Lung cancer is a leading cause of cancer death and disease burden in many countries. Understanding of the biological pathways involved in lung cancer aetiology is required to identify key biomolecules that could be of significant clinical value, either as predictive, prognostic or diagnostic markers, or as targets for the development of novel therapies to treat this disease, in addition to smoking avoidance strategies. Genome-wide association studies (GWAS) have enabled significant progress in the past 5 years in investigating genetic susceptibility to lung cancer. Large scale, multi-cohort GWAS of mainly Caucasian, smoking, populations have identified strong associations for lung cancer mapped to chromosomal regions 15q [nicotinic acetylcholine receptor (nAChR) subunits: CHRNA3, CHRNA5], 5p (TERT-CLPTM1L locus) and 6p (BAT3-MSH5). Some studies in Asian populations of smokers have found similar risk loci, whereas GWAS in never smoking Asian females have identified associations in other chromosomal regions, e.g., 3q (TP63), that are distinct from smoking-related lung cancer risk loci. GWAS of smoking behaviour have identified risk loci for smoking quantity at 15q (similar genes to lung cancer susceptibility: CHRNA3, CHRNA5) and 19q (CYP2A6). Other genes have been mapped for smoking initiation and smoking cessation. In chronic obstructive pulmonary disease (COPD), which is a known risk factor for lung cancer, GWAS in large cohorts have also found CHRNA3 and CHRNA5 single nucleotide polymorphisms (SNPs) mapping at 15q as risk loci, as well as other regions at 4q31 (HHIP), 4q24 (FAM13A) and 5q (HTR4). The overlap in risk loci between lung cancer, smoking behaviour and COPD may be due to the effects of nicotine addiction; however, more work needs to be undertaken to explore the potential direct effects of nicotine and its metabolites in gene-environment interaction in these phenotypes. Goals of future genetic susceptibility studies of lung cancer should focus on refining the strongest risk loci in a wide range of populations with lung cancer, and integrating other clinical and biomarker information, in order to achieve the aim of personalised therapy for lung cancer.