FTO Obesity Variant Circuitry and Adipocyte Browning in Humans
Open accessAbstract
BACKGROUND: Genomewide association studies can be used to identify disease-relevant genomic regions, but interpretation of the data is challenging. The FTO region harbors the strongest genetic association with obesity, yet the mechanistic basis of this association remains elusive. METHODS: We examined epigenomic data, allelic activity, motif conservation, regulator expression, and gene coexpression patterns, with the aim of dissecting the regulatory circuitry and mechanistic basis of the association between the FTO region and obesity. We validated our predictions with the use of directed perturbations in samples from patients and from mice and with endogenous CRISPR-Cas9 genome editing in samples from patients. RESULTS: Our data indicate that the FTO allele associated with obesity represses mitochondrial thermogenesis in adipocyte precursor cells in a tissue-autonomous manner. The rs1421085 T-to-C single-nucleotide variant disrupts a conserved motif for the ARID5B repressor, which leads to derepression of a potent preadipocyte enhancer and a doubling of IRX3 and IRX5 expression during early adipocyte differentiation. This results in a cell-autonomous developmental shift from energy-dissipating beige (brite) adipocytes to energy-storing white adipocytes, with a reduction in mitochondrial thermogenesis by a factor of 5, as well as an increase in lipid storage. Inhibition of Irx3 in adipose tissue in mice reduced body weight and increased energy dissipation without a change in physical activity or appetite. Knockdown of IRX3 or IRX5 in primary adipocytes from participants with the risk allele restored thermogenesis, increasing it by a factor of 7, and overexpression of these genes had the opposite effect in adipocytes from nonrisk-allele carriers. Repair of the ARID5B motif by CRISPR-Cas9 editing of rs1421085 in primary adipocytes from a patient with the risk allele restored IRX3 and IRX5 repression, activated browning expression programs, and restored thermogenesis, increasing it by a factor of 7. CONCLUSIONS: Our results point to a pathway for adipocyte thermogenesis regulation involving ARID5B, rs1421085, IRX3, and IRX5, which, when manipulated, had pronounced pro-obesity and anti-obesity effects. (Funded by the German Research Center for Environmental Health and others.).
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The strongest associated obesity GWAS signal lies within an intron of FTO but principally influences expression of distal genes IRX3 and IRX5.
“One such example comes from the strongest associated obesity GWAS signal that lies within an intron of the FTO gene, but principally influences the expression of distal genes IRX3 and IRX517–19.”
Primary human cell lines are limited by the complex nature and convergence of multiple cell types causing most complex diseases.
“But their use is limited by the complex nature and convergence of multiple cell types causing most complex diseases.”
From Multi-omics approaches to disease (2017) · cites this paper as [16]
Substantial enrichment for genes involved in mitochondrial functions and lipid metabolism was observed, suggesting possible roles in thermogenesis.
“Substantial enrichment for genes involved in mitochondrial functions and lipid metabolism was observed, which suggests possible roles in thermogenesis.”
From Multi-omics approaches to disease (2017) · cites this paper as [16]
Adipocyte size and mitochondrial DNA content were shown to differ significantly between 24 risk alleles and 34 non-risk alleles, consistent with an adipocyte-autonomous effect on energy balance.
“Adipocyte size and mitochondrial DNA content were then studied for 24 risk alleles and 34 non-risk alleles and shown to differ significantly, consistent with an adipocyte-autonomous effect on energy balance.”
From Multi-omics approaches to disease (2017) · cites this paper as [16]
The causal variant at the FTO locus was predicted using cross-species conservation and targeted editing with CRISPR-Cas9 identified a single nucleotide variant that disrupts ARID5B repressor binding.
“Finally, the causal variant at the FTO locus was predicted using cross-species conservation and targeted editing with CRISPR-Cas9 identified a single nucleotide variant that disrupts ARID5B repressor binding.”
From Multi-omics approaches to disease (2017) · cites this paper as [16]
A long enhancer active in mesenchymal adipocyte progenitors was shown to differ in activity between risk and non-risk haplotype at the FTO locus.
“A long enhancer active in mesenchymal adipocyte progenitors was shown to differ in activity between risk and non-risk haplotype.”
From Multi-omics approaches to disease (2017) · cites this paper as [16]
Transcriptomics, employing either expression arrays or RNA-Seq, has proven to be particularly useful for identifying causal genes at GWAS loci.
“Thus, transcriptomics, employing either expression arrays or RNA-Seq (Box 1), has proven to be particularly useful for identifying causal genes at GWAS loci [16, 22–24].”
From Multi-omics approaches to disease (2017) · cites this paper as [16]
More recent examples have focused on the relationship between obesity-associated variants intronic to FTO, altered expression of IRX3 and IRX5, and adipocyte and hypothalamic function.
“More recent examples have focused on the relationship between obesity-associated variants intronic to FTO, altered expression of IRX3 IRX5, and adipocyte102 and hypothalamic103 function.”
From A brief history of human disease genetics (2020) · cites this paper as [102]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Genome engineering using the CRISPR-Cas9 system
Ran, F. A., Hsu, P. D., Wright, J., Agarwala, V., Scott, D. A., & Zhang, F. (2013) · Nature Protocols · doi:10.1038/nprot.2013.143 · Cited by 11,133 · see citing works
Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities
Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,628 · see citing works
Topological domains in mammalian genomes identified by analysis of chromatin interactions
Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829 · see citing works
Self-citationIntegrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
Self-citationLippa, N. C., & Sanderson, S. C. (2013) · Human Heredity · doi:10.1159/000353712 · Cited by 6,093 · 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
Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., Perry, J. R. B., Elliott, K. S., Lango, H., Rayner, N. W., et al. (2007) · Science · doi:10.1126/science.1141634 · Cited by 4,531 · see citing works
Identification and Importance of Brown Adipose Tissue in Adult Humans
Cypess, A. M., Lehman, S., Williams, G., Tal, I., Rodman, D., Goldfine, A. B., Kuo, F. C., Palmer, E. L., Tseng, Y.-H., Doria, A., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0810780 · Cited by 4,399 · see citing works
Cold-Activated Brown Adipose Tissue in Healthy Men
van Marken Lichtenbelt, W. D., Vanhommerig, J. W., Smulders, N. M., Drossaerts, J. M. A. F. L., Kemerink, G. J., Bouvy, N. D., Schrauwen, P., & Teule, G. J. J. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808718 · Cited by 3,452 · see citing works
Beige Adipocytes Are a Distinct Type of Thermogenic Fat Cell in Mouse and Human
Wu, J., Boström, P., Sparks, L. M., Li, Y., Choi, J. H., Giang, A.-H., Khandekar, M. J., Virtanen, K. A., Nuutila, P., Schaart, G., et al. (2012) · Cell · doi:10.1016/j.cell.2012.05.016 · Cited by 3,299 · see citing works
Functional Brown Adipose Tissue in Healthy Adults
Virtanen, K. A., Lidell, M. E., Orava, J., Heglind, M., Westergren, R., Niemi, T., Taittonen, M., Laine, J., Savisto, N.-J., Enerbäck, S., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808949 · Cited by 3,058 · see citing works
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
MAGIC, Speliotes, E. K., on behalf of Procardis Consortium, Willer, C. J., Berndt, S. I., Monda, K. L., Thorleifsson, G., Jackson, A. U., Allen, H. L., Lindgren, C. M., et al. (2010) · Nature Genetics · doi:10.1038/ng.686 · Cited by 2,912 · see citing works
Self-citationThe NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707 · see citing works
Overweight, Obesity, and Mortality in a Large Prospective Cohort of Persons 50 to 71 Years Old
Adams, K. F., Schatzkin, A. G., Harris, T. B., Kipnis, V., Mouw, T., Ballard-Barbash, R. R., Hollenbeck, A. R., & Leitzmann, M. F. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa055643 · Cited by 2,115 · see citing works
Variation in FTO contributes to childhood obesity and severe adult obesity
Christian Dina, C., Meyre, D., Gallina, S., Durand, E., Körner, A., Jacobson, P., Carlsson, L. M. S., Kieß, W., Vatin, V., Lecœur, C., et al. (2007) · Nature Genetics · doi:10.1038/ng2048 · Cited by 1,620 · see citing works
Common variants near MC4R are associated with fat mass, weight and risk of obesity
The Prostate, L., Loos, R. J. F., KORA, Study, N. H., Initiative, D. G., Study, T. S., The Wellcome Trust Case Control Consortium, FUSION, Lindgren, C. M., Li, S., et al. (2008) · Nature Genetics · doi:10.1038/ng.140 · Cited by 1,325 · see citing works
Prdm16 determines the thermogenic program of subcutaneous white adipose tissue in mice
Seale, P., Conroe, H. M., Estall, J. L., Kajimura, S., Frontini, A., Ishibashi, J., Cohen, P., Cinti, S., & Spiegelman, B. M. (2010) · Journal of Clinical Investigation · doi:10.1172/jci44271 · Cited by 1,241 · see citing works
Obesity-associated variants within FTO form long-range functional connections with IRX3
Smemo, S., Tena, J. J., Kim, K.-H., Gamazon, E. R., Sakabe, N. J., Gómez-Marín, C., Aneas, I., Credidio, F. L., Sobreira, D. R., Wasserman, N. F., et al. (2014) · Nature · doi:10.1038/nature13138 · Cited by 1,219 · see citing works
Self-citationInactivation of the Fto gene protects from obesity
Fischer, J., Koch, L., Emmerling, C., Vierkotten, J., Peters, T., Brüning, J. C., & Rüther, U. (2009) · Nature · doi:10.1038/nature07848 · Cited by 943 · see citing works
Cohen, P., Levy, J. D., Zhang, Y., Frontini, A., Kolodin, D., Svensson, K. J., Lo, J. C., Zeng, X., Ye, L., Khandekar, M. J., et al. (2014) · Cell · doi:10.1016/j.cell.2013.12.021 · Cited by 878 · see citing works
Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index
Speliotes, E. K., Willer, C. J., Berndt, S. I., Monda, K. L., Þorleifsson, G., Jackson, A., Allen, H. L., Lindgren, C. M., Luan, J., Mägi, R., et al. (2010) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/d3bn-7g86 · Cited by 767 · see citing works
The bigger picture of FTO—the first GWAS-identified obesity gene
Loos, R. J. F., & Yeo, G. S. H. (2013) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2013.227 · Cited by 724 · see citing works
UCP1 in Brite/Beige Adipose Tissue Mitochondria Is Functionally Thermogenic
Shabalina, I. G., Petrovič, N., de Jong, J. M. A., Kalinovich, A., Cannon, B., & Nedergaard, J. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.10.044 · Cited by 637 · see citing works
Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues
Ernst, J., & Kellis, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3157 · Cited by 352 · see citing works
Self-citationIRF4 Is a Key Thermogenic Transcriptional Partner of PGC-1α
Kong, X., Banks, A. S., Liu, T., Kazak, L., Rao, R. R., Cohen, P., Wang, X., Yu, S., Lo, J. C., Tseng, Y., et al. (2014) · Cell · doi:10.1016/j.cell.2014.04.049 · Cited by 315 · see citing works
Ragvin, A., Moro, E., Fredman, D., Navratilova, P., Drivenes, Ø., Engström, P. G., Alonso, M. E., Mustienes, E. D. L. C., Skarmeta, J. L. G., Tavares, M. J., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911591107 · Cited by 211 · see citing works
Brown adipose tissue and its therapeutic potential
Lidell, M. E., Betz, M. J., & Enerbäck, S. (2014) · Journal of Internal Medicine · doi:10.1111/joim.12255 · Cited by 163 · see citing works
Stratigopoulos, G., Carli, J. F. M., O’Day, D. R., Wang, L., LeDuc, C. A., Lanzano, P., Chung, W. K., ROSENBAUM, M. S., Egli, D. M., Doherty, D., et al. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2014.04.009 · Cited by 152 · see citing works
Claussnitzer, M., Dankel, S. N., Klocke, B., Grallert, H., Glunk, V., Berulava, T., Lee, H., Oskolkov, N., Fadista, J., Ehlers, K., et al. (2014) · Cell · doi:10.1016/j.cell.2013.10.058 · Cited by 126 · see citing works
Self-citationInverse relationship between obesity and FTO gene expression in visceral adipose tissue in humans
Klöting, N., Schleinitz, D., Ruschke, K., Berndt, J., Faßhauer, M., Tönjes, A., Schön, M. R., Kovacs, P. L., Stümvoll, M., & Blüher, M. (2008) · Diabetologia · doi:10.1007/s00125-008-0928-9 · Cited by 113 · see citing works
Grunnet, L. G., Nilsson, E., Ling, C., Hansen, T. F., Pedersen, O. B., Groop, L., Vaag, A. A., & Poulsen, P. (2009) · Diabetes · doi:10.2337/db09-0205 · Cited by 101 · see citing works
Genetic Variation at the <i>FTO</i> Locus Influences <i>RBL2</i> Gene Expression
Jowett, J. B. M., Curran, J. E., Johnson, M. P., Carless, M. A., Göring, H. H. H., Dyer, T. D., Cole, S. A., Comuzzie, A. G., MacCluer, J. W., Moses, E. K., et al. (2010) · Diabetes · doi:10.2337/db09-1277 · Cited by 61 · see citing works
Whitson, R. H., Huang, T. H., & Itakura, K. (1999) · Biochemical and Biophysical Research Communications · doi:10.1006/bbrc.1999.0643 · Cited by 55 · see citing works
Modulator recognition factor-2 regulates triglyceride metabolism in adipocytes
Yamakawa, T., Sugimoto, K., Whitson, R. H., & Itakura, K. (2009) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2009.11.049 · Cited by 26 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Functional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083 · see citing works
10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719 · see citing works
Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352 · see citing works
The Human Transcription Factors
Lambert, S. A., Jolma, A., Campitelli, L. F., Das, P. K., Yin, Y., Albu, M., Chen, X., Taipale, J., Hughes, T. R., & Weirauch, M. T. (2018) · Cell · doi:10.1016/j.cell.2018.01.029 · Cited by 3,399 · see citing works
An Expanded View of Complex Traits: From Polygenic to Omnigenic
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017) · Cell · doi:10.1016/j.cell.2017.05.038 · Cited by 2,851 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
Orienting the causal relationship between imprecisely measured traits using GWAS summary data.
Hemani, G., Tilling, K., & Davey Smith, G. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007081 · Cited by 2,607 · see citing works
The role of m6A modification in the biological functions and diseases
Jiang, X., Liu, B., Nie, Z., Duan, L., Xiong, Q., Jin, Z., Yang, C., & Chen, Y. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00450-x · Cited by 2,335 · see citing works
The genetics of obesity: From discovery to biology
Loos, R. J. F., & Yeo, G. S. H. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00414-z · Cited by 1,291 · see citing works
Detection and interpretation of shared genetic influences on 42 human traits
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016) · Nature Genetics · doi:10.1038/ng.3570 · Cited by 1,151 · see citing works
Self-citationIntegrative omics for health and disease
Karczewski, K. J., & Snyder, M. P. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2018.4 · Cited by 1,158 · see citing works
Dumesic, D. A., Oberfield, S. E., Stener-Victorin, E., Marshall, J. C., Laven, J. S., & Legro, R. S. (2015) · Endocrine Reviews · doi:10.1210/er.2015-1018 · Cited by 1,020 · see citing works
The Post-GWAS Era: From Association to Function
Gallagher, M. D., & Chen-Plotkin, A. S. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.04.002 · Cited by 822 · see citing works
RNA splicing is a primary link between genetic variation and disease
Li, Y. I., van de Geijn, B., Raj, A., Knowles, D. A., Petti, A. A., Golan, D., Gilad, Y., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad9417 · Cited by 660 · see citing works
A brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Self-citationIntegrative analysis of omics summary data reveals putative mechanisms underlying complex traits
Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685 · see citing works
Obesity Pathogenesis: An Endocrine Society Scientific Statement
Schwartz, M. W., Seeley, R. J., Zeltser, L. M., Drewnowski, A., Ravussin, E., Redman, L. M., & Leibel, R. L. (2017) · Endocrine Reviews · doi:10.1210/er.2017-00111 · Cited by 727 · see citing works
Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0092-1 · Cited by 491 · see citing works
Schwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532 · see citing works
Self-citationDrug development in the era of precision medicine
Dugger, S. A., Platt, A., & Goldstein, D. B. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.226 · Cited by 493 · see citing works
Mumbach, M. R., Satpathy, A. T., Boyle, E. A., Dai, C., Gowen, B. G., Cho, S. W., Nguyen, M. L., Rubin, A. J., Granja, J. M., Kazane, K. R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3963 · Cited by 490 · see citing works
Tewhey, R., Kotliar, D., Park, D. S., Liu, B., Winnicki, S., Reilly, S. K., Andersen, K. G., Mikkelsen, T. S., Lander, E. S., Schaffner, S. F., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.027 · Cited by 480 · see citing works
Contrasting the Genetic Architecture of 30 Complex Traits from Summary Association Data
Shi, H., Kichaev, G., & Pasaniuc, B. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.05.013 · Cited by 387 · see citing works
The Genetics of Transcription Factor DNA Binding Variation
Deplancke, B., Alpern, D., & Gardeux, V. (2016) · Cell · doi:10.1016/j.cell.2016.07.012 · Cited by 390 · see citing works
A compendium of promoter-centered long-range chromatin interactions in the human genome
Jung, I., Schmitt, A., Diao, Y., Lee, A. J., Liu, T., Yang, D., Tan, C., Eom, J., Chan, M., Chee, S., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0494-8 · Cited by 362 · see citing works
Genetics and epigenetics in obesity
Rohde, K., Keller, M., la Cour Poulsen, L., Blüher, M., Kovacs, P., & Böttcher, Y. (2019) · Metabolism · doi:10.1016/j.metabol.2018.10.007 · Cited by 355 · see citing works
Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits
Ulirsch, J. C., Nandakumar, S. K., Wang, L., Giani, F. C., Zhang, X., Rogov, P., Melnikov, A., McDonel, P., Do, R., Mikkelsen, T. S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.048 · Cited by 328 · see citing works
Porcu, E., Rüeger, S., Lepik, K., eQTLGen Consortium, Agbessi, M., Ahsan, H., Alves, I., Andiappan, A., Arindrarto, W., Awadalla, P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10936-0 · Cited by 322 · see citing works
Where Are the Disease-Associated eQTLs?
Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works
Akbari, P., Gilani, A., Sosina, O., Kosmicki, J. A., Khrimian, L., Fang, Y.-Y., Persaud, T., Garcia, V., Sun, D., Li, A., et al. (2021) · Science · doi:10.1126/science.abf8683 · Cited by 276 · see citing works
Exposing the exposures responsible for type 2 diabetes and obesity
Franks, P. W., & McCarthy, M. I. (2016) · Science · doi:10.1126/science.aaf5094 · Cited by 265 · see citing works
Swanson, S. A., Tiemeier, H., Ikram, M. A., & Hernán, M. A. (2017) · Epidemiology · doi:10.1097/ede.0000000000000699 · Cited by 202 · see citing works
Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
Gazal, S., Weissbrod, O., Hormozdiari, F., Dey, K. K., Nasser, J., Jagadeesh, K. A., Weiner, D. J., Shi, H., Fulco, C. P., O’Connor, L. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01087-y · Cited by 181 · see citing works
Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions
Ernst, J., Melnikov, A., Zhang, X., Wang, L., Rogov, P., Mikkelsen, T. S., & Kellis, M. (2016) · Nature Biotechnology · doi:10.1038/nbt.3678 · Cited by 162 · see citing works
Self-citationGasperini, M., Findlay, G. M., McKenna, A., Milbank, J. H., Lee, C., Zhang, M. D., Cusanovich, D. A., & Shendure, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.010 · Cited by 143 · see citing works
Zou, S., Toh, J. D. W., Wong, K. H. Q., Gao, Y.-G., Hong, W., & Woon, E. C. Y. (2016) · Scientific Reports · doi:10.1038/srep25677 · Cited by 158 · see citing works
Inferring relevant cell types for complex traits by using single-cell gene expression
Calderon, D., Bhaskar, A., Knowles, D. A., Golan, D., Raj, T., Fu, A. Q., & Pritchard, J. K. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.009 · Cited by 127 · see citing works
Genetic Associations with Obstructive Sleep Apnea Traits in Hispanic/Latino Americans
Cade, B. E., Chen, H., Stilp, A. M., Gleason, K. J., Sofer, T., Ancoli-Israel, S., Arens, R., Bell, G. I., Below, J. E., Bjonnes, A. C., et al. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201512-2431oc · Cited by 134 · see citing works
Genetic determinants of polycystic ovary syndrome: Progress and future directions
Jones, M. R., & Goodarzi, M. O. (2016) · Fertility and Sterility · doi:10.1016/j.fertnstert.2016.04.040 · Cited by 117 · see citing works
Origins of human disease: the chrono-epigenetic perspective
Oh, E. S., & Petronis, A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00348-6 · Cited by 108 · see citing works
Wu, Y., Zheng, Z., Visscher, P. M., & Yang, J. (2017) · Genome biology · doi:10.1186/s13059-017-1216-0 · Cited by 100 · see citing works
Genetic risk for obesity predicts nucleus accumbens size and responsivity to real-world food cues
Rapuano, K. M., Zieselman, A. L., Kelley, W. M., Sargent, J. D., Heatherton, T. F., & Gilbert-Diamond, D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1605548113 · Cited by 96 · see citing works
Genetic Regulatory Mechanisms of Smooth Muscle Cells Map to Coronary Artery Disease Risk Loci
Liu, B., Pjanic, M., Wang, T., Nguyen, T., Gloudemans, M., Rao, A., Castano, V. G., Nurnberg, S., Rader, D. J., Elwyn, S., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.001 · Cited by 94 · see citing works
Obesity and eating behavior from the perspective of twin and genetic research
Silventoinen, K., & Konttinen, H. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.12.012 · Cited by 80 · see citing works
The GIANT consortium, Wood, A. R., Tyrrell, J., Beaumont, R., Jones, S. E., Tuke, M. A., Ruth, K. S., Yaghootkar, H., Freathy, R. M., Murray, A., et al. (2016) · Diabetologia · doi:10.1007/s00125-016-3908-5 · Cited by 74 · see citing works
Ndiaye, F. K., Huyvaert, M., Ortalli, A., Canouil, M., Lecoeur, C., Verbanck, M., Lobbens, S., Khamis, A., Marselli, L., Marchetti, P., et al. (2020) · International Journal of Obesity · doi:10.1038/s41366-019-0428-7 · Cited by 66 · see citing works
Tan, Z., Kong, M., Wen, S., Tsang, K. Y., Niu, B., Hartmann, C., Chan, D., Hui, C.-C., & Cheah, K. S. E. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.4132 · Cited by 52 · see citing works
Self-citationGuo, Y., Wan, Z., Zhao, P., Wei, M., Liu, Y., Bu, T., Sun, W., Li, Z., & Yuan, L. (2021) · Journal of Nanobiotechnology · doi:10.1186/s12951-021-01145-3 · Cited by 60 · see citing works
Lin, Z., Wang, X., Bustin, K. A., Shishikura, K., McKnight, N. R., He, L., Suciu, R. M., Hu, K., Han, X., Ahmadi, M., et al. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00616 · Cited by 43 · see citing works
Concepts and Misconceptions about the Polygenic Additive Model Applied to Disease
Visscher, P. M., & Wray, N. R. (2015) · Human Heredity · doi:10.1159/000446931 · Cited by 30 · see citing works
Chiou, J., Zeng, C., Cheng, Z., Han, J. Y., Schlichting, M., Huang, S., Wang, J., Sui, Y., Deogaygay, A., Okino, M.-L., et al. (2019) · doi:10.1101/693671 · Cited by 189 · see citing works
Burton, E. A., Argenziano, M., Cook, K., Ridler, M., Lu, S., Su, C., Manduchi, E., Littleton, S. H., Leonard, M. E., Hodge, K. M., et al. (2024) · doi:10.1101/2024.08.22.609230 · Cited by 3 · see citing works
- Authors
- Claussnitzer, M., Dankel, S. N., Kim, K.-H., Quon, G., Meuleman, W., Haugen, C., Glunk, V., Sousa, I. S., Beaudry, J. L., Puviindran, V., Abdennur, N. A., Liu, J., Svensson, P.-A., Hsu, Y.-H., Drucker, D. J., Mellgren, G., Hui, C.-C., Hauner, H., & Kellis, M.
- Affiliations
- Broad Institute (US)
- Haukeland University Hospital (NO)
- University of Toronto (CA)
- TUM Klinikum (DE)
- Mount Sinai Hospital (CA)
- University of Gothenburg (SE)
- Beth Israel Deaconess Medical Center (US)
- ORCID
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2015-09-03
- Volume
- 373
- Issue
- 10
- Pages
- 895-907
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC4959911
- OpenAlex
- W1636205509
- Cited by
- 1,332
- References
- 34
- Citations by year
- 2026: 73 · 2025: 105 · 2024: 93 · 2023: 92 · 2022: 120 · 2021: 145 · 2020: 133 · 2019: 121 · 2018: 132 · 2017: 140 · 2016: 142 · 2015: 35 · 2014: 1
- FWCI
- 110.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Site cant be reached (submittedVersion)
- Funders
- Heart and Stroke Foundation of Canada
- Australian Government
- Technische Universität München
- Hospital for Sick Children
- National Institutes of Health
- Canadian Institutes of Health Research
- Helmholtz Zentrum München
- National Human Genome Research Institute (R01HG008155, RC1HG005334, R01HG004037)
- National Institute of General Medical Sciences (R01GM113708)
- Topics
- Health Sciences › Medicine › Physiology › Adipose Tissue and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- FTO, obesity, adipocyte browning, mitochondrial thermogenesis, ARID5B, IRX3, IRX5, rs1421085, preadipocyte differentiation, white adipocytes, beige adipocytes, CRISPR/Cas9 genome editing, epigenetic regulation
- MeSH terms
- Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Alleles, Animals, Base Sequence, Genetic Engineering, Humans, Mitochondria, Molecular Sequence Data, Obesity, Phenotype, Proteins, Risk, Gene Expression, RNA Editing, Adipocytes, Thermogenesis, Mice, Epigenomics, Clustered Regularly Interspaced Short Palindromic Repeats
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
