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  • 标题:Analyses of nicotine metabolism biomarker genetics stratified by sex in African and European Americans
  • 本地全文:下载
  • 作者:Meghan J. Chenoweth ; Lisa Sanderson Cox ; Nikki L. Nollen
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2021
  • 卷号:11
  • DOI:10.1038/s41598-021-98883-z
  • 语种:English
  • 出版社:Springer Nature
  • 摘要:Nicotine is inactivated by the polymorphic CYP2A6 enzyme to cotinine and then to 3′hydroxycotinine. The Nicotine Metabolite Ratio (NMR; 3′hydroxycotinine/cotinine) is a heritable nicotine metabolism biomarker, varies with sex and ancestry, and influences smoking cessation and disease risk. We conducted sex-stratified genome-wide association studies of the NMR in European American (EA) and African American (AA) smokers (NCT01314001, NCT00666978). In EA females (n = 389) and males (n = 541), one significant (P < 5e−8) chromosome 19 locus was found (top variant: rs56113850, CYP2A6 (intronic), for C vs. T: females: beta = 0.67, P = 7.5e−22, 21.8% variation explained; males: beta = 0.75, P = 1.2e−37, 26.1% variation explained). In AA females (n = 503) and males (n = 352), the top variant was found on chromosome 19 but differed by sex (females: rs11878604, CYP2A6 (~ 16 kb 3′), for C vs. T: beta = − 0.71, P = 6.6e−26, 16.2% variation explained; males: rs3865454, CYP2A6 (~ 7 kb 3′), for G vs. T: beta = 0.64, P = 1.9e−19, 18.9% variation explained). In AA females, a significant region was found on chromosome 12 (top variant: rs12425845: P = 5.0e−9, TMEM132C (~ 1 Mb 5′), 6.1% variation explained) which was not significant in AA males. In AA males, significant regions were found on chromosomes 6 (top variant: rs9379805: P = 4.8e−9, SLC17A2 (~ 8 kb 5′), 8.0% variation explained) and 16 (top variant: rs77368288: P = 3.5e−8, ZNF469 (~ 92 kb 5′), 7.1% variation explained) which were not significant in AA females. Further investigation of these associations outside of chromosome 19 is required, as they did not replicate. Understanding how sex and ancestry influence nicotine metabolism genetics may improve personalized approaches for smoking cessation and risk prediction for tobacco-related diseases.
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