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Genetic load: genomic estimates and applications in non-model animals

Nature Reviews Genetics volume 23, pages 492–503 (2022)Cite this article

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Abstract

Genetic variation, which is generated by mutation, recombination and gene flow, can reduce the mean fitness of a population, both now and in the future. This ‘genetic load’ has been estimated in a wide range of animal taxa using various approaches. Advances in genome sequencing and computational techniques now enable us to estimate the genetic load in populations and individuals without direct fitness estimates. Here, we review the classic and contemporary literature of genetic load. We describe approaches to quantify the genetic load in whole-genome sequence data based on evolutionary conservation and annotations. We show that splitting the load into its two components — the realized load (or expressed load) and the masked load (or inbreeding load) — can improve our understanding of the population genetics of deleterious mutations.

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Fig. 1: Effects of Ne on genetic load partition.
Fig. 2: Effects of demographic bottlenecks on genetic load partition.
Fig. 3: Genetic load proxies used with whole genomes in wild animals.

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References

  1. Ohta, T. Slightly deleterious mutant substitutions in evolution. Nature 246, 96–98 (1973).

    Article  CAS  PubMed  Google Scholar 

  2. Haldane, J. B. S. The cost of natural selection. J. Genet. 55, 511 (1957).

    Article  Google Scholar 

  3. Haldane, J. B. S. The effect of variation of fitness. Am. Nat. 71, 337–349 (1937).

    Article  Google Scholar 

  4. Muller, H. J. Our load of mutations. Am. J. Hum. Genet. 2, 111–176 (1950). This work presents the first explicit use of the term ‘genetic load’ with an explanation of the relationship between the mutation rate and load, and controversial speculation on the possible effects of relaxed selection in humans.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Crow, J. F. Some possibilities for measuring selection intensities in man. Hum. Biol. 30, 3–13 (1958).

    Google Scholar 

  6. Crow, J. F. in Mathematical Topics in Population Genetics (ed. Kojima, K.) 128–177 (Springer, 1970).

  7. IUCN Standards and Petitions Committee. The IUCN red list of threatened species. IUCN https://www.iucn.org/resources/conservation-tools/iucn-red-list-threatened-species (2021).

  8. Barrett, S. C. H. & Charlesworth, D. Effects of a change in the level of inbreeding on the genetic load. Nature 352, 522–524 (1991).

    Article  CAS  PubMed  Google Scholar 

  9. Saccheri, I. J., Lloyd, H. D., Helyar, S. J. & Brakefield, P. M. Inbreeding uncovers fundamental differences in the genetic load affecting male and female fertility in a butterfly. Proc. Biol. Sci. 272, 39–46 (2005).

    PubMed  Google Scholar 

  10. van Oosterhout, C. Mutation load is the spectre of species conservation. Nat. Ecol. Evol. 4, 1004–1006 (2020).

    Article  PubMed  Google Scholar 

  11. van Oosterhout, C. et al. The guppy as a conservation model: implications of parasitism and inbreeding for reintroduction success. Conserv. Biol. J. Soc. Conserv. Biol. 21, 1573–1583 (2007).

    Google Scholar 

  12. Fox, C. W., Scheibly, K. L. & Reed, D. H. Experimental evolution of the genetic load and its implications for the genetic basis of inbreeding depression. Evol. Int. J. Org. Evol 62, 2236–2249 (2008).

    Article  Google Scholar 

  13. Morton, N. E., Crow, J. F. & Muller, H. J. An estimate of the mutational damage in man from data on consanguineous marriages. Proc. Natl Acad. Sci. USA 42, 855–863 (1956). This work presents a definition of the lethal equivalents and uses regression analysis to estimate the inbreeding load or masked load.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cavalli-Sforza, L. L. & Bodmer, W. Human Population Genetics (Dover Publications, 1971).

  15. Caballero, A., Bravo, I. & Wang, J. Inbreeding load and purging: implications for the short-term survival and the conservation management of small populations. Heredity 118, 177–185 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Nietlisbach, P., Muff, S., Reid, J. M., Whitlock, M. C. & Keller, L. F. Nonequivalent lethal equivalents: models and inbreeding metrics for unbiased estimation of inbreeding load. Evol. Appl. 12, 266–279 (2019).

    Article  PubMed  Google Scholar 

  17. Mathur, S. & DeWoody, J. A. Genetic load has potential in large populations but is realized in small inbred populations. Evol. Appl. 14, 1540–1557 (2021). This empirical study measures load components in populations with different demography and the interplay between inbreeding effects and purging.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Mathur, S., Tomec˘ek, J., Tarango-Arámbula, L., Perez, R. & DeWoody, A. An evolutionary perspective on contemporary genetic load in threatened species to inform future conservation efforts. Preprint at Authorea https://doi.org/10.22541/au.162495929.94655412/v1 (2021).

