a novel risk SNP rs114020893 in the lncRNA NEXN-AS1 region at 1p31.1 remained statistically significant (odds ratio = 1.17; 95% confidence interval = 1.11-1.24; P = 8.31 × 10 -9 ). In further in silico analysis, rs114020893 was predicted to change the secondary structure of the lncRNA. Our finding indicates that SNP rs114020893 of NEXN-AS1 at 1p31.1 may contribute to lung cancer susceptibility.

To answer the question as to which commercial high-density SNP chip covers most of the human genome given a fixed budget, we compared the performance of 12 chips of different sizes released by Affymetrix and Illumina for the European, Asian, and African populations. These include Affymetrix' relatively new population-optimized arrays, whose SNP sets are each tailored toward a specific ethnicity. Our evaluation of the chips included the use of two measures, efficiency and cost-benefit ratio, which we developed as supplements to genetic coverage. Unlike coverage, these measures factor in the price of a chip or its substitute size (number of SNPs on chip), allowing comparisons to be drawn between differently priced chips. In this fashion, we identified the Affymetrix population-optimized arrays as offering the most cost-effective coverage for the Asian and African population. For the European population, we established the Illumina Human Omni 2.5-8 as the preferred choice. Interestingly, the Affymetrix chip tailored toward an Eastern Asian subpopulation performed well for all three populations investigated. However, our coverage estimates calculated for all chips proved much lower than those advertised by the producers. All our analyses were based on the 1000 Genome Project as reference population.