Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects
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Abstract
Body mass and body fat composition are of clinical interest due to their links to cardiovascular- and metabolic diseases. Fat stored in the trunk has been suggested to be more pathogenic compared to fat stored in other compartments. In this study, we perform genome-wide association studies (GWAS) for the proportion of body fat distributed to the arms, legs and trunk estimated from segmental bio-electrical impedance analysis (sBIA) for 362,499 individuals from the UK Biobank. 98 independent associations with body fat distribution are identified, 29 that have not previously been associated with anthropometric traits. A high degree of sex-heterogeneity is observed and the effects of 37 associated variants are stronger in females compared to males. Our findings also implicate that body fat distribution in females involves mesenchyme derived tissues and cell types, female endocrine tissues as well as extracellular matrix maintenance and remodeling.
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The genetic factors that help determine fat distribution are largely distinct from those that affect overall adiposity as estimated by BMI.
“Importantly, the genetic factors that help determine fat distribution are largely distinct from those that affect overall adiposity as estimated by BMI.78,94”
From Definition and diagnostic criteria of clinical obesity (2025) · cites this paper as [94]
The Results (‘Overlap with findings from previous GWAS’; page unavailable) identify 29 body-fat-distribution signals not previously associated with an anthropometric trait, supporting some genetic distinction. But the same section reports that the strongest arm-fat-ratio associations occur at known BMI and adiposity loci, while ‘Correlation between fat ratios and anthropometric traits’ reports a strong genetic correlation between arm fat ratio and BMI in females. The blanket description ‘largely distinct’ omits this substantial overlap and variation by fat compartment and sex. (LLM, conf=0.87)
Experimental — accuracy not yet measuredGenome-wide association studies have identified several hundred common variants that influence food intake, basal metabolic rate, and the energy used during a fixed amount of exercise.
“Genome-wide association studies have identified several hundred common variants that influence food intake, basal metabolic rate, and the energy used during a fixed amount of exercise.92–94”
From Definition and diagnostic criteria of clinical obesity (2025) · cites this paper as [94]
The Introduction (page unavailable) says GWAS have identified hundreds of loci associated with body mass and body fat distribution, not variants influencing food intake, basal metabolic rate, or energy used during a fixed amount of exercise. Searches for those three outcomes found no passage in our copy addressing the attributed claim. (LLM, conf=0.76)
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1 retracted
Ezzat, S., Zheng, L., Florez, J., Stefan, N., Mayr, T., Hliang, M., Jablonski, K., Harden, M. V., Stančáková, A., Laakso, M., et al. (2013) · Cell Metabolism · doi:10.1016/j.cmet.2013.05.002 · Cited by 24 · see citing works
RetractedControlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., Mullany, E. C., Biryukov, S., Abbafati, C., Abera, S. F., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60460-8 · Cited by 11,952 · see citing works
(1998) · PsycEXTRA Dataset · doi:10.1037/e565682010-001 · Cited by 9,452 · see citing works
Health effects of overweight and obesity in 195 countries over 25 years
Collaborators, T. G. 2. O. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1614362 · Cited by 7,918 · see citing works
SIFT: predicting amino acid changes that affect protein function
Ng, P. C. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg509 · Cited by 6,601 · see citing works
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559 · see citing works
METAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534 · see citing works
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255 · see citing works
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
Predicting Functional Effect of Human Missense Mutations Using PolyPhen‐2
Adzhubei, I., Jordan, D. M., & Sunyaev, S. R. (2013) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0720s76 · Cited by 3,705 · see citing works
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487 · see citing works
Self-citationSubcutaneous and visceral adipose tissue: their relation to the metabolic syndrome
Wajchenberg, B. L. (2000) · Endocrine Reviews · doi:10.1210/edrv.21.6.0415 · Cited by 2,961 · see citing works
(1998) · PubMed · Cited by 2,737 · see citing works
The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog)
MacArthur, J., Bowler, E., Cerezo, M., Gil, L., Hall, P., Hastings, E., Junkins, H., McMahon, A., Milano, A., Morales, J., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1133 · Cited by 2,161 · see citing works
Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
The International Consortium for Blood Pressure Genome-Wide Association Studies (2011) · Nature · doi:10.1038/nature10405 · Cited by 2,033 · see citing works
The Electronic Medical Records and Genomics (eMERGE) Consortium, Wood, A. R., The MIGen Consortium, The PAGE Consortium, Study, T. L. C., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., et al. (2014) · Nature Genetics · doi:10.1038/ng.3097 · Cited by 1,853 · see citing works
GenABEL: an R library for genome-wide association analysis
Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm108 · Cited by 1,836 · see citing works
New genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580 · see citing works
Common variants at 30 loci contribute to polygenic dyslipidemia
Kathiresan, S., Willer, C. J., Peloso, G. M., Demissie, S., Musunuru, K., Schadt, E. E., Kaplan, L., Bennett, D., Li, Y., Tanaka, T., et al. (2009) · Nature Genetics · doi:10.1038/ng.291 · Cited by 1,364 · see citing works
Genome-wide association study identifies eight loci associated with blood pressure
Wellcome Trust Case Control Consortium, Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M. D., Bochud, M., Coin, L., Najjar, S. S., Zhao, J. H., Heath, S. C., et al. (2009) · Nature Genetics · doi:10.1038/ng.361 · Cited by 1,215 · see citing works
van der Harst, P., & Verweij, N. (2018) · Circulation Research · doi:10.1161/circresaha.117.312086 · Cited by 1,141 · see citing works
The UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040 · see citing works
MAGIC, Heid, I. M., Jackson, A. U., Randall, J. C., Winkler, T. W., Qi, L., Steinthorsdottir, V., Thorleifsson, G., Zillikens, M. C., Speliotes, E. K., et al. (2010) · Nature Genetics · doi:10.1038/ng.685 · Cited by 931 · see citing works
Self-citationBiological interpretation of genome-wide association studies using predicted gene functions
Pers, T. H., Karjalainen, J. M., Chan, Y., Westra, H.-J., Wood, A. R., Yang, J., Lui, J. C., Vedantam, S., Gustafsson, S., Esko, T., et al. (2015) · Nature Communications · doi:10.1038/ncomms6890 · Cited by 787 · see citing works
Winkler, T. W., Justice, A. E., Graff, M., Barata, L., Feitosa, M. F., Chu, S. H., Czajkowski, J., Esko, T., Fall, T., Kilpeläinen, T. O., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005378 · Cited by 664 · see citing works
Self-citationThe ADAMTS (A Disintegrin and Metalloproteinase with Thrombospondin motifs) family
Kelwick, R. J. R., Desanlis, I., Wheeler, G. N., & Edwards, D. R. (2015) · Genome biology · doi:10.1186/s13059-015-0676-3 · Cited by 654 · see citing works
GWAMA: software for genome-wide association meta-analysis
Mägi, R., & Morris, A. P. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-288 · Cited by 603 · see citing works
Versican: a versatile extracellular matrix proteoglycan in cell biology
Wight, T. N. (2002) · Current Opinion in Cell Biology · doi:10.1016/s0955-0674(02)00375-7 · Cited by 564 · see citing works
Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology
23andMe Research Team, Ferreira, M. A., collaborators, A., BIOS consortium, Study, L. C., Vonk, J. M., Baurecht, H., Marenholz, I., Tian, C., Hoffman, J. D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3985 · Cited by 550 · see citing works
Warren, H. R., Εvangelou, Ε., Cabrera, C. P., Gao, H., Ren, M., Mifsud, B., Ntalla, I., Surendran, P., Liu, C., Cook, J. P., et al. (2017) · Nature Genetics · doi:10.1038/ng.3768 · Cited by 545 · see citing works
Genetic and Nongenetic Determinants of Regional Fat Distribution
Bouchard, C., Després, J., & Mauriège, P. (1993) · Endocrine Reviews · doi:10.1210/edrv-14-1-72 · Cited by 486 · see citing works
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., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003500 · Cited by 409 · see citing works
Self-citationGenetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile
Kilpeläinen, T. O., Zillikens, M. C., Stančákova, A., Finucane, F. M., Ried, J. S., Langenberg, C., Zhang, W., Beckmann, J. S., Luan, J., Vandenput, L., et al. (2011) · Nature Genetics · doi:10.1038/ng.866 · Cited by 335 · see citing works
Genetic Basis for Sex Differences in Obesity and Lipid Metabolism
Link, J. C., & Reue, K. (2017) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-071816-064827 · Cited by 293 · see citing works
New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk
Lu, Y., Day, F. R., Gustafsson, S., Buchkovich, M. L., Na, J., Bataille, V. A., Cousminer, D. L., Dastani, Z., Drong, A., Esko, T., et al. (2016) · Nature Communications · doi:10.1038/ncomms10495 · Cited by 285 · see citing works
Fox, C. S., Liu, Y., White, C. C., Feitosa, M. F., Smith, A. V., Heard‐Costa, N. L., Lohman, K., Johnson, A. D., Foster, M. C., Greenawalt, D. M., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002695 · Cited by 276 · see citing works
Cancer progression and tumor cell motility are associated with the FGFR4 Arg(388) allele.
