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.

  • Analysis
  • Published:

Human gene essentiality

Nature Reviews Genetics volume 19, pages 51–62 (2018)Cite this article

Subjects

Key Points

  • A gene is considered essential when loss of its function compromises the viability or fitness of the organism.

  • Large-scale, population genome analyses in humans allow the observation of genes that do not tolerate loss of function, that is, are essential, and genes that tolerate biallelic loss of function, that is, are dispensable.

  • Human essential genes may not be captured in mouse knockout mouse models or recapitulated in cellular assays.

  • Observing the phenotypic consequences of loss-of-function variants is now used to anticipate drug safety and efficacy and guide drug discovery.

Abstract

A gene can be defined as essential when loss of its function compromises viability of the individual (for example, embryonic lethality) or results in profound loss of fitness. At the population level, identification of essential genes is accomplished by observing intolerance to loss-of-function variants. Several computational methods are available to score gene essentiality, and recent progress has been made in defining essentiality in the non-coding genome. Haploinsufficiency is emerging as a critical aspect of gene essentiality: approximately 3,000 human genes cannot tolerate loss of one of the two alleles. Genes identified as essential in human cell lines or knockout mice may be distinct from those in living humans. Reconciling these discrepancies in how we evaluate gene essentiality has applications in clinical genetics and may offer insights for drug development.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Rank correlation of essential gene sets across human population data and cell-based CRISPR–Cas9 screens.
Figure 2: Consistency of essentiality calls across human in vivo, mouse in vivo, and CRISPR–Cas9 cell line data sets.
Figure 3: Core set of essential genes in mice and humans.
Figure 4: Pathogenic variants in essential genes.

Similar content being viewed by others

References

  1. Maniloff, J. The minimal cell genome: “on being the right size”. Proc. Natl Acad. Sci. USA 93, 10004–10006 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hutchison III, C. A. et al. Global transposon mutagenesis and a minimal Mycoplasma genome. Science 286, 2165–2169 (1999).

    Article  Google Scholar 

  3. Hutchison III, C. A., et al. Design and synthesis of a minimal bacterial genome. Science 351, aad6253 (2016).

    Article  CAS  Google Scholar 

  4. Liu, G. et al. Gene essentiality is a quantitative property linked to cellular evolvability. Cell 163, 1388–1399 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Luo, H., Lin, Y., Gao, F., Zhang, C. T. & Zhang, R. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res. 42, D574–D580 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Deutschbauer, A. M. et al. Mechanisms of haploinsufficiency revealed by genome-wide profiling in yeast. Genetics 169, 1915–1925 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cirulli, E. T. et al. A whole-genome analysis of premature termination codons. Genomics 98, 337–342 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Rausell, A. et al. Analysis of stop-gain and frameshift variants in human innate immunity genes. PLoS Comput. Biol. 10, e1003757 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Rivas, M. A. et al. Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science 348, 666–669 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Montgomery, S. B., Lappalainen, T., Gutierrez-Arcelus, M. & Dermitzakis, E. T. Rare and common regulatory variation in population-scale sequenced human genomes. PLoS Genet. 7, e1002144 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Huang, N., Lee, I., Marcotte, E. M. & Hurles, M. E. Characterising and predicting haploinsufficiency in the human genome. PLoS Genet. 6, e1001154 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl Acad. Sci. USA 113, 11901–11906 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016). This paper presents the identification by ExAC of 3,230 genes with near-complete depletion of predicted protein-truncating variants. This work describes the widely used pLI score to identify essential genes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Chamary, J. V., Parmley, J. L. & Hurst, L. D. Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat. Rev. Genet. 7, 98–108 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Hunt, R. C., Simhadri, V. L., Iandoli, M., Sauna, Z. E. & Kimchi-Sarfaty, C. Exposing synonymous mutations. Trends Genet. 30, 308–321 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. Petrovski, S., Wang, Q., Heinzen, E. L., Allen, A. S. & Goldstein, D. B. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS Genet. 9, e1003709 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rackham, O. J., Shihab, H. A., Johnson, M. R. & Petretto, E. EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization. Nucleic Acids Res. 43, e33 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014). This is an influential paper describing context-dependent mutation rates across the genome. It forms the basis for several sores of essentiality.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fadista, J., Oskolkov, N., Hansson, O. & Groop, L. LoFtool: a gene intolerance score based on loss-of-function variants in 60 706 individuals. Bioinformatics 33, 471–474 (2016).

