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.

Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity

Subjects

Abstract

A major goal of biomedicine is to understand the function of every gene in the human genome1. Loss-of-function mutations can disrupt both copies of a given gene in humans and phenotypic analysis of such ‘human knockouts’ can provide insight into gene function. Consanguineous unions are more likely to result in offspring carrying homozygous loss-of-function mutations. In Pakistan, consanguinity rates are notably high2. Here we sequence the protein-coding regions of 10,503 adult participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS), designed to understand the determinants of cardiometabolic diseases in individuals from South Asia3. We identified individuals carrying homozygous predicted loss-of-function (pLoF) mutations, and performed phenotypic analysis involving more than 200 biochemical and disease traits. We enumerated 49,138 rare (<1% minor allele frequency) pLoF mutations. These pLoF mutations are estimated to knock out 1,317 genes, each in at least one participant. Homozygosity for pLoF mutations at PLA2G7 was associated with absent enzymatic activity of soluble lipoprotein-associated phospholipase A2; at CYP2F1, with higher plasma interleukin-8 concentrations; at TREH, with lower concentrations of apoB-containing lipoprotein subfractions; at either A3GALT2 or NRG4, with markedly reduced plasma insulin C-peptide concentrations; and at SLC9A3R1, with mediators of calcium and phosphate signalling. Heterozygous deficiency of APOC3 has been shown to protect against coronary heart disease4,5; we identified APOC3 homozygous pLoF carriers in our cohort. We recruited these human knockouts and challenged them with an oral fat load. Compared with family members lacking the mutation, individuals with APOC3 knocked out displayed marked blunting of the usual post-prandial rise in plasma triglycerides. Overall, these observations provide a roadmap for a ‘human knockout project’, a systematic effort to understand the phenotypic consequences of complete disruption of genes in humans.

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: Homozygous pLoF burden in PROMIS is driven by excess autozygosity.
Figure 2: Carriers of PLA2G7 splice mutation have diminished Lp-PLA2 mass and activity but similar risk for coronary heart disease when compared to non-carriers.
Figure 3: APOC3 pLoF homozygotes have diminished fasting triglycerides and blunted post-prandial lipaemia.
Figure 4: Simulations anticipate many more homozygous pLoF genes in the PROMIS cohort.

Similar content being viewed by others

References

  1. Eisenberg, D., Marcotte, E. M., Xenarios, I. & Yeates, T. O. Protein function in the post-genomic era. Nature 405, 823–826 (2000)

    Article  CAS  PubMed  Google Scholar 

  2. Bittles, A. H., Mason, W. M., Greene, J. & Rao, N. A. Reproductive behavior and health in consanguineous marriages. Science 252, 789–794 (1991)

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Saleheen, D. et al. The Pakistan Risk of Myocardial Infarction Study: a resource for the study of genetic, lifestyle and other determinants of myocardial infarction in South Asia. Eur. J. Epidemiol. 24, 329–338 (2009)

    Article  PubMed  PubMed Central  Google Scholar 

  4. Crosby, J. et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N. Engl. J. Med. 371, 22–31 (2014)

    Article  PubMed  CAS  Google Scholar 

  5. Jørgensen, A. B., Frikke-Schmidt, R., Nordestgaard, B. G. & Tybjærg-Hansen, A. Loss-of-function mutations in APOC3 and risk of ischemic vascular disease. N. Engl. J. Med. 371, 32–41 (2014)

    Article  PubMed  CAS  Google Scholar 

  6. Narasimhan, V. M. et al. Health and population effects of rare gene knockouts in adult humans with related parents. Science 352, 474–477 (2016)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Sulem, P. et al. Identification of a large set of rare complete human knockouts. Nat. Genet. 47, 448–452 (2015)

    Article  CAS  PubMed  Google Scholar 

  8. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Di Angelantonio, E. et al. Lipid-related markers and cardiovascular disease prediction. J. Am. Med. Assoc. 307, 2499–2506 (2012)

    CAS  Google Scholar 

  10. Gregson, J. M. et al. Genetic invalidation of Lp-Pla2 as a therapeutic target: large-scale study of five functional Lp-Pla2-lowering alleles. Eur. J. Prev. Cardiol. (2016)

  11. Polfus, L. M., Gibbs, R. A. & Boerwinkle, E. Coronary heart disease and genetic variants with low phospholipase A2 activity. N. Engl. J. Med. 372, 295–296 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. White, H. D. et al. Darapladib for preventing ischemic events in stable coronary heart disease. N. Engl. J. Med. 370, 1702–1711 (2014)

    Article  CAS  PubMed  Google Scholar 

  13. O’Donoghue, M. L. et al. Effect of darapladib on major coronary events after an acute coronary syndrome: the SOLID-TIMI 52 randomized clinical trial. J. Am. Med. Assoc. 312, 1006–1015 (2014)

