1932

Abstract

Natural highly fecund populations abound. These range from viruses to gadids. Many highly fecund populations are economically important. Highly fecund populations provide an important contrast to the low-fecundity organisms that have traditionally been applied in evolutionary studies. A key question regarding high fecundity is whether large numbers of offspring are produced on a regular basis, by few individuals each time, in a sweepstakes mode of reproduction. Such reproduction characteristics are not incorporated into the classical Wright–Fisher model, the standard reference model of population genetics, or similar types of models, in which each individual can produce only small numbers of offspring relative to the population size. The expected genomic footprints of population genetic models of sweepstakes reproduction are very different from those of the Wright–Fisher model. A key, immediate issue involves identifying the footprints of sweepstakes reproduction in genomic data. Whole-genome sequencing data can be used to distinguish the patterns made by sweepstakes reproduction from the patterns made by population growth in a population evolving according to the Wright–Fisher model (or similar models). If the hypothesis of sweepstakes reproduction cannot be rejected, then models of sweepstakes reproduction and associated multiple-merger coalescents will become at least as relevant as the Wright–Fisher model (or similar models) and the Kingman coalescent, the cornerstones of mathematical population genetics, in further discussions of evolutionary genomics of highly fecund populations.

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2020-11-23
2024-10-06
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Literature Cited

  1. 1. 
    Agrios G. 2005. Plant Pathology Amsterdam: Academic
    [Google Scholar]
  2. 2. 
    Anderson C, Khan MA, Catanzariti AM, Jack CA, Nemri A et al. 2016. Genome analysis and avirulence gene cloning using a high-density RADseq linkage map of the flax rust fungus, Melampsora lini. BMC Genom. 17:667
    [Google Scholar]
  3. 3. 
    Árnason E. 2004. Mitochondrial cytochrome b variation in the high-fecundity Atlantic cod: trans-Atlantic clines and shallow gene genealogy. Genetics 166:1871–85
    [Google Scholar]
  4. 4. 
    Árnason E, Halldórsdóttir K. 2015. Nucleotide variation and balancing selection at the Ckma gene in Atlantic cod: analysis with multiple merger coalescent models. PeerJ 3:e786
    [Google Scholar]
  5. 5. 
    Árnason E, Halldórsdóttir K. 2019. Codweb: Whole-genome sequencing uncovers extensive reticulations fueling adaptation among Atlantic, Arctic, and Pacific gadids. Sci. Adv. 5:eaat8788
    [Google Scholar]
  6. 6. 
    Bah B, Pardoux E. 2015. The Λ-lookdown model with selection. Stoch. Process. Appl. 125:1089–126
    [Google Scholar]
  7. 7. 
    Bah B, Sow AB, Pardoux E 2012. A look-down model with selection. Springer Proceedings in Mathematics and Statistics1–28 Berlin/Heidelberg, Ger: Springer
    [Google Scholar]
  8. 8. 
    Barton N, Etheridge A, Véber A 2017. The infinitesimal model: definition, derivation, and implications. Theor. Popul. Biol. 118:50–73
    [Google Scholar]
  9. 9. 
    Berestycki N. 2009. Recent progress in coalescent theory. Ensaios Mat 16:1–193
    [Google Scholar]
  10. 10. 
    Bertoin J. 2010. Exchangeable coalescents Cours d'école doctorale, Int. Cent. Meet. Math. Luminy, Marseille, Fr., Sept 20–24
    [Google Scholar]
  11. 11. 
    Bertoin J, Legall J. 2005. Stochastic flows associated to coalescent processes. II. Stochastic differential equations. Ann. Inst. Henri Poincaré B 41:307–33
    [Google Scholar]
  12. 12. 
    Bhaskar A, Clark A, Song Y 2014. Distortion of genealogical properties when the sample size is very large. PNAS 111:2385–90
    [Google Scholar]
  13. 13. 
    Birkner M, Blath J. 2008. Computing likelihoods for coalescents with multiple collisions in the infinitely many sites model. J. Math. Biol. 57:435–65
    [Google Scholar]
  14. 14. 
    Birkner M, Blath J. 2009. Measure-valued diffusions, coalescents and genetic inference. Trends in Stochastic Analysis J Blath, P Mörters, M Scheutzow 329–63 Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  15. 15. 