Bange, J., Prechtl, D., Cheburkin, Y. V., Specht, K., Harbeck, N., Schmitt, M., Knyazeva, T., Müller, S., Gärtner, S., Sures, I., et al. (2002) · PubMed · Cited by 275 · see citing works
Adipose extracellular matrix remodelling in obesity and insulin resistance
Lin, D., Chun, T., & Kang, L. (2016) · Biochemical Pharmacology · doi:10.1016/j.bcp.2016.05.005 · Cited by 239 · see citing works
the CARDIoGRAM Consortium, Bis, J. C., Kavousi, M., Franceschini, N., Isaacs, A., Abecasis, G. R., Schminke, U., Post, W. S., Smith, A. V., Cupples, L. A., et al. (2011) · Nature Genetics · doi:10.1038/ng.920 · Cited by 212 · see citing works
Myth and paradox of coronary risk and the menopause
Tunstall‐Pedoe, H. (1998) · The Lancet · doi:10.1016/s0140-6736(97)11321-6 · Cited by 178 · see citing works
Wain, L. V., Vaez, A., Jansen, R., Joehanes, R., van der Most, P. J., Erzurumluoglu, A. M., O’Reilly, P. F., Cabrera, C. P., Warren, H. R., Rose, L. M., et al. (2017) · Hypertension · doi:10.1161/hypertensionaha.117.09438 · Cited by 163 · see citing works
Yaghootkar, H., Lotta, L. A., Tyrrell, J., Smit, R. A. J., Jones, S. E., Donnelly, L. A., Beaumont, R. N., Campbell, A. I., Tuke, M. A., Hayward, C., et al. (2016) · Diabetes · doi:10.2337/db15-1671 · Cited by 152 · see citing works
plink: AnRPackage for Linking Mixed-Format Tests Using IRT-Based Methods
Weeks, J. P. (2010) · Journal of Statistical Software · doi:10.18637/jss.v035.i12 · Cited by 136 · see citing works
Meta‐analysis of sex‐specific genome‐wide association studies
Mägi, R., Lindgren, C. M., & Morris, A. P. (2010) · Genetic Epidemiology · doi:10.1002/gepi.20540 · Cited by 115 · see citing works
He, M., Xu, M., Zhang, B., Liang, J., Chen, P., Lee, J.-Y., Johnson, T. A., Li, H., Yang, X., Dai, J., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddu583 · Cited by 106 · see citing works
Bekhouche, M., Leduc, C., Dupont, L., Janssen, L., Delolme, F., Goff, S. V., Smargiasso, N., Baiwir, D., Mazzucchelli, G., Zanella‐Cléon, I., et al. (2016) · The FASEB Journal · doi:10.1096/fj.15-279869 · Cited by 102 · see citing works
The extracellular matrix in cancer progression: Role of hyalectan proteoglycans and ADAMTS enzymes
Binder, M. J., McCoombe, S. G., Williams, E. D., McCulloch, D. R., & Ward, A. C. (2016) · Cancer Letters · doi:10.1016/j.canlet.2016.11.001 · Cited by 77 · see citing works
Wen, W., Kato, N., Hwang, J., Guo, X., Tabara, Y., Li, H., Dorajoo, R., Yang, X., Tsai, F., Li, S., et al. (2016) · Scientific Reports · doi:10.1038/srep17958 · Cited by 75 · see citing works
Meta and pooled analyses of FGFR4 Gly388Arg polymorphism as a cancer prognostic factor
Frullanti, E., Berking, C., Harbeck, N., Jézéquel, P., Haugen, A., Mawrin, C., Parise, O., Sasaki, H., Tsuchiya, N., & Dragani, T. A. (2011) · European Journal of Cancer Prevention · doi:10.1097/cej.0b013e3283457274 · Cited by 55 · see citing works
Mally, K., Trentmann, J., Heller, M., & Dittmar, M. (2011) · European Journal of Applied Physiology · doi:10.1007/s00421-010-1795-x · Cited by 49 · see citing works
Wakil, S. M., Ram, R., Muiya, N. P., Andres, E., Mazhar, N., Hagos, S., Alshahid, M., Meyer, B. F., Morahan, G., & Dzimiri, N. (2016) · Clinical Genetics · doi:10.1111/cge.12761 · Cited by 22 · see citing works
Flagged first, then by citations
1 of the 56 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Definition and diagnostic criteria of clinical obesity
Rubino, F., Cummings, D. E., Eckel, R. H., Cohen, R. V., Wilding, J. P. H., Brown, W. A., Stanford, F. C., Batterham, R. L., Farooqi, I. S., Farpour-Lambert, N. J., et al. (2025) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00316-4 · Cited by 1,811 · see citing works
Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations
Boer, C. G., Hatzikotoulas, K., Southam, L., Stefánsdóttir, L., Zhang, Y., de Almeida, R. C., Wu, T. T., Zheng, J., Hartley, A. E., Teder‐Laving, M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.038 · Cited by 441 · see citing works
Larsson, S. C., Bäck, M., Rees, J. M. B., Mason, A. M., & Burgess, S. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz388 · Cited by 436 · see citing works
Metabolically Healthy Obesity: Criteria, Epidemiology, Controversies, and Consequences
Tsatsoulis, A. A., & Paschou, S. Α. (2020) · Current Obesity Reports · doi:10.1007/s13679-020-00375-0 · Cited by 263 · see citing works
Common genetic variation influencing human white matter microstructure
Zhao, B., Li, T., Yang, Y., Wang, X., Luo, T., Shan, Y., Zhu, Z., Xiong, D., Hauberg, M. E., Bendl, J. M., et al. (2021) · Science · doi:10.1126/science.abf3736 · Cited by 217 · see citing works
Genetics of Obesity: What We Have Learned Over Decades of Research
Bouchard, C. (2021) · Obesity · doi:10.1002/oby.23116 · Cited by 219 · see citing works
