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Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods

Published online by Cambridge University Press:  06 January 2023

Ji Ke Open the ORCID record for Ji Ke [Opens in a new window] ,
Wenjing Gao ,
Biqi Wang ,
Weihua Cao ,
Jun Lv ,
Canqing Yu ,
Tao Huang ,
Dianjianyi Sun ,
Chunxiao Liao  and
Yuanjie Pang
...Show all authors
Ji Ke
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Wenjing Gao*
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Biqi Wang
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Weihua Cao
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Jun Lv
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Canqing Yu
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Tao Huang
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Dianjianyi Sun
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Chunxiao Liao
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Yuanjie Pang
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
Zengchang Pang
Affiliation:
Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China
Liming Cong
Affiliation:
Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
Hua Wang
Affiliation:
Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Xianping Wu
Affiliation:
Sichuan Center for Disease Control and Prevention, Chengdu, China
Yu Liu
Affiliation:
Heilongjiang Provincial Center for Disease Control and Prevention, Harbin, China
Liming Li*
Affiliation:
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
*
Authors for correspondence: Wenjing Gao, Email: pkuepigwj@126.com; Liming Li, Email: lmleeph@vip.163.com
Authors for correspondence: Wenjing Gao, Email: pkuepigwj@126.com; Liming Li, Email: lmleeph@vip.163.com
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Abstract

It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.

Keywords

TwinsBMIlipidsstructural equation modelgenome-wide association study
Type
Article
Information
Twin Research and Human Genetics , Volume 25 , Issue 6 , December 2022 , pp. 234 - 244
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of International Society for Twin Studies

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References

Abbasi, F., Blasey, C., & Reaven, G. M. (2013). Cardiometabolic risk factors and obesity: Does it matter whether BMI or waist circumference is the index of obesity? American Journal of Clinical Nutrition, 98, 637–640. http://doi.org/10.3945/ajcn.112.047506 CrossRefGoogle ScholarPubMed
Ainsworth, B. E., Haskell, W. L., Whitt, M. C., Irwin, M. L., Swartz, A. M., Strath, S. J., O’Brien, W. L., Bassett, D. R. Jr, Schmitz, K. H., Emplaincourt, P. O., Jacobs, D. R. Jr, & Leon, A. S. (2000). Compendium of physical activities: An update of activity codes and MET intensities. Medicine & Science in Sports & Exercise, 32, S498–S504. http://doi.org/10.1097/00005768-200009001-00009 CrossRefGoogle ScholarPubMed
Al-Attar, S. A., Pollex, R. L., Ban, M. R., Young, T. K., Bjerregaard, P., Anand, S. S., Yusuf, S., Zinman, B., Harris, S. B., Hanley, A. J., Connelly, P. W., Huff, M. W., & Hegele, R. A. (2008). Association between the FTO rs9939609 polymorphism and the metabolic syndrome in a non-Caucasian multi-ethnic sample. Cardiovascular Diabetology, 7, 5. http://doi.org/10.1186/1475-2840-7-5 CrossRefGoogle Scholar
Alshehry, Z. H., Mundra, P. A., Barlow, C. K., Mellett, N. A., Wong, G., McConville, M. J., Simes, J., Tonkin, A. M., Sullivan, D. R., Barnes, E. H., Nestel, P. J., Kingwell, B. A., Marre, M., Neal, B., Poulter, N. R., Rodgers, A., Williams, B., Zoungas, S., Hillis, G. S., … Meikle, P. J. (2016). Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus. Circulation, 134, 1637–1650. http://doi.org/10.1161/CIRCULATIONAHA.116.023233 CrossRefGoogle ScholarPubMed