    Article  Google Scholar 

  19. Lewontin, R. The Genetic Basis of Evolutionary Change (Columbia Univ. Press, 1974).

  20. Wang, J., Hill, W. G., Charlesworth, D. & Charlesworth, B. Dynamics of inbreeding depression due to deleterious mutations in small populations: mutation parameters and inbreeding rate. Genet. Res. 74, 165–178 (1999).

    Article  CAS  PubMed  Google Scholar 

  21. Glémin, S. How are deleterious mutations purged? Drift versus nonrandom mating. Evolution 57, 2678–2687 (2003).

    Article  PubMed  Google Scholar 

  22. Grossen, C., Guillaume, F., Keller, L. F. & Croll, D. Purging of highly deleterious mutations through severe bottlenecks in Alpine ibex. Nat. Commun. 11, 1001 (2020). This analysis of whole-genome sequence data shows how highly deleterious mutations are purged, whereas mildly deleterious mutations accumulate in populations characterized by different series of bottleneck events.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kardos, M. et al. The crucial role of genome-wide genetic variation in conservation. Proc. Natl Acad. Sci. USA 118, e2104642118 (2021). This work is a critical evaluation of the use of functional genetic diversity in conservation genetics, concluding that the conservation of genome-wide genetic variation should be prioritized.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mills, R. E. et al. Natural genetic variation caused by small insertions and deletions in the human genome. Genome Res. 21, 830–839 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Boffelli, D. et al. Phylogenetic shadowing of primate sequences to find functional regions of the human genome. Science 299, 1391 (2003).

    Article  CAS  PubMed  Google Scholar 

  26. Cooper, G. et al. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15, 901–913 (2005). This work introduces GERP to identify genomic regions that are evolutionarily constrained (that is, exhibit nucleotide substitution deficits) using whole-genome alignments.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Asthana, S., Roytberg, M., Stamatoyannopoulos, J. & Sunyaev, S. Analysis of sequence conservation at nucleotide resolution. PLOS Comput. Biol. 3, e254 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Braasch, I. et al. The spotted gar genome illuminates vertebrate evolution and facilitates human–teleost comparisons. Nat. Genet. 48, 427–437 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huber, C. D., Kim, B. Y. & Lohmueller, K. E. Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution. PLOS Genet. 16, e1008827 (2020). This work analyses and discusses the use and limitations of GERP scores to identify deleterious mutations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLOS Comput. Biol. 6, e1001025 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012). This paper introduces software to categorize the effects of genetic variants based on gene annotations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185, 862–864 (1974).

    Article  CAS  PubMed  Google Scholar 

  35. Herrgard, S. et al. Prediction of deleterious functional effects of amino acid mutations using a library of structure-based function descriptors. Proteins 53, 806–816 (2003).

    Article  CAS  PubMed  Google Scholar 

  36. Harris, K. & Pritchard, J. K. Rapid evolution of the human mutation spectrum. eLife 6, e24284 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Context dependence, ancestral misidentification, and spurious signatures of natural selection. Mol. Biol. Evol. 24, 1792–1800 (2007).

    Article  CAS  PubMed  Google Scholar 

  38. Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 76, 7.20.1–7.20.41 (2013).

    Google Scholar 

  40. McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014). This work introduces CADD to estimate the fitness effect of known variants and expand this prediction to new mutations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Ritchie, G. R. S., Dunham, I., Zeggini, E. & Flicek, P. Functional annotation of noncoding sequence variants. Nat. Methods 11, 294–296 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Groß, C., de Ridder, D. & Reinders, M. Predicting variant deleteriousness in non-human species: applying the CADD approach in mouse. BMC Bioinformatics 19, 373 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Bejerano, G. et al. Ultraconserved elements in the human genome. Science 304, 1321 (2004). This work identifies, annotates and analyses ultraconserved elements in the vertebrate genome.

    Article  CAS  PubMed  Google Scholar 

  46. Pál, C., Papp, B. & Hurst, L. D. Highly expressed genes in yeast evolve slowly. Genetics 158, 927–931 (2001).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Drummond, D. A. & Wilke, C. O. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134, 341–352 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhang, J. & Yang, J.-R. Determinants of the rate of protein sequence evolution. Nat. Rev. Genet. 16, 409–420 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Marek, A. & Tomala, K. The contribution of purifying selection, linkage, and mutation bias to the negative correlation between gene expression and polymorphism density in yeast populations. Genome Biol. Evol. 10, 2986–2996 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Schubert, M. et al. Prehistoric genomes reveal the genetic foundation and cost of horse domestication. Proc. Natl Acad. Sci. USA 111, E5661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Van Der Valk, T., Manuel, M., de, Marques-Bonet, T. & Guschanski, K. Estimates of genetic load suggest frequent purging of deleterious alleles in small populations. Preprint at bioRxiv https://doi.org/10.1101/696831 (2021).