Sex differences in genetic architecture in the UK Biobank
Bernabeu, E., Canela‐Xandri, O., Rawlik, K., Talenti, A., Prendergast, J. G. D., & Tenesa, A. (2021) · Nature Genetics · doi:10.1038/s41588-021-00912-0 · Cited by 196 · see citing works
Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots
Agrawal, S., Wang, M., Klarqvist, M. D. R., Smith, K., Shin, J. H., Dashti, H., Diamant, N., Choi, S. H., Jurgens, S. J., Ellinor, P. T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30931-2 · Cited by 152 · see citing works
Adipose tissue hyperplasia and hypertrophy in common and syndromic obesity-the case of BBS obesity
Horwitz, A., & Birk, R. (2023) · Nutrients · doi:10.3390/nu15153445 · Cited by 143 · see citing works
Sex Differences in Genomic Drivers of Adipose Distribution and Related Cardiometabolic Disorders
Lumish, H. S., O'Reilly, M. E., & Reilly, M. P. (2019) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.119.313154 · Cited by 118 · see citing works
Leptin-activated hypothalamic BNC2 neurons acutely suppress food intake
Tan, H. L., Yin, L., Tan, Y., Ivanov, J., Plucińska, K., Ilanges, A., Herb, B. R., Wang, P., Kosse, C., Cohen, P., et al. (2024) · Nature · doi:10.1038/s41586-024-08108-2 · Cited by 106 · see citing works
Freuer, D., Linseisen, J., & Meisinger, C. (2021) · Metabolism · doi:10.1016/j.metabol.2021.154732 · Cited by 100 · see citing works
Sex-specific genetic effects across biomarkers
Flynn, E., Tanigawa, Y., Rodríguez, F., Altman, R. B., Sinnott-Armstrong, N., & Rivas, M. A. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-020-00712-w · Cited by 87 · see citing works
Sun, C., Kovacs, P. L., & Guiu‐Jurado, E. (2021) · Genes · doi:10.3390/genes12060841 · Cited by 96 · see citing works
Johansson, Å., Andreassen, O. A., Brunak, S., Franks, P. W., Hedman, H., Loos, R. J. F., Meder, B., Melén, E., Wheelock, C. E., & Jacobsson, B. (2023) · Journal of Internal Medicine · doi:10.1111/joim.13640 · Cited by 98 · see citing works
Self-citationChronic stress, epigenetics, and adipose tissue metabolism in the obese state
Xiao, Y., Liu, D., Cline, M. A., & Gilbert, E. R. (2020) · Nutrition & Metabolism · doi:10.1186/s12986-020-00513-4 · Cited by 87 · see citing works
Sanchez‐Roige, S., Jennings, M. V., Thorpe, H. H. A., Mallari, J. E., van der Werf, L. C., Bianchi, S. B., Huang, Y., Lee, C., Mallard, T. T., Barnes, S. A., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02453-y · Cited by 74 · see citing works
Brettle, H., Tran, V., Drummond, G. R., Franks, A. E., Petrovski, S., Vinh, A., & Jelinic, M. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.971048 · Cited by 78 · see citing works
Adiposity and sex-specific cancer risk
Rask‐Andersen, M., Ivansson, E. L., Höglund, J., Ek, W. E., Karlsson, T., & Johansson, Å. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.05.010 · Cited by 71 · see citing works
Self-citationSex differences in the oral microbiome, host traits, and their causal relationships
Liu, X., Tong, X., Jie, Z., Zhu, J., Tian, L., Sun, Q., Ju, Y., Zou, L., Lu, H., Qiu, X., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105839 · Cited by 66 · see citing works
Genetics and epigenetics in the obesity phenotyping scenario
Trang, B. K., & Grant, S. F. A. (2023) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-023-09804-6 · Cited by 66 · see citing works
Blencowe, M., Chen, X., Zhao, Y., Itoh, Y., McQuillen, C. N., Han, Y., Shou, B. L., McClusky, R., Reue, K., Arnold, A. P., et al. (2022) · Genome Research · doi:10.1101/gr.275965.121 · Cited by 63 · see citing works
Agius, R., Pace, N. P., & Fava, S. (2023) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3725 · Cited by 62 · see citing works
Sex and Gender Differences in Obesity: Biological, Sociocultural, and Clinical Perspectives
Kim, H., Kim, S., & Sung, M. (2025) · The World Journal of Men s Health · doi:10.5534/wjmh.250126 · Cited by 69 · see citing works
A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes
Coral, D., Fernandez-Tajes, J. J., Tsereteli, N., Pomares‐Millan, H., Fitipaldi, H., Mutie, P. M., Atabaki‐Pasdar, N., Kalamajski, S., Poveda, A., Miller‐Fleming, T. W., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-022-00731-5 · Cited by 51 · see citing works
Gene–Environment Interactions on Body Fat Distribution
Li, X., & Qi, L. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20153690 · Cited by 53 · see citing works
The Genetic Basis of Obesity and Related Metabolic Diseases in Humans and Companion Animals
Wallis, N. J., & Raffan, E. (2020) · Genes · doi:10.3390/genes11111378 · Cited by 52 · see citing works
Deng, Y.-T., Li, Y.-Z., Huang, S.-Y., Ou, Y.-N., Zhang, W., Chen, S.-D., Zhang, Y.-R., Yang, L., Dong, Q., Feng, J.-F., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01604-9 · Cited by 45 · see citing works