Beyene, H. B., Olshansky, G., T. Smith, A. A., Giles, C., Huynh, K., Cinel, M., Mellett, N. A., Cadby, G., Hung, J., Hui, J., Beilby, J., Watts, G. F., Shaw, J. E., Moses, E. K., Magliano, D. J., & Meikle, P. J. (2020). High-coverage plasma lipidomics reveals novel sex-specific lipidomic fingerprints of age and BMI: Evidence from two large population cohort studies. PLOS Biology, 18, e3000870. http://doi.org/10.1371/journal.pbio.3000870 CrossRefGoogle ScholarPubMed
Bragg, F., Tang, K., Guo, Y., Iona, A., Du, H., Holmes, M. V., Bian, Z, Kartsonaki, C., Chen, Y., Yang, L., Sun, Q., Dong, C., Chen, J., Collins, R., Peto, R., Li, L., & Chen, Z., & China Kadoorie Biobank (CKB) Collaborative Group. (2018). Associations of general and central adiposity with incident diabetes in Chinese men and women. Diabetes Care, 41, 494–502. http://doi.org/10.2337/dc17-1852 CrossRefGoogle ScholarPubMed
Cadby, G., Melton, P. E., McCarthy, N. S., Almeida, M., Williams-Blangero, S., Curran, J. E., VandeBerg, J. L., Hui, J., Beilby, J., Musk, A. W., James, A. L., Hung, J., Blangero, J., & Moses, E. K. (2018). Pleiotropy of cardiometabolic syndrome with obesity-related anthropometric traits determined using empirically derived kinships from the Busselton health study. Human Genetics, 137, 45–53. http://doi.org/10.1007/s00439-017-1856-x CrossRefGoogle ScholarPubMed
Cadby, G., Melton, P. E., McCarthy, N. S., Giles, C., Mellett, N. A., Huynh, K., Hung, J., Beilby, J., Dubé, M. P., Watts, G. F., Blangero, J., Meikle, P. J., & Moses, E. K. (2020). Heritability of 596 lipid species and genetic correlation with cardiovascular traits in the Busselton family heart study. Journal of Lipid Research, 61, 537–545. http://doi.org/10.1194/jlr.RA119000594 CrossRefGoogle ScholarPubMed
Chen, B., Luo, L., Wei, X., Gong, D., & Jin, L. (2018). Altered plasma miR-144 as a novel biomarker for coronary artery disease. Annals of Clinical & Laboratory Science, 48, 440–445. https://www.ncbi.nlm.nih.gov/pubmed/30143484 Google ScholarPubMed
Chien, K. L., Hsu, H. C., Chen, W. J., Chen, M. F., Su, T. C., & Lee, Y. T. (2007). Familial aggregation of metabolic syndrome among the Chinese: Report from the Chin-Shan community family study. Diabetes Research and Clinical Practice, 76, 418–424. http://doi.org/10.1016/j.diabres.2006.09.026 CrossRefGoogle ScholarPubMed
Christakoudi, S., Evangelou, E., Riboli, E., & Tsilidis, K. K. (2021). GWAS of allometric body-shape indices in UK Biobank identifies loci suggesting associations with morphogenesis, organogenesis, adrenal cell renewal and cancer. Scientific Reports, 11, 10688. http://doi.org/10.1038/s41598-021-89176-6 CrossRefGoogle ScholarPubMed
Davezac, N., Baldin, V., Blot, J., Ducommun, B., & Tassan, J. P. (2002). Human pEg3 kinase associates with and phosphorylates CDC25B phosphatase: A potential role for pEg3 in cell cycle regulation. Oncogene, 21, 7630–7641. http://doi.org/10.1038/sj.onc.1205870 CrossRefGoogle ScholarPubMed
de Aguiar Vallim, T. Q., Tarling, E. J., Kim, T., Civelek, M., Baldan, A., Esau, C., & Edwards, P. A. (2013). MicroRNA-144 regulates hepatic ATP binding cassette transporter A1 and plasma high-density lipoprotein after activation of the nuclear receptor farnesoid X receptor. Circulation Research, 112, 1602–1612. http://doi.org/10.1161/CIRCRESAHA.112.300648 CrossRefGoogle ScholarPubMed
Dennerlein, S., Rozanska, A., Wydro, M., Chrzanowska-Lightowlers, Z. M., & Lightowlers, R. N. (2010). Human ERAL1 is a mitochondrial RNA chaperone involved in the assembly of the 28S small mitochondrial ribosomal subunit. Biochemical Journal, 430, 551–558. http://doi.org/10.1042/BJ20100757 CrossRefGoogle ScholarPubMed