    Article  Google Scholar 

  52. Willemsen, D., Cui, R., Reichard, M. & Valenzano, D. R. Intra-species differences in population size shape life history and genome evolution. eLife 9, e55794 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Orlando, L. & Librado, P. Origin and evolution of deleterious mutations in horses. Genes 10, 649 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  54. Khan, A. et al. Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. Proc. Natl Acad. Sci. USA 118, e2023018118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Liu, L. et al. Genetic consequences of long-term small effective population size in the critically endangered pygmy hog. Evol. Appl. 14, 710–720 (2021).

    Article  CAS  PubMed  Google Scholar 

  56. Xue, Y. et al. Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding. Science 348, 242–245 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wang, P. et al. Genomic consequences of long-term population decline in brown eared pheasant. Mol. Biol. Evol. 38, 263–273 (2021).

    Article  CAS  PubMed  Google Scholar 

  58. Hu, Y. et al. Genomic evidence for two phylogenetic species and long-term population bottlenecks in red pandas. Sci. Adv. 6, eaax5751 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Leroy, T. et al. Island songbirds as windows into evolution in small populations. Curr. Biol. 31, 1303–1310.e4 (2021).

    Article  CAS  PubMed  Google Scholar 

  60. Jay, P. et al. Mutation load at a mimicry supergene sheds new light on the evolution of inversion polymorphisms. Nat. Genet. 53, 288–293 (2021). This empirical study on a model butterfly shows the combined effects of selective advantages and load accumulation due to chromosomal inversions.

    Article  CAS  PubMed  Google Scholar 

  61. de Valles-Ibáñez, G. et al. Genetic load of loss-of-function polymorphic variants in great apes. Genome Biol. Evol. 8, 871–877 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Kutschera, V. E. et al. Purifying selection in corvids is less efficient on islands. Mol. Biol. Evol. 37, 469–474 (2020).

    Article  CAS  PubMed  Google Scholar 

  63. Robinson, J. A. et al. Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. Sci. Adv. 5, eaau0757 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Robinson, J. A. et al. Genomic flatlining in the endangered island fox. Curr. Biol. 26, 1183–1189 (2016).

    Article  CAS  PubMed  Google Scholar 

  65. Ochoa, A. & Gibbs, H. L. Genomic signatures of inbreeding and mutation load in a threatened rattlesnake. Mol. Ecol. 00, 1–16 (2021).

    CAS  Google Scholar 

  66. Dussex, N. et al. Population genomics reveals the impact of long-term small population size in the critically endangered kākāpō. Cell Genomics 1, 100002 (2021).

    Article  CAS  Google Scholar 

  67. Benazzo, A. et al. Survival and divergence in a small group: the extraordinary genomic history of the endangered Apennine brown bear stragglers. Proc. Natl Acad. Sci. USA 114, E9589–E9597 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Do, R. et al. No evidence that selection has been less effective at removing deleterious mutations in Europeans than in Africans. Nat. Genet. 47, 126–131 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Henn, B. M., Botigué, L. R., Bustamante, C. D., Clark, A. G. & Gravel, S. Estimating the mutation load in human genomes. Nat. Rev. Genet. 16, 333–343 (2015). This review shows that the predicted burden of deleterious alleles can differ substantially in human populations, depending on the methodology used to calculate the load.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Simons, Y. B. & Sella, G. The impact of recent population history on the deleterious mutation load in humans and close evolutionary relatives. Curr. Opin. Genet. Dev. 41, 150–158 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Simons, Y. B., Turchin, M. C., Pritchard, J. K. & Sella, G. The deleterious mutation load is insensitive to recent population history. Nat. Genet. 46, 220–224 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lohmueller, K. E. The distribution of deleterious genetic variation in human populations. Curr. Opin. Genet. Dev. 29, 139–146 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Wallace, B. Studies on irradiated populations of Drosophila melanogaster. J. Genet. 54, 280–293 (1956).

    Article  Google Scholar 

  74. Keightley, P. D. Nature of deleterious mutation load in Drosophila. Genetics 144, 1993–1999 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Wallace, B. The estimation of adaptive values of experimental populations. Evolution 6, 333–341 (1952).

    Article  Google Scholar 

  76. Mukai, T., Chigusa, S. I., Mettler, L. E. & Crow, J. F. Mutation rate and dominance of genes affecting viability in Drosophila melanogaster. Genetics 72, 335–355 (1972).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Domínguez-García, S., García, C., Quesada, H. & Caballero, A. Accelerated inbreeding depression suggests synergistic epistasis for deleterious mutations in Drosophila melanogaster. Heredity 123, 709–722 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Whitlock, M. C. Fixation of new alleles and the extinction of small populations: drift load, beneficial alleles, and sexual selection. Evolution 54, 1855–1861 (2000).

