SNP rs16969968 as a Strong Predictor of Nicotine Dependence and Lung Cancer Risk in a North Indian Population

Document Type : Research Articles


1 Department of Genetics, University of Delhi South Campus, New Delhi, India.

2 Department of Biochemistry, Dr Ram ManoharLohia Hospital, Baba Kharak Singh Marg, New Delhi, Delhi, India.

3 Department of Pulmonary Medicine and Sleep Disorder, All India Institute of Medical Sciences, New Delhi, Delhi, India.


Background: The 15q24-25 loci contain genes (CHRNA5 and CHRNA3) encoding nicotinic acetylcholine receptor subunits. We here determined for the first time the association of genetic variants rs16969968 and rs3743074 in CHRNA5 and CHRNA3, respectively, on nicotine dependence and lung cancer risk in a North Indian population by a case-control approach. Methods: Venous blood samples were obtained from 324 participants (108 lung cancer patients and 216 healthy individuals). DNA was extracted and PCR amplified with primers flanking the SNPs rs16969968 and rs3743074. Amplicons were subjected to sequencing and logistic regression was used to analyze association between variables. Results: The risk variant SNP rs16969968 in both heterozygous and homozygous forms appeared to exert a significant effect on nicotine dependence [GA (OR=2.77) and AA (OR=2.53)]. As expected, smoking was strongly associated with lung cancer (OR= 2.62). Risk allele rs16969968 in CHRNA5 also showed a significant association with increased lung cancer risk in our cohort, alone (OR= 4.99) and with smoking as a co-variable (OR= 4.28). Comparison of our analysis with other populations suggested that individuals with rs16969968 risk allele in the Indian population are more susceptible to lung cancer. Conclusion: Overall, the results strongly indicated that, in our cohort North Indian population, the genetic variant rs16969968, but not rs3743074, is significantly associated with both nicotine dependence and increased risk of lung cancer. While the results are significant, there is further need to increase the sample size and improve precision of our risk prediction.


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