GEM: scalable and flexible gene–environment interaction analysis in millions of samples
Westerman, K. E., Pham, D. T., Hong, L., Chen, Y., Sevilla-González, M. D. R., Sung, Y. J., Sun, Y. V., Morrison, A. C., Chen, H., & Manning, A. K. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab223 · Cited by 45 · see citing works
Heterogeneity in Obesity: Genetic Basis and Metabolic Consequences
Sulc, J., Winkler, T. W., Heid, I. M., & Kutalik, Z. (2020) · Current Diabetes Reports · doi:10.1007/s11892-020-1285-4 · Cited by 44 · see citing works
Karoulias, S. Z., Taye, N., Stanley, S. A., & Hubmacher, D. (2020) · Biomolecules · doi:10.3390/biom10040596 · Cited by 44 · see citing works
Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images
Goallec, A. L., Diai, S., Collin, S., Prost, J.-B., Vincent, T., & Patel, C. J. (2022) · Nature Communications · doi:10.1038/s41467-022-29525-9 · Cited by 43 · see citing works
Associations between chronotype, MTNR1B genotype and risk of type 2 diabetes in UK Biobank
Tan, X. W., Ciuculete, D. M., Schiöth, H. B., & Benedict, C. (2019) · Journal of Internal Medicine · doi:10.1111/joim.12994 · Cited by 41 · see citing works
Ma, M., Liu, X., Jia, G., Geng, B., & Xia, Y. (2022) · BMC Endocrine Disorders · doi:10.1186/s12902-022-01087-3 · Cited by 40 · see citing works
Szczerbiński, Ł., & Florez, J. C. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(23)00232-2 · Cited by 38 · see citing works
Meng, M., Yang, L., Wu, X., Gan, Z., Pan, W., Liu, K., Li, C., Qin, N., & Li, J. (2020) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2020.156311 · Cited by 40 · see citing works
Genetic and Epigenetic Interplay Within a COLGALT2 Enhancer Associated With Osteoarthritis
Kehayova, Y. S., Watson, E., Wilkinson, J. M., Loughlin, J. A., & Rice, S. J. (2021) · Arthritis & Rheumatology · doi:10.1002/art.41738 · Cited by 40 · see citing works
Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes
Pan, D. Z., Miao, Z., Comenho, C., Rajkumar, S., Koka, A., Lee, S. H., Alvarez, M., Kamińska, D., Ko, A., Sinsheimer, J. S., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00939-2 · Cited by 39 · see citing works
Tóth, B. B., Arianti, R., Shaw, A., Vámos, A., Veréb, Z., Póliska, S., Győry, F., Bacsó, Z., Fésüs, L., & Kristóf, E. (2020) · Cells · doi:10.3390/cells9040987 · Cited by 8 · see citing works
Zong, Q., Bundkirchen, K., Neunaber, C., & Noack, S. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24054831 · Cited by 38 · see citing works
Peters, T., Nüllig, L., Antel, J., Naaresh, R., Laabs, B., Tegeler, L., Amhaouach, C., Libuda, L., Hinney, A., & Hebebrand, J. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00373 · Cited by 36 · see citing works
Zhang, X., Τheodoratou, E., Li, X., Farrington, S. M., Law, P., Broderick, P., Walker, M., Klimentidis, Y. C., Rees, J. M. B., Houlston, R. S., et al. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01236-2 · Cited by 35 · see citing works
Your height affects your health: genetic determinants and health-related outcomes in Taiwan
Chiou, J., Cheng, C., Liang, W., Chou, C.-H., Wang, C., Lin, W., Chiu, M.-L., Cheng, W., Lin, C., Lin, T., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02450-w · Cited by 33 · see citing works
Kim, M. S., Song, M., Kim, S., Kim, B., Kang, W., Kim, J. Y., Myung, W., Lee, I., Do, R., Khera, A. V., et al. (2023) · Obesity · doi:10.1002/oby.23722 · Cited by 33 · see citing works
Freuer, D., Linseisen, J., O’Mara, T. A., Leitzmann, M. F., Baurecht, H., Baumeister, S., & Meisinger, C. (2021) · Cancers · doi:10.3390/cancers13205053 · Cited by 29 · see citing works
Shang, P., Li, W., Liu, G., Zhang, J., Li, M., Wu, L., Wang, K., & Chamba, Y. (2019) · International Journal of Genomics · doi:10.1155/2019/5070975 · Cited by 28 · see citing works
Ostinelli, G., Vijay, J., Vohl, M., Grundberg, E., & Tchernof, A. (2021) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2021.111220 · Cited by 27 · see citing works
McDonald, M., Kumar, P. L., Srinivasasainagendra, V., Nair, A., Rocco, A., Wilson, A. C., Chiles, J. W., Richman, J., Pinson, S. A., Dennis, R. A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01221-w · Cited by 27 · see citing works
Ghatan, S., van Rooij, J., van Hoek, M., Boer, C. G., Felix, J. F., Kavousi, M., Jaddoe, V. W. V., Sijbrands, E. J. G., Medina‐Gómez, C., Rivadeneira, F., et al. (2024) · Genome Medicine · doi:10.1186/s13073-023-01255-7 · Cited by 26 · see citing works
Deep learning and genome-wide association meta-analyses of bone marrow adiposity in the UK Biobank
Xu, W., Mesa‐Eguiagaray, I., Morris, D. M., Wang, C., Gray, C. D., Sjöström, S., Papanastasiou, G., Badr, S., Paccou, J., Li, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55422-4 · Cited by 28 · see citing works
The current review of adolescent obesity: the role of genetic factors
