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Power and limits of selection genome scans on temporal data from a selfing population

de Navascués, Miguel1, 2, 3  ; Becheler, Arnaud1, 4 ; Gay, Laurène5; Ronfort, Joëlle5; Loridon, Karine5; Vitalis, Renaud1, 3 
1 CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, Univ Montpellier, Montpellier, France
2 Human Evolution, Department of Organismal Biology, Uppsala University, Uppsala, Sweden
3 Institut de Biologie Computationnelle, Montpellier, France
4 Current affiliation: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, USA
5 AGAP, INRAE, CIRAD, IRD, Montpellier SupAgro, Univ Montpellier, Montpellier, France

10.24072/pcjournal.47 - Peer Community Journal, Volume 1 (2021), article no. e37.

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Tracking genetic changes of populations through time allows a more direct study of the evolutionary processes acting on the population than a single contemporary sample. Several statistical methods have been developed to characterize the demography and selection from temporal population genetic data. However, these methods are usually developed under the assumption of outcrossing reproduction and might not be applicable when there is substantial selfing in the population. Here, we focus on a method to detect loci under selection based on a genome scan of temporal differentiation, adapting it to the particularities of selfing populations. Selfing reduces the effective recombination rate and can extend hitch-hiking effects to the whole genome, erasing any local signal of selection on a genome scan. Therefore, selfing is expected to reduce the power of the test. By means of simulations, we evaluate the performance of the method under scenarios of adaptation from new mutations or standing variation at different rates of selfing. We find that the detection of loci under selection in predominantly selfing populations remains challenging even with the adapted method. Still, selective sweeps from standing variation on predominantly selfing populations can leave some signal of selection around the selected site thanks to historical recombination before the sweep. Under this scenario, ancestral advantageous alleles at low frequency leave the strongest local signal, while new advantageous mutations leave no local footprint of the sweep.
Published online:
DOI: 10.24072/pcjournal.47
de Navascués, Miguel 1, 2, 3; Becheler, Arnaud 1, 4; Gay, Laurène 5; Ronfort, Joëlle 5; Loridon, Karine 5; Vitalis, Renaud 1, 3

1 CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, Univ Montpellier, Montpellier, France
2 Human Evolution, Department of Organismal Biology, Uppsala University, Uppsala, Sweden
3 Institut de Biologie Computationnelle, Montpellier, France
4 Current affiliation: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, USA
5 AGAP, INRAE, CIRAD, IRD, Montpellier SupAgro, Univ Montpellier, Montpellier, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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de Navascués, Miguel; Becheler, Arnaud; Gay, Laurène; Ronfort, Joëlle; Loridon, Karine; Vitalis, Renaud. Power and limits of selection genome scans on temporal data from a selfing population. Peer Community Journal, Volume 1 (2021), article  no. e37. doi : 10.24072/pcjournal.47. https://peercommunityjournal.org/articles/10.24072/pcjournal.47/

Peer reviewed and recommended by PCI : 10.24072/pci.evolbiol.100110

[1] Anderson EC et al., Monte Carlo Evaluation of the Likelihood for Ne From Temporally Spaced Samples, Volume 156 (2000), pp. 2109-2118

[2] Baron, E.; Richirt, J.; Villoutreix, R.; Amsellem, L.; Roux, F. The genetics of intra‐ and interspecific competitive response and effect in a local population of an annual plant species, Functional Ecology, Volume 29 (2015) no. 10, pp. 1361-1370 | DOI

[3] Barrett, R.; Schluter, D. Adaptation from standing genetic variation, Trends in Ecology & Evolution, Volume 23 (2008) no. 1, pp. 38-44 | DOI

[4] Berg, J. J.; Coop, G. A Coalescent Model for a Sweep of a Unique Standing Variant, Genetics, Volume 201 (2015) no. 2, pp. 707-725 | DOI

[5] Biek, R.; Pybus, O. G.; Lloyd-Smith, J. O.; Didelot, X. Measurably evolving pathogens in the genomic era, Trends in Ecology & Evolution, Volume 30 (2015) no. 6, pp. 306-313 | DOI