    Article  CAS  Google Scholar 

  14. Carr, B. A., Wan, J., Hines, R. N. & Yost, G. S. Characterization of the human lung CYP2F1 gene and identification of a novel lung-specific binding motif. J. Biol. Chem. 278, 15473–15483 (2003)

    Article  CAS  PubMed  Google Scholar 

  15. Standiford, T. J. et al. Interleukin-8 gene expression by a pulmonary epithelial cell line. A model for cytokine networks in the lung. J. Clin. Invest. 86, 1945–1953 (1990)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Murray, I. A., Coupland, K., Smith, J. A., Ansell, I. D. & Long, R. G. Intestinal trehalase activity in a UK population: establishing a normal range and the effect of disease. Br. J. Nutr. 83, 241–245 (2000)

    Article  CAS  PubMed  Google Scholar 

  17. Christiansen, D. et al. Humans lack iGb3 due to the absence of functional iGb3-synthase: implications for NKT cell development and transplantation. PLoS Biol. 6, e172 (2008)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Dahl, K., Buschard, K., Gram, D. X., d’Apice, A. J. & Hansen, A. K. Glucose intolerance in a xenotransplantation model: studies in alpha-gal knockout mice. APMIS 114, 805–811 (2006)

    Article  CAS  PubMed  Google Scholar 

  19. Casu, A. et al. Insulin secretion and glucose metabolism in alpha 1,3-galactosyltransferase knock-out pigs compared to wild-type pigs. Xenotransplantation 17, 131–139 (2010)

    Article  PubMed  Google Scholar 

  20. Schneider, M. R. & Wolf, E. The epidermal growth factor receptor ligands at a glance. J. Cell. Physiol. 218, 460–466 (2009)

    Article  CAS  PubMed  Google Scholar 

  21. Wang, G. X. et al. The brown fat-enriched secreted factor Nrg4 preserves metabolic homeostasis through attenuation of hepatic lipogenesis. Nat. Med. 20, 1436–1443 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Murtazina, R. et al. Tissue-specific regulation of sodium/proton exchanger isoform 3 activity in Na+/H+ exchanger regulatory factor 1 (NHERF1) null mice. cAMP inhibition is differentially dependent on NHERF1 and exchange protein directly activated by cAMP in ileum versus proximal tubule. J. Biol. Chem. 282, 25141–25151 (2007)

    Article  CAS  PubMed  Google Scholar 

  23. Karim, Z. et al. NHERF1 mutations and responsiveness of renal parathyroid hormone. N. Engl. J. Med. 359, 1128–1135 (2008)

    Article  CAS  PubMed  Google Scholar 

  24. Huff, M. W. & Hegele, R. A. Apolipoprotein C-III: going back to the future for a lipid drug target. Circ. Res. 112, 1405–1408 (2013)

    Article  CAS  PubMed  Google Scholar 

  25. Pollin, T. I. et al. A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection. Science 322, 1702–1705 (2008)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gaudet, D. et al. Antisense inhibition of apolipoprotein C-III in patients with hypertriglyceridemia. N. Engl. J. Med. 373, 438–447 (2015)

    Article  CAS  PubMed  Google Scholar 

  27. Gaudet, D. et al. Targeting APOC3 in the familial chylomicronemia syndrome. N. Engl. J. Med. 371, 2200–2206 (2014)

    Article  PubMed  CAS  Google Scholar 

  28. Graham, M. J. et al. Antisense oligonucleotide inhibition of apolipoprotein C-III reduces plasma triglycerides in rodents, nonhuman primates, and humans. Circ. Res. 112, 1479–1490 (2013)

    Article  CAS  PubMed  Google Scholar 

  29. Brown, S. D. & Moore, M. W. Towards an encyclopaedia of mammalian gene function: the International Mouse Phenotyping Consortium. Dis. Model. Mech. 5, 289–292 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Scott, E. M. et al. Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery. Nat. Genet. 48, 1071–1076 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kooner, J. S. et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat. Genet. 43, 984–989 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Do, R. et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102–106 (2015)

    Article  CAS  PubMed  Google Scholar 

  35. Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  36. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics http://dx.doi.org/10.1002/0471250953.bi1110s43 (2013)

  40. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Karczewski, K. J. Loftee (Loss-of-Function Transcript Effect Estimator), https://github.com/konradjk/loftee (2015)

  42. Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91, 839–848 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5, e15004 (2010)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hunter-Zinck, H. et al. Population genetic structure of the people of Qatar. Am. J. Hum. Genet. 87, 17–25 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lander, E. S. & Botstein, D. Homozygosity mapping: a way to map human recessive traits with the DNA of inbred children. Science 236, 1567–1570 (1987)