Yılmaz, B., & Karadağ, M. G. (2020) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2020-0480 · Cited by 25 · see citing works
Wang, M., Yeung, S. L. A., Luo, S., Jang, H., Ho, H. S., Sharp, S. J., Wijndaele, K., Brage, S., Wareham, N. J., & Kim, Y. (2023) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2023.08.002 · Cited by 24 · see citing works
Klimentidis, Y. C., Zhao, C., Gonzalez‐Garay, M. L., Grigoriadis, D., Sackey, E., Pittman, A. M., Østergaard, P., & Herbst, K. L. (2022) · European Journal of Human Genetics · doi:10.1038/s41431-022-01231-6 · Cited by 23 · see citing works
Wang, C., Zhu, Y., Liu, Z., Long, H., Ruan, Z., & Zhao, S. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1011896 · Cited by 23 · see citing works
Chehadeh, S. E. H., Osman, W. M., Nazar, S., JERMAN, L. A., Alghafri, A., Sajwani, A., Alawlaqi, M., AlObeidli, M., Jelinek, H. F., Anouti, F. A., et al. (2020) · Gene · doi:10.1016/j.gene.2020.144509 · Cited by 22 · see citing works
Aging and Adiposity—Focus on Biological Females at Midlife and Beyond
Rehman, A., Lathief, S., Charoenngam, N., & Pal, L. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052972 · Cited by 24 · see citing works
Li, J. S., Riggins, K. S., Yang, L., Chen, C., Castro, P., Alfarkh, W., Zarrin‐Khameh, N., Scheurer, M. E., Creighton, C. J., Musher, B., et al. (2025) · Clinical Epigenetics · doi:10.1186/s13148-025-01817-z · Cited by 20 · see citing works
Limited evidence for blood eQTLs in human sexual dimorphism
Porcu, E., Claringbould, A., Weihs, A., Lepik, K., Richardson, T. G., Völker, U., Santoni, F. A., Teumer, A., Franke, L., Reymond, A., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01088-w · Cited by 20 · see citing works
Reis, H. H. T., Brito, C. J., Sillero‐Quintana, M., da Silva, A. G., Fernández-Cuevas, I., Cerqueira, M. S., Werneck, F. Z., & Marins, J. C. B. (2022) · Journal of Thermal Biology · doi:10.1016/j.jtherbio.2022.103424 · Cited by 20 · see citing works
Sakane, S., Hikita, H., Shirai, K., Sakamoto, T., Narumi, R., Adachi, J., Kakita, N., Yamada, Y., Toyoda, H., Takahashi, H., et al. (2024) · Hepatology Communications · doi:10.1097/hc9.0000000000000448 · Cited by 20 · see citing works
Saeed, S., Janjua, Q. M., Haseeb, A., Khanam, R., Durand, E., Vaillant, E., Ning, L., Badreddine, A., Berberian, L., Boissel, M., et al. (2022) · Diabetes · doi:10.2337/db21-0373 · Cited by 20 · see citing works
Борисович, Н. В., Николаевна, Н. О., Churnosova, M., Aristova, I., Ponomarenko, M., Reshetnikova, Y., Churnosov, V., Николаевна, С. И., Ponomarenko, I., Efremova, O. A., et al. (2024) · Arthroplasty · doi:10.1186/s42836-023-00229-9 · Cited by 19 · see citing works
Moon, C. Y., Schilder, B. M., Raj, T., & Huang, K. (2021) · iScience · doi:10.1016/j.isci.2021.102550 · Cited by 16 · see citing works
Genetic association of the PERIOD3 (PER3) Clock gene with extreme obesity
de Azevedo, P. G., Miranda, L. R., Nicolau, E., Alves, R. B., Bicalho, M. A. C., Couto, P. P., Ramos, A. V., de Souza, R. P., Longhi, R., Friedman, E., et al. (2021) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2021.06.006 · Cited by 16 · see citing works
Pazokitoroudi, A., Liu, Z., Dahl, A., Zaitlen, N., Rosset, S., & Sankararaman, S. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.05.015 · Cited by 16 · see citing works
Power, G. M., Tobias, J. H., Frayling, T. M., Tyrrell, J., Hartley, A. E., Heron, J., Smith, G. D., & Richardson, T. G. (2023) · European Journal of Epidemiology · doi:10.1007/s10654-023-00986-6 · Cited by 15 · see citing works
The evolutionary impact and influence of oestrogens on adipose tissue structure and function
Bardhi, O., Palmer, B. F., & Clegg, D. J. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0207 · Cited by 15 · see citing works
Cho, H., Jin, H., & Eom, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.669215 · Cited by 15 · see citing works
The causal effect of life course adiposity on periodontitis: A Mendelian randomization study
Li, W., He, Y., Zheng, Q., & Deng, X. (2022) · Journal of Periodontology · doi:10.1002/jper.21-0632 · Cited by 15 · see citing works
Le, T. H., Crouse, W. L., Keele, G. R., Holl, K., Seshie, O., Tschannen, M., Craddock, A. L., Das, S. K., Szalanczy, A., McDonald, B., et al. (2022) · Diabetes · doi:10.2337/db22-0252 · Cited by 15 · see citing works
Genetic Basis of Obesity and Type 2 Diabetes in Africans: Impact on Precision Medicine
Doumatey, A. P., Ekoru, K., Adeyemo, A., & Rotimi, C. N. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1215-5 · Cited by 15 · see citing works
Abaj, F., Saeedy, S. A. G., & Mirzaei, K. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-01050-6 · Cited by 15 · see citing works
Can adult polygenic scores improve prediction of body mass index in childhood?
Lange, K., Kerr, J. A., Mansell, T., O'Sullivan, J. M., Burgner, D. P., Clifford, S. A., Olds, T., Dwyer, T., Wake, M. A., & Saffery, R. (2022) · International Journal of Obesity · doi:10.1038/s41366-022-01130-2 · Cited by 16 · see citing works
Beyond apples and pears: sex-specific genetics of body fat percentage
Roshandel, D., Lu, T., Paterson, A. D., & Dash, S. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1274791 · Cited by 14 · see citing works
The Fat-to-Lean Mass Ratio Is Associated with Hyperinsulinemia in Healthy Mexican Adolescents
Gamboa‐Gómez, C. I., Guerrero‐Romero, F., Aradillas-García, C., Rodríguez‐Morán, M., & Simental‐Mendía, L. E. (2020) · Journal of the American College of Nutrition · doi:10.1080/07315724.2020.1752845 · Cited by 14 · see citing works
Feng, Q., Yuan, S., Yang, Q., Lu, Y., Loos, R. J. F., Li, G. H., Fei, Y., Tsoi, M. F., Cheung, C. L., & Cheung, B. M. Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-74657-x · Cited by 14 · see citing works
Liu, Y., Zhang, X., Lee, J., Smelser, D. T., Cade, B. E., Chen, H., Zhou, H., Kirchner, H. L., Lin, X., Mukherjee, S., et al. (2021) · International Journal of Obesity · doi:10.1038/s41366-021-00817-2 · Cited by 14 · see citing works
Giontella, A., Lotta, L. A., Overton, J. D., Baras, A., Minuz, P., Melander, O., Gill, D., & Fava, C. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.020405 · Cited by 11 · see citing works
Stanley, A., Schuna, J. M., Yang, S., Kennedy, S., Heo, M., Wong, M. C., Shepherd, J., & Heymsfield, S. B. (2020) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqaa194 · Cited by 13 · see citing works
Livingstone, K. M., Tan, M. H., Abbott, G., Duckham, R. L., Croft, L., Ward, J., McEvoy, M., Keske, M. A., Austin, C. M., & Bowe, S. J. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.692677 · Cited by 13 · see citing works
Identification of Scd5 as a functional regulator of visceral fat deposition and distribution
Zhang, Q., Sun, S., Zhang, Y., Wang, X., & Li, Q. (2022) · iScience · doi:10.1016/j.isci.2022.103916 · Cited by 13 · see citing works
Saliba‐Gustafsson, P., Justesen, J. M., Ranta, A., Sharma, D., Bielczyk-Maczyńska, E., Li, J., Najmi, L. A., Apodaka‐Biguri, M., Aspichueta, P., Björck, H. M., et al. (2024) · Hepatology · doi:10.1097/hep.0000000000001066 · Cited by 13 · see citing works
LCoRL Regulates Growth and Metabolism
Wyler, S. C., Gahlot, S., Bideyan, L., Yip, C., Dushime, J., Chen, B., Lee, J. J., Tinajero, A., Limboy, C., Bordash, S., et al. (2024) · Endocrinology · doi:10.1210/endocr/bqae146 · Cited by 13 · see citing works
Pitfalls in performing genome-wide association studies on ratio traits
McCaw, Z. R., Dey, R., Somineni, H., Amar, D., Mukherjee, S., Sandor, K. P., Karaletsos, T., Koller, D., Aschard, H., Smith, G. D., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100406 · Cited by 12 · see citing works
Breathlessness across generations: results from the RHINESSA generation study
Ekström, M. P., Johannessen, A., Abramson, M. J., Benediktsdóttir, B., Franklin, K. A., Gíslason, Þ., Real, F. G., Holm, M., Janson, C., Jögi, R., et al. (2021) · Thorax · doi:10.1136/thoraxjnl-2021-217271 · Cited by 13 · see citing works
Yoshiji, S., Tanaka, D., Minamino, H., Lu, T., Butler‐Laporte, G., Murakami, T., Fujita, Y., Richards, J. B., & Inagaki, N. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.899625 · Cited by 11 · see citing works
Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review
Wang, J., Gu, R., Kong, X., Luan, S., & Luo, Y. L. L. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.110986 · Cited by 11 · see citing works
Zhu, H., Yi, X., He, M., Wu, S., Li, M., & Gao, S. (2025) · Metabolism · doi:10.1016/j.metabol.2025.156303 · Cited by 12 · see citing works
Shared genetic influences on resting‐state functional networks of the brain