[6] Bollback, J. P.; York, T. L.; Nielsen, R. Estimation of 2Nes From Temporal Allele Frequency Data, Genetics, Volume 179 (2008) no. 1, pp. 497-502 | DOI

[7] Bonhomme, M.; Boitard, S.; San Clemente, H.; Dumas, B.; Young, N.; Jacquet, C. Genomic Signature of Selective Sweeps Illuminates Adaptation ofMedicago truncatulato Root-Associated Microorganisms, Molecular Biology and Evolution, Volume 32 (2015) no. 8, pp. 2097-2110 | DOI

[8] Bonnin, I.; Ronfort, J.; Wozniak, F.; Olivieri, I. Spatial effects and rare outcrossing events in Medicago truncatula (Fabaceae), Molecular Ecology, Volume 10 (2001) no. 6, pp. 1371-1383 | DOI

[9] Brüniche-Olsen, A.; Austin, J. J.; Jones, M. E.; Holland, B. R.; Burridge, C. P. Detecting Selection on Temporal and Spatial Scales: A Genomic Time-Series Assessment of Selective Responses to Devil Facial Tumor Disease, PLOS ONE, Volume 11 (2016) no. 3 | DOI

[10] Buffalo, V.; Coop, G. The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data, Genetics, Volume 213 (2019) no. 3, pp. 1007-1045 | DOI

[11] Burgarella, C.; Gayral, P.; Ballenghien, M.; Bernard, A.; David, P.; Jarne, P.; Correa, A.; Hurtrez-Boussès, S.; Escobar, J.; Galtier, N.; Glémin, S. Molecular Evolution of Freshwater Snails with Contrasting Mating Systems, Molecular Biology and Evolution, Volume 32 (2015) no. 9, pp. 2403-2416 | DOI

[12] Charlesworth, B. Evolutionary Rates in Partially Self-Fertilizing Species, The American Naturalist, Volume 140 (1992) no. 1, pp. 126-148 | DOI

[13] Clo, J.; Ronfort, J.; Abu Awad, D. Hidden genetic variance contributes to increase the short‐term adaptive potential of selfing populations, Journal of Evolutionary Biology, Volume 33 (2020) no. 9, pp. 1203-1215 | DOI

[14] Cvijović, I.; Good, B. H.; Desai, M. M. The Effect of Strong Purifying Selection on Genetic Diversity, Genetics, Volume 209 (2018) no. 4, pp. 1235-1278 | DOI

[15] DiCiccio, T.; Efron, B. More accurate confidence intervals in exponential families, Biometrika, Volume 79 (1992) no. 2, pp. 231-245 | DOI

[16] Diver, C. Fossil Records of Mendelian Mutants, Nature, Volume 124 (1929) no. 3118, p. 183-183 | DOI

[17] Dobzhansky T, Genetics of natural populations IX. Temporal changes in the composition of populations of Drosophila pseudoobscura, Genetics, Volume 28 (1943), pp. 162-186

[18] Drummond AJ et al. , Estimating Mutation Parameters, Population History and Genealogy Simultaneously From Temporally Spaced Sequence Data, Genetics, Volume 161 (2002), pp. 1307-1320

[19] Etterson, J. R.; Franks, S. J.; Mazer, S. J.; Shaw, R. G.; Gorden, N. L. S.; Schneider, H. E.; Weber, J. J.; Winkler, K. J.; Weis, A. E. Project Baseline: An unprecedented resource to study plant evolution across space and time, American Journal of Botany, Volume 103 (2016) no. 1, pp. 164-173 | DOI

[20] Fan, J.; Gunderson, K. L.; Bibikova, M.; Yeakley, J. M.; Chen, J.; Wickham Garcia, E.; Lebruska, L. L.; Laurent, M.; Shen, R.; Barker, D. [3] Illumina Universal Bead Arrays, Methods in Enzymology, Elsevier, 2006, pp. 57-73 | DOI

[21] Fan et al., Highly Parallel SNP Genotyping, Cold Spring Harbor Symposia on Quantitative Biology, Volume 68 (2003), pp. 69-78 | DOI