Eden, E., Navon, R., Steinfeld, I., Lipson, D., & Yakhini, Z. (2009). GOrilla: A tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics, 10, 48. http://doi.org/10.1186/1471-2105-10-48 CrossRefGoogle ScholarPubMed
Elks, C. E., den Hoed, M., Zhao, J. H., Sharp, S. J., Wareham, N. J., Loos, R. J., & Ong, K. K. (2012). Variability in the heritability of body mass index: A systematic review and meta-regression. Frontiers in Endocrinology, 3, 29. http://doi.org/10.3389/fendo.2012.00029 CrossRefGoogle ScholarPubMed
Emdin, C. A., Khera, A. V., Natarajan, P., Klarin, D., Zekavat, S. M., Hsiao, A. J., & Kathiresan, S. (2017). Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA, 317, 626–634. http://doi.org/10.1001/jama.2016.21042 CrossRefGoogle ScholarPubMed
Fall, T., Hagg, S., Ploner, A., Magi, R., Fischer, K., Draisma, H. H., Sarin, A. P., Benyamin, B., Ladenvall, C., Åkerlund, M., Kals, M., Esko, T., Nelson, C. P., Kaakinen, M., Huikari, V., Mangino, M., Meirhaeghe, A., Kristiansson, K., Nuotio, M. L., … ENGAGE Consortium. (2015). Age- and sex-specific causal effects of adiposity on cardiovascular risk factors. Diabetes, 64, 1841–1852. http://doi.org/10.2337/db14-0988 CrossRefGoogle ScholarPubMed
Fan, M., Lv, J., & He, P. (2014). Chinese guidelines for data processing and analysis concerning the International Physical Activity Questionnaire. Zhonghua Liu Xing Bing Xue Za Zhi, 35, 961–964. https://www.ncbi.nlm.nih.gov/pubmed/25376692 Google ScholarPubMed
Flegal, K. M., Graubard, B. I., Williamson, D. F., & Gail, M. H. (2007). Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA, 298, 2028–2037. http://doi.org/10.1001/jama.298.17.2028 CrossRefGoogle ScholarPubMed
Fox, C. S., Liu, Y., White, C. C., Feitosa, M., Smith, A. V., Heard-Costa, N., Lohman, K., GIANT Consortium, MAGIC Consortium, GLGC Consortium, Johnson, A. D., Foster, M. C., Greenawalt, D. M., Griffin, P., Ding, J., Newman, A. B., Tylavsky, F., Miljkovic, I., Kritchevsky, S. B., … Borecki, I. B. (2012). Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women. PLOS Genetics, 8, e1002695. http://doi.org/10.1371/journal.pgen.1002695 CrossRefGoogle ScholarPubMed
Gao, L., Wu, L., Zhang, M., Zhao, X., Cheng, H., & Mi, J. (2018). Gender-specific association of the rs6499640 polymorphism in the FTO gene with plasma lipid levels in Chinese children. Genetics and Molecular Biology, 41, 397–402. http://doi.org/10.1590/1678-4685-GMB-2017-0107 CrossRefGoogle ScholarPubMed
Gao, W., Cao, W., Lv, J., Yu, C., Wu, T., Wang, S., Meng, L, Wang, D., Wang, Z., Pang, Z., Yu, M., Wang, H., Wu, X., Dong, Z., Wu, F., Jiang, G., Wang, X., Liu, Y., Deng, J., … Li, L. (2019). The Chinese National Twin Registry: A ‘gold mine’ for scientific research. Journal of Internal Medicine, 286, 299–308. http://doi.org/10.1111/joim.12926 CrossRefGoogle Scholar
Gaudet, P., Livstone, M. S., Lewis, S. E., & Thomas, P. D. (2011). Phylogenetic-based propagation of functional annotations within the Gene Ontology Consortium. Briefings in Bioinformatics, 12, 449–462. http://doi.org/10.1093/bib/bbr042 CrossRefGoogle ScholarPubMed
Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (BMI Mediated Effects), Lu, Y., Hajifathalian, K., Ezzati, M., Woodward, M., Rimm, E. B., & Danaei, G. (2014). Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: A pooled analysis of 97 prospective cohorts with 1.8 million participants. Lancet, 383, 970–983. http://doi.org/10.1016/S0140-6736(13)61836-X Google ScholarPubMed
