Review of population history reconstruction methods in conservation biology

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Abstract

Demographic history reconstruction is based on the estimation of effective population size (Ne), which is inferred and interpreted in various fields of evolutionary and conservation biology. Interest in Ne estimation is growing, as the key evolutionary forces and their are linked to Ne, and genetic data become increasingly accessible. However, what is effective population size, and how can we obtain an estimate of effective population size? In this review, we describe the history of the term “Ne” and explore existing methods for obtaining historical and contemporary estimates of changes in effective population size. We provide a detailed overview of methods based on sequential Markovian coalescence (SMC), generalized phylogenetic coalescence (G-PhoCS), identity by descent (IBD) and identity by state (IBS) similarity, as well as methods using allele frequency spectrum (AFS). For each method, we briefly summarize the underlying theory and note its advantages and disadvantages. In the final section of the review, we present examples of the use of these methods for various non-model species with conservation status.

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About the authors

Azamat A. Totikov

Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University

Author for correspondence.
Email: a.totickov1@gmail.com
ORCID iD: 0000-0003-1236-631X
SPIN-code: 9767-3971
Scopus Author ID: 57265434800

research assistant; postgraduate student

Russian Federation, Novosibirsk; Novosibirsk

Andrey A. Tomarovsky

Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences; Novosibirsk State University

Email: andrey.tomarovsky@gmail.com
ORCID iD: 0000-0002-6414-704X
SPIN-code: 6727-8664
Scopus Author ID: 57264872500

research assistant; postgraduate student

Russian Federation, Novosibirsk; Novosibirsk

Aliya R. Yakupova

Saint Petersburg National Research University of Information Technologies, Mechanics and Optics

Email: aliyah.yakupova@gmail.com
ORCID iD: 0000-0003-1486-0864
SPIN-code: 4292-0609
Scopus Author ID: 57264122200

master

Russian Federation, Saint Petersburg

Alexander S. Graphodatsky

Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences

Email: graf@mcb.nsc.ru
ORCID iD: 0000-0002-8282-1085
SPIN-code: 4436-9033
Scopus Author ID: 7003878913

Dr. Sci. (Biol.), head of the Department of Diversity and Evolution of Genomes, head of the Laboratory of Animal Cytogenetics

Russian Federation, Novosibirsk

Sergei F. Kliver

Independent researcher

Email: mahajrod@gmail.com
ORCID iD: 0000-0002-2965-3617
SPIN-code: 8635-4259
Scopus Author ID: 56449314300

