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Pleiotropy of genetic variants on obesity and smoking phenotypes : Results from the Oncoarray Project of The International Lung Cancer Consortium

Wang, Tao; Moon, Jee Young; Wu, Yiqun; Amos, Christopher I.; Hung, Rayjean J.; Tardon, Adonina; Andrew, Angeline; Chen, Chu; Christiani, David C. and Albanes, Demetrios, et al. (2017) In PloS one 12(9). p.0185660-0185660
Abstract

Obesity and cigarette smoking are correlated through complex relationships. Common genetic causes may contribute to these correlations. In this study, we selected 241 loci potentially associated with body mass index (BMI) based on the Genetic Investigation of ANthropometric Traits (GIANT) consortium data and calculated a BMI genetic risk score (BMI-GRS) for 17,037 individuals of European descent from the Oncoarray Project of the International Lung Cancer Consortium (ILCCO). Smokers had a significantly higher BMI-GRS than never-smokers (p = 0.016 and 0.010 before and after adjustment for BMI, respectively). The BMI-GRS was also positively correlated with pack-years of smoking (p<0.001) in smokers. Based on causal network inference... (More)

Obesity and cigarette smoking are correlated through complex relationships. Common genetic causes may contribute to these correlations. In this study, we selected 241 loci potentially associated with body mass index (BMI) based on the Genetic Investigation of ANthropometric Traits (GIANT) consortium data and calculated a BMI genetic risk score (BMI-GRS) for 17,037 individuals of European descent from the Oncoarray Project of the International Lung Cancer Consortium (ILCCO). Smokers had a significantly higher BMI-GRS than never-smokers (p = 0.016 and 0.010 before and after adjustment for BMI, respectively). The BMI-GRS was also positively correlated with pack-years of smoking (p<0.001) in smokers. Based on causal network inference analyses, seven and five of 241 SNPs were classified to pleiotropic models for BMI/smoking status and BMI/pack-years, respectively. Among them, three and four SNPs associated with smoking status and pack-years (p<0.05), respectively, were followed up in the ever-smoking data of the Tobacco, Alcohol and Genetics (TAG) consortium. Among these seven candidate SNPs, one SNP (rs11030104, BDNF) achieved statistical significance after Bonferroni correction for multiple testing, and three suggestive SNPs (rs13021737, TMEM18; rs11583200, ELAVL4; and rs6990042, SGCZ) achieved a nominal statistical significance. Our results suggest that there is a common genetic component between BMI and smoking, and pleiotropy analysis can be useful to identify novel genetic loci of complex phenotypes.

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@article{8d88a7cf-499c-46cd-8383-865b8a114742,
  abstract     = {<p>Obesity and cigarette smoking are correlated through complex relationships. Common genetic causes may contribute to these correlations. In this study, we selected 241 loci potentially associated with body mass index (BMI) based on the Genetic Investigation of ANthropometric Traits (GIANT) consortium data and calculated a BMI genetic risk score (BMI-GRS) for 17,037 individuals of European descent from the Oncoarray Project of the International Lung Cancer Consortium (ILCCO). Smokers had a significantly higher BMI-GRS than never-smokers (p = 0.016 and 0.010 before and after adjustment for BMI, respectively). The BMI-GRS was also positively correlated with pack-years of smoking (p&lt;0.001) in smokers. Based on causal network inference analyses, seven and five of 241 SNPs were classified to pleiotropic models for BMI/smoking status and BMI/pack-years, respectively. Among them, three and four SNPs associated with smoking status and pack-years (p&lt;0.05), respectively, were followed up in the ever-smoking data of the Tobacco, Alcohol and Genetics (TAG) consortium. Among these seven candidate SNPs, one SNP (rs11030104, BDNF) achieved statistical significance after Bonferroni correction for multiple testing, and three suggestive SNPs (rs13021737, TMEM18; rs11583200, ELAVL4; and rs6990042, SGCZ) achieved a nominal statistical significance. Our results suggest that there is a common genetic component between BMI and smoking, and pleiotropy analysis can be useful to identify novel genetic loci of complex phenotypes.</p>},
  author       = {Wang, Tao and Moon, Jee Young and Wu, Yiqun and Amos, Christopher I. and Hung, Rayjean J. and Tardon, Adonina and Andrew, Angeline and Chen, Chu and Christiani, David C. and Albanes, Demetrios and Heijden, Erik H.F.M.van der and Duell, Eric and Rennert, Gadi and Goodman, Gary and Liu, Geoffrey and Mckay, James D. and Yuan, Jian Min and Field, John K. and Manjer, Jonas and Grankvist, Kjell and Kiemeney, Lambertus A. and Marchand, Loic Le and Teare, M. Dawn and Schabath, Matthew B. and Johansson, Mattias and Aldrich, Melinda C. and Davies, Michael and Johansson, Mikael and Tsao, Ming Sound and Caporaso, Neil and Lazarus, Philip and Lam, Stephen and Bojesen, Stig E. and Arnold, Susanne and Wu, Xifeng and Zong, Xuchen and Hong, Yun Chul and Ho, Gloria Y.F.},
  issn         = {1932-6203},
  language     = {eng},
  number       = {9},
  pages        = {0185660--0185660},
  publisher    = {Public Library of Science},
  series       = {PloS one},
  title        = {Pleiotropy of genetic variants on obesity and smoking phenotypes : Results from the Oncoarray Project of The International Lung Cancer Consortium},
  url          = {http://dx.doi.org/10.1371/journal.pone.0185660},
  volume       = {12},
  year         = {2017},
}