[22] Feder, A. F.; Kryazhimskiy, S.; Plotkin, J. B. Identifying Signatures of Selection in Genetic Time Series, Genetics, Volume 196 (2014) no. 2, pp. 509-522 | DOI

[23] Fisher, R. A.; Ford, E. B. The spread of a gene in natural conditions in a colony of the moth Panaxia dominula L., Heredity, Volume 1 (1947) no. 2, pp. 143-174 | DOI

[24] Foll, M.; Shim, H.; Jensen, J. D. WFABC: a Wright-Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data, Molecular Ecology Resources, Volume 15 (2014) no. 1, pp. 87-98 | DOI

[25] Frachon, L.; Libourel, C.; Villoutreix, R.; Carrère, S.; Glorieux, C.; Huard-Chauveau, C.; Navascués, M.; Gay, L.; Vitalis, R.; Baron, E.; Amsellem, L.; Bouchez, O.; Vidal, M.; Le Corre, V.; Roby, D.; Bergelson, J.; Roux, F. Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time, Nature Ecology & Evolution, Volume 1 (2017) no. 10, pp. 1551-1561 | DOI

[26] François, O.; Martins, H.; Caye, K.; Schoville, S. D. Controlling false discoveries in genome scans for selection, Molecular Ecology, Volume 25 (2016) no. 2, pp. 454-469 | DOI

[27] Franks, S. J.; Hamann, E.; Weis, A. E. Using the resurrection approach to understand contemporary evolution in changing environments, Evolutionary Applications, Volume 11 (2017) no. 1, pp. 17-28 | DOI

[28] Franks, S. J.; Kane, N. C.; O'Hara, N. B.; Tittes, S.; Rest, J. S. Rapid genome‐wide evolution in Brassica rapa populations following drought revealed by sequencing of ancestral and descendant gene pools, Molecular Ecology, Volume 25 (2016) no. 15, pp. 3622-3631 | DOI

[29] Gay, L. Medicago truncatula SNP temporal data from: “Power and limits of selection genome scans on temporal data from a selfing population”. Data INRAE. https://doi.org/10.15454/, (2020)

[30] Gilbert, K. J.; Whitlock, M. C. Evaluating methods for estimating local effective population size with and without migration, Evolution, Volume 69 (2015) no. 8, pp. 2154-2166 | DOI

[31] Glémin, S.; Ronfort, J. Adaptation and maladaptation in selfing and outcrossing species: new mutations versus standing variation, Evolution, Volume 67 (2012) no. 1, pp. 225-240 | DOI

[32] Golding GB, ; Strobeck C, Linkage Disequilibrium in a Finite Population That Is Partially Selfing, Genetics, Volume 94 (1980), pp. 777-789

[33] Goldringer, I.; Bataillon, T. On the Distribution of Temporal Variations in Allele Frequency, Genetics, Volume 168 (2004) no. 1, pp. 563-568 | DOI

[34] Goodwillie, C.; Stewart, E. Cleistogamy and Hybridization in Two Subspecies ofTriodanis perfoliata(Campanulaceae), Rhodora, Volume 115 (2013) no. 961, pp. 42-60 | DOI

[35] Haldane, J. B. S. A mathematical theory of natural and artificial selection. part ii the influence of partial self-fertilisation, inbreeding, assortative mating, and selective fertilisation on the composition of mendelian populations, and on natural selection., Biological Reviews, Volume 1 (1924) no. 3, pp. 158-163 | DOI

[36] Haldane, J. B. S. A Mathematical Theory of Natural and Artificial Selection, Part V: Selection and Mutation, Mathematical Proceedings of the Cambridge Philosophical Society, Volume 23 (1927) no. 7, pp. 838-844 | DOI

[37] Hartfield, M.; Bataillon, T. Selective Sweeps Under Dominance and Inbreeding, G3 Genes|Genomes|Genetics, Volume 10 (2020) no. 3, pp. 1063-1075 | DOI

[38] Hartfield, M.; Bataillon, T.; Glémin, S. The Evolutionary Interplay between Adaptation and Self-Fertilization, Trends in Genetics, Volume 33 (2017) no. 6, pp. 420-431 | DOI

