Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

FTO genotype is associated with phenotypic variability of body mass index

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Test statistics (–log10(P values)) for association with BMI variability in the discovery meta-analysis of SNPs at the FTO locus against their physical location.

References

  1. Hill, W. G. & Mulder, H. A. Genetic analysis of environmental variation. Genet Res. 92, 381–395 (2010)

    Article  Google Scholar 

  2. Ansel, J. et al. Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genet. 4, e1000049 (2008)

    Article  Google Scholar 

  3. Wolc, A., White, I. M., Avendano, S. & Hill, W. G. Genetic variability in residual variation of body weight and conformation scores in broiler chickens. Poult. Sci. 88, 1156–1161 (2009)

    CAS  PubMed  Google Scholar 

  4. Jimenez-Gomez, J. M., Corwin, J. A., Joseph, B., Maloof, J. N. & Kliebenstein, D. J. Genomic analysis of QTLs and genes altering natural variation in stochastic noise. PLoS Genet. 7, e1002295 (2011)

    Article  CAS  Google Scholar 

  5. Frayling, T. M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007)

    Article  CAS  ADS  Google Scholar 

  6. Dina, C. et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nature Genet. 39, 724–726 (2007)

    Article  CAS  Google Scholar 

  7. Scuteri, A. et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 3, e115 (2007)

    Article  Google Scholar 

  8. Kilpeläinen, T. O. et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med. 8, e1001116 (2011)

    Article  Google Scholar 

  9. Bell, C. G. et al. Integrated genetic and epigenetic analysis identifies haplotype-specific methylation in the FTO type 2 diabetes and obesity susceptibility locus. PLoS ONE 5, e14040 (2010)

    Article  ADS  Google Scholar 

  10. Almén, M. S. et al. Genome wide analysis reveals association of a FTO gene variant with epigenetic changes. Genomics 99, 132–137 (2012)

    Article  Google Scholar 

  11. Falconer, D. S. Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genet. Res. 56, 57–70 (1990)

    Article  Google Scholar 

  12. Jinks, J. L. & Connolly, V. Selection for specific and general response to environmental differences. Heredity 30, 33–40 (1973)

    Article  Google Scholar 

  13. Mackay, T. F. & Lyman, R. F. Drosophila bristles and the nature of quantitative genetic variation. Phil. Trans. R. Soc. Lond. B 360, 1513–1527 (2005)

    Article  CAS  Google Scholar 

  14. Ros, M. et al. Evidence for genetic control of adult weight plasticity in the snail Helix aspersa. Genetics 168, 2089–2097 (2004)

    Article  Google Scholar 

  15. Ordas, B., Malvar, R. A. & Hill, W. G. Genetic variation and quantitative trait loci associated with developmental stability and the environmental correlation between traits in maize. Genet Res. 90, 385–395 (2008)

    Article  CAS  Google Scholar 

  16. Yang, Y., Christensen, O. F. & Sorensen, D. Use of genomic models to study genetic control of environmental variance. Genet. Res. 93, 125–138 (2011)

    Article  CAS  Google Scholar 

  17. Rönnegård, L. & Valdar, W. Detecting major genetic loci controlling phenotypic variability in experimental crosses. Genetics 188, 435–447 (2011)

    Article  Google Scholar 

  18. Paré, G., Cook, N. R., Ridker, P. M. & Chasman, D. I. On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women’s Genome Health Study. PLoS Genet. 6, e1000981 (2010)

    Article  Google Scholar 

  19. Martin, N. G., Rowell, D. M. & Whitfield, J. B. Do the MN and Jk systems influence environmental variability in serum lipid levels? Clin. Genet. 24, 1–14 (1983)

    Article  CAS  Google Scholar 

  20. Visscher, P. M. & Posthuma, D. Statistical power to detect genetic loci affecting environmental sensitivity. Behav. Genet. 40, 728–733 (2010)

    Article  Google Scholar 

  21. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999)

