Lu Liu1,2, Yu-Fang Pei2,3, Tao-Le Liu4, Hong-Wen Deng5,6, Lei Zhang7,8, Wen-Zhu Hu1,2, Xiao-Lin Yang1,2, Shan-Cheng Li9, Rong Hai10, Shu Ran11, Lan Juan Zhao12, Hui Shen12, Qing Tian12, Hong-Mei Xiao13, Kun Zhang14. 1. Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China. 2. Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China. 3. Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, Suzhou, China. 4. Center for Circadian Clock, School of Biology & Basic Medical Sciences, Medical College of Soochow University, Suzhou, China. 5. Tulane Center for Genomics and Bioinformatics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA. hdeng2@tulane.edu. 6. School of Basic Medical Sciences, Central South University, 410000, Changsha, China. hdeng2@tulane.edu. 7. Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou, China. lzhang6@suda.edu.cn. 8. Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China. lzhang6@suda.edu.cn. 9. Department of Orthopedics, the Second Affiliated Hospital, Soochow University, Suzhou, China. 10. Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China. 11. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China. 12. Tulane Center for Genomics and Bioinformatics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA. 13. School of Basic Medical Sciences, Central South University, 410000, Changsha, China. 14. Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, 70125, USA.
Abstract
OBJECTIVES: Aiming to uncover the genetic basis of abdominal obesity, we performed a genome-wide association study (GWAS) meta-analysis of trunk fat mass adjusted by trunk lean mass (TFMadj) and followed by a series of functional investigations. SUBJECTS: A total of 11,569 subjects from six samples were included into the GWAS meta-analysis. METHODS: Meta-analysis was performed by a weighted fixed-effects model. In silico replication analysis was performed in the UK-Biobank (UKB) sample (N = 331,093) and in the GIANT study (N up to 110,204). Cis-expression QTL (cis-eQTL) analysis, dual-luciferase reporter assay and electrophoresis mobility shift assay (EMSA) were conducted to examine the functional relevance of the identified SNPs. At last, differential gene expression analysis (DGEA) was performed. RESULTS: We identified an independent SNP rs12409479 at 1p21 (MAF = 0.07, p = 7.26 × 10-10), whose association was replicated by the analysis of TFM in the UKB sample (one-sided p = 3.39 × 10-3), and was cross-validated by the analyses of BMI (one-sided p = 0.03) and WHRadj (one-sided p = 0.04) in the GIANT study. Cis-eQTL analysis demonstrated that allele A at rs12409479 was positively associated with PTBP2 expression level in subcutaneous adipose tissue (N = 385, p = 4.15 × 10-3). Dual-luciferase reporter assay showed that the region repressed PTBP2 gene expression by downregulating PTBP2 promoter activity (p < 0.001), and allele A at rs12409479 induced higher luciferase activity than allele G did (p = 4.15 × 10-3). EMSA experiment implied that allele A was more capable of binding to unknown transcription factors than allele G. Lastly, DGEA showed that the level of PTBP2 expression was higher in individuals with obesity than in individuals without obesity (N = 20 and 11, p = 0.04 and 9.22 × 10-3), suggesting a regulatory role in obesity development. CONCLUSIONS: Taken together, we hypothesize a regulating path from rs12409479 to trunk fat mass development through its allelic specific regulation of PTBP2 gene expression, thus providing some novel insight into the genetic basis of abdominal obesity.
OBJECTIVES: Aiming to uncover the genetic basis of abdominal obesity, we performed a genome-wide association study (GWAS) meta-analysis of trunk fat mass adjusted by trunk lean mass (TFMadj) and followed by a series of functional investigations. SUBJECTS: A total of 11,569 subjects from six samples were included into the GWAS meta-analysis. METHODS: Meta-analysis was performed by a weighted fixed-effects model. In silico replication analysis was performed in the UK-Biobank (UKB) sample (N = 331,093) and in the GIANT study (N up to 110,204). Cis-expression QTL (cis-eQTL) analysis, dual-luciferase reporter assay and electrophoresis mobility shift assay (EMSA) were conducted to examine the functional relevance of the identified SNPs. At last, differential gene expression analysis (DGEA) was performed. RESULTS: We identified an independent SNP rs12409479 at 1p21 (MAF = 0.07, p = 7.26 × 10-10), whose association was replicated by the analysis of TFM in the UKB sample (one-sided p = 3.39 × 10-3), and was cross-validated by the analyses of BMI (one-sided p = 0.03) and WHRadj (one-sided p = 0.04) in the GIANT study. Cis-eQTL analysis demonstrated that allele A at rs12409479 was positively associated with PTBP2 expression level in subcutaneous adipose tissue (N = 385, p = 4.15 × 10-3). Dual-luciferase reporter assay showed that the region repressed PTBP2 gene expression by downregulating PTBP2 promoter activity (p < 0.001), and allele A at rs12409479 induced higher luciferase activity than allele G did (p = 4.15 × 10-3). EMSA experiment implied that allele A was more capable of binding to unknown transcription factors than allele G. Lastly, DGEA showed that the level of PTBP2 expression was higher in individuals with obesity than in individuals without obesity (N = 20 and 11, p = 0.04 and 9.22 × 10-3), suggesting a regulatory role in obesity development. CONCLUSIONS: Taken together, we hypothesize a regulating path from rs12409479 to trunk fat mass development through its allelic specific regulation of PTBP2 gene expression, thus providing some novel insight into the genetic basis of abdominal obesity.
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