    Article  CAS  PubMed  Google Scholar 

  79. Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    Article  CAS  PubMed  Google Scholar 

  80. Groß, C. et al. Prioritizing sequence variants in conserved non-coding elements in the chicken genome using chCADD. PLOS Genet. 16, e1009027 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Groß, C. et al. pCADD: SNV prioritisation in Sus scrofa. Genet. Sel. Evol. 52, 4 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Doekes, H. P., Bijma, P. & Windig, J. J. How depressing is inbreeding? A meta-analysis of 30 years of research on the effects of inbreeding in livestock. Genes 12, 926 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Leroy, G. Inbreeding depression in livestock species: review and meta-analysis. Anim. Genet. 45, 618–628 (2014).

    Article  CAS  PubMed  Google Scholar 

  84. Doekes, H. P. et al. Inbreeding depression due to recent and ancient inbreeding in Dutch Holstein–Friesian dairy cattle. Genet. Sel. Evol. 51, 54 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Stoffel, M. A., Johnston, S. E., Pilkington, J. G. & Pemberton, J. M. Mutation load decreases with haplotype age in wild Soay sheep. Evol. Lett. 5, 187–195 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Pausch, H. et al. Homozygous haplotype deficiency reveals deleterious mutations compromising reproductive and rearing success in cattle. BMC Genomics 16, 312 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. VanRaden, P. M., Olson, K. M., Null, D. J. & Hutchison, J. L. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J. Dairy. Sci. 94, 6153–6161 (2011).

    Article  CAS  PubMed  Google Scholar 

  88. Derks, M. F. L. et al. Loss of function mutations in essential genes cause embryonic lethality in pigs. PLOS Genet. 15, e1008055 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Charlier, C. et al. NGS-based reverse genetic screen for common embryonic lethal mutations compromising fertility in livestock. Genome Res. 26, 1333–1341 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Tait-Burkard, C. et al. Livestock 2.0 — genome editing for fitter, healthier, and more productive farmed animals. Genome Biol. 19, 204 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Clark, E. L. et al. From FAANG to fork: application of highly annotated genomes to improve farmed animal production. Genome Biol. 21, 285 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Johnsson, M. et al. Removal of alleles by genome editing (RAGE) against deleterious load. Genet. Sel. Evol. 51, 14 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Robinson, J. A., Brown, C., Kim, B. Y., Lohmueller, K. E. & Wayne, R. K. Purging of strongly deleterious mutations explains long-term persistence and absence of inbreeding depression in island foxes. Curr. Biol. 28, 3487–3494.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Crnokrak, P. & Barrett, S. C. H. Perspective: purging the genetic load: a review of the experimental evidence. Evolution 56, 2347–2358 (2002).

    Article  PubMed  Google Scholar 

  95. Schumer, M. et al. Natural selection interacts with recombination to shape the evolution of hybrid genomes. Science 360, 656 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Van Der Valk, T., Díez-del-Molino, D., Marques-Bonet, T., Guschanski, K. & Dalén, L. Historical genomes reveal the genomic consequences of recent population decline in eastern gorillas. Curr. Biol. 29, 165–170.e6 (2019). This paper shows the relevance of museum samples to monitor genetic load accumulation in declining populations.

    Article  PubMed  CAS  Google Scholar 

  97. Cui, R. et al. Relaxed selection limits lifespan by increasing mutation load. Cell 178, 385–399.e20 (2019).

    Article  CAS  PubMed  Google Scholar 

  98. Feng, S. et al. The genomic footprints of the fall and recovery of the crested ibis. Curr. Biol. 29, 340–349.e7 (2019). This work evaluates temporal genomic erosion by directly comparing historical and contemporary re-sequenced genomes of the crested ibis, a species once on the brink of extinction.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Bortoluzzi, C. et al. The type of bottleneck matters: insights into the deleterious variation landscape of small managed populations. Evol. Appl. 13, 330–341 (2020).

    Article  CAS  PubMed  Google Scholar 

  100. Beichman, A. C. et al. Aquatic adaptation and depleted diversity: a deep dive into the genomes of the sea otter and giant otter. Mol. Biol. Evol. 36, 2631–2655 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. van Oosterhout, C. et al. Inbreeding depression and genetic load of sexually selected traits: how the guppy lost its spots. J. Evol. Biol. 16, 273–281 (2003).

    Article  PubMed  Google Scholar 

  102. de Manuel, M. et al. The evolutionary history of extinct and living lions. Proc. Natl Acad. Sci. USA 117, 10927–10934 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Moran, B. M. et al. The genomic consequences of hybridization. eLife 10, e69016 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Jones, M. R., Mills, L. S., Jensen, J. D. & Good, J. M. The origin and spread of locally adaptive seasonal camouflage in snowshoe hares. Am. Nat. 196, 316–332 (2020).

    Article  PubMed  Google Scholar 

  105. Díez-del-Molino, D., Sánchez-Barreiro, F., Barnes, I., Gilbert, M. T. P. & Dalén, L. Quantifying temporal genomic erosion in endangered species. Trends Ecol. Evol. 33, 176–185 (2018).

    Article  PubMed  Google Scholar 

  106. Hedrick, P. W. & Garcia-Dorado, A. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31, 940–952 (2016).

    Article  PubMed  Google Scholar 

  107. Van Der Valk, T. et al. The genome of the endangered Dryas monkey provides new insights into the evolutionary history of the vervets. Mol. Biol. Evol. 37, 183–194 (2020).

    Article  PubMed  CAS  Google Scholar 

  108. Pérez-Pereira, N. et al. Long-term exhaustion of the inbreeding load in Drosophila melanogaster. Heredity 127, 373–383 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Hansson, B., Morales, H. E. & van Oosterhout, C. Comment on “Individual heterozygosity predicts translocation success in threatened desert tortoises”. Science 372, eabh1105 (2021).

    Article  CAS  PubMed  Google Scholar 

  110. Pérez-Pereira, N., Caballero, A. & García-Dorado, A. Reviewing the consequences of genetic purging on the success of rescue programs. Conserv. Genet. 23, 1–17 (2022).

    Article  Google Scholar 

  111. Ralls, K., Sunnucks, P., Lacy, R. C. & Frankham, R. Genetic rescue: a critique of the evidence supports maximizing genetic diversity rather than minimizing the introduction of putatively harmful genetic variation. Biol. Conserv. 251, 108784 (2020). This work presents a direct critique of the view that genetic rescue should avoid the use of individuals from large populations.

    Article  Google Scholar 

  112. von Seth, J. et al. Genomic insights into the conservation status of the world’s last remaining Sumatran rhinoceros populations. Nat. Commun. 12, 2393 (2021).

    Article  CAS  Google Scholar 

  113. Marsden, C. D. et al. Bottlenecks and selective sweeps during domestication have increased deleterious genetic variation in dogs. Proc. Natl Acad. Sci. USA 113, 152–157 (2016).

    Article  CAS  PubMed  Google Scholar 

  114. Kim, B. Y., Huber, C. D. & Lohmueller, K. E. Deleterious variation shapes the genomic landscape of introgression. PLOS Genet. 14, e1007741 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Agrawal, A. F. & Whitlock, M. C. Mutation load: the fitness of individuals in populations where deleterious alleles are abundant. Annu. Rev. Ecol. Evol. Syst. 43, 115–135 (2012).

    Article  Google Scholar 

  116. Huber, C. D., Durvasula, A., Hancock, A. M. & Lohmueller, K. E. Gene expression drives the evolution of dominance. Nat. Commun. 9, 2750 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Henn, B. M. et al. Distance from sub-Saharan Africa predicts mutational load in diverse human genomes. Proc. Natl Acad. Sci. USA 113, E440 (2016). This work shows that purifying selection at the conserved genomic position is stronger in African compared with non-African populations, but many deleterious mutations evolved as nearly neutral during the out-of-Africa expansion.

    Article  CAS  PubMed  Google Scholar 

  118. Boyko, A. R. et al. Assessing the evolutionary impact of amino acid mutations in the human genome. PLOS Genet. 4, e1000083 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  119. Rhie, A. et al. Towards complete and error-free genome assemblies of all vertebrate species. Nature 592, 737–746 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Feng, S. et al. Dense sampling of bird diversity increases power of comparative genomics. Nature 587, 252–257 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Lewin, H. A. et al. Earth BioGenome Project: sequencing life for the future of life. Proc. Natl Acad. Sci. USA 115, 4325 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Genereux, D. P. et al. A comparative genomics multitool for scientific discovery and conservation. Nature 587, 240–245 (2020).

    Article  CAS  Google Scholar 

  123. Armstrong, J. et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature 587, 246–251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Chen, J., Bataillon, T., Glémin, S. & Lascoux, M. Hunting for beneficial mutations: conditioning on SIFT scores when estimating the distribution of fitness effect of new mutations. Genome Biol. Evol. 14, evab151 (2021).

    Article  PubMed Central  Google Scholar 

  125. Hemmings, N. L., Slate, J. & Birkhead, T. R. Inbreeding causes early death in a passerine bird. Nat. Commun. 3, 863 (2012).

    Article  CAS  PubMed  Google Scholar 

  126. Kono, T. J. Y. et al. Comparative genomics approaches accurately predict deleterious variants in plants. G3 8, 3321–3329 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Frazer, J. et al. Disease variant prediction with deep generative models of evolutionary data. Nature 599, 91–95 (2021).

    Article  CAS  PubMed  Google Scholar 

  128. Tichkule, S. et al. VIVID: a web application for variant interpretation and visualisation in multidimensional analyses. Preprint at bioRxiv https://doi.org/10.1101/2021.11.16.468904 (2021).

    Article  Google Scholar 

  129. Kyriazis, C. C., Wayne, R. K. & Lohmueller, K. E. Strongly deleterious mutations are a primary determinant of extinction risk due to inbreeding depression. Evol. Lett. 5, 33–47 (2021).

    Article  PubMed  Google Scholar 

  130. Bosse, M. No “doom” in chicken domestication? PLOS Genet. 15, e1008089 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Makino, T. et al. Elevated proportions of deleterious genetic variation in domestic animals and plants. Genome Biol. Evol. 10, 276–290 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Librado, P. et al. Ancient genomic changes associated with domestication of the horse. Science 356, 442–445 (2017).

    Article  CAS  PubMed  Google Scholar 

  133. Rogers, R. L. & Slatkin, M. Excess of genomic defects in a woolly mammoth on Wrangel island. PLOS Genet. 13, e1006601 (2017). This work uses ancient DNA data to show the accumulation of detrimental mutations (deletions, stop codons) and retrogenes in small populations.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  134. Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).

    Article  Google Scholar 

  135. Chang, D. & Shapiro, B. Using ancient DNA and coalescent-based methods to infer extinction. Biol. Lett. 12, 20150822 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  136. Martiniano, R., Garrison, E., Jones, E. R., Manica, A. & Durbin, R. Removing reference bias and improving indel calling in ancient DNA data analysis by mapping to a sequence variation graph. Genome Biol. 21, 250 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Günther, T. & Nettelblad, C. The presence and impact of reference bias on population genomic studies of prehistoric human populations. PLOS Genet. 15, e1008302 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  138. Marciniak, S. & Perry, G. H. Harnessing ancient genomes to study the history of human adaptation. Nat. Rev. Genet. 18, 659–674 (2017).

    Article  CAS  PubMed  Google Scholar 

  139. Orlando, L., Gilbert, M. T. P. & Willerslev, E. Reconstructing ancient genomes and epigenomes. Nat. Rev. Genet. 16, 395–408 (2015).

    Article  CAS  PubMed  Google Scholar 

  140. Pääbo, S. et al. Genetic analyses from ancient DNA. Annu. Rev. Genet. 38, 645–679 (2004).

    Article  PubMed  CAS  Google Scholar 

  141. Flanagan, S. P., Forester, B. R., Latch, E. K., Aitken, S. N. & Hoban, S. Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol. Appl. 11, 1035–1052 (2018). This work presents a practical road map for effectively implementing genomics into species management and conservation.

    Article  PubMed  Google Scholar 

  142. Schiffels, S. & Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46, 919–925 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Heled, J. & Drummond, A. J. Bayesian inference of population size history from multiple loci. BMC Evol. Biol. 8, 289 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Santiago, E. et al. Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Mol. Biol. Evol. 37, 3642–3653 (2020).

    Article  CAS  PubMed  Google Scholar 

  145. Grace, M. K. et al. Testing a global standard for quantifying species recovery and assessing conservation impact. Conserv. Biol. 35, 1833–1849 (2021).

    Article  PubMed  Google Scholar 

  146. Teixeira, J. C. & Huber, C. D. The inflated significance of neutral genetic diversity in conservation genetics. Proc. Natl Acad. Sci. USA 118, e2015096118 (2021). This perspective piece argues that loss of neutral genetic diversity does not predict extinction risk.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. García-Dorado, A. & Caballero, A. Neutral genetic diversity as a useful tool for conservation biology. Conserv. Genet. 22, 541–545 (2021).

    Article  Google Scholar 

  148. DeWoody, J. A., Harder, A. M., Mathur, S. & Willoughby, J. R. The long-standing significance of genetic diversity in conservation. Mol. Ecol. 30, 4147–4154 (2021).

    Article  PubMed  Google Scholar 

  149. Willoughby, J. et al. The reduction of genetic diversity in threatened vertebrates and new recommendations regarding IUCN conservation rankings. Biol. Conserv. 191, 495–503 (2015).

    Article  Google Scholar 

  150. Li, H. et al. Large numbers of vertebrates began rapid population decline in the late 19th century. Proc. Natl Acad. Sci. USA 113, 14079–14084 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Hansson, B. & Westerberg, L. On the correlation between heterozygosity and fitness in natural populations. Mol. Ecol. 11, 2467–2474 (2002).

    Article  PubMed  Google Scholar 

  152. Harrisson, K. A., Pavlova, A., Telonis-Scott, M. & Sunnucks, P. Using genomics to characterize evolutionary potential for conservation of wild populations. Evol. Appl. 7, 1008–1025 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  153. Frankham, R. Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow. Mol. Ecol. 24, 2610–2618 (2015).

    Article  PubMed  Google Scholar 

  154. Rellstab, C., Dauphin, B. & Exposito-Alonso, M. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14, 1202–1212 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal)adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).

    Article  Google Scholar 

  156. Whiteley, A. R., Fitzpatrick, S. W., Funk, W. C. & Tallmon, D. A. Genetic rescue to the rescue. Trends Ecol. Evol. 30, 42–49 (2015).

    Article  PubMed  Google Scholar 

  157. Haller, B. C. & Messer, P. W. SLiM 3: forward genetic simulations beyond the Wright–Fisher model. Mol. Biol. Evol. 36, 632–637 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol. Conserv. 170, 56–63 (2014).

    Article  Google Scholar 

  159. Lynch, M., Conery, J. & Burger, R. Mutation accumulation and the extinction of small populations. Am. Nat. 146, 489–518 (1995).

    Article  Google Scholar 

  160. Wallace, B. Fifty years of genetic load. J. Hered. 78, 134–142 (1987).

    Article  CAS  PubMed  Google Scholar 

  161. Kimura, M., Maruyama, T. & Crow, J. F. The mutation load in small populations. Genetics 48, 1303–1312 (1963).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Lynch, M. & Gabriel, W. Mutation load and the survival of small populations. Evolution 44, 1725–1737 (1990).

    Article  PubMed  Google Scholar 

  163. Whitlock, M. C., Ingvarsson, P. K. & Hatfield, T. Local drift load and the heterosis of interconnected populations. Heredity 84, 452–457 (2000).

    Article  PubMed  Google Scholar 

  164. Willi, Y., Griffin, P. & Van Buskirk, J. Drift load in populations of small size and low density. Heredity 110, 296–302 (2013).

    Article  CAS  PubMed  Google Scholar 

  165. Bürger, R. & Lynch, M. Evolution and extinction in a changing environment: a quantitative-genetic analysis. Evolution 49, 151–163 (1995).

    PubMed  Google Scholar 

  166. Chevin, L.-M., Cotto, O. & Ashander, J. Stochastic evolutionary demography under a fluctuating optimum phenotype. Am. Nat. 190, 786–802 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Kimura, M. & Crow, J. F. The number of alleles that can be maintained in a finite population. Genetics 49, 725–738 (1964).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. García-Ramos, G. & Kirkpatrick, M. Genetic models of adaptation and gene flow in peripheral populations. Evolution 51, 21–28 (1997).

    Article  PubMed  Google Scholar 

  169. Bolnick, D. I. & Nosil, P. Natural selection in populations subject to a migration load. Evolution 61, 2229–2243 (2007).

    Article  PubMed  Google Scholar 

  170. Fierst, J. L. & Hansen, T. F. Genetic architecture and postzygotic reproductive isolation: evolution of Bateson–Dobzhansky–Muller incompatibilities in a polygenic model. Evolution 64, 675–693 (2010).

    Article  PubMed  Google Scholar 

  171. Charlesworth, B. & Barton, N. H. Recombination load associated with selection for increased recombination. Genet. Res. 67, 27–41 (1996).

    Article  CAS  PubMed  Google Scholar 

  172. Haag, C. R. & Roze, D. Genetic load in sexual and asexual diploids: segregation, dominance and genetic drift. Genetics 176, 1663–1678 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  173. Nunez, J. C. B. et al. Ecological load and balancing selection in circumboreal barnacles. Mol. Biol. Evol. 38, 676–685 (2021).

    Article  CAS  PubMed  Google Scholar 

  174. Van Valen, L. M. A new evolutionary law. Evol. Theory 1, 1–30 (1973).

    Google Scholar 

  175. Crow, J. F. & Morton, N. E. The genetic load due to mother–child incompatibility. Am. Nat. 94, 413–419 (1960).

    Article  Google Scholar 

  176. Sandler, L. & Novitski, E. Meiotic drive as an evolutionary force. Am. Nat. 91, 105–110 (1957).

    Article  Google Scholar 

  177. Lindholm, A. K. et al. The ecology and evolutionary dynamics of meiotic drive. Trends Ecol. Evol. 31, 315–326 (2016).

    Article  PubMed  Google Scholar 

  178. Gompel, N. & Prud’homme, B. The causes of repeated genetic evolution. Dev. Biol. 332, 36–47 (2009).

    Article  CAS  PubMed  Google Scholar 

  179. Hansen, T. F. & Price, D. K. Age- and sex-distribution of the mutation load. Genetica 106, 251–262 (1999).

    Article  CAS  PubMed  Google Scholar 

  180. Mee, J. A. & Yeaman, S. Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations. Am. Nat. 194, 529–540 (2019).

    Article  PubMed  Google Scholar 

  181. Stone, J. L. Sheltered load associated with S-alleles in Solanum carolinense. Heredity 92, 335–342 (2004).

    Article  CAS  PubMed  Google Scholar 

  182. van Oosterhout, C. A new theory of MHC evolution: beyond selection on the immune genes. Proc. R. Soc. B 276, 657–665 (2009).