Guedes, E. C., Franca, G. S., Lino, C. A., Koyama, F. C., Moreira Ldo, N., Alexandre, J. G., Barreto-Chaves, M. L., Galante, P. A., & Diniz, G. P. (2016). MicroRNA expression signature is altered in the cardiac remodeling induced by high fat diets. Journal of Cellular Physiology, 231, 1771–1783. http://doi.org/10.1002/jcp.25280 CrossRefGoogle ScholarPubMed
Harris, J. R., Tambs, K., & Magnus, P. (1995). Sex-specific effects for body mass index in the new Norwegian twin panel. Genetic Epidemiology, 12, 251–265. http://doi.org/10.1002/gepi.1370120303 CrossRefGoogle ScholarPubMed
He, M., Cornelis, M. C., Franks, P. W., Zhang, C., Hu, F. B., & Qi, L. (2010). Obesity genotype score and cardiovascular risk in women with type 2 diabetes mellitus. Arteriosclerosis, Thrombosis, and Vascular Biology, 30, 327–332. http://doi.org/10.1161/ATVBAHA.109.196196 CrossRefGoogle ScholarPubMed
Hebbar, P., Abubaker, J. A., Abu-Farha, M., Alsmadi, O., Elkum, N., Alkayal, F., John, S. E., Channanath, A., Iqbal, R., Pitkaniemi, J., Tuomilehto, J., Sladek, R., Al-Mulla, F., & Thanaraj, T. A. (2021). Genome-wide landscape establishes novel association signals for metabolic traits in the Arab population. Human Genetics, 140, 505–528. http://doi.org/10.1007/s00439-020-02222-7 CrossRefGoogle ScholarPubMed
Heller, D. A., de Faire, U., Pedersen, N. L., Dahlen, G., & McClearn, G. E. (1993). Genetic and environmental influences on serum lipid levels in twins. New England Journal of Medicine, 328, 1150–1156. http://doi.org/10.1056/NEJM199304223281603 CrossRefGoogle ScholarPubMed
Herskind, A. M., McGue, M., Sorensen, T. I., & Harvald, B. (1996). Sex and age specific assessment of genetic and environmental influences on body mass index in twins. International Journal of Obesity and Related Metabolic Disorders, 20, 106–113. https://www.ncbi.nlm.nih.gov/pubmed/8646246 Google ScholarPubMed
Hong, K. W., Go, M. J., Jin, H. S., Lim, J. E., Lee, J. Y., Han, B. G., Hwang, S. Y., Lee, S. H., Park, H. K., Cho, Y. S., & Oh, B. (2010). Genetic variations in ATP2B1, CSK, ARSG and CSMD1 loci are related to blood pressure and/or hypertension in two Korean cohorts. Journal of Human Hypertension, 24, 367–372. http://doi.org/10.1038/jhh.2009.86 CrossRefGoogle ScholarPubMed
Howie, B. N., Donnelly, P., & Marchini, J. (2009). A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLOS Genetics, 5, e1000529. http://doi.org/10.1371/journal.pgen.1000529 CrossRefGoogle ScholarPubMed
Hu, Y. W., Hu, Y. R., Zhao, J. Y., Li, S. F., Ma, X., Wu, S. G., Lu, J. B., Qiu, Y. R., Sha, Y. H., Wang, Y. C., Gao, J. J., Zheng, L., & Wang, Q. (2014). An agomir of miR-144-3p accelerates plaque formation through impairing reverse cholesterol transport and promoting pro-inflammatory cytokine production. PLoS One, 9, e94997. http://doi.org/10.1371/journal.pone.0094997 CrossRefGoogle ScholarPubMed
Kim, Y. K., Hwang, M. Y., Kim, Y. J., Moon, S., Han, S., & Kim, B. J. (2016). Evaluation of pleiotropic effects among common genetic loci identified for cardio-metabolic traits in a Korean population. Cardiovascular Diabetology, 15, 20. http://doi.org/10.1186/s12933-016-0337-1 CrossRefGoogle Scholar
Kring, S. I., Holst, C., Zimmermann, E., Jess, T., Berentzen, T., Toubro, S., Hansen, T., Astrup, A., Pedersen, O., & Sorensen, T. I. (2008). FTO gene associated fatness in relation to body fat distribution and metabolic traits throughout a broad range of fatness. PLoS One, 3, e2958. http://doi.org/10.1371/journal.pone.0002958 CrossRefGoogle ScholarPubMed
Li, L., Gao, W., Yu, C., Lv, J., Cao, W., Zhan, S., Wang, S., Wu, C., & Hu, Y. (2013). The Chinese National Twin Registry: An update. Twin Research and Human Genetics, 16, 86–90. http://doi.org/10.1017/thg.2012.148 CrossRefGoogle ScholarPubMed
