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.

Genetic studies of body mass index yield new insights for obesity biology

Abstract

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10−8), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for 2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

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: Cumulative variance explained and example of secondary signals.
Figure 2: Tissues and reconstituted gene sets significantly enriched for genes within BMI-associated loci.

Similar content being viewed by others

References

  1. Maes, H. H., Neale, M. C. & Eaves, L. J. Genetic and environmental factors in relative body weight and human adiposity. Behav. Genet. 27, 325–351 (1997)

    CAS  PubMed  Google Scholar 

  2. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Zaitlen, N. et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Fall, T. & Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Mol. Cell. Endocrinol. 382, 740–757 (2014)

    CAS  PubMed  Google Scholar 

  5. 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)

    CAS  PubMed  Google Scholar 

  6. Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nature Genet. 41, 25–34 (2009)

    CAS  PubMed  Google Scholar 

  7. Voight, B. F. et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 8, e1002793 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Kilpeläinen, T. O. et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nature Genet. 43, 753–760 (2011)

    PubMed  Google Scholar 

  9. Bradfield, J. P. et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nature Genet. 44, 526–531 (2012)

    CAS  PubMed  Google Scholar 

  10. Monda, K. L. et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nature Genet. 45, 690–696 (2013)

    CAS  PubMed  Google Scholar 

  11. Berndt, S. I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nature Genet. 45, 501–512 (2013)

    CAS  PubMed  Google Scholar 

  12. Guo, Y. et al. Gene-centric meta-analyses of 108 912 individuals confirm known body mass index loci and reveal three novel signals. Hum. Mol. Genet. 22, 184–201 (2013)

    CAS  PubMed  Google Scholar 

  13. Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nature Genet. 46, 1173–1186 (2014)

    CAS  PubMed  Google Scholar 

  14. Maller, J. B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nature Genet. 44, 1294–1301 (2012)

    CAS  PubMed  Google Scholar 

  15. Wakefield, J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 81, 208–227 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Peters, U. et al. A systematic mapping approach of 16q12.2/FTO and BMI in more than 20,000 African Americans narrows in on the underlying functional variation: results from the Population Architecture using Genomics and Epidemiology (PAGE) study. PLoS Genet. 9, e1003171 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Juster, F. T. & Suzman, R. An overview of the Health and Retirement Study. J. Hum. Resour. 30, S7–S56 (1995)

    Google Scholar 

  18. Bouchonville, M. et al. Weight loss, exercise or both and cardiometabolic risk factors in obese older adults: results of a randomized controlled trial. Int. J. Obes. 38, 423–431 (2013)

    Google Scholar 

  19. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature Genet. 44, 369–375 (2012)

    CAS  PubMed  Google Scholar 

  21. Pers, T. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 5, 5890 (2014)

    Google Scholar 

  22. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012)

  23. Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nature Biotechnol. 28, 1045–1048 (2010)

    CAS  Google Scholar 

  24. Segrè, A. V., Groop, L., Mootha, V. K., Daly, M. J. & Altshuler, D. Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 6, e1001058 (2010)

    PubMed  PubMed Central  Google Scholar 

  25. Wullschleger, S., Loewith, R. & Hall, M. N. TOR signaling in growth and metabolism. Cell 124, 471–484 (2006)

    CAS  PubMed  Google Scholar 

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009)

    PubMed  PubMed Central  Google Scholar 

  28. Mägi, R. et al. Contribution of 32 GWAS-identified common variants to severe obesity in European adults referred for bariatric surgery. PLoS ONE 8, e70735 (2013)

    ADS  PubMed  PubMed Central  Google Scholar 

  29. Lee, A. W. et al. Functional inactivation of the genome-wide association study obesity gene neuronal growth regulator 1 in mice causes a body mass phenotype. PLoS ONE 7, e41537 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yang, Y., Atasoy, D., Su, H. H. & Sternson, S. M. Hunger states switch a flip-flop memory circuit via a synaptic AMPK-dependent positive feedback loop. Cell 146, 992–1003 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Wu, Q., Clark, M. S. & Palmiter, R. D. Deciphering a neuronal circuit that mediates appetite. Nature 483, 594–597 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Shen, Y., Fu, W. Y., Cheng, E. Y., Fu, A. K. & Ip, N. Y. Melanocortin-4 receptor regulates hippocampal synaptic plasticity through a protein kinase A-dependent mechanism. J. Neurosci. 33, 464–472 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Gibbs, J. W., III, Sombati, S., DeLorenzo, R. J. & Coulter, D. A. Cellular actions of topiramate: blockade of kainate-evoked inward currents in cultured hippocampal neurons. Epilepsia 41 (suppl. 1). S10–S16 (2000)

    CAS  Google Scholar 

  34. Poulsen, C. F. et al. Modulation by topiramate of AMPA and kainate mediated calcium influx in cultured cerebral cortical, hippocampal and cerebellar neurons. Neurochem. Res. 29, 275–282 (2004)

    CAS  PubMed  Google Scholar 

  35. Henao-Mejia, J. et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482, 179–185 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Frazer, K. A. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007)

