Review of population history reconstruction methods in conservation biology

Abstract

Demographic history reconstruction is based on the estimation of effective population size (N_e), 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 N_e, 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 “N_e” 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.

Keywords

conservation biology / genomics / population genetics / demography / genetic diversity / demographic history / effective population size

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] The IUCN Red List of Threatened Species [Электронный ресурс]. IUCN Red List of Threatened Species. Режим доступа: https://www.iucnredlist.org/en. Дата обращения: 11.03.2022.

[3] 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)

[4] Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines [Электронный ресурс]. PNAS. Режим доступа: https://www.pnas.org/doi/abs/10.1073/pnas.1704949114. Дата обращения: 04.03. 2022.

[5] Lande R. Genetics and Demography in Biological Conservation. Science. 1988;241(4872):1455–1460. DOI: 10.1126/science.3420403

[6] Lande R. Genetics and Demography in Biological Conservation // Science. 1988. Vol. 241, No. 4872. P. 1455–1460. DOI: 10.1126/science.3420403

[7] 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).

[8] Fred W., Allendorf W., Funk C., et al. Conservation and the Genomics of Populations. Oxford University Press [Электронный ресурс]. Режим доступа: https://www.global.oup.com/ukhe/product/conservation-and-the-genomics-of-populations-9780198856573 Дата обращения: 09.04.2022.

[9] 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

[10] Hare M.P., Nunney L., Schwartz M.K., et al. Understanding and estimating effective population size for practical application in marine species management // Conserv Biol. 2011. Vol. 25, No. 3. P. 438–449. DOI: 10.1111/j.1523-1739.2010.01637.x

[11] 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

[12] Cammen K.M., Schultz T.F., Don Bowen W., et al. Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds // Ecol Evol. 2018. Vol. 8, No. 13. P. 6599–6614. DOI: 10.1002/ece3.4143

[13] 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

[14] Luikart G., Ryman N., Tallmon D.A., et al. Estimation of census and effective population sizes: the increasing usefulness of DNA-based approaches // Conserv Genet. 2010. Vol. 11, No. 2. P. 355–373. DOI: 10.1007/s10592-010-0050-7

[15] Waples RS. What Is Ne, Anyway? J Hered. 2022;113(4):371–379. DOI: 10.1093/jhered/esac023

[16] Waples R.S. What Is Ne, Anyway? // J Hered. 2022. Vol. 113, No. 4. P. 371–379. DOI: 10.1093/jhered/esac023

[17] Wright S. Evolution in Mendelian Populations. Genetics. 1931;16(2):97–159. DOI: 10.1093/genetics/16.2.97

[18] Wright S. Evolution in Mendelian Populations // Genetics. 1931. Vol. 16, No. 2. P. 97–159. DOI: 10.1093/genetics/16.2.97

[19] 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.

[20] Crow J.F. Breeding structure of populations. II. Effective population number. In: Statistics and Mathematics in Biology. Ed. by O. Kempthorne, T.A. Bancroft, J.W. Gowen. Ames, Ia: Iowa State Univ. Press, 1954. P. 543–556.

[21] Ewens WJ. Mathematical population genetics. Springer Verlag; 1979. 325 p.

[22] Ewens W.J. Mathematical population genetics. Springer Verlag, 1979. 325 p.

[23] 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

[24] Ewens W.J. On the concept of the effective population size // Theor Popul Biol. 1982. Vol. 21, No. 3. P. 373–378. DOI: 10.1016/0040-5809(82)90024-7

[25] Nordborg M, Krone M. Separation of time scales and convergence to the coalescent in structured populations. Oxford: Oxford University Press; 2002. P. 194–232.

[26] Nordborg M., Krone M. Separation of time scales and convergence to the coalescent in structured populations. Oxford: Oxford University Press, 2002. P. 194–232.

[27] 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

[28] Sjödin P., Kaj I., Krone S., et al. On the Meaning and Existence of an Effective Population Size // Genetics. 2005. Vol. 169, No. 2. P. 1061–1070. DOI: 10.1534/genetics.104.026799

[29] 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

[30] 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. Vol. 28, No. 8. P. 1904–1918. DOI: 10.1111/mec.15027

[31] 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

[32] Waples R.S. Spatial-temporal stratifications in natural populations and how they affect understanding and estimation of effective population size // Mol Ecol Resour. 2010. Vol. 10, No. 5. P. 785–796. DOI: 10.1111/j.1755-0998.2010.02876.x

[33] 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

[34] Nadachowska-Brzyska K., Konczal M., Babik W. Navigating the temporal continuum of effective population size // Methods Ecol Evol. 2022. Vol. 13, No. 1. P. 22–41. DOI: 10.1111/2041-210X.13740