independent researcher

Georgia, Batumi

References

  1. The IUCN Red List of Threatened Species [Enternet]. IUCN Red List of Threatened Species. Available from: https://www.iucnredlist.org/en (accessed: 2022 March 11).
  2. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines [Enternet]. PNAS. Available from: https://www.pnas.org/doi/abs/10.1073/pnas.1704949114 (accessed: 2022 March 04)
  3. Lande R. Genetics and Demography in Biological Conservation. Science. 1988;241(4872):1455–1460. doi: 10.1126/science.3420403
  4. Fred W, Allendorf W, Funk C, et al. Conservation and the Genomics of Populations. Oxford University Press. [Enternet]. Available from: https://www.global.oup.com/ukhe/product/conservation-and-the-genomics-of-populations-9780198856573 (accessed: 2022 April 9).
  5. Hare MP, Nunney L, Schwartz MK, et al. Understanding and estimating effective population size for practical application in marine species management. Conserv Biol. 2011;25(3):438–449. doi: 10.1111/j.1523-1739.2010.01637.x
  6. Cammen KM, Schultz TF, Don Bowen W, et al. Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds. Ecol Evol. 2018;8(13):6599–6614. doi: 10.1002/ece3.4143
  7. Luikart G, Ryman N, Tallmon DA, et al. Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches. Conserv Genet. 2010;11(2):355–373. doi: 10.1007/s10592-010-0050-7
  8. Waples RS. What Is Ne, Anyway? J Hered. 2022;113(4):371–379. doi: 10.1093/jhered/esac023
  9. Wright S. Evolution in Mendelian Populations. Genetics. 1931;16(2):97–159. doi: 10.1093/genetics/16.2.97
  10. Crow JF. Breeding structure of populations. II. Effective population number. In: Statistics and Mathematics in Biology. Ed by O. Kempthorne, T.A. Bancroft, J.W. Gowen. Iowa State Univ. Press. Ames. Ia. 1954. P. 543–556.
  11. Ewens WJ. Mathematical population genetics. Springer Verlag; 1979. 325 p.
  12. Ewens WJ. On the concept of the effective population size. Theor Popul Biol. 1982;21(3):373–378. doi: 10.1016/0040-5809(82)90024-7
  13. Nordborg M, Krone M. Separation of time scales and convergence to the coalescent in structured populations. Oxford: Oxford University Press; 2002. P. 194–232.
  14. Sjödin P, Kaj I, Krone S, et al. On the Meaning and Existence of an Effective Population Size. Genetics. 2005;169(2):1061–1070. doi: 10.1534/genetics.104.026799
  15. Ryman N, Laikre L, Hössjer O. Do estimates of contemporary effective population size tell us what we want to know? Mol Ecol Mol Ecol. 2019;28(8):1904–1918. doi: 10.1111/mec.15027
  16. Waples RS. Spatial-temporal stratifications in natural populations and how they affect understanding and estimation of effective population size. Mol Ecol Resour. 2010;10(5):785–796. doi: 10.1111/j.1755-0998.2010.02876.x
  17. Nadachowska-Brzyska K, Konczal M, Babik W. Navigating the temporal continuum of effective population size. Methods Ecol Evol. 2022;13(1):22–41. doi: 10.1111/2041-210X.13740
  18. Beichman AC, Huerta-Sanchez E, Lohmueller KE. Using Genomic Data to Infer Historic Population Dynamics of Nonmodel Organisms. Annu Rev Ecol Evol Syst. 2018;49(1):433–456. doi: 10.1146/annurev-ecolsys-110617-062431
  19. Wang J, Santiago E, Caballero A. Prediction and estimation of effective population size. Heredity. 2016;117(4):193–206. doi: 10.1038/hdy.2016.43
  20. Walsh B, Lynch M. Evolution and Selection of Quantitative Traits. Oxford University Press; 2018. 1490 p.
  21. Charlesworth B. Fundamental concepts in genetics: effective population size and patterns of molecular evolution and variation. Nat Rev Genet. 2009;10(3):195–205. doi: 10.1038/nrg2526
  22. Waples RS, Do C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol Appl. 2010;3(3):244–262. doi: 10.1111/j.1752-4571.2009.00104.x
  23. Marandel F, Lorance P, Berthelé O, et al. Estimating effective population size of large marine populations, is it feasible? Fish Fish. 2019;20(1):189–198. doi: 10.1111/faf.12338
  24. Gilbert KJ, Whitlock MC. Evaluating methods for estimating local effective population size with and without migration. Evol Int J Org Evol. 2015;69(8):2154–2166. doi: 10.1111/evo.12713
  25. Palstra FP, Fraser DJ. Effective/census population size ratio estimation: a compendium and appraisal. Ecol Evol. 2012;2(9): 2357–2365. doi: 10.1002/ece3.329
  26. Robinson JA, Räikkönen J, Vucetich LM, et al. Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. Sci Adv. 2019;5(5):eaau0757. doi: 10.1126/sciadv.aau0757
  27. Griffiths RC, Tavaré S. The age of a mutation in a general coalescent tree. Commun Stat Stoch Models. 1998;14(1–2):273–295. doi: 10.1080/15326349808807471
  28. Wakeley J, Hey J. Estimating Ancestral Population Parameters. Genetics. 1997;145(3):847–855. doi: 10.1093/genetics/145.3.847
  29. Nielsen R. Estimation of Population Parameters and Recombination Rates From Single Nucleotide Polymorphisms. Genetics. 2000;154(2):931–942. doi: 10.1093/genetics/154.2.931
  30. Gutenkunst RN, Hernandez RD, Williamson SH, et al. Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data. PLOS Genet. 2009;5(10): e1000695. doi: 10.1371/journal.pgen.1000695
  31. Jouganous J, Long W, Ragsdale AP, et al. Inferring the Joint Demographic History of Multiple Populations: Beyond the Diffusion Approximation. Genetics. 2017;206(3):1549–1567. doi: 10.1534/genetics.117.200493
  32. Excoffier L, Dupanloup I, Huerta-Sánchez E, et al. Robust Demographic Inference from Genomic and SNP Data. PLOS Genet. 2013;9(10): e1003905. doi: 10.1371/journal.pgen.1003905
  33. Jenkins PA, Mueller JW, Song YS. General Triallelic Frequency Spectrum Under Demographic Models with Variable Population Size. Genetics. 2014;196(1):295–311. doi: 10.1534/genetics.113.158584
  34. Marth GT, Czabarka E, Murvai J, et al. The allele frequency spectrum in genome-wide human variation data reveals signals of differential demographic history in three large world populations. Genetics. 2004;166(1):351–372. doi: 10.1534/genetics.166.1.351
  35. Adams AM, Hudson RR. Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms. Genetics. 2004;168(3):1699–1712. doi: 10.1534/genetics.104.030171
  36. Voight BF, Adams AM, Frisse LA, et al. Interrogating multiple aspects of variation in a full resequencing data set to infer human population size changes. Proc Natl Acad Sci. 2005;102(51): 18508–18513. doi: 10.1073/pnas.0507325102
  37. Chen H, Green RE, Pääbo S, et al. The Joint Allele-Frequency Spectrum in Closely Related Species. Genetics. 2007;177(1):387–398. doi: 10.1534/genetics.107.070730
  38. Myers S, Fefferman C, Patterson N. Can one learn history from the allelic spectrum? Theor Popul Biol. 2008;73(3):342–348. doi: 10.1016/j.tpb.2008.01.001
  39. Evans SN, Shvets Y, Slatkin M. Non-equilibrium theory of the allele frequency spectrum. Theor Popul Biol. 2007;71(1):109–119. doi: 10.1016/j.tpb.2006.06.005
  40. Williamson SH, Hernandez R, Fledel-Alon A, et al. Simultaneous inference of selection and population growth from patterns of variation in the human genome. Proc Natl Acad Sci USA. 2005;102(22):7882–7887. doi: 10.1073/pnas.0502300102
  41. Beeravolu CR, Hickerson MJ, Frantz LAF, et al. Blockwise site frequency spectra for inferring complex population histories and recombination. bioRxiv. 2017:077958. doi: 10.1101/077958
  42. Ragsdale AP, Gravel S. Unbiased Estimation of Linkage Disequilibrium from Unphased Data. Mol Biol Evol. 2020;37(3):923–932. doi: 10.1093/molbev/msz265
  43. Kamm JA, Terhorst J, Song YS. Efficient Computation of the Joint Sample Frequency Spectra for Multiple Populations. J Comput Graph Stat. 2017;26(1):182–194. doi: 10.1080/10618600.2016.1159212
  44. Kamm J, Terhorst J, Durbin R, et al. Efficiently inferring the demographic history of many populations with allele count data. J Am Stat Assoc. 2020;115(531):1472–1487. doi: 10.1080/01621459.2019.1635482
  45. Noskova E, Ulyantsev V, Koepfli KP, et al. GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data. Gigascience. 2020;9(3):giaa005. doi: 10.1093/gigascience/giaa005
  46. Noskova E, Abramov N, Iliutkin S, et al. GADMA2: more efficient and flexible demographic inference from genetic data. bioRxiv. 2022;2022:06.14.496083. doi: 10.1101/2022.06.14.496083
  47. Li H, Durbin R. Inference of human population history from individual whole-genome sequences: 7357. Nature. 2011;475(7357): 493–496. doi: 10.1038/nature10231
  48. Schiffels S, Durbin R. Inferring human population size and separation history from multiple genome sequences. Nat Genet. 2014;46(8):919–925. doi: 10.1038/ng.3015
  49. Terhorst J, Kamm JA, Song YS. Robust and scalable inference of population history from hundreds of unphased whole-genomes. Nat Genet. 2017;49(2):303–309. doi: 10.1038/ng.3748
  50. Kingman JFC. On the genealogy of large populations. J Appl Probab. 1982;19:27–43. doi: 10.2307/3213548
  51. Hudson RR. Testing the constant-rate neutral allele model with protein sequence data. Evolution. 1983;37(1):203–217. doi: 10.2307/2408186
  52. Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE. 1989;77(2):257–286. doi: 10.1109/5.18626
  53. Zucchini W, MacDonald IL, Langrock R. Hidden Markov Models for Time Series: An Introduction Using R. 2nd ed. New York: Chapman and Hall/CRC; 2016. 398 p. doi: 10.1201/b20790
  54. Hobolth A, Christensen OF, Mailund T, et al. Genomic Relationships and Speciation Times of Human, Chimpanzee, and Gorilla Inferred from a Coalescent Hidden Markov Model. PLoS Genet. 2007;3(2):e7. doi: 10.1371/journal.pgen.0030007
  55. McVean GAT, Cardin NJ. Approximating the coalescent with recombination. Philos Trans R Soc Lond B Biol Sci. 2005;360(1459): 1387–1393. doi: 10.1098/rstb.2005.1673
  56. Marjoram P, Wall JD. Fast “coalescent” simulation. BMC Genet. 2006;7:16. doi: 10.1186/1471-2156-7-16
  57. Mazet O, Rodríguez W, Grusea S, et al. On the importance of being structured: instantaneous coalescence rates and human evolution — lessons for ancestral population size inference? 4. Heredity. 2016;116(4):362–371. doi: 10.1038/hdy.2015.104
  58. Paul JS, Song YS. Blockwise HMM computation for large-scale population genomic inference. Bioinformatics. 2012;28(15): 2008–2015. doi: 10.1093/bioinformatics/bts314
  59. Gusev A, Lowe JK, Stoffel M, et al. Whole population, genome-wide mapping of hidden relatedness. Genome Res. 2009;19(2): 318–326. doi: 10.1101/gr.081398.108
  60. Nait Saada J, Kalantzis G, Shyr D, et al. Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. Nat Commun. 2020;11(1):6130. doi: 10.1038/s41467-020-19588-x
  61. Palamara PF, Terhorst J, Song YS, et al. High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability: 9. Nat Genet. 2018;50(9): 1311–1317. doi: 10.1038/s41588-018-0177-x
  62. Harris K, Nielsen R. Inferring Demographic History from a Spectrum of Shared Haplotype Lengths. PLOS Genet. 2013;9(6):e1003521. doi: 10.1371/journal.pgen.1003521
  63. Liu S, Lorenzen ED, Fumagalli M, et al. Population Genomics Reveal Recent Speciation and Rapid Evolutionary Adaptation in Polar Bears. Cell. 2014;157(4):785–794. doi: 10.1016/j.cell.2014.03.054
  64. Bosse M, Megens H-J, Madsen O, et al. Untangling the hybrid nature of modern pig genomes: a mosaic derived from biogeographically distinct and highly divergent Sus scrofa populations. Mol Ecol. 2014;23(16):4089–4102. doi: 10.1111/mec.12807
  65. Gronau I, Hubisz MJ, Gulko B, et al. Bayesian inference of ancient human demography from individual genome sequences: 10. Nat Genet. 2011;43(10):1031–1034. doi: 10.1038/ng.937
  66. Schrider DR, Shanku AG, Kern AD. Effects of Linked Selective Sweeps on Demographic Inference and Model Selection. Genetics. 2016;204(3):1207–1223. doi: 10.1534/genetics.116.190223
  67. Feng S, Fang Q, Barnett R, et al. The Genomic Footprints of the Fall and Recovery of the Crested Ibis. Curr Biol. 2019;29(2):340–349.e7. doi: 10.1016/j.cub.2018.12.008
  68. de Manuel M, Barnett R, Sandoval-Velasco M, et al. The evolutionary history of extinct and living lions. Proc Natl Acad Sci. 2020;117(20):10927–10934. doi: 10.1073/pnas.1919423117
  69. Sodeland M, Jentoft S, Jorde PE, et al. Stabilizing selection on Atlantic cod supergenes through a millennium of extensive exploitation. Proc Natl Acad Sci USA. 2022;119(8): e2114904119. doi: 10.1073/pnas.2114904119
  70. Chavez DE, Gronau I, Hains T, et al. Comparative genomics uncovers the evolutionary history, demography, and molecular adaptations of South American canids. Proc Natl Acad Sci USA. 2022;119(34): e2205986119. doi: 10.1073/pnas.2205986119
  71. de Ferran V, Figueiró HV, de Jesus Trindade F, et al. Phylogenomics of the world’s otters. Curr Biol. 2022;32(16):3650–3658.e4. doi: 10.1016/j.cub.2022.06.036
  72. Abascal F, Corvelo A, Cruz F, et al. Extreme genomic erosion after recurrent demographic bottlenecks in the highly endangered Iberian lynx. Genome Biol. 2016;17(1):251. doi: 10.1186/s13059-016-1090-1
  73. Ekblom R, Brechlin B, Persson J, et al. Genome sequencing and conservation genomics in the Scandinavian wolverine population. Conserv Biol. 2018;32(6):1301–1312. doi: 10.1111/cobi.13157

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2. Fig. 1. Use of methods for reconstructing demographic history from 2012 to 2022, separated by species with a global conservation status (according to IUCN data): a — stacked histogram of the total number of species studied by year. The colors indicate the proportion of species by conservation status; b — proportion of approach groups used in studies of species with each conservation status. Global status: LC — Least Concern, NT — Near Threatened, VU — Vulnerable, EN — Endangered, CR — Critically Endangered, EW — Extinct in the Wild, EX — Extinct, DD — Data Deficient, NE — Not Evaluated. Methods: AFS — methods using the allele frequency spectrum; SMC — methods using sequential Markovian coalescence; AFS + SMC — use of a combined approach

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3. Рис. 2. Использование методов реконструкции демографической истории в научных работах в период с 2012 по 2022 г. с разделением по типам методов: а — стековая гистограмма общего количества публикаций по годам. Цветами показано соотношение применяемых групп методов; b — cоотношение применяемых групп методов с 2012 по 2022 г. AFS — методы, использующие спектр частот аллелей; SMC — методы, использующие последовательную марковскую коалесценцию; AFS + SMC — комбинированный подход

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4. Рис. 3. Хронология развития ключевых программных инструментов реконструкции демографической истории

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5. Fig. 4. Decision tree for selecting a method for reconstructing demographic history

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