    Article  ADS  CAS  PubMed  Google Scholar 

  47. Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wright, S. Coefficients of Inbreeding and Relationship. Am. Nat. 56, 330–338 (1922)

    Article  Google Scholar 

  49. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Eppig, J. T., Blake, J. A., Bult, C. J., Kadin, J. A. & Richardson, J. E. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 43, D726–D736 (2015)

    Article  CAS  PubMed  Google Scholar 

  53. Georgi, B., Voight, B. F. & Bućan, M. From mouse to human: evolutionary genomics analysis of human orthologs of essential genes. PLoS Genet. 9, e1003484 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Fuchs, M. et al. The p400 complex is an essential E1A transformation target. Cell 106, 297–307 (2001)

    Article  CAS  PubMed  Google Scholar 

  55. Fazzio, T. G., Huff, J. T. & Panning, B. An RNAi screen of chromatin proteins identifies Tip60-p400 as a regulator of embryonic stem cell identity. Cell 134, 162–174 (2008)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006)

    Article  CAS  PubMed  Google Scholar 

  57. Sambrook, J. & Russell, D. W. Purification of nucleic acids by extraction with phenol:chloroform. CSH Protoc. http://dx.doi.org/10.1101/pdb.prot4455 (2006)

  58. Mosteller, R. D. Simplified calculation of body-surface area. N. Engl. J. Med. 317, 1098 (1987)

    CAS  PubMed  Google Scholar 

  59. Maraki, M. et al. Validity of abbreviated oral fat tolerance tests for assessing postprandial lipemia. Clin. Nutr. 30, 852–857 (2011)

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

D.S. is supported by grants from the National Institutes of Health, the Fogarty International, the Wellcome Trust, the British Heart Foundation, and Pfizer. P.N. is supported by the John S. LaDue Memorial Fellowship in Cardiology from Harvard Medical School. H.-H.W. is supported by a grant from the Samsung Medical Center, Korea (SMO116163). S.K. is supported by the Ofer and Shelly Nemirovsky MGH Research Scholar Award and by grants from the National Institutes of Health (R01HL107816), the Donovan Family Foundation, and Fondation Leducq. Exome sequencing was supported by a grant from the NHGRI (5U54HG003067-11) to S.G. and E.S.L. D.G.M. is supported by a grant from the National Institutes of Health (R01GM104371). J.D. holds a British Heart Foundation Chair, European Research Council Senior Investigator Award, and NIHR Senior Investigator Award. The Cardiovascular Epidemiology Unit at the University of Cambridge, which supported the field work and genotyping of PROMIS, is funded by the UK Medical Research Council, British Heart Foundation, and NIHR Cambridge Biomedical Research Centre. In recognition for PROMIS fieldwork and support, we also acknowledge contributions made by the following: M. Z. Ozair, U. Ahmed, A. Hakeem, H. Khalid, K. Shahid, F. Shuja, A. Kazmi, M. Qadir Hameed, N. Khan, S. Khan, A. Ali, M. Ali, S. Ahmed, M. W. Khan, M. R. Khan, A. Ghafoor, M. Alam, R. Ahmed, M. I. Javed, A. Ghaffar, T. B. Mirza, M. Shahid, J. Furqan, M. I. Abbasi, T. Abbas, R. Zulfiqar, M. Wajid, I. Ali, M. Ikhlaq, D. Sheikh, M. Imran, M. Walker, N. Sarwar, S. Venorman, R. Young, A. Butterworth, H. Lombardi, B. Kaur and N. Sheikh. Fieldwork in the PROMIS study has been supported through funds available to investigators at the Center for Non-Communicable Diseases, Pakistan and the University of Cambridge, UK.

Author information

Authors and Affiliations

Authors

Contributions

Sample recruitment and phenotyping was performed by D.S., P.F., J.D., A.R., M.Z., M.S., A.I., S.A., F.Ma., M.I., S.A., K.T., N.H.M., K.S.Z., N.Q., M.I., S.Z.R., F.Me., K.M., N.A., and R.M.K. D.S., P.F., J.D., and W.Z. performed array-based genotyping and runs-of-homozygosity analyses. Exome sequencing was coordinated by D.S., N.G., S.G., E.S.L., D.J.R., and S.K. P.N., W.Z., H.H.W., and R.D. performed exome-sequencing quality control and association analyses. P.N., I.M.A., K.J.K., A.H.O., B.W., and D.G.M. performed variant annotation. D.S., S.K., and D.J.R. performed confirmatory genotyping and lipoprotein biomarker assays. D.S. and A.R. conducted recall-based studies for the APOC3 knockouts. P.N. and M.J.D. performed bioinformatics simulations. P.N. and K.E.S. performed constraint score analyses. D.S., P.N., and S.K. designed the study and wrote the paper. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Danish Saleheen or Sekar Kathiresan.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 pLoF mutations are typically seen in very few individuals.

