Although independent genome wide association studies have identified variants associated with cigarette use, these variants currently explain very little of the phenotypic variation because genetic effects due to common alleles are quite small and the detection of signals requires very large sample sizes. GWAS are underpowered to detect these effects. To overcome the issue of power and false-positive findings, meta-analysis statistically synthesizes information from multiple studies (41). The largest genetic meta-analysis of cigarette use conducted by the Tobacco and Genetics Consortium included sixteen GWAS and found five significant loci. Each of the five loci was associated with only one specific smoking phenotype: nonsynonymous rs6265 on BDNF and smoking initiation (OR = 1.06, 95%CI: 1.04, 1.08, p-value = 1.8 × 10−8); nonsynonymous rs1051730 in 15q25 on nicotinic receptor gene CHRNA3 (β = 1.03, SE = 0.053, p-value = 2.8 × 10−73), rs1329650 on 10q25 (β = 0.367, SE = 0.059, p-value = 5.7 × 10−10), and rs3733829 in 9p13 of EGLN2 (β = 0.333, SE = 0.058, p-value = 1.0 × 10−8) and number of cigarettes per day, and rs3025343 near