    Google Scholar 

  23. Bartha, I. et al. The characteristics of heterozygous protein truncating variants in the human genome. PLoS Comput Biol 11, e1004647 (2015). This study highlights rare heterozygous variants as an unexplored source of diversity of phenotypic traits and diseases. It describes the lack of compensation at expression level (haploinsufficiency).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cassa, C. A. et al. Estimating the selective effects of heterozygous protein-truncating variants from human exome data. Nat. Genet. 49, 806–810 (2017). This paper describes a large set of essential genes that are likely to have crucial functions but have not yet been characterized.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dang, V. T., Kassahn, K. S., Marcos, A. E. & Ragan, M. A. Identification of human haploinsufficient genes and their genomic proximity to segmental duplications. Eur. J. Hum. Genet. 16, 1350–1357 (2008).

    Article  CAS  PubMed  Google Scholar 

  26. Khurana, E., Fu, Y., Chen, J. & Gerstein, M. Interpretation of genomic variants using a unified biological network approach. PLoS Comput. Biol. 9, e1002886 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Steinberg, J., Honti, F., Meader, S. & Webber, C. Haploinsufficiency predictions without study bias. Nucleic Acids Res. 43, e101 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shihab, H. A., Rogers, M. F., Campbell, C. & Gaunt, T. R. HIPred: an integrative approach to predicting haploinsufficient genes. Bioinformatics 33, 1751–1757 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Giaever, G. & Nislow, C. The yeast deletion collection: a decade of functional genomics. Genetics 197, 451–465 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fraser, A. Essential Human Genes. Cell Syst. 1, 381–382 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Dickerson, J. E., Zhu, A., Robertson, D. L. & Hentges, K. E. Defining the role of essential genes in human disease. PLoS ONE 6, e27368 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Khuri, S. & Wuchty, S. Essentiality and centrality in protein interaction networks revisited. BMC Bioinformatics 16, 109 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Vinayagam, A. et al. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proc. Natl Acad. Sci. USA 113, 4976–4981 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Georgi, B., Voight, B. F. & Bucan, 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 

  35. Cannavo, E. et al. Genetic variants regulating expression levels and isoform diversity during embryogenesis. Nature 541, 402–406 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Jeong, H., Mason, S. P., Barabasi, A. L. & Oltvai, Z. N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    Article  CAS  PubMed  Google Scholar 

  37. Zhang, X., Acencio, M. L. & Lemke, N. Predicting essential genes and proteins based on machine learning and network topological features: a comprehensive review. Front. Physiol. 7, 75 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Rosenthal, N. & Brown, S. The mouse ascending: perspectives for human-disease models. Nat. Cell Biol. 9, 993–999 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Ayadi, A. et al. Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project. Mamm. Genome 23, 600–610 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Justice, M. J. & Dhillon, P. Using the mouse to model human disease: increasing validity and reproducibility. Dis. Model. Mech. 9, 101–103 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Prado, A., Canal, I. & Ferrus, A. The haplolethal region at the 16F gene cluster of Drosophila melanogaster: structure and function. Genetics 151, 163–175 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Howell, G. R., Munroe, R. J. & Schimenti, J. C. Transgenic rescue of the mouse t complex haplolethal locus Thl1. Mamm. Genome 16, 838–846 (2005).

    Article  CAS  PubMed  Google Scholar 

  46. Dickinson, M. E. et al. High-throughput discovery of novel developmental phenotypes. Nature 537, 508–514 (2016). This is the largest study from the International Mouse Phenotyping Consortium. It identifies 410 lethal genes during the production of the first 1,751 mouse gene knockouts.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Dey, G., Jaimovich, A., Collins, S. R., Seki, A. & Meyer, T. Systematic discovery of human gene function and principles of modular organization through phylogenetic profiling. Cell Rep. http://dx.doi.org/10.1016/j.celrep.2015.01.025 (2015).

  48. Edwards, A. M. et al. Too many roads not taken. Nature 470, 163–165 (2011).

    Article  CAS  PubMed  Google Scholar 

  49. Ganna, A. et al. Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum. Preprint at http://biorxiv.org/content/early/2017/06/09/148247 (2017).