[35] 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

[36] Beichman A.C., Huerta-Sanchez E., Lohmueller K.E. Using Genomic Data to Infer Historic Population Dynamics of Nonmodel Organisms // Annu Rev Ecol Evol. Syst. 2018. Vol. 49, No. 1. P. 433–456. DOI: 10.1146/annurev-ecolsys-110617-062431

[37] 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

[38] Wang J., Santiago E., Caballero A. Prediction and estimation of effective population size // Heredity. 2016. Vol. 117, No. 4. P. 193–206. DOI: 10.1038/hdy.2016.43

[39] Walsh B, Lynch M. Evolution and Selection of Quantitative Traits. Oxford University Press; 2018. 1490 p.

[40] Walsh B., Lynch M. Evolution and Selection of Quantitative Traits. Oxford University Press, 2018. 1490 p.

[41] 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

[42] Charlesworth B. Fundamental concepts in genetics: effective population size and patterns of molecular evolution and variation // Nat Rev Genet. 2009. Vol. 10, No. 3. P. 195–205. DOI: 10.1038/nrg2526

[43] 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

[44] Waples R.S., 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. Vol. 3, No. 3. P. 244–262. DOI: 10.1111/j.1752-4571.2009.00104.x

[45] 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

[46] Marandel F., Lorance P., Berthelé O., et al. Estimating effective population size of large marine populations, is it feasible? // Fish Fish. 2019. Vol. 20, No. 1. P. 189–198. DOI: 10.1111/faf.12338

[47] 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

[48] Gilbert K.J., Whitlock M.C. Evaluating methods for estimating local effective population size with and without migration // Evol Int J Org Evol. 2015. Vol. 69, No. 8. P. 2154–2166. DOI: 10.1111/evo.12713

[49] 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

[50] Palstra F.P., Fraser D.J. Effective/census population size ratio estimation: a compendium and appraisal // Ecol Evol. 2012. Vol. 2, No. 9. P. 2357–2365. DOI: 10.1002/ece3.329

[51] 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

[52] Robinson J.A., Räikkönen J., Vucetich L.M., et al. Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction // Sci Adv. 2019. Vol. 5, No. 5. P. eaau0757. DOI: 10.1126/sciadv.aau0757

[53] 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

[54] Griffiths R.C., Tavaré S. The age of a mutation in a general coalescent tree // Commun Stat Stoch Models. 1998. Vol. 14, No. 1–2. P. 273–295. DOI: 10.1080/15326349808807471

[55] Wakeley J, Hey J. Estimating Ancestral Population Parameters. Genetics. 1997;145(3):847–855. DOI: 10.1093/genetics/145.3.847

[56] Wakeley J., Hey J. Estimating Ancestral Population Parameters // Genetics. 1997. Vol. 145, No. 3. P. 847–855. DOI: 10.1093/genetics/145.3.847

[57] 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

[58] Nielsen R. Estimation of Population Parameters and Recombination Rates From Single Nucleotide Polymorphisms // Genetics. 2000. Vol. 154, No. 2. P. 931–942. DOI: 10.1093/genetics/154.2.931

[59] 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

[60] Gutenkunst R.N., Hernandez R.D., Williamson S.H., et al. Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data // PLOS Genet. 2009. Vol. 5, No. 10. P. e1000695. DOI: 10.1371/journal.pgen.1000695

[61] 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

[62] Jouganous J., Long W., Ragsdale A.P., et al. Inferring the Joint Demographic History of Multiple Populations: Beyond the Diffusion Approximation // Genetics. 2017. Vol. 206, No. 3. P. 1549–1567. DOI: 10.1534/genetics.117.200493

[63] 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

[64] Excoffier L., Dupanloup I., Huerta-Sánchez E., et al. Robust Demographic Inference from Genomic and SNP Data // PLOS Genet. 2013. Vol. 9, No. 10. P. e1003905. DOI: 10.1371/journal.pgen.1003905

[65] 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

[66] Jenkins P.A., Mueller J.W., Song Y.S. General Triallelic Frequency Spectrum Under Demographic Models with Variable Population Size // Genetics. 2014. Vol. 196, No. 1. P. 295–311. DOI: 10.1534/genetics.113.158584

[67] 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

[68] Marth G.T., 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. Vol. 166, No. 1. P. 351–372. DOI: 10.1534/genetics.166.1.351

[69] 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

[70] Adams A.M., Hudson R.R. Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms // Genetics. 2004. Vol. 168, No. 3. P. 1699–1712. DOI: 10.1534/genetics.104.030171

[71] 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

[72] Voight B.F., Adams A.M., Frisse L.A., et al. Interrogating multiple aspects of variation in a full resequencing data set to infer human population size changes // Proc Natl Acad Sci. 2005. Vol. 102, No. 51. P. 18508–18513. DOI: 10.1073/pnas.0507325102

[73] 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

[74] Chen H., Green R.E., Pääbo S., et al. The Joint Allele-Frequency Spectrum in Closely Related Species // Genetics. 2007. Vol. 177, No. 1. P. 387–398. DOI: 10.1534/genetics.107.070730

[75] 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

[76] Myers S., Fefferman C., Patterson N. Can one learn history from the allelic spectrum? // Theor Popul Biol. 2008. Vol. 73, No. 3. P. 342–348. DOI: 10.1016/j.tpb.2008.01.001

[77] 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

[78] Evans S.N., Shvets Y., Slatkin M. Non-equilibrium theory of the allele frequency spectrum // Theor Popul Biol. 2007. Vol. 71, No. 1. P. 109–119. DOI: 10.1016/j.tpb.2006.06.005

[79] 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

[80] Williamson S.H., 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. Vol. 102, No. 22. P. 7882–7887. DOI: 10.1073/pnas.0502300102

[81] 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

[82] Beeravolu C.R., Hickerson M.J., Frantz L.A.F., et al. Blockwise site frequency spectra for inferring complex population histories and recombination // bioRxiv. 2017. P. 077958. DOI: 10.1101/077958

[83] 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

[84] Ragsdale A.P., Gravel S. Unbiased Estimation of Linkage Disequilibrium from Unphased Data // Mol Biol Evol. 2020. Vol. 37, No. 3. P. 923–932. DOI: 10.1093/molbev/msz265

[85] 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

[86] Kamm J.A., Terhorst J., Song Y.S. Efficient Computation of the Joint Sample Frequency Spectra for Multiple Populations // J Comput Graph Stat. 2017. Vol. 26, No. 1. P. 182–194. DOI: 10.1080/10618600.2016.1159212

[87] 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

[88] 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. Vol. 115, No. 531. P. 1472–1487. DOI: 10.1080/01621459.2019.1635482

[89] 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

[90] Noskova E., Ulyantsev V., Koepfli K.P., et al. GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data // Gigascience. 2020. Vol. 9, No. 3. P. giaa005. DOI: 10.1093/gigascience/giaa005

[91] 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

[92] Noskova E., Abramov N., Iliutkin S., et al. GADMA2: more efficient and flexible demographic inference from genetic data // bioRxiv. 2022. P. 2022.06.14.496083. DOI: 10.1101/2022.06.14.496083

[93] 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

[94] Li H., Durbin R. Inference of human population history from individual whole-genome sequences: 7357 // Nature. 2011. Vol. 475, No. 7357. P. 493–496. DOI: 10.1038/nature10231

[95] 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

[96] Schiffels S., Durbin R. Inferring human population size and separation history from multiple genome sequences // Nat Genet. 2014. Vol. 46, No. 8. P. 919–925. DOI: 10.1038/ng.3015

[97] 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

[98] Terhorst J., Kamm J.A., Song Y.S. Robust and scalable inference of population history from hundreds of unphased whole-genomes // Nat Genet. 2017. Vol. 49, No. 2. P. 303–309. DOI: 10.1038/ng.3748

[99] Kingman JFC. On the genealogy of large populations. J Appl Probab. 1982;19:27–43. DOI: 10.2307/3213548

[100] Kingman J.F.C. On the genealogy of large populations // J Appl Probab. 1982. Vol. 19. P. 27–43. DOI: 10.2307/3213548

[101] Hudson RR. Testing the constant-rate neutral allele model with protein sequence data. Evolution. 1983;37(1):203–217. DOI: 10.2307/2408186

[102] Hudson R.R. Testing the constant-rate neutral allele model with protein sequence data // Evolution. 1983. Vol. 37, No. 1. P. 203–217. DOI: 10.2307/2408186

[103] 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

[104] Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition // Proc IEEE. 1989. Vol. 77, No. 2. P. 257–286. DOI: 10.1109/5.18626

[105] 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

[106] Zucchini W., MacDonald I.L., 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

[107] 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

[108] Hobolth A., Christensen O.F., Mailund T., et al. Genomic Relationships and Speciation Times of Human, Chimpanzee, and Gorilla Inferred from a Coalescent Hidden Markov Model // PLoS Genet. 2007. Vol. 3, No. 2. P. e7. DOI: 10.1371/journal.pgen.0030007

[109] 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

[110] McVean G.A.T., Cardin N.J. Approximating the coalescent with recombination // Philos Trans R Soc Lond B Biol Sci. 2005. Vol. 360, No. 1459. P. 1387–1393. DOI: 10.1098/rstb.2005.1673