    ADS  CAS  PubMed  Google Scholar 

  38. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nature Protocols 9, 1192–1212 (2014)

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  MATH  Google Scholar 

  41. Wen, W. et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nature Genet. 44, 307–311 (2012)

    CAS  PubMed  Google Scholar 

  42. Randall, J. C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 9, e1003500 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)

    PubMed  PubMed Central  Google Scholar 

  44. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nature Methods 7, 248–249 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  45. NHLBI Exome Sequencing Project (ESP), Exome Variant Server; http://evs.gs.washington.edu/EVS/

  46. Ng, P. C. & Henikoff, S. Predicting deleterious amino acid substitutions. Genome Res. 11, 863–874 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Mills, R. E. et al. Mapping copy number variation by population-scale genome sequencing. Nature 470, 59–65 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008)

    ADS  CAS  PubMed  Google Scholar 

  49. Zhong, H., Yang, X., Kaplan, L. M., Molony, C. & Schadt, E. E. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am. J. Hum. Genet. 86, 581–591 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Grundberg, E. et al. Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nature Genet. 44, 1084–1089 (2012)

    CAS  PubMed  Google Scholar 

  51. Dixon, A. L. et al. A genome-wide association study of global gene expression. Nature Genet. 39, 1202–1207 (2007)

    CAS  PubMed  Google Scholar 

  52. Fehrmann, R. S. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 7, e1002197 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Nelis, M. et al. Genetic structure of Europeans: a view from the North-East. PLoS ONE 4, e5472 (2009)

    ADS  PubMed  PubMed Central  Google Scholar 

  54. Myers, A. J. et al. A survey of genetic human cortical gene expression. Nature Genet. 39, 1494–1499 (2007)

    CAS  PubMed  Google Scholar 

  55. Westra, H. J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nature Genet. 45, 1238–1243 (2013)

    CAS  PubMed  Google Scholar 

  56. Morris, A. P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nature Genet. 44, 981–990 (2012)

    CAS  PubMed  Google Scholar 

  57. Deloukas, P. et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nature Genet. 45, 25–33 (2013)

    CAS  PubMed  Google Scholar 

  58. Ehret, G. B. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011)

    ADS  CAS  PubMed  Google Scholar 

  59. Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature http://dx.doi.org/nature14132 (this issue)

  60. Willer, C. et al. Discovery and refinement of loci associated with lipid levels. Nature Genet. 45, 1274–1283 (2013)

    CAS  PubMed  Google Scholar 

  61. Scott, R. A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nature Genet. 44, 991–1005 (2012)

    CAS  PubMed  Google Scholar 

  62. Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nature Genet. 44, 659–669 (2012)

    CAS  PubMed  Google Scholar 

  63. Saxena, R. et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nature Genet. 42, 142–148 (2010)

    CAS  PubMed  Google Scholar 

  64. Dastani, Z. et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 8, e1002607 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Pattaro, C. et al. Genome-wide association and functional follow-up reveals new loci for kidney function. PLoS Genet. 8, e1002584 (2012)

    PubMed  PubMed Central  Google Scholar 

  66. Böger, C. A. et al. CUBN is a gene locus for albuminuria. J. Am. Soc. Nephrol. 22, 555–570 (2011)

    PubMed  PubMed Central  Google Scholar 

  67. Stolk, L. et al. Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways. Nature Genet. 44, 260–268 (2012)

    CAS  PubMed  Google Scholar 

  68. Elks, C. E. et al. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nature Genet. 42, 1077–1085 (2010)

    CAS  PubMed  Google Scholar 

  69. Williams, W. W. et al. Association testing of previously reported variants in a large case-control meta-analysis of diabetic nephropathy. Diabetes 61, 2187–2194 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Sandholm, N. et al. New susceptibility loci associated with kidney disease in type 1 diabetes. PLoS Genet. 8, e1002921 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  72. Li, Q., Brown, J. B., Huang, H. & Bickel, P. J. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 5, 1752–1779 (2011)

    MathSciNet  MATH  Google Scholar 

  73. Feng, J., Liu, T., Qin, B., Zhang, Y. & Liu, X. S. Identifying ChIP-seq enrichment using MACS. Nature Protocols 7, 1728–1740 (2012)

    CAS  PubMed  Google Scholar 

  74. Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012)

    ADS  PubMed  Google Scholar 

  75. Fehrmann, R. S. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nature Genet. 47, 115–125 (2015)

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

A full list of acknowledgements can be found in the Supplementary Information.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

A full list of author contributions can be found in the Supplementary Information.

Corresponding authors

Correspondence to Joel N. Hirschhorn, Ruth J. F. Loos or Elizabeth K. Speliotes.

Ethics declarations

Competing interests

Competing interests statement: G.T., V.S., U.T. and K.S. are employed by deCODE Genetics/Amgen, Inc. I.B. and spouse own stock in GlaxoSmithKline and Incyte, Ltd. C.B. is a consultant for Weight Watchers, Pathway Genomics, NIKE and Gatorade PepsiCo.