Li, Y., Cao, K., Zhu, G., Fang, W., Chen, C., Wang, X., Zhao, P., Guo, J., Ding, T., Guan, L., Zhang, Q., Guo, W., Fei, Z., & Wang, L. (2019). Genomic analyses of an extensive collection of wild and cultivated accessions provide new insights into peach breeding history. Genome Biology, 20, 36. http://doi.org/10.1186/s13059-019-1648-9 CrossRefGoogle ScholarPubMed
Liao, C., Gao, W., Cao, W., Lv, J., Yu, C., Wang, S., Zhou, B., Pang, Z., Cong, L., Wang, H., Wu, X., & Li, L. (2015). Associations of body composition measurements with serum lipid, glucose and insulin profile: A Chinese twin study. PLoS One, 10, e0140595. http://doi.org/10.1371/journal.pone.0140595 CrossRefGoogle ScholarPubMed
Lin, C. C., Peyser, P. A., Kardia, S. L., Li, C. I., Liu, C. S., Chu, J. S., Lin, W. Y., & Li, T. C. (2014). Heritability of cardiovascular risk factors in a Chinese population ¾Taichung Community Health Study and Family Cohort. Atherosclerosis, 235, 488–495. http://doi.org/10.1016/j.atherosclerosis.2014.05.939 CrossRefGoogle Scholar
Lin, M. L., Park, J. H., Nishidate, T., Nakamura, Y., & Katagiri, T. (2007). Involvement of maternal embryonic leucine zipper kinase (MELK) in mammary carcinogenesis through interaction with Bcl-G, a pro-apoptotic member of the Bcl-2 family. Breast Cancer Research, 9, R17. http://doi.org/10.1186/bcr1650 CrossRefGoogle ScholarPubMed
Liu, C. T., Monda, K. L., Taylor, K. C., Lange, L., Demerath, E. W., Palmas, W., Wojczynski, M. K., Ellis, J. C., Vitolins, M. Z., Liu, S., Papanicolaou, G. J., Irvin, M. R., Xue, L., Griffin, P. J., Nalls, M. A., Adeyemo, A., Liu, J., Li, G., Ruiz-Narvaez, E. A., Chen, W. M., … Fox, C. S. (2013). Genome-wide association of body fat distribution in African ancestry populations suggests new loci. PLOS Genetics, 9, e1003681. http://doi.org/10.1371/journal.pgen.1003681 CrossRefGoogle ScholarPubMed
Lotta, L. A., Wittemans, L. B. L., Zuber, V., Stewart, I. D., Sharp, S. J., Luan, J., Day, F. R., Li, C., Bowker, N., Cai, L., De Lucia Rolfe, E., Khaw, K. T., Perry, J. R. B., O’Rahilly, S., Scott, R. A., Savage, D. B., Burgess, S., Wareham, N. J., & Langenberg, C. (2018). Association of genetic variants related to gluteofemoral versus abdominal fat distribution with type 2 diabetes, coronary disease, and cardiovascular risk factors. JAMA, 320, 2553–2563. http://doi.org/10.1001/jama.2018.19329 CrossRefGoogle Scholar
Mahaney, M. C., Blangero, J., Comuzzie, A. G., VandeBerg, J. L., Stern, M. P., & MacCluer, J. W. (1995). Plasma HDL cholesterol, triglycerides, and adiposity. A quantitative genetic test of the conjoint trait hypothesis in the San Antonio Family Heart Study. Circulation, 92, 3240–3248. http://doi.org/10.1161/01.cir.92.11.3240 CrossRefGoogle ScholarPubMed
Mather, L., Blom, V., Bergstrom, G., & Svedberg, P. (2016). An underlying common factor, influenced by genetics and unique environment, explains the covariation between major depressive disorder, generalized anxiety disorder, and burnout: A Swedish twin study. Twin Research and Human Genetics, 19, 619–627. http://doi.org/10.1017/thg.2016.73 CrossRefGoogle ScholarPubMed
Nock, N. L., Wang, X., Thompson, C. L., Song, Y., Baechle, D., Raska, P., Stein, C. M., & Gray-McGuire, C. (2009). Defining genetic determinants of the metabolic syndrome in the Framingham Heart Study using association and structural equation modeling methods. BMC Proceedings, 3, S50. http://doi.org/10.1186/1753-6561-3-s7-s50 CrossRefGoogle ScholarPubMed
Orstavik, R. E., Kendler, K. S., Czajkowski, N., Tambs, K., & Reichborn-Kjennerud, T. (2007). Genetic and environmental contributions to depressive personality disorder in a population-based sample of Norwegian twins. Journal of Affective Disorders, 99, 181–189. http://doi.org/10.1016/j.jad.2006.09.011 CrossRefGoogle Scholar