    Birkner M, Blath J, Eldon B 2013. An ancestral recombination graph for diploid populations with skewed offspring distribution. Genetics 193:255–90
    [Google Scholar]
  16. 16. 
    Birkner M, Blath J, Möhle M, Steinrücken M, Tams J 2009. A modified lookdown construction for the Xi-Fleming-Viot process with mutation and populations with recurrent bottlenecks. ALEA Lat. Am. J. Probab. Math. Stat. 6:25–61
    [Google Scholar]
  17. 17. 
    Birkner M, Blath J, Steinrücken M 2011. Importance sampling for Λ-coalescents in the infinitely many sites model. Theor. Popul. Biol. 79:155–73
    [Google Scholar]
  18. 18. 
    Birkner M, Liu H, Sturm A 2018. Coalescent results for diploid exchangeable population models. Electron. J. Probab. 23:49
    [Google Scholar]
  19. 19. 
    Blath J, Cronjäger MC, Eldon B, Hammer M 2016. The site-frequency spectrum associated with Ξ-coalescents. Theor. Popul. Biol. 110:36–50
    [Google Scholar]
  20. 20. 
    Boom J, Boulding E, Beckenbach A 1994. Mitochondrial DNA variation in introduced populations of Pacific oyster, Crassostrea gigas, in British Columbia. Can. J. Fish. Aquat. Sci. 51:1608–14
    [Google Scholar]
  21. 21. 
    Byeon SY, Oh HJ, Kim S, Yun SH, Kang JH et al. 2019. The origin and population genetic structure of the ‘golden tide’ seaweeds, Sargassum horneri, in Korean waters. Sci. Rep. 9:7757
    [Google Scholar]
  22. 22. 
    Canino MF, Spies IB, Cunningham KM, Hauser L, Grant WS 2010. Multiple ice-age refugia in Pacific cod. Gadus macrocephalus. Mol. Ecol. 19:4339–51
    [Google Scholar]
  23. 23. 
    Carr SM, Marshall HD. 2008. Intraspecific phylogeographic genomics from multiple complete mtDNA genomes in Atlantic cod (Gadus morhua): origins of the “codmother,” transatlantic vicariance and midglacial population expansion. Genetics 180:381–89
    [Google Scholar]
  24. 24. 
    Chan J, Perrone V, Spence J, Jenkins P, Mathieson S, Song Y 2018. A likelihood-free inference framework for population genetic data using exchangeable neural networks. Proceedings of the 31st Conference on Advances in Neural Information Processing Systems (NIPS 2018) S Bengio, H Wallach, H Larochelle, K Grauman, N Cesa-Bianchi, R Garnett San Diego, CA: Salk Inst.
    [Google Scholar]
  25. 25. 
    Chandler AC. 1955. Introduction to Parasitology New York: Wiley. , 8th. ed.
    [Google Scholar]
  26. 26. 
    Christie MR, Johnson DW, Stallings CD, Hixon MA 2010. Self-recruitment and sweepstakes reproduction amid extensive gene flow in a coral-reef fish. Mol. Ecol. 19:1042–57
    [Google Scholar]
  27. 27. 
    Coffman AJ, Hsieh PH, Gravel S, Gutenkunst RN 2015. Computationally efficient composite likelihood statistics for demographic inference. Mol. Biol. Evol. 33:591–93
    [Google Scholar]
  28. 28. 
    Cohen DM, Inada T, Iwamoto T, Scialabba N 1990. FAO Species Catalogue, Vol. 10: Gadiform Fishes of the World Rome: FAO
    [Google Scholar]
  29. 29. 
    Conner JK. 2001. How strong is natural selection. ? Trends Ecol. Evol. 16:215–17
    [Google Scholar]
  30. 30. 
    Dawson DA, Li Z. 2012. Stochastic equations, flows and measure-valued processes. Ann. Probab. 40:813–57
    [Google Scholar]
  31. 31. 
    Der R, Epstein C, Plotkin JB 2012. Dynamics of neutral and selected alleles when the offspring distribution is skewed. Genetics 191:1331–44
    [Google Scholar]
  32. 32. 