  50. Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).

    Article  CAS  PubMed  Google Scholar 

  51. Stenson, P. D. et al. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 133, 1–9 (2014).

    Article  CAS  PubMed  Google Scholar 

  52. Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Narasimhan, V. M., Xue, Y. & Tyler-Smith, C. Human knockout carriers: dead, diseased, healthy, or improved? Trends Mol. Med. 22, 341–351 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 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  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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 

  56. Lim, E. T. et al. Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genet. 10, e1004494 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Saleheen, D. et al. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature 544, 235–239 (2017). This provides a roadmap for a 'human knockout project' to understand the phenotypic consequences of complete disruption of genes in humans.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Nagy, E. & Maquat, L. E. A rule for termination-codon position within intron-containing genes: when nonsense affects RNA abundance. Trends Biochem. Sci. 23, 198–199 (1998).

    Article  CAS  PubMed  Google Scholar 

  59. Lykke-Andersen, S. & Jensen, T. H. Nonsense-mediated mRNA decay: an intricate machinery that shapes transcriptomes. Nat. Rev. Mol. Cell Biol. 16, 665–677 (2015).

    Article  CAS  PubMed  Google Scholar 

  60. Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Esteller, M. Non-coding RNAs in human disease. Nat. Rev. Genet. 12, 861–874 (2011).

    Article  CAS  PubMed  Google Scholar 

  62. Makrythanasis, P. & Antonarakis, S. E. Pathogenic variants in non-protein-coding sequences. Clin. Genet. 84, 422–428 (2013).

    Article  CAS  PubMed  Google Scholar 

  63. Gordon, C. T. & Lyonnet, S. Enhancer mutations and phenotype modularity. Nat. Genet. 46, 3–4 (2014).

    Article  CAS  PubMed  Google Scholar 

  64. Smedley, D. et al. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am. J. Hum. Genet. 99, 595–606 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Harmston, N., Baresic, A. & Lenhard, B. The mystery of extreme non-coding conservation. Phil. Trans. R. Soc. B 368, 20130021 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wright, J. B. & Sanjana, N. E. CRISPR screens to discover functional noncoding elements. Trends Genet. 32, 526–529 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  CAS  Google Scholar 

  68. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ionita-Laza, I., McCallum, K., Xu, B. & Buxbaum, J. D. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214–220 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Khurana, E. et al. Role of non-coding sequence variants in cancer. Nat. Rev. Genet. 17, 93–108 (2016).

    Article  CAS  PubMed  Google Scholar 

  72. Aggarwala, V. & Voight, B. F. An expanded sequence context model broadly explains variability in polymorphism levels across the human genome. Nat. Genet. 48, 349–355 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. di Iulio, J. et al. The human non-coding genome defined by genetic diversity. Nat. Genet. (in the press) (2017).

  74. Fulco, C. P. et al. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science 354, 769–773 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Korkmaz, G. et al. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat. Biotechnol. 34, 192–198 (2016).

    Article  CAS  PubMed  Google Scholar 

  76. Sanjana, N. E. et al. High-resolution interrogation of functional elements in the noncoding genome. Science 353, 1545–1549 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Zhu, S. et al. Genome-scale deletion screening of human long non-coding RNAs using a paired-guide RNA CRISPR-Cas9 library. Nat. Biotechnol. 34, 1279–1286 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Kathiresan, S. Developing medicines that mimic the natural successes of the human genome: lessons from NPC1L1, HMGCR, PCSK9, APOC3, and CETP. J. Am. Coll. Cardiol. 65, 1562–1566 (2015).

    Article  PubMed  Google Scholar 

  79. Este, J. A. & Telenti, A. HIV entry inhibitors. Lancet 370, 81–88 (2007).

    Article  CAS  PubMed  Google Scholar 

  80. Kuehn, H. S. et al. Immune dysregulation in human subjects with heterozygous germline mutations in CTLA4. Science 345, 1623–1627 (2014). This is a report of haploinsufficiency linked to a severe immune disease in several unrelated adults that escaped diagnosis for years. It serves as a model of the syndromes to come.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Sabatine, M. S. et al. Evolocumab and clinical outcomes in patients with cardiovascular disease. N. Engl. J. Med. 376, 1713–1722 (2017). This is a clinical trial of a drug built on the knowledge of the cardiovascular phenotype of a human PCSK9 truncation.

    Article  CAS  PubMed  Google Scholar 

  82. Samocha, K. E. et al. Regional missense constraint improves variant deleteriousness prediction. Preprint at http://biorxiv.org/content/early/2017/06/12/148353 (2017).

  83. Sohail, M. et al. Negative selection in humans and fruit flies involves synergistic epistasis. Science 356, 539–542 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Kellis, M. et al. Defining functional DNA elements in the human genome. Proc. Natl Acad. Sci. USA 111, 6131–6138 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank Drs Ewen Kirkness and Michael Hicks for valuable comments. The authors are employees of Human Longevity, Inc.

Author information

Authors and Affiliations

  1. Human Longevity Inc., San Diego, 92121, California, USA

    István Bartha, Julia di Iulio, J. Craig Venter & Amalio Telenti

  2. J. Craig Venter Institute, Capricorn Lane, La Jolla, 92037, California, USA

    J. Craig Venter & Amalio Telenti

Authors
  1. István Bartha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julia di Iulio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Craig Venter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amalio Telenti
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors substantially contributed to discussion of content and to reviewing/editing the manuscript before submission. I.B., J.d.I. and A.T. researched data for the article and contributed to writing the manuscript.

Corresponding authors

Correspondence to J. Craig Venter or Amalio Telenti.

Ethics declarations

Competing interests

The authors are employees of Human Longevity, Inc. There is no commercial interest or intellectual property associated with this work.

Related links

PowerPoint slides

Supplementary information

Supplementary information

Supplementary information S1 (box) (PDF 422 kb)

Supplementary information

Supplementary information S2 (table) (XLSX 3420 kb)

Supplementary information

Supplementary information S3 (table) (TXT 70 kb)

Glossary

Minimal genome

A genome limited to the essential genes for life.

Robustness

The ability of a biological system to keep its behaviour unchanged under perturbation.

Redundancy

The possibility of having a function encoded by more than one gene.

Evolvability

The degree to which an organism can generate adaptive solutions to future environments through heritable phenotypic variation.

Exome

The subset of the genome that is part of mature RNAs and translated into proteins.

Protein truncation

A truncated, incomplete and usually nonfunctional protein product. Generally, the result of stop-gain, frameshift or splice-donor genetic variants.

Loss-of-function variants

Genetic variants that severely disrupt the function of a protein. These can be missense (a change of the codon resulting in a change in the amino acid) or nonsense and protein-truncating variants.

Haploinsufficiency

In a diploid organism, having only a single functional copy of a gene (with the other copy inactivated by mutation), which is insufficient to maintain proper gene function.

Stop-gain variants

Also known as nonsense variants, changes in the genetic material that result in premature termination of the translated protein.

Saturate

When referring to the generation of gene variants genome-wide, the sample size at which all positions in the genome are seen variant at least once.

Frameshift variants

Deletions or insertions in the protein-coding region, the lengths of which are not divisible by three, thus disrupting the reading frame of the gene.

Synonymous variants

A change of nucleotide that does not lead to changes in the amino-acid sequence of a protein.

Neutral variation

Genetic variants that are not subjects of natural selection.

ROC curve

(Receiver operating characteristic curve). A visual and quantitative method of evaluating the performance of binary classifiers. The true positive rate of a classifier is plotted against the false-positive rate.

Expression quantitative trait loci

(eQTLs). Loci where variation is associated with differential expression of a gene.

Haploid

Of cells, containing a single set of chromosomes.

Ploidy

The number of sets of chromosomes in a cell.

Hemizygosity

The absence of one copy of a gene in diploid cells.

Compound heterozygosity

The state in which both alleles of a gene carry a (deleterious) variant, but those variants are different.

Nonsense-mediated mRNA decay

(NMD). A cellular pathway that serves to recognize and degrade mRNAs with translation termination codons that are positioned in abnormal contexts.

Haplotype phasing

The assignment of an allele to one of the two copies of the chromosomes (maternal and paternal).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bartha, I., di Iulio, J., Venter, J. et al. Human gene essentiality. Nat Rev Genet 19, 51–62 (2018). https://doi.org/10.1038/nrg.2017.75

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg.2017.75

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research