[111] Marjoram P, Wall JD. Fast “coalescent” simulation. BMC Genet. 2006;7:16. DOI: 10.1186/1471-2156-7-16

[112] Marjoram P., Wall J.D. Fast “coalescent” simulation // BMC Genet. 2006. Vol. 7. P. 16. DOI: 10.1186/1471-2156-7-16

[113] 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

[114] 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? // Heredity. 2016. Vol. 116, No. 4. P. 362–371. DOI: 10.1038/hdy.2015.104

[115] Paul JS, Song YS. Blockwise HMM computation for large-scale population genomic inference. Bioinformatics. 2012;28(15): 2008–2015. DOI: 10.1093/bioinformatics/bts314

[116] Paul J.S., Song Y.S. Blockwise HMM computation for large-scale population genomic inference // Bioinformatics. 2012. Vol. 28, No. 15. P. 2008–2015. DOI: 10.1093/bioinformatics/bts314

[117] 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

[118] Gusev A., Lowe J.K., Stoffel M., et al. Whole population, genome-wide mapping of hidden relatedness // Genome Res. 2009. Vol. 19, No. 2. P. 318–326. DOI: 10.1101/gr.081398.108

[119] 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

[120] 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. Vol. 11, No. 1. P. 6130. DOI: 10.1038/s41467-020-19588-x

[121] 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

[122] Palamara P.F., Terhorst J., Song Y.S., et al. High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability: 9 // Nat Genet. 2018. Vol. 50, No. 9. P. 1311–1317. DOI: 10.1038/s41588-018-0177-x

[123] 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

[124] Harris K., Nielsen R. Inferring Demographic History from a Spectrum of Shared Haplotype Lengths // PLOS Genet. 2013. Vol. 9, No. 6. P. e1003521. DOI: 10.1371/journal.pgen.1003521

[125] 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

[126] Liu S., Lorenzen E.D., Fumagalli M., et al. Population Genomics Reveal Recent Speciation and Rapid Evolutionary Adaptation in Polar Bears // Cell. 2014. Vol. 157, No. 4. P. 785–794. DOI: 10.1016/j.cell.2014.03.054

[127] 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

[128] 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. Vol. 23, No. 16. P. 4089–4102. DOI: 10.1111/mec.12807

[129] 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

[130] Gronau I., Hubisz M.J., Gulko B., et al. Bayesian inference of ancient human demography from individual genome sequences: 10 // Nat Genet. 2011. Vol. 43, No. 10. P. 1031–1034. DOI: 10.1038/ng.937

[131] 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

[132] Schrider D.R., Shanku A.G., Kern A.D. Effects of Linked Selective Sweeps on Demographic Inference and Model Selection // Genetics. 2016. Vol. 204, No. 3. P. 1207–1223. DOI: 10.1534/genetics.116.190223

[133] 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

[134] Feng S., Fang Q., Barnett R., et al. The Genomic Footprints of the Fall and Recovery of the Crested Ibis // Curr Biol. 2019. Vol. 29, No. 2. P. 340–349.e7. DOI: 10.1016/j.cub.2018.12.008

[135] 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

[136] de Manuel M., Barnett R., Sandoval-Velasco M., et al. The evolutionary history of extinct and living lions // Proc Natl Acad Sci. 2020. Vol. 117, No. 20. P. 10927–10934. DOI: 10.1073/pnas.1919423117

[137] 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

[138] Sodeland M., Jentoft S., Jorde P.E., et al. Stabilizing selection on Atlantic cod supergenes through a millennium of extensive exploitation // Proc Natl Acad Sci. USA. 2022. Vol. 119, No. 8. P. e2114904119. DOI: 10.1073/pnas.2114904119

[139] 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

[140] Chavez D.E., 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. Vol. 119, No. 34. P. e2205986119. DOI: 10.1073/pnas.2205986119

[141] 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

[142] de Ferran V., Figueiró H.V., de Jesus Trindade F., et al. Phylogenomics of the world’s otters // Curr Biol. 2022. Vol. 32, No. 16. P. 3650–3658.e4. DOI: 10.1016/j.cub.2022.06.036

[143] 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

[144] Abascal F., Corvelo A., Cruz F., et al. Extreme genomic erosion after recurrent demographic bottlenecks in the highly endangered Iberian lynx // Genome Biol. 2016. Vol. 17, No. 1. P. 251. DOI: 10.1186/s13059-016-1090-1

[145] 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

[146] Ekblom R., Brechlin B., Persson J., et al. Genome sequencing and conservation genomics in the Scandinavian wolverine population // Conserv Biol. 2018. Vol. 32, No. 6. P. 1301–1312. DOI: 10.1111/cobi.13157

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