    Der R, Plotkin JB. 2014. The equilibrium allele frequency distribution for a population with reproductive skew. Genetics 196:1199–216
    [Google Scholar]
  33. 33. 
    Donnelly P, Kurtz TG. 1999. Particle representations for measure-valued population models. Ann. Probab. 27:166–205
    [Google Scholar]
  34. 34. 
    Donnelly P, Tavare S. 1995. Coalescents and genealogical structure under neutrality. Annu. Rev. Genet. 29:401–21
    [Google Scholar]
  35. 35. 
    Duong T. 2019. ks: kernel smoothing, version 1.11.6. R Software Package
    [Google Scholar]
  36. 36. 
    Durrett R, Schweinsberg J. 2004. Approximating selective sweeps. Theor. Popul. Biol. 66:129–38
    [Google Scholar]
  37. 37. 
    Eldon B, Birkner M, Blath J, Freund F 2015. Can the site-frequency spectrum distinguish exponential population growth from multiple-merger coalescents. ? Genetics 199:841–56
    [Google Scholar]
  38. 38. 
    Eldon B, Freund F. 2018. Genealogical properties of subsamples in highly fecund populations. J. Stat. Phys. 172:175–207
    [Google Scholar]
  39. 39. 
    Eldon B, Stephan W. 2018. Evolution of highly fecund haploid populations. Theor. Popul. Biol. 119:48–56
    [Google Scholar]
  40. 40. 
    Eldon B, Wakeley J. 2006. Coalescent processes when the distribution of offspring number among individuals is highly skewed. Genetics 172:2621–33
    [Google Scholar]
  41. 41. 
    Etheridge A. 2011. Some Mathematical Models from Population Genetics Berlin/Heidelberg, Ger: Springer
    [Google Scholar]
  42. 42. 
    Ewens WJ. 2004. Mathematical Population Genetics New York: Springer
    [Google Scholar]
  43. 43. 
    Foucart C. 2013. The impact of selection in the Λ-Wright-Fisher model. Electron. Commun. Probab. 18:1–10
    [Google Scholar]
  44. 44. 
    Freund F. 2020. Cannings models, population size changes and multiple-merger coalescents. J. Math. Biol. 80:1497–521
    [Google Scholar]
  45. 45. 
    Fu Y. 2006. Exact coalescent for the Wright-Fisher model. Theor. Popul. Biol. 69:385–94
    [Google Scholar]
  46. 46. 
    Fumagalli M. 2013. Assessing the effect of sequencing depth and sample size in population genetics inferences. PLOS ONE 8:e79667
    [Google Scholar]
  47. 47. 
    Gao Y, Zhang C, Yuan L, Ling Y, Wang X et al. 2019. PGG.Han: the Han Chinese genome database and analysis platform. Nucleic Acids Res 48:D971–76
    [Google Scholar]
  48. 48. 
    Gerzabek G, Oddou-Muratorio S, Hampe A 2016. Temporal change and determinants of maternal reproductive success in an expanding oak forest stand. J. Ecol. 105:39–48
    [Google Scholar]
  49. 49. 
    Gillespie JH. 2000. Genetic drift in an infinite population: the pseudohitchhiking model. Genetics 155:909–19
    [Google Scholar]
  50. 50. 
    Gillespie JH. 2004. Population Genetics: A Concise Guide Baltimore, MD: Johns Hopkins Univ. Press
    [Google Scholar]
  51. 51. 
    Griffiths R, Tavaré S. 1994. Ancestral inference in population genetics. Stat. Sci. 9:307–19
    [Google Scholar]
  52. 52. 
    Griffiths R, Tavaré S. 1994. Sampling theory for neutral alleles in a varying environment. Philos. Trans. R. Soc. B 344:403–10
    [Google Scholar]
  53. 53. 
    Griffiths R, Tavaré S. 1994. Simulating probability distributions in the coalescent. Theor. Popul. Biol. 46:131–59
    [Google Scholar]
  54. 54. 
    Haller BC, Messer PW. 2019. SLiM 3: forward genetic simulations beyond the WrightFisher model. Mol. Biol. Evol. 36:632–37
    [Google Scholar]
  55. 55. 
    Han E, Sinsheimer JS, Novembre J 2013. Characterizing bias in population genetic inferences from low-coverage sequencing data. Mol. Biol. Evol. 31:723–35
    [Google Scholar]
  56. 56. 
    Harvey BP, McKeown NJ, Rastrick SPS, Bertolini C, Foggo A et al. 2016. Individual and population-level responses to ocean acidification. Sci. Rep. 6:20194
    [Google Scholar]
  57. 57. 
    Hedgecock D. 1994. Genetics and evolution of aquatic organisms. Genet. Evol. Aquat. Org. 122:122–34
    [Google Scholar]
  58. 58. 
    Hedgecock D, Pudovkin AI. 2011. Sweepstakes reproductive success in highly fecund marine fish and shellfish: a review and commentary. Bull. Mar. Sci. 87:971–1002
    [Google Scholar]
  59. 59. 
    Hoban SM, Mezzavilla M, Gaggiotti OE, Benazzo A, van Oosterhout C, Bertorelle G 2013. High variance in reproductive success generates a false signature of a genetic bottleneck in populations of constant size: a simulation study. BMC Bioinform 14:309
    [Google Scholar]
  60. 60. 
    Hoscheit P, Pybus OG. 2019. The multifurcating skyline plot. Virus Evol 5:vez031
    [Google Scholar]
  61. 61. 
    Hovmøller MS, Sørensen CK, Walter S, Justesen AF 2011. Diversity of Puccinia striiformis on cereals and grasses. Annu. Rev. Phytopathol. 49:197–217
    [Google Scholar]
  62. 62. 
    Hubert S, Higgins B, Borza T, Bowman S 2010. Development of a SNP resource and a genetic linkage map for Atlantic cod (Gadus morhua). BMC Genom 11:191
    [Google Scholar]
  63. 63. 
    Hughes AR, Hanley TC, Byers JE, Grabowski JH, McCrudden T et al. 2019. Genetic diversity and phenotypic variation within hatchery-produced oyster cohorts predict size and success in the field. Ecol. Appl. 29:e01940
    [Google Scholar]
  64. 64. 
    Huillet T, Möhle M. 2011. Population genetics models with skewed fertilities: forward and backward analysis. Stoch. Models 27:521–54
    [Google Scholar]
  65. 65. 
    Huillet T, Möhle M. 2013. On the extended Moran model and its relation to coalescents with multiple collisions. Theor. Popul. Biol. 87:5–14
    [Google Scholar]
  66. 66. 
    Hutchings JA. 2000. Collapse and recovery of marine fishes. Nature 406:882–85
    [Google Scholar]
  67. 67. 
    Hutchings JA, Myers RA. 1994. What can be learned from the collapse of a renewable resource? Atlantic cod, Gadus morhua, of Newfoundland and Labrador. Can. J. Fish. Aquat. Sci. 51:2126–46
    [Google Scholar]
  68. 68. 
    Hyman LH. 1951. The Invertebrates: Platyhelminthes and Rhynchocoela, the Acoelomate Bilateria, Vol. 2 New York: McGraw-Hill
    [Google Scholar]
  69. 69. 
    Irwin KK, Laurent S, Matuszewski S, Vuilleumier S, Ormond L et al. 2016. On the importance of skewed offspring distributions and background selection in virus population genetics. Heredity 117:393–99
    [Google Scholar]
  70. 70. 
    Jennings JB, Calow P. 1975. The relationship between high fecundity and the evolution of entoparasitism. Oecologia 21:109–15
    [Google Scholar]
  71. 71. 
    Kato M, Vasco DA, Sugino R, Narushima D, Krasnitz A 2017. Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer. R. Soc. Open Sci. 4:171060
    [Google Scholar]
  72. 72. 
    Keightley PD, Campos JL, Booker TR, Charlesworth B 2016. Inferring the frequency spectrum of derived variants to quantify adaptive molecular evolution in protein-coding genes of Drosophila melanogaster. . Genetics 203:975–84
    [Google Scholar]
  73. 73. 
    Kelleher J, Etheridge AM, McVean G 2016. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLOS Comput. Biol. 12:e1004842
    [Google Scholar]
  74. 74. 