The site-frequency spectrum of synonymous, missense, and high-confidence pLoF mutations is represented. Points represent the proportion of variants within a 1 × 10−4 minor allele frequency bin for each variant category. Lines represent the cumulative proportions of variants categories. The bottom inset highlights that most pLoF variants are often seen in no more than one or two individuals. The top inset highlights that virtually all pLoF mutations are very rare.

Extended Data Figure 2 Intersection of homozygous pLoF genes between PROMIS and other cohorts.

We compared the counts and overlap of unique homozygous pLoF genes in PROMIS with other exome sequenced cohorts.

Extended Data Figure 3 QQ-plot of recessive model pLoF association analysis across phenotypes.

Analyses to determine whether homozygous pLoF carrier status was associated with traits was performed where there were at least two homozygous pLoF carriers phenotyped per trait. The observed versus the expected results from 15,263 associations are displayed here demonstrating an excess of associations beyond a Bonferroni threshold.

Extended Data Figure 4 Carriers of pLoF alleles in CYP2F1 have increased IL-8 concentrations.

Participants who had pLoF mutations in the CYP2F1 gene had higher concentrations of IL-8, whereas heterozygotes had a more modest effect when compared to the rest of the cohort of non-carriers. IL-8 concentration is natural log transformed. Bars represent 1.5× interquartile range beyond the 25th and 75th percentiles.

Extended Data Figure 5 Carriers of pLoF alleles in TREH have decreased concentrations of several lipoprotein subfractions.

Participants who had pLoF mutations in the TREH gene had lower concentrations of several lipoprotein subfractions. Bars represent 1.5× interquartile range beyond the 25th and 75th percentiles.

Extended Data Figure 6 Nondiabetic homozygous pLoF carriers for A3GALT2 have diminished insulin C-peptide concentrations.

Among nondiabetics, those who were homozygous pLoF for A3GALT2 had substantially lower fasting insulin C-peptide concentrations. This observation was not evident in nondiabetic heterozygous pLoF A3GALT2 participants. Insulin C-peptide is natural log transformed. Bars represent 1.5× interquartile range beyond the 25th and 75th percentiles.

Extended Data Figure 7 Example of a second polymorphism in-phase which rescues a putative protein-truncating mutation.

Short-reads that align to genomic positions 65,339,112 to 65,339,132 on chromosome 1 are displayed for one individual with a putative homozygous pLoF genotype in this region. The SNP at position 65,339,122 from G to T is annotated as a nonsense mutation in the JAK1 gene. However, all three homozygotes of this mutation carried a tandem SNP in the same codon (A to G at 65,339,124) thus resulting in a glutamine and effectively rescuing the protein-truncating mutation.

Extended Data Figure 8 Anticipated number of genes knocked out with increasing sample sizes by minimum knockout count.

We simulate the number of genes expected to be knocked out by minimum knockout count per gene at increasing sample sizes. We perform this simulation with and without the observed inbreeding.

Extended Data Figure 9 PROMIS participants have an excess burden of runs of homozygosity compared with other populations.

Consanguinity leads to regions of genomic segments that are identical by descent and can be observed as runs of homozygosity. Using genome-wide array data in 17,744 PROMIS participants and reference samples from the International HapMap3, the burden of runs of homozygosity (minimum 1.5 Mb) per individual was derived and population-specific distributions are displayed, with outliers removed. This highlights the higher median runs of homozygosity burden in PROMIS and the higher proportion of individuals with very high burdens.

Extended Data Figure 10 Down-sampling of synonymous and high confidence pLoF variants to validate simulation.

a, b, We ran simulations to estimate the number of unique, completely knocked out genes at increasing sample sizes. Before applying our model, we first applied this approach to a range of sample sizes below 7,078 for variants that were not under constraint, synonymous variants (a), and for high-confidence null variants (b). At the observed sample size, we did not observe significant selection. We expect that at increasing sample sizes, there may be a subset of genes that will not be tolerated in a homozygous pLoF state. In fact, our estimates are slightly more conservative when comparing outbred simulations with a recent description of >100,000 Icelanders using a more liberal definition for pLoF mutations.

Related audio

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-9, the full legend for Supplementary Table 1 (supplied as a separate spreadsheet) and Supplementary References. (PDF 621 kb)

Supplementary Table

This file contains Supplementary Table 1 – see the Supplementary Information document for the full description. (XLSX 241 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saleheen, D., Natarajan, P., Armean, I. et al. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature 544, 235–239 (2017). https://doi.org/10.1038/nature22034

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature22034

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