Additional information

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

A list of authors and affiliations appears in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Study design.

*The SNP counts reflect sample size filter of n ≥ 50,000. §Counts represent the primary European sex-combined analysis. Please see Extended Data Table 1 for counts for secondary analyses.

Extended Data Figure 2 Genetic characterization of BMI-associated variants.

a, Plot of the cumulative phenotypic variance explained by each locus ordered by decreasing effect size. b, The relationship between effect size and allele frequency. Previously identified loci are blue circles and novel loci are red triangles. c, Quantile–quantile (Q–Q) plot of meta-analysis P values for all 1,909 BMI-replication SNPs (blue) and after removing SNPs near the 97 associated loci (green). d, Histogram of cumulative effect of BMI risk alleles. Mean BMI for each bin is shown by the black dots (with standard deviation) and corresponds to the right-hand y axis.

Extended Data Figure 3 Partitioning the variance in and risk prediction from SNP-derived predictor.

a, b, The analyses were performed using 2,758 full sibling pairs from the TwinGene cohort (a) and 1,622 pairs from the QIMR cohort (b). The SNP-based predictor was adjusted for the first 20 principal components. The variance of the SNP-based predictor can be partitioned into four components (Vg, Ve, Cg and Ce) using the within-family prediction analysis, in which Vg is the variance explained by real SNP effects, Cg is the covariance between predictors attributed to the real effects of SNPs that are not in LD but correlated due to population stratification, Ve is the accumulated variance due to the errors in estimating SNP effects, and Ce is the covariance between predictors attributed to errors in estimating the effects of SNPs that are correlated due to population stratification. Error bars reflect s.e.m. of estimates. c, The prediction R2 shown on the y axis is the squared correlation between phenotype and SNP-based genetic predictor in unrelated individuals from the TwinGene (n = 5,668) and QIMR (n = 3,953) studies. The number shown in each column is the number of SNPs selected from the GCTA joint and conditional analysis at a range of P-value thresholds. In each case, the predictor was adjusted by the first 20 principal components. The column in orange is the average prediction R2 weighted by sample size over the two cohorts. The dashed grey line is the value inferred from the within-family prediction analyses using this equation R2 = (Vg + Cg)2/(Vg + Ve + Cg + Ce).

Extended Data Figure 4 Comparison of BMI-associated index SNPs across ethnicities.

a, b, BMI effects observed in European ancestry individuals (x axes) compared to African ancestry (a) or Asian ancestry (b) individuals (y axes). c, d, Allele frequencies between ancestry groups, as in a and b. e, f, Comparison of the estimates of explained variance. In all plots, novel loci are in red and previously identified loci are in blue.

Extended Data Figure 5 Effects of BMI-associated loci on related metabolic traits.

Unsupervised hierarchical clustering of the 97 BMI-associated loci (y axis) on 23 related metabolic traits (x axis). The top row shows the a priori expected relationship with BMI (green is concordant effect direction, purple is opposite). Loci with statistically significant concordant direction of effect are highlighted in green, and significant but opposing effects are in purple. Grey indicates a non-significant relationship and those with no information are in white. The key in the top left corner also shows the count of gene–phenotype pairs in each category (cyan bars).

Extended Data Figure 6 Bubble chart representing the genetic overlap across traits at BMI susceptibility loci.

Each bubble represents a trait for which association results were requested for the 97 GWS BMI loci. The size of the bubble is proportional to the number of BMI-increasing loci with a significant association. A line connects each pair of bubbles with thickness proportional to the number of significant loci shared between the traits. Traits tested include the current study BMI SNPs, African-American BMI (AA BMI), hip circumference (HIP), HIP adjusted for BMI (HIPadjBMI), waist circumference (WC), waist circumference adjusted for BMI (WCadjBMI), waist-to-hip ratio (WHR), waist-to-hip ratio adjusted for BMI (WHRadjBMI), height, adiponectin, coronary artery disease (CAD), diastolic blood pressure (DBP), systolic blood pressure (SBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), triglycerides (TG), type 2 diabetes (T2D), fasting glucose (FG), fasting insulin (FI), fasting insulin adjusted for BMI (FIadjBMI), two-hour glucose (Glu2hr), diabetic nephropathy (Diab_Neph), age at menopause (AgeMenopause), and age at menarche (AgeMenarche).

Extended Data Table 1 Descriptive characteristics of meta-analyses
Extended Data Table 2 Previously known GWS BMI loci in European meta-analysis
Extended Data Table 3 Association of the GWS SNPs for BMI with cis-gene expression (cis-eQTLs)
Extended Data Table 4 Putative coding variants in LD (r2 ≥ 0.7) with GWS BMI loci

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-10, Author Contributions and detailed Acknowledgements. (PDF 9393 kb)

Supplementary Data

This file contains Supplementary Tables 1-25. (XLSX 1588 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Locke, A., Kahali, B., Berndt, S. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015). https://doi.org/10.1038/nature14177

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature14177

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