    Kimura M. 1957. Some problems of stochastic processes in genetics. Ann. Math. Stat. 28:882–901
    [Google Scholar]
  75. 75. 
    Kimura M. 1969. The number of heterozygous nucleotide sites maintained in a finite population due to steady flux of mutations. Genetics 61:893–903
    [Google Scholar]
  76. 76. 
    Kingman J. 2000. Origins of the coalescent: 1974–1982. Genetics 156:1461–63
    [Google Scholar]
  77. 77. 
    Kingman JFC. 1982. The coalescent. Stoch. Process. Appl. 13:235–48
    [Google Scholar]
  78. 78. 
    Kingman JFC. 1982. Exchangeability and evolution of large populations. Exchangeability in Probability and Statistics G Koch, F Spizzichino 97–112 Amsterdam: North-Holland
    [Google Scholar]
  79. 79. 
    Kingman JFC. 1982. On the genealogy of large populations. J. Appl. Probab. 19:A27–43
    [Google Scholar]
  80. 80. 
    Kirubakaran TG, Andersen Ø, Moser M, Arnyasi M, McGinnity P et al. 2020. A nanopore based chromosome-level assembly representing Atlantic cod from the Celtic Sea. G3 10:8 https://doi.org/10.1534/g3.120.401423
    [Crossref] [Google Scholar]
  81. 81. 
    Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P et al. 2012. Rate of de novo mutations and the importance of father's age to disease risk. Nature 488:471–75
    [Google Scholar]
  82. 82. 
    Koskela J. 2018. Multi-locus data distinguishes between population growth and multiple merger coalescents. Stat. Appl. Genet. Mol. Biol. 17:20170011
    [Google Scholar]
  83. 83. 
    Koskela J, Berenguer MW. 2019. Robust model selection between population growth and multiple merger coalescents. Math. Biosci. 311:1–12
    [Google Scholar]
  84. 84. 
    Kumar S, Subramanian S. 2002. Mutation rates in mammalian genomes. PNAS 99:803–8
    [Google Scholar]
  85. 85. 
    Larribe F, Fearnhead P. 2011. On composite likelihoods in statistical genetics. Stat. Sin. 21:43–69
    [Google Scholar]
  86. 86. 
    Li G, Hedgecock D. 1998. Genetic heterogeneity, detected by PCR-SSCP, among samples of larval Pacific oysters (Crassostrea gigas) supports the hypothesis of large variance in reproductive success. Can. J. Fish. Aquat. Sci. 55:1025–33
    [Google Scholar]
  87. 87. 
    Lucas ER, Miles A, Harding NJ, Clarkson CS, Lawniczak MK et al. 2019. Whole-genome sequencing reveals high complexity of copy number variation at insecticide resistance loci in malaria mosquitoes. Genome Res 29:1250–61
    [Google Scholar]
  88. 88. 
    Martinez AS, Willoughby JR, Christie MR 2018. Genetic diversity in fishes is influenced by habitat type and life-history variation. Ecol. Evol. 8:12022–31
    [Google Scholar]
  89. 89. 
    Matuszewski S, Hildebrandt ME, Achaz G, Jensen JD 2017. Coalescent processes with skewed offspring distributions and nonequilibrium demography. Genetics 208:323–38
    [Google Scholar]
  90. 90. 
    Mavárez J, Linares M. 2008. Homoploid hybrid speciation in animals. Mol. Ecol. 17:4181–85
    [Google Scholar]
  91. 91. 
    Melfi A, Viswanath D. 2018. Single and simultaneous binary mergers in Wright-Fisher genealogies. Theor. Popul. Biol. 121:60–71
    [Google Scholar]
  92. 92. 
    Möhle M. 1998. Robustness results for the coalescent. J. Appl. Probab. 35:438–47
    [Google Scholar]
  93. 93. 
    Möhle M, Sagitov S. 2001. A classification of coalescent processes for haploid exchangeable population models. Ann. Probab. 29:1547–62
    [Google Scholar]
  94. 94. 
    Möhle M, Sagitov S. 2003. Coalescent patterns in diploid exchangeable population models. J. Math. Biol. 47:337–52
    [Google Scholar]
  95. 95. 
    Moran EV, Clark JS. 2012. Causes and consequences of unequal seedling production in forest trees: a case study in red oaks. Ecology 93:1082–94
    [Google Scholar]
  96. 96. 
    Naqvi MA, Jamil T, Naqvi SZ, Memon MA, Aimulajiang K et al. 2020. Immunodiagnostic potential of recombinant tropomyosin during prepatent Haemonchus contortus infection in goat. Res. Vet. Sci. 128:197–204
    [Google Scholar]
  97. 97. 
    Niwa HS, Nashida K, Yanagimoto T 2016. Reproductive skew in Japanese sardine inferred from DNA sequences. J. Mar. Sci. 73:2181–89
    [Google Scholar]
  98. 98. 
    Parenti P. 2003. Family Molidae Bonaparte 1832—molas or ocean sunfishes Annot. Checkl. Fish. 18, Calif. Acad. Sci San Francisco:
    [Google Scholar]
  99. 99. 
    Pedersen C, Rasmussen SW, Giese H 2002. A genetic map of Blumeria graminis based on functional genes, avirulence genes, and molecular markers. Fungal Genet. Biol. 35:235–46
    [Google Scholar]
  100. 100. 
    Pimentel D, Lach L, Zuniga R, Morrison D 2000. Environmental and economic costs of nonindigenous species in the United States. BioScience 50:53–65
    [Google Scholar]
  101. 101. 
    Pimentel D, Zuniga R, Morrison D 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. 52:273–88
    [Google Scholar]
  102. 102. 
    Pitman J. 1999. Coalescents with multiple collisions. Ann. Probab. 27:1870–902
    [Google Scholar]
  103. 103. 
    Provine WB. 2001. The Origins of Theoretical Population Genetics: With a New Afterword Chicago: Univ. Chicago Press
    [Google Scholar]
  104. 104. 
    R Core Team 2014. R: a language and environment for statistical computing. Statistical Software R Found. Stat. Comput Vienna:
    [Google Scholar]
  105. 105. 
    Rabadán R, Blumberg AJ. 2019. Topological Data Analysis for Genomics and Evolution: Topology in Biology Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  106. 106. 
    Retel C, Märkle H, Becks L, Feulner P 2019. Ecological and evolutionary processes shaping viral genetic diversity. Viruses 11:220
    [Google Scholar]
  107. 107. 
    Rossman AY. 2008. The impact of invasive fungi on agricultural ecosystems in the United States. Biol. Invasions 11:97–107
    [Google Scholar]
  108. 108. 
    Ruggeri P, Splendiani A, Muri CD, Fioravanti T, Santojanni A et al. 2016. Coupling demographic and genetic variability from archived collections of European anchovy (Engraulis encrasicolus). PLOS ONE 11:e0151507
    [Google Scholar]
  109. 109. 
    Sackman AM, Harris RB, Jensen JD 2019. Inferring demography and selection in organisms characterized by skewed offspring distributions. Genetics 211:1019–28
    [Google Scholar]
  110. 110. 
    Sagitov S. 1999. The general coalescent with asynchronous mergers of ancestral lines. J. Appl. Probab. 36:1116–25
    [Google Scholar]
  111. 111. 
    Sagitov S. 2003. Convergence to the coalescent with simultaneous mergers. J. Appl. Probab. 40:839–54
    [Google Scholar]
  112. 112. 
    Sankaran T. 1954. The natural enemies of Ceroplasses pseudoceriferous green (Hemiptera-cicada). J. Sci. Res. Banaras Hindu Univ. 5:100–19
    [Google Scholar]
  113. 113. 
    Sargsyan O, Wakeley J. 2008. A coalescent process with simultaneous multiple mergers for approximating the gene genealogies of many marine organisms. Theor. Popul. Biol. 74:104–14
    [Google Scholar]
  114. 114. 
    Schmidt J. 1921. New studies of sun-fishes made during the “Dana” expedition, 1920. Nature 107:76–79
    [Google Scholar]
  115. 115. 
    Schweinsberg J. 2000. Coalescents with simultaneous multiple collisions. Electron. J. Probab. 5:1–50
    [Google Scholar]
  116. 116. 