Pang, Y., Kartsonaki, C., Turnbull, I., Guo, Y., Chen, Y., Clarke, R., Bian, Z., Bragg, F., Millwood, I. Y., Yang, L., Huang, Y., Yang, Y., Zhang, X., Chen, J., Li, L., Holmes, M. V., & Chen, Z. (2019). Adiposity in relation to risks of fatty liver, cirrhosis and liver cancer: A prospective study of 0.5 million Chinese adults. Scientific Reports, 9, 785. http://doi.org/10.1038/s41598-018-36460-7 CrossRefGoogle ScholarPubMed
Pang, Z., Zhang, D., Li, S., Duan, H., Hjelmborg, J., Kruse, T. A., Kyvik, K. O., Christensen, K., & Tan, Q. (2010). Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins. Diabetologia, 53, 2554–2561. http://doi.org/10.1007/s00125-010-1907-5 CrossRefGoogle ScholarPubMed
Parr, C. L., Batty, G. D., Lam, T. H., Barzi, F., Fang, X., Ho, S. C., Jee, S. H., Ansary-Moghaddam, A., Jamrozik, K., Ueshima, H., Woodward, M., Huxley, R. R., & Asia-Pacific Cohort Studies Collaboration. (2010). Body-mass index and cancer mortality in the Asia-Pacific Cohort Studies Collaboration: Pooled analyses of 424,519 participants. Lancet Oncology, 11, 741–752. http://doi.org/10.1016/S1470-2045(10)70141-8 CrossRefGoogle ScholarPubMed
Ramirez, C. M., Rotllan, N., Vlassov, A. V., Davalos, A., Li, M., Goedeke, L., Aranda, J. F., Cirera-Salinas, D., Araldi, E., Salerno, A., Wanschel, A., Zavadil, J., Castrillo, A., Kim, J., Suárez, Y., & Fernandez-Hernando, C. (2013). Control of cholesterol metabolism and plasma high-density lipoprotein levels by microRNA-144. Circulation Research, 112, 1592–1601. http://doi.org/10.1161/CIRCRESAHA.112.300626 CrossRefGoogle ScholarPubMed
Ran, S., Pei, Y. F., Liu, Y. J., Zhang, L., Han, Y. Y., Hai, R., Tian, Q., Lin, Y., Yang, T. L., Guo, Y. F., Shen, H., Thethi, I. S., Zhu, X. Z., & Deng, H. W. (2013). Bivariate genome-wide association analyses identified genes with pleiotropic effects for femoral neck bone geometry and age at menarche. PLoS One, 8, e60362. http://doi.org/10.1371/journal.pone.0060362 CrossRefGoogle ScholarPubMed
Randall, J. C., Winkler, T. W., Kutalik, Z., Berndt, S. I., Jackson, A. U., Monda, K. L., Kilpeläinen, T. O., Esko, T., Mägi, R., Li, S., Workalemahu, T., Feitosa, M. F., Croteau-Chonka, D. C., Day, F. R., Fall, T., Ferreira, T., Gustafsson, S., Locke, A. E., Mathieson, I., … Heid, I. M. (2013). Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLOS Genetics, 9, e1003500. http://doi.org/10.1371/journal.pgen.1003500 CrossRefGoogle ScholarPubMed
Rankinen, T., Sarzynski, M. A., Ghosh, S., & Bouchard, C. (2015). Are there genetic paths common to obesity, cardiovascular disease outcomes, and cardiovascular risk factors? Circulation Research, 116, 909–922. http://doi.org/10.1161/CIRCRESAHA.116.302888 CrossRefGoogle ScholarPubMed
Rhee, E. P., Ho, J. E., Chen, M. H., Shen, D., Cheng, S., Larson, M. G., Ghorbani, A., Shi, X., Helenius, I. T., O’Donnell, C. J., Souza, A. L., Deik, A., Pierce, K. A., Bullock, K., Walford, G. A., Vasan, R. S., Florez, J. C., Clish, C., Yeh, J. R., … Gerszten, R. E. (2013). A genome-wide association study of the human metabolome in a community-based cohort. Cell Metabolism, 18, 130–143. http://doi.org/10.1016/j.cmet.2013.06.013 CrossRefGoogle Scholar
Rohde, K., Keller, M., la Cour Poulsen, L., Bluher, M., Kovacs, P., & Bottcher, Y. (2019). Genetics and epigenetics in obesity. Metabolism, 92, 37–50. http://doi.org/10.1016/j.metabol.2018.10.007 CrossRefGoogle ScholarPubMed
Schorr, M., Dichtel, L. E., Gerweck, A. V., Valera, R. D., Torriani, M., Miller, K. K., & Bredella, M. A. (2018). Sex differences in body composition and association with cardiometabolic risk. Biology of Sex Differences, 9, 28. http://doi.org/10.1186/s13293-018-0189-3 CrossRefGoogle ScholarPubMed
Schousboe, K., Willemsen, G., Kyvik, K. O., Mortensen, J., Boomsma, D. I., Cornes, B. K., Davis, C. J., Fagnani, C., Hjelmborg, J., Kaprio, J., De Lange, M., Luciano, M., Martin, N. G., Pedersen, N., Pietiläinen, K. H., Rissanen, A., Saarni, S., Sørensen, T. I., Van Baal, G. C., & Harris, J. R. (2003). Sex differences in heritability of BMI: A comparative study of results from twin studies in eight countries. Twin Research, 6, 409–421. http://doi.org/10.1375/136905203770326411 CrossRefGoogle ScholarPubMed
Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., & Smoller, J. W. (2013). Pleiotropy in complex traits: challenges and strategies. Nature Reviews Genetics, 14, 483–495. http://doi.org/10.1038/nrg3461 CrossRefGoogle ScholarPubMed
Souren, N. Y., Paulussen, A. D., Loos, R. J., Gielen, M., Beunen, G., Fagard, R., Derom, C., Vlietinck, R., & Zeegers, M. P. (2007). Anthropometry, carbohydrate and lipid metabolism in the East Flanders Prospective Twin Survey: Heritabilities. Diabetologia, 50, 2107–2116. http://doi.org/10.1007/s00125-007-0784-z CrossRefGoogle ScholarPubMed
Sun, P. C., Uppaluri, R., Schmidt, A. P., Pashia, M. E., Quant, E. C., Sunwoo, J. B., Gollin, S. M., & Scholnick, S. B. (2001). Transcript map of the 8p23 putative tumor suppressor region. Genomics, 75, 17–25. http://doi.org/10.1006/geno.2001.6587 CrossRefGoogle ScholarPubMed
Tang, W., Hong, Y., Province, M. A., Rich, S. S., Hopkins, P. N., Arnett, D. K., Pankow, J. S., Miller, M. B., & Eckfeldt, J. H. (2006). Familial clustering for features of the metabolic syndrome: the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study. Diabetes Care, 29, 631–636. http://doi.org/10.2337/diacare.29.03.06.dc05-0679 CrossRefGoogle ScholarPubMed
Tao, S., Li, Y., Xiao, Z., Cen, R., Zhang, H., Zhuo, Y., Zhou, B., Chen, P., Li, Y., Liao, Y., Folsom, A., Stamler, J., Warnick, G. R., & Williams, O. D. (1992). Serum lipids and their correlates in Chinese urban and rural populations of Beijing and Guangzhou. PRC-USA Cardiovascular and Cardiopulmonary Epidemiology Research Group. International Journal of Epidemiology, 21, 893–903. http://doi.org/10.1093/ije/21.5.893 CrossRefGoogle Scholar
Wang, B., Gao, W., Yu, C., Cao, W., Lv, J., Wang, S., Pang, Z, Cong, L., Wang, H., Wu, X., & Li, L. (2015). Determination of zygosity in adult Chinese twins using the 450K methylation array versus questionnaire data. PLoS One, 10 e0123992. http://doi.org/10.1371/journal.pone.0123992 CrossRefGoogle ScholarPubMed
Wang, X., Zheng, Y., Ma, Y., Du, L., Chu, F., Gu, H., Dahlgren, R. A., Li, Y., & Wang, H. (2018). Lipid metabolism disorder induced by up-regulation of miR-125b and miR-144 following beta-diketone antibiotic exposure to F0-zebrafish (Danio rerio). Ecotoxicology and Environmental Safety, 164, 243–252. http://doi.org/10.1016/j.ecoenv.2018.08.027 CrossRefGoogle ScholarPubMed
Weiss, L. A., Pan, L., Abney, M., & Ober, C. (2006). The sex-specific genetic architecture of quantitative traits in humans. Nature Genetics, 38, 218–222. http://doi.org/10.1038/ng1726 CrossRefGoogle ScholarPubMed
Willer, C. J., Schmidt, E. M., Sengupta, S., Peloso, G. M., Gustafsson, S., Kanoni, S., Ganna, A., Chen, J., Buchkovich, M. L., Mora, S., Beckmann, J. S., Bragg-Gresham, J. L., Chang, H. Y., Demirkan, A., Den Hertog, H. M., Do, R., Donnelly, L. A., Ehret, G. B., Esko, T., … Global Lipids Genetics Consortium. (2013). Discovery and refinement of loci associated with lipid levels. Nature Genetics, 45, 1274–1283. http://doi.org/10.1038/ng.2797 Google ScholarPubMed
Winkler, T. W., Justice, A. E., Graff, M., Barata, L., Feitosa, M. F., Chu, S., Czajkowski, J., Esko, T., Fall, T., Kilpeläinen, T. O., Lu, Y., Mägi, R., Mihailov, E., Pers, T. H., Rüeger, S., Teumer, A., Ehret, G. B., Ferreira, T., Heard-Costa, N. L., … Loos, R. J. (2015). The influence of age and sex on genetic associations with adult body size and shape: A large-scale genome-wide interaction study. PLOS Genetics, 11, e1005378. http://doi.org/10.1371/journal.pgen.1005378 CrossRefGoogle ScholarPubMed
Wurtz, P., Wang, Q., Kangas, A. J., Richmond, R. C., Skarp, J., Tiainen, M., Tynkkynen, T, Soininen, P., Havulinna, A. S., Kaakinen, M., Viikari, J. S., Savolainen, M. J., Kähönen, M., Lehtimäki, T., Männistö, S., Blankenberg, S., Zeller, T., Laitinen, J., Pouta, A., … Ala-Korpela, M. (2014). Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change. PLOS Medicine, 11, e1001765. http://doi.org/10.1371/journal.pmed.1001765 CrossRefGoogle ScholarPubMed
Yan, Y. X., Xiao, H. B., Zhang, J., Wang, S., Dong, J., & Wu, L. J. (2021). Pri-miR-144 rs9279 is associated with type 2 diabetes and regulation of stress response. Journal of Cellular Physiology, 236, 561–569. http://doi.org/10.1002/jcp.29883 CrossRefGoogle ScholarPubMed
Yang, J., Bakshi, A., Zhu, Z., Hemani, G., Vinkhuyzen, A. A., Nolte, I. M., van Vliet-Ostaptchouk, J. V., Snieder, H., Lifelines Cohort, Study, Esko, T., Milani, L., Mägi, R., Metspalu, A., Hamsten, A., Magnusson, P. K., Pedersen, N. L., Ingelsson, E., & Visscher, P. M. (2015). Genome-wide genetic homogeneity between sexes and populations for human height and body mass index. Human Molecular Genetics, 24, 7445–7449. http://doi.org/10.1093/hmg/ddv443 CrossRefGoogle ScholarPubMed
Zhou, B., Li, L., Lyu, J., Yu, C., Wang, S., Pang, Z., Cong, L., Dong, Z., Wu, F., Wang, H., Wu, X., Jiang, G., Wang, X., Wang, B., Gao, W., & Cao, W. (2015). Heritability of body mass index on Chinese adult twins from nine provinces/cities in China. Zhonghua Liu Xing Bing Xue Za Zhi, 36, 299–303. https://www.ncbi.nlm.nih.gov/pubmed/25975537 Google ScholarPubMed
Zhou, X., & Stephens, M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nature Genetics, 44, 821–824. http://doi.org/10.1038/ng.2310 CrossRefGoogle ScholarPubMed
Zhou, X., & Stephens, M. (2014). Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nature Methods, 11, 407–409. http://doi.org/10.1038/nmeth.2848 CrossRefGoogle ScholarPubMed
Zhu, H., & Leung, S. W. (2015). Identification of microRNA biomarkers in type 2 diabetes: A meta-analysis of controlled profiling studies. Diabetologia, 58, 900–911. http://doi.org/10.1007/s00125-015-3510-2 CrossRefGoogle ScholarPubMed
Zhu, S., Heymsfield, S. B., Toyoshima, H., Wang, Z., Pietrobelli, A., & Heshka, S. (2005). Race-ethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. American Journal of Clinical Nutrition, 81, 409–415. http://doi.org/10.1093/ajcn.81.2.409 CrossRefGoogle ScholarPubMed
Zhu, X., Feng, T., Tayo, B. O., Liang, J., Young, J. H., Franceschini, N., Smith, J. A., Yanek, L. R., Sun, Y. V., Edwards, T. L., Chen, W., Nalls, M., Fox, E., Sale, M., Bottinger, E., Rotimi, C., COGENT BP Consortium, Liu, Y., McKnight, B., … Redline, S. (2015). Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. American Journal of Human Genetics, 96, 21–36. http://doi.org/10.1016/j.ajhg.2014.11.011 CrossRefGoogle ScholarPubMed
Zhu, Z., Guo, Y., Shi, H., Liu, C. L., Panganiban, R. A., Chung, W., O’Connor, L. J., Himes, B. E., Gazal, S., Hasegawa, K., Camargo, C. A. Jr, Qi, L., Moffatt, M. F., Hu, F. B., Lu, Q., Cookson, W. O. C., & Liang, L. (2020). Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. Journal of Allergy and Clinical Immunology, 145, 537–549. http://doi.org/10.1016/j.jaci.2019.09.035 CrossRefGoogle ScholarPubMed
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