Guimaraes, J., Sprooten, E., Beckmann, C. F., Franke, B., & Bralten, J. (2022) · Human Brain Mapping · doi:10.1002/hbm.25712 · Cited by 10 · see citing works
Non-coding RNA network associated with obesity and rheumatoid arthritis
Auer, E. D., Santos, D. D. C., de Lima, I. J. V., & Boldt, A. B. W. (2022) · Immunobiology · doi:10.1016/j.imbio.2022.152281 · Cited by 11 · see citing works
Lee, S., Bijsterbosch, J. D., Alfaro‐Almagro, F., Elliott, L. T., McCarthy, P., Taschler, B., Sala‐Llonch, R., Beckmann, C. F., Duff, E. P., Smith, S. M., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119779 · Cited by 12 · see citing works
Carbeck, K., Arcese, P., Lovette, I., Pruett, C., Winker, K., & Walsh, J. (2023) · Nature Communications · doi:10.1038/s41467-023-42786-2 · Cited by 10 · see citing works
Vas, V., Háhner, T., Kudlik, G., Ernszt, D., Kvell, K., Kuti, D., Kovács, K., Tóvári, J., Trexler, M., Merő, B. L., et al. (2019) · Cells · doi:10.3390/cells8080831 · Cited by 11 · see citing works
Campolo, J., Corradi, E., Rizzardi, A., Parolini, M., Dellanoce, C., Guglielmo, M. D. D., Tarlarini, P., Cattaneo, M., Trivella, M., & Maria, R. D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229152 · Cited by 10 · see citing works
Ahn, Y., Lee, H., & Cho, Y. S. (2020) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s281529 · Cited by 11 · see citing works
Tejaas: reverse regression increases power for detecting trans-eQTLs
Banerjee, S., Simonetti, F. L., Detrois, K. E., Kaphle, A., Mitra, R., Nagial, R., & Söding, J. (2021) · Genome biology · doi:10.1186/s13059-021-02361-8 · Cited by 10 · see citing works
Blaj, I., Tetens, J., Bennewitz, J., Thaller, G. E., & Falker‐Gieske, C. (2022) · BMC Genomics · doi:10.1186/s12864-022-08716-0 · Cited by 10 · see citing works
Illuminating the ‘healthy obese’ phenotype
Rask‐Andersen, M., & Johansson, Å. (2023) · Nature Metabolism · doi:10.1038/s42255-022-00733-3 · Cited by 10 · see citing works
Self-citationLee, Y., Kang, Y.-R., Chang, Y., Kim, J., & Sung, M. (2025) · Obesity Reviews · doi:10.1111/obr.70026 · Cited by 13 · see citing works
Body composition and testosterone in men: a Mendelian randomization study
Ikehata, Y., Hachiya, T., Kobayashi, T., Ide, H., & Horie, S. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1277393 · Cited by 11 · see citing works
- Authors
- Rask-Andersen, M., Karlsson, T., Ek, W. E., & Johansson, Å.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- Johansson, Åsa
- Venue
- Nature Communications
- Published
- 2019-01-21
- Volume
- 10
- Issue
- 1
- Pages
- 339-339
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6341104
- OpenAlex
- W2908665501
- Cited by
- 245
- References
- 56
- Citations by year
- 2026: 17 · 2025: 25 · 2024: 22 · 2023: 37 · 2022: 63 · 2021: 41 · 2020: 29 · 2019: 11
- FWCI
- 21.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Åke Wiberg Stiftelse
- Svenska Sällskapet för Medicinsk Forskning
- Hjärt-Lungfonden
- Medical Research Council (MC_PC_17228)
- Uppsala Multidisciplinary Center for Advanced Computational Science
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
- Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
- Keywords
- body fat distribution, trunk fat, sex-specific genetic effects, segmental bioelectrical impedance analysis, anthropometric traits, mesenchyme, extracellular matrix remodeling, obesity genetics
- MeSH terms
- Adipose Tissue, Anthropometry, Arm, Body Weight, Endocrine Glands, Female, Gene Expression Regulation, United Kingdom, Humans, Leg, Male, Obesity, Sex Factors, Body Mass Index, Electric Impedance, Biological Specimen Banks, Genetic Heterogeneity, Polymorphism, Single Nucleotide, Gene Expression Profiling, Waist-Hip Ratio, Body Fat Distribution, Genome-Wide Association Study, Genetic Loci, Torso
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