    Schweinsberg J. 2003. Coalescent processes obtained from supercritical Galton–Watson processes. Stoch. Process. Appl. 106:107–39
    [Google Scholar]
  117. 117. 
    Sheehan S, Song YS. 2016. Deep learning for population genetic inference. PLOS Comput. Biol. 12:e1004845
    [Google Scholar]
  118. 118. 
    Simon M, Cordo C. 1997. Inheritance of partial resistance to Septoria tritici in wheat (Triticum aestivum): limitation of pycnidia and spore production. Agronomie 17:343–47
    [Google Scholar]
  119. 119. 
    Soliai MM, Meyer SE, Udall JA, Elzinga DE, Hermansen RA et al. 2014. De novo genome assembly of the fungal plant pathogen Pyrenophora semeniperda. . PLOS ONE 9:e87045
    [Google Scholar]
  120. 120. 
    Star B, Nederbragt AJ, Jentoft S, Grimholt U, Malmstrøm M et al. 2011. The genomic sequence of Atlantic cod reveals a unique immune system. Nature 477:207–10
    [Google Scholar]
  121. 121. 
    Tajima F. 1983. Evolutionary relationships of DNA sequences in finite populations. Genetics 105:437–60
    [Google Scholar]
  122. 122. 
    Timm A, Yin J. 2012. Kinetics of virus production from single cells. Virology 424:11–17
    [Google Scholar]
  123. 123. 
    Tindale NB. 1932. Revision of the Australian ghost moths (Lepidoptera homoneura, family Hepialidae). Part I. Rec. S. Aust. Mus. 4:497–536
    [Google Scholar]
  124. 124. 
    Tørresen OK, Star B, Jentoft S, Reinar WB, Grove H et al. 2017. An improved genome assembly uncovers prolific tandem repeats in Atlantic cod. BMC Genom 18:95
    [Google Scholar]
  125. 125. 
    Turelli M, Barton NH. 1994. Genetic and statistical analyses of strong selection on polygenic traits: What, me normal. ? Genetics 138:913–41
    [Google Scholar]
  126. 126. 
    Varin C, Reid N, Firth D 2011. An overview of composite likelihood methods. Stat. Sin. 21:5–42
    [Google Scholar]
  127. 127. 
    Wakeley J. 2007. Coalescent Theory Greenwood Village, CO: Roberts & Co.
    [Google Scholar]
  128. 128. 
    Wakeley J, Takahashi T. 2003. Gene genealogies when the sample size exceeds the effective size of the population. Mol. Biol. Evol. 20:208–2013
    [Google Scholar]
  129. 129. 
    Wallen RM, Perlin MH. 2018. An overview of the function and maintenance of sexual reproduction in dikaryotic fungi. Front. Microbiol. 9:503
    [Google Scholar]
  130. 130. 
    Wand MP. 2014. KernSmooth: functions for kernel smoothing for Wand & Jones (1995), version 2.23-12. R Software Package
    [Google Scholar]
  131. 131. 
    Wand MP, Jones MC. 1994. Kernel Smoothing London: Chapman & Hall/CRC
    [Google Scholar]
  132. 132. 
    Waples RS. 2016. Tiny estimates of the ratio in marine fishes: Are they real. ? J. Fish Biol. 89:2479–504
    [Google Scholar]
  133. 133. 
    Watterson GA. 1975. On the number of segregating sites in genetical models without recombination. Theor. Popul. Biol. 7:256–76
    [Google Scholar]
  134. 134. 
    Williams GC. 1975. Sex and Evolution Princeton, NJ: Princeton Univ. Press
    [Google Scholar]
  135. 135. 
    Williams GC, Mitton JB. 1973. Why reproduce sexually. ? J. Theor. Biol. 39:545–54
    [Google Scholar]
  136. 136. 
    Wu D, Dou J, Chai X, Bellis C, Wilm A et al. 2019. Large-scale whole-genome sequencing of three diverse Asian populations in Singapore. Cell 179:736–49.e15
    [Google Scholar]
  137. 137. 
    Zhu L, Bustamante CD. 2005. A composite-likelihood approach for detecting directional selection from DNA sequence data. Genetics 170:1411–21
    [Google Scholar]
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