Won-Jun Lee, Ji Eun Lim, Ji-One Kang, Tae-Woong Ha, Hae-Un Jung, Dong Jun Kim, Eun Ju Baek, Han Kyul Kim, Ju Yeon Chung, Bermseok Oh
{"title":"Smoking-Interaction Loci Affect Obesity Traits: A Gene-Smoking Stratified Meta-Analysis of 545,131 Europeans.","authors":"Won-Jun Lee, Ji Eun Lim, Ji-One Kang, Tae-Woong Ha, Hae-Un Jung, Dong Jun Kim, Eun Ju Baek, Han Kyul Kim, Ju Yeon Chung, Bermseok Oh","doi":"10.1159/000525749","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples.</p><p><strong>Methods: </strong>We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI and computed the stratified p values (pstratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group.</p><p><strong>Results: </strong>We identified four genome-wide significant loci in interactions with the smoking status (pstratified < 5 × 10-8): rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity.</p><p><strong>Conclusions: </strong>Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.</p>","PeriodicalId":18030,"journal":{"name":"Lifestyle Genomics","volume":"15 3","pages":"87-97"},"PeriodicalIF":2.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lifestyle Genomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000525749","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Although many studies have investigated the association between smoking and obesity, very few have analyzed how obesity traits are affected by interactions between genetic factors and smoking. Here, we aimed to identify the loci that affect obesity traits via smoking status-related interactions in European samples.
Methods: We performed stratified analysis based on the smoking status using both the UK Biobank (UKB) data (N = 334,808) and the Genetic Investigation of ANthropometric Traits (GIANT) data (N = 210,323) to identify gene-smoking interaction for obesity traits. We divided the UKB subjects into two groups, current smokers and nonsmokers, based on the smoking status, and performed genome-wide association study (GWAS) for body mass index (BMI), waist circumference adjusted for BMI (WCadjBMI), and waist-hip ratio adjusted for BMI (WHRadjBMI) in each group. And then we carried out the meta-analysis using both GWAS summary statistics of UKB and GIANT for BMI, WCadjBMI, and WHRadjBMI and computed the stratified p values (pstratified) based on the differences between meta-analyzed estimated beta coefficients with standard errors in each group.
Results: We identified four genome-wide significant loci in interactions with the smoking status (pstratified < 5 × 10-8): rs336396 (INPP4B) and rs12899135 (near CHRNB4) for BMI, and rs998584 (near VEGFA) and rs6916318 (near RSPO3) for WHRadjBMI. Moreover, we annotated the biological functions of the SNPs using expression quantitative trait loci (eQTL) and GWAS databases, along with publications, which revealed possible mechanisms underlying the association between the smoking status-related genetic variants and obesity.
Conclusions: Our findings suggest that obesity traits can be modified by the smoking status via interactions with genetic variants through various biological pathways.
期刊介绍:
Lifestyle Genomics aims to provide a forum for highlighting new advances in the broad area of lifestyle-gene interactions and their influence on health and disease. The journal welcomes novel contributions that investigate how genetics may influence a person’s response to lifestyle factors, such as diet and nutrition, natural health products, physical activity, and sleep, amongst others. Additionally, contributions examining how lifestyle factors influence the expression/abundance of genes, proteins and metabolites in cell and animal models as well as in humans are also of interest. The journal will publish high-quality original research papers, brief research communications, reviews outlining timely advances in the field, and brief research methods pertaining to lifestyle genomics. It will also include a unique section under the heading “Market Place” presenting articles of companies active in the area of lifestyle genomics. Research articles will undergo rigorous scientific as well as statistical/bioinformatic review to ensure excellence.