[39] Hartfield, M.; Glémin, S. Limits to Adaptation in Partially Selfing Species, Genetics, Volume 203 (2016) no. 2, pp. 959-974 | DOI

[40] Hereford, J. A Quantitative Survey of Local Adaptation and Fitness Trade‐Offs, The American Naturalist, Volume 173 (2009) no. 5, pp. 579-588 | DOI

[41] Hermisson, J.; Pennings, P. S. Soft Sweeps, Genetics, Volume 169 (2005) no. 4, pp. 2335-2352 | DOI

[42] Hivert, V.; Leblois, R.; Petit, E. J.; Gautier, M.; Vitalis, R. Measuring Genetic Differentiation from Pool-seq Data, Genetics, Volume 210 (2018) no. 1, pp. 315-330 | DOI

[43] Hoban, S.; Kelley, J. L.; Lotterhos, K. E.; Antolin, M. F.; Bradburd, G.; Lowry, D. B.; Poss, M. L.; Reed, L. K.; Storfer, A.; Whitlock, M. C. Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions, The American Naturalist, Volume 188 (2016) no. 4, pp. 379-397 | DOI

[44] Hodgins, K. A.; Yeaman, S. Mating system impacts the genetic architecture of adaptation to heterogeneous environments, New Phytologist, Volume 224 (2019) no. 3, pp. 1201-1214 | DOI

[45] Huber, C. D.; Nordborg, M.; Hermisson, J.; Hellmann, I. Keeping It Local: Evidence for Positive Selection in Swedish Arabidopsis thaliana, Molecular Biology and Evolution, Volume 31 (2014) no. 11, pp. 3026-3039 | DOI

[46] Innan, H.; Kim, Y. Pattern of polymorphism after strong artificial selection in a domestication event, Proceedings of the National Academy of Sciences, Volume 101 (2004) no. 29, pp. 10667-10672 | DOI

[47] Johri, P.; Charlesworth, B.; Jensen, J. D. Toward an Evolutionarily Appropriate Null Model: Jointly Inferring Demography and Purifying Selection, Genetics, Volume 215 (2020) no. 1, pp. 173-192 | DOI

[48] Jullien, M. Analyse temporelle de la diversité en régime autogame : Approches théorique et empirique. PhD, Montpellier SupAgro, Montpellier. ((2019))

[49] Jullien, M.; Navascués, M.; Ronfort, J.; Loridon, K.; Gay, L. Structure of multilocus genetic diversity in predominantly selfing populations, Heredity, Volume 123 (2019) no. 2, pp. 176-191 | DOI

[50] Kellogg VL, ; Bell RG, Studies of variation in insects, Proceedings of the Washington Academy of Sciences, Volume 6 (1904), pp. 203-332

[51] Kerfoot, W. C.; Weider, L. J. Experimental paleoecology (resurrection ecology): Chasing Van Valen's Red Queen hypothesis, Limnology and Oceanography, Volume 49 (2004) no. 4part2, pp. 1300-1316 | DOI

[52] Kimura, M.; Ohta, T. The age of a neutral mutant persisting in a finite population, Genetics, Volume 75 (1973) no. 1, pp. 199-212 | DOI

[53] Kon, K.; Blacklow, W. Polymorphism, Outcrossing and Polyploidy in Bromus diandrus and B. rigidus, Australian Journal of Botany, Volume 38 (1990) no. 6 | DOI

[54] Krimbas, C. B.; Tsakas, S. The Genetics of Dacus oleae. V. Changes of Esterase Polymorphism in a Natural Population Following Insecticide Control-Selection or Drift?, Evolution, Volume 25 (1971) no. 3 | DOI

[55] Larson, S. R.; Cartier, E.; Mccracken, C. L.; Dyer, D. Mode of reproduction and amplified fragment length polymorphism variation in purple needlegrass (Nassella pulchra): utilization of natural germplasm sources, Molecular Ecology, Volume 10 (2001) no. 5, pp. 1165-1177 | DOI

[56] Lenormand, T.; Nougué, O.; Jabbour-Zahab, R.; Arnaud, F.; Dezileau, L.; Chevin, L.-M.; Sánchez, M. I. Resurrection ecology inArtemia, Evolutionary Applications, Volume 11 (2017) no. 1, pp. 76-87 | DOI

[57] Leonardi, M.; Librado, P.; Der Sarkissian, C.; Schubert, M.; Alfarhan, A. H.; Alquraishi, S. A.; Al-Rasheid, K. A. S.; Gamba, C.; Willerslev, E.; Orlando, L. Evolutionary Patterns and Processes: Lessons from Ancient DNA, Systematic Biology, Volume 66 (2017), p. e1-e29 | DOI

[58] Li, C. C. Population Genetics, University of Chicago Press, Chicago, (1955)

[59] Lin, C. H.; Yeakley, J. M.; McDaniel, T. K.; Shen, R. Medium- to High-Throughput SNP Genotyping Using VeraCode Microbeads, DNA and RNA Profiling in Human Blood, Humana Press, Totowa, NJ, 2009, pp. 129-142 | DOI

[60] Loridon, K.; Burgarella, C.; Chantret, N.; Martins, F.; Gouzy, J.; Prospéri, J.; Ronfort, J. Single‐nucleotide polymorphism discovery and diversity in the model legume M edicago truncatula, Molecular Ecology Resources, Volume 13 (2013) no. 1, pp. 84-95 | DOI

[61] Messer, P. W. SLiM: Simulating Evolution with Selection and Linkage, Genetics, Volume 194 (2013) no. 4, pp. 1037-1039 | DOI

[62] Navascués, M.; Vitalis, R. DriftTest-Evaluation. A suite of R scripts to evaluate the performance of DriftTest for detection of selection with temporal data. Zenodo. https://doi.org/10.5281/ zenodo.1194666, (2020)

[63] Navascués, M.; Vitalis, R. DriftTest v1.0.5. A computer program to detect selection from tem- poral genetic differentiation. Zenodo. https://doi.org/10.5281/zenodo.1194662, (2020)

[64] Nei M, ; Tajima F , Genetic Drift and Estimation of Effective Population Size, Genetics, Volume 98 (1981), pp. 625-640

[65] Nielsen, R. Molecular Signatures of Natural Selection, Annual Review of Genetics, Volume 39 (2005) no. 1, pp. 197-218 | DOI

[66] Nordborg, M. Structured Coalescent Processes on Different Time Scales, Genetics, Volume 146 (1997) no. 4, pp. 1501-1514 | DOI

[67] Nordborg M, Linkage Disequilibrium, Gene Trees and Selfing: An Ancestral Recombination Graph With Partial Self-Fertilization, Genetics, Volume 154 (2000), pp. 923-929

[68] Novak, S. J.; Mack, R. N.; Soltis, D. E. Genetic variation in Bromus tectorum (poaceae): population differentiation in its north american range, American Journal of Botany, Volume 78 (1991) no. 8, pp. 1150-1161 | DOI

[69] Orr HA, ; Betancourt AJ, Haldane’s Sieve and Adaptation From the Standing Genetic Variation., Genetics, Volume 157 (2001), pp. 875-884

[70] Pollak, E. On the Theory of Partially Inbreeding Finite Populations. I. Partial Selfing, Genetics, Volume 117 (1987) no. 2, pp. 353-360 | DOI

[71] Pool, J. E.; Hellmann, I.; Jensen, J. D.; Nielsen, R. Population genetic inference from genomic sequence variation, Genome Research, Volume 20 (2010) no. 3, pp. 291-300 | DOI

[72] R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/, (2018)

[73] Rambaut, A. Estimating the rate of molecular evolution: incorporating non-contemporaneous sequences into maximum likelihood phylogenies, Bioinformatics, Volume 16 (2000) no. 4, pp. 395-399 | DOI

[74] Rausher, M. D.; Delph, L. F. Commentary: When does understanding phenotypic evolution require identification of the underlying genes?, Evolution, Volume 69 (2015) no. 7, pp. 1655-1664 | DOI

