Yu Liu1, Wenshan Gao1, Claudia Koellmann2, Sigrid Le Clerc3, Anke Hüls2, Bingjie Li1, Qianqian Peng1, Sijie Wu1, Anan Ding1, Yajun Yang4, Li Jin5, Jean Krutmann6, Tamara Schikowski7, Jean-François Zagury8, Sijia Wang9. 1. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. 2. IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany. 3. Laboratoire Génomique, Bioinformatique et Applications, Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers, Paris, France. 4. State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China. 5. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China. 6. IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany; Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany. 7. IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany. Electronic address: Tamara.Schikowski@IUF-Duesseldorf.de. 8. Laboratoire Génomique, Bioinformatique et Applications, Chaire de Bioinformatique, EA4627, Conservatoire National des Arts et Métiers, Paris, France. Electronic address: zagury@cnam.fr. 9. CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China. Electronic address: wangsijia@picb.ac.cn.
Abstract
BACKGROUND: The progression of human skin aging has a strong genetic basis. However, recent studies have mainly focused on Caucasian populations and we have thus performed a genetic association study on skin aging signs in Han Chinese population. OBJECTIVE: To investigate genetic risk factors in skin aging in Han Chinese female, we performed a genome-wide association study. METHODS: We collected genotype data from 1534 Han Chinese female from Taizhou cohort and evaluated 15 skin aging phenotypes by using the validated skin aging SCINEXA™ score. Genetic associations were tested by linear and logistic regression analyses and adjusted for potential confounders. RESULTS: Six genomic regions significantly associated with a risk for skin aging were revealed : 6q24.2 (rs3804540, P=4.6×10-9, additive model) with size of pigmented spots on forehead, 10q26.13 (rs4962295, P=1.9 ×10-8, additive model) with wrinkles under eyes, 15q21.1 (rs28392847, P=1.6×10-8, additive model) with crow's feet, 2p25.1 (rs191497052, P=5.5×10-9, dominant model) with telangiectasia, 13q34 (rs3825460, P=3.7×10-8, dominant model) with size of pigmented spots on cheeks and 16p13.11(rs76053540, P=5.0×10-9, dominant model) with nasolabialfold. The signal on 15q21.1 was replicated in the meta-analysis with two independent Caucasian cohorts (P=8.6×10-10). We have also successfully replicated in our cohort an association between SNP rs1048943 of gene CYP1A1 (P=7.1 × 10-4) and pigmented spots on cheeks previously described in Caucasian cohort. CONCLUSIONS: Our study has identified new genetic risk factors for signs of skin aging in the Han Chinese. This study suggests there are differences in genetic susceptibility to skin aging between Caucasians and the Han Chinese.
BACKGROUND: The progression of human skin aging has a strong genetic basis. However, recent studies have mainly focused on Caucasian populations and we have thus performed a genetic association study on skin aging signs in Han Chinese population. OBJECTIVE: To investigate genetic risk factors in skin aging in Han Chinese female, we performed a genome-wide association study. METHODS: We collected genotype data from 1534 Han Chinese female from Taizhou cohort and evaluated 15 skin aging phenotypes by using the validated skin aging SCINEXA™ score. Genetic associations were tested by linear and logistic regression analyses and adjusted for potential confounders. RESULTS: Six genomic regions significantly associated with a risk for skin aging were revealed : 6q24.2 (rs3804540, P=4.6×10-9, additive model) with size of pigmented spots on forehead, 10q26.13 (rs4962295, P=1.9 ×10-8, additive model) with wrinkles under eyes, 15q21.1 (rs28392847, P=1.6×10-8, additive model) with crow's feet, 2p25.1 (rs191497052, P=5.5×10-9, dominant model) with telangiectasia, 13q34 (rs3825460, P=3.7×10-8, dominant model) with size of pigmented spots on cheeks and 16p13.11(rs76053540, P=5.0×10-9, dominant model) with nasolabialfold. The signal on 15q21.1 was replicated in the meta-analysis with two independent Caucasian cohorts (P=8.6×10-10). We have also successfully replicated in our cohort an association between SNP rs1048943 of gene CYP1A1 (P=7.1 × 10-4) and pigmented spots on cheeks previously described in Caucasian cohort. CONCLUSIONS: Our study has identified new genetic risk factors for signs of skin aging in the Han Chinese. This study suggests there are differences in genetic susceptibility to skin aging between Caucasians and the Han Chinese.
Authors: Sara Rhaissa Rezende Dos Reis; Suyene Rocha Pinto; Frederico Duarte de Menezes; Ramon Martinez-Manez; Eduardo Ricci-Junior; Luciana Magalhaes Rebelo Alencar; Edward Helal-Neto; Aline Oiveira da Silva de Barros; Patricia Cristina Lisboa; Ralph Santos-Oliveira Journal: Pharm Res Date: 2020-01-22 Impact factor: 4.200