    Article  PubMed  CAS  Google Scholar 

  183. Brady, S. P. et al. Understanding maladaptation by uniting ecological and evolutionary perspectives. Am. Nat. 194, 495–515 (2019).

    Article  PubMed  Google Scholar 

  184. Rice, W. & Chippindale, A. The evolution of hybrid infertility: perpetual coevolution between gender-specific and sexually antagonistic genes. Genetics 116, 179–188 (2002).

    CAS  Google Scholar 

  185. Hedrick, P. Genetics of Populations (Jones & Bartlett Learning, 2011).

  186. García-Dorado, A. Understanding and predicting the fitness decline of shrunk populations: inbreeding, purging, mutation, and standard selection. Genetics 190, 1461–1476 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  187. Jinks, J. L. & Jones, R. M. Estimation of the components of heterosis. Genetics 43, 223–234 (1958).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank D. Charlesworth and A. Caballero for helpful comments on a previous version of the manuscript. C.v.O. was supported by the Royal Society International Collaborations Award (ICA\R1\201194) and the Earth and Life Systems Alliance (ELSA). G.B. and F.R. were supported by the University of Ferrara (Italy). G.B., F.R., A.I. and E.T. were funded by the MIUR PRIN 2017 grant 201794ZXTL to G.B. M.B. was financially supported by the Dutch NWO Veni grant n. 016.Veni.181.050. H.E.M. was funded by an EMBO long-term fellowship (grant 1111-2018) and the European Union’s Horizon 2020 research and innovation programme under a Marie Sklodowska-Curie grant (840519). C.B. is funded by the Wellcome grant WT207492.

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Author notes

  1. These authors contributed equally: Giorgio Bertorelle, Francesca Raffini, Hernán E. Morales, Cock van Oosterhout.

Authors and Affiliations

  1. Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy

    Giorgio Bertorelle & Francesca Raffini

  2. Wageningen University and Research — Animal Breeding & Genomics, Wageningen, Netherlands

    Mirte Bosse

  3. Ecology and Evolution, VU University Amsterdam, Amsterdam, Netherlands

    Mirte Bosse

  4. Department of Genetics, University of Cambridge, Cambridge, UK

    Chiara Bortoluzzi

  5. Wellcome Sanger Institute, Hinxton, UK

    Chiara Bortoluzzi

  6. Department of Biology, University of Florence, Sesto Fiorentino, Italy

    Alessio Iannucci

  7. Department of Life and Environmental Sciences, Marche Polytechnic University, Ancona, Italy

    Emiliano Trucchi

  8. Section for Evolutionary Genomics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Hernán E. Morales

  9. Centre for Evolutionary Hologenomics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Hernán E. Morales

  10. School of Environmental Sciences, University of East Anglia, Norwich, UK

    Cock van Oosterhout

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Contributions

G.B., F.R., C.B., H.E.M. and C.v.O. researched the literature. G.B., F.R., E.T., H.E.M. and C.v.O. substantially contributed to discussions of the content. G.B., F.R., M.B., E.T., H.E.M. and C.v.O. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Giorgio Bertorelle or Cock van Oosterhout.

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Glossary

Fitness

A measure of the capability of an individual or genotype to survive and reproduce.

Deleterious mutations

Poorly adapted genetic variants that reduce fitness relative to the wild-type variant, thereby contributing to the genetic load. The mutations that remain deleterious across alternative environments are called ‘unconditionally deleterious’.

Inbreeding depression

The reduction of fitness due to breeding between relatives.

Effective population size

(Ne). The number of individuals in an idealized population that shows the same amount of genetic drift (that is, random fluctuation of allele frequencies and loss of gene diversity) as the actual population.

Bottlenecks

(Also known as population bottlenecks). Sharp reductions in the effective population size (Ne) over one or multiple generations.

Genetic rescue

Artificial (re)introduction of new (or rare) genetic variants into a population with the aims of reducing inbreeding depression, increasing genetic variation and population viability.

Anchor species

A species that is added to the genome alignment between two evolutionarily distantly related taxa to help connect them and facilitate the identification of conserved elements.

Mutational spectrum

The rate of different types of DNA mutations in different sequence contexts.

DNA sequence context

Nucleotide composition around the focal mutation.

Ultraconserved elements

Highly conserved genomic regions with (near) identical nucleotide sequences in evolutionarily distant taxa.

Runs of homozygosity

Long and contiguous regions of a diploid genome where all nucleotide sites are homozygous.

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Bertorelle, G., Raffini, F., Bosse, M. et al. Genetic load: genomic estimates and applications in non-model animals. Nat Rev Genet 23, 492–503 (2022). https://doi.org/10.1038/s41576-022-00448-x

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