[75] Robertson, A. Inbreeding in artificial selection programmes, Genetical Research, Volume 2 (1961) no. 2, pp. 189-194 | DOI

[76] Ronfort, J.; Glemin, S. Mating system, Haldane's sieve, and the domestication process, Evolution, Volume 67 (2013), pp. 1518-1526 | DOI

[77] Roze, D. Background Selection in Partially Selfing Populations, Genetics, Volume 203 (2016) no. 2, pp. 937-957 | DOI

[78] Ryman, N.; Allendorf, F. W.; Jorde, P. E.; Laikre, L.; Hössjer, O. Samples from subdivided populations yield biased estimates of effective size that overestimate the rate of loss of genetic variation, Molecular Ecology Resources, Volume 14 (2014) no. 1, pp. 87-99 | DOI

[79] Sanders, T. B.; Hamrick, J. L. Variation in the Breeding System of Elymus canadensis, Evolution, Volume 34 (1980) no. 1, pp. 117-122 | DOI

[80] Santiago, E.; Caballero, A. Effective size of populations under selection., Genetics, Volume 139 (1995) no. 2, pp. 1013-1030 | DOI

[81] Schemske, D. W. Evolution of Reproductive Characteristics in Impatiens (Balsaminaceae): The Significance of Cleistogamy and Chasmogamy, Ecology, Volume 59 (1978) no. 3, pp. 596-613 | DOI

[82] Schlötterer, C.; Kofler, R.; Versace, E.; Tobler, R.; Franssen, S. U. Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation, Heredity, Volume 114 (2015) no. 5, pp. 431-440 | DOI

[83] Schrider, D. R.; Kern, A. D. Supervised Machine Learning for Population Genetics: A New Paradigm, Trends in Genetics, Volume 34 (2018) no. 4, pp. 301-312 | DOI

[84] Siol, M.; Prosperi, J. M.; Bonnin, I.; Ronfort, J. How multilocus genotypic pattern helps to understand the history of selfing populations: a case study in Medicago truncatula, Heredity, Volume 100 (2008) no. 5, pp. 517-525 | DOI

[85] Skoglund, P.; Sjödin, P.; Skoglund, T.; Lascoux, M.; Jakobsson, M. Investigating Population History Using Temporal Genetic Differentiation, Molecular Biology and Evolution, Volume 31 (2014) no. 9, pp. 2516-2527 | DOI

[86] Storey, J. D. A direct approach to false discovery rates, Journal of the Royal Statistical Society: Series B (Statistical Methodology), Volume 64 (2002) no. 3, pp. 479-498 | DOI

[87] Storey JD, ; Bass AJ, ; Dabney A, ; Robinson D, qvalue: Q-value estimation for false discovery rate control. R package version 2.26.0, http://github.com/jdstorey/qvalue, (2021)

[88] Turner, S. qqman: Q-Q and Manhattan plots for GWAS data. R package version 0.1.4., (2017)

[89] Vitalis, R.; Couvet, D. Two-locus identity probabilities and identity disequilibrium in a partially selfing subdivided population, Genetical Research, Volume 77 (2001) no. 1, pp. 67-81 | DOI

[90] Wang, J.; Santiago, E.; Caballero, A. Prediction and estimation of effective population size, Heredity, Volume 117 (2016) no. 4, pp. 193-206 | DOI

[91] Wang J, ; Whitlock MC, Estimating Effective Population Size and Migration Rates From Genetic Samples Over Space and Time, Genetics, Volume 1963 (2003), pp. 429-446

[92] Waples, R. S. A generalized approach for estimating effective population size from temporal changes in allele frequency., Genetics, Volume 121 (1989) no. 2, pp. 379-391 | DOI

[93] Weir, B. S.; Cockerham, C. C. Estimating F-Statistics for the Analysis of Population Structure, Evolution, Volume 38 (1984) no. 6 | DOI

[94] Whitehead, M. R.; Lanfear, R.; Mitchell, R. J.; Karron, J. D. Plant Mating Systems Often Vary Widely Among Populations, Frontