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FTO genotype is associated with phenotypic variability of body mass index
Open accessAbstract
A meta-analysis of genome-wide association studies of phenotypic variation for height and body mass index in human populations using 170,000 samples shows that one single nucleotide polymorphism at the FTO locus, which is associated with obesity, is also associated with phenotypic variation. Genome-wide association studies have successfully detected thousands of single nucleotide polymorphisms (SNPs) associated with complex traits in human populations. These studies tested the associations between SNPs and a phenotype — a disease or quantitative trait — expressed as a mean of the trait. This study is different, testing for associations between SNPs and variations of the phenotype, using more than 100,000 samples on height and body mass index in human populations. The authors find that one SNP at the FTO locus, which is known to be associated with obesity, is also associated with phenotypic variability. These results demonstrate that it is possible to find genetic variants that associate with variability and that between-person variability in obesity can partly be explained by genotype at the FTO locus. However, most genetic variants are not associated with phenotypic variance, or their effects on variability are very small. There is evidence across several species for genetic control of phenotypic variation of complex traits1,2,3,4, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using ∼170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype)5,6,7, is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of ∼0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI8, possibly mediated by DNA methylation9,10. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.
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Citations
Cites
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
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
N6-Methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO
Jia, G., Fu, Y., Zhao, X., Dai, Q., Zheng, G., Yang, Y., Yi, C., Lindahl, T., Pan, T., Yang, Y.-G., et al. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.687 · Cited by 4,106
Genomic Control for Association Studies
Devlin, B., & Roeder, K. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00997.x · Cited by 3,162
LocusZoom: regional visualization of genome-wide association scan results
Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., & Willer, C. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq419 · Cited by 2,707
Self-citationA new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945
Self-citationGenABEL: 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
Self-citationScuteri, A., Sanna, S., Chen, W.-M., Uda, M., Albai, G., Strait, J., Najjar, S., Nagaraja, R., Orrú, M., Usala, G., et al. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030115 · Cited by 1,724
Self-citationPhysical Activity and the Association of Common FTO Gene Variants With Body Mass Index and Obesity
Rampersaud, E., Mitchell, B. D., Pollin, T. I., Fu, M., Shen, H., O’Connell, J. R., Ducharme, J. L., Hines, S., Sack, P., Naglieri, R., et al. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.16.1791 · Cited by 299
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Goodrich, J. K., Waters, J. L., Poole, A. C., Sutter, J. L., Koren, O., Blekhman, R., Beaumont, M., Van Treuren, W., Knight, R., Bell, J. T., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.053 · Cited by 3,164
“Variation in host genetics can also underlie susceptibility to metabolic disease (Frayling et al., 2007; Frazer et al., 2009; Herbert et al., 2006; Yang et al., 2012).”
Zheng, J., Erzurumluoglu, A. M., Elsworth, B. L., Kemp, J. P., Howe, L., Haycock, P. C., Hemani, G., Tansey, K., Laurin, C., Early Genetics and Lifecourse Epidemiology (EAGLE) Eczema Consortium, et al. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw613 · Cited by 869
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
the Alzheimer’s Disease Neuroimaging Initiative, E. C., Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., et al. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9269-5 · Cited by 838
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
Self-citationThe Key Role of Epigenetics in Human Disease Prevention and Mitigation
Feinberg, A. P. (2018) · New England Journal of Medicine · doi:10.1056/nejmra1402513 · Cited by 638
Metadata
- Authors
- Yang, J., Loos, R. J. F., Powell, J. E., Medland, S. E., Speliotes, E. K., Chasman, D. I., Rose, L. M., Thorleifsson, G., Steinthorsdottir, V., Mägi, R., Waite, L., Vernon Smith, A., Yerges-Armstrong, L. M., Monda, K. L., Hadley, D., Mahajan, A., Li, G., Kapur, K., Vitart, V., Huffman, J. E., Wang, S. R., Palmer, C., Esko, T., Fischer, K., Hua Zhao, J., Demirkan, A., Isaacs, A., Feitosa, M. F., Luan, J., Heard-Costa, N. L., White, C., Jackson, A. U., Preuss, M., Ziegler, A., Eriksson, J., Kutalik, Z., Frau, F., Nolte, I. M., Van Vliet-Ostaptchouk, J. V., Hottenga, J.-J., Jacobs, K. B., Verweij, N., Goel, A., Medina-Gomez, C., Estrada, K., Lynn Bragg-Gresham, J., Sanna, S., Sidore, C., Tyrer, J., Teumer, A., Prokopenko, I., Mangino, M., Lindgren, C. M., Assimes, T. L., Shuldiner, A. R., Hui, J., Beilby, J. P., McArdle, W. L., Hall, P., Haritunians, T., Zgaga, L., Kolcic, I., Polasek, O., Zemunik, T., Oostra, B. A., Juhani Junttila, M., Grönberg, H., Schreiber, S., Peters, A., Hicks, A. A., Stephens, J., Foad, N. S., Laitinen, J., Pouta, A., Kaakinen, M., Willemsen, G., Vink, J. M., Wild, S. H., Navis, G., Asselbergs, F. W., Homuth, G., John, U., Iribarren, C., Harris, T., Launer, L., Gudnason, V., O’Connell, J. R., Boerwinkle, E., Cadby, G., Palmer, L. J., James, A. L., Musk, A. W., Ingelsson, E., Psaty, B. M., Beckmann, J. S., Waeber, G., Vollenweider, P., Hayward, C., Wright, A. F., Rudan, I., Groop, L. C., Metspalu, A., Khaw, K.-T., van Duijn, C. M., Borecki, I. B., Province, M. A., Wareham, N. J., Tardif, J.-C., Huikuri, H. V., Adrienne Cupples, L., Atwood, L. D., Fox, C. S., Boehnke, M., Collins, F. S., Mohlke, K. L., Erdmann, J., Schunkert, H., Hengstenberg, C., Stark, K., Lorentzon, M., Ohlsson, C., Cusi, D., Staessen, J. A., Van der Klauw, M. M., Pramstaller, P. P., Kathiresan, S., Jolley, J. D., Ripatti, S., Jarvelin, M.-R., de Geus, E. J. C., Boomsma, D. I., Penninx, B., Wilson, J. F., Campbell, H., Chanock, S. J., van der Harst, P., Hamsten, A., Watkins, H., Hofman, A., Witteman, J. C., Zillikens, M. C., Uitterlinden, A. G., Rivadeneira, F., Carola Zillikens, M., Kiemeney, L. A., Vermeulen, S. H., Abecasis, G. R., Schlessinger, D., Schipf, S., Stumvoll, M., Tönjes, A., Spector, T. D., North, K. E., Lettre, G., McCarthy, M. I., Berndt, S. I., Heath, A. C., Madden, P. A. F., Nyholt, D. R., Montgomery, G. W., Martin, N. G., McKnight, B., Strachan, D. P., Hill, W. G., Snieder, H., Ridker, P. M., Thorsteinsdottir, U., Stefansson, K., Frayling, T. M., Hirschhorn, J. N., Goddard, M. E., & Visscher, P. M.
- Venue
- Nature
- Published
- 2012-09-16
- Volume
- 490
- Issue
- 7419
- Pages
- 267-272
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2104382557
- Cited by
- 423
- References
- 10
- FWCI
- 22.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Yang, J., Loos, R. J. F., Powell, J. E., Medland, S. E., Speliotes, E. K., Chasman, D. I., Rose, L. M., Thorleifsson, G., Steinthorsdottir, V., Mägi, R., Waite, L., Vernon Smith, A., Yerges-Armstrong, L. M., Monda, K. L., Hadley, D., Mahajan, A., Li, G., Kapur, K., Vitart, V., . . . Visscher, P. M. (2012). FTO genotype is associated with phenotypic variability of body mass index. Nature. https://doi.org/10.1038/nature11401
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