    Article  CAS  Google Scholar 

  22. Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genet. 42, 937–948 (2010)

    Article  CAS  Google Scholar 

  23. Struchalin, M. V., Dehghan, A., Witteman, J. C., van Duijn, C. & Aulchenko, Y. S. Variance heterogeneity analysis for detection of potentially interacting genetic loci: method and its limitations. BMC Genet. 11, 92 (2010)

    Article  Google Scholar 

  24. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010)

    Article  CAS  ADS  Google Scholar 

  25. Williams, P. T. Quantile-specific penetrance of genes affecting lipoproteins, adiposity and height. PLoS ONE 7, e28764 (2012)

    Article  CAS  ADS  Google Scholar 

  26. Silventoinen, K. et al. Modification effects of physical activity and protein intake on heritability of body size and composition. Am. J. Clin. Nutr. 90, 1096–1103 (2009)

    Article  CAS  Google Scholar 

  27. Andreasen, C. H. et al. Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation. Diabetes 57, 95–101 (2008)

    Article  CAS  Google Scholar 

  28. Rampersaud, E. et al. Physical activity and the association of common FTO gene variants with body mass index and obesity. Arch. Intern. Med. 168, 1791–1797 (2008)

    Article  Google Scholar 

  29. Jia, G. et al. N6-Methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nature Chem. Biol. 7, 885–887 (2011)

    Article  CAS  Google Scholar 

  30. Toperoff, G. et al. Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood. Hum. Mol. Genet. 21, 371–383 (2012)

    Article  CAS  Google Scholar 

  31. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007)

  32. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genom × 10−wide association studies by imputation of genotypes. Nature Genet. 39, 906–913 (2007)

    Article  CAS  Google Scholar 

  33. Li, Y., Willer, C. J., Ding, J., Scheet, P. & Abecasis, G. R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010)

    Article  Google Scholar 

  34. Aulchenko, Y. S., Ripke, S., Isaacs, A. & van Duijn, C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007)

    Article  CAS  Google Scholar 

  35. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010)

    Article  CAS  Google Scholar 

  36. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge funding from the Australian National Health and Medical Research Council (NHMRC grants 241944, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 496688, 552485, 613672, 613601 and 1011506), the US National Institutes of Health (grants AA07535, AA10248, AA014041, AA13320, AA13321, AA13326, DA12854 and GM057091) and the Australian Research Council (ARC grant DP1093502). A detailed list of acknowledgements by study is provided in the Supplementary Information. We apologize to authors whose work we could not cite owing to space restrictions.

Author information

Authors and Affiliations

Authors

Contributions

P.M.V., M.E.G. and J.Y. conceived and designed the study. J.Y. and P.M.V. derived the analytical theory. J.Y. performed the meta-analyses and simulations. J.Y. and P.M.V. wrote the first draft of the manuscript. J.Y., D.I.C., J.H.Z. and R.J.F.L. performed further statistical verification analyses. D.P.S., W.G.H., R.J.F.L., S.I.B. and H. Snieder contributed important additional concepts and critically reviewed the manuscript before submission. S.E.M., P.A.F.M., A.C.H., N.G.M., D.R.N. and G.W.M. contributed the individual-level genotype and phenotype data of the QIMR cohort. T.M.F., J.N.H. and R.J.F.L. liaised with the GIANT consortium for this project. The cohort-specific contributions of all other authors are provided in the Supplementary Information.

Corresponding author

Correspondence to Peter M. Visscher.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-10, Supplementary Tables 1-5, Supplementary Notes and Data, Supplementary References, Supplementary Acknowledgements (study-specific) and Supplementary Author Contributions. Supplementary Tables 4, 5 and the Acknowledgements were corrected on 31 January 2013. (PDF 1652 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, J., Loos, R., Powell, J. et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267–272 (2012). https://doi.org/10.1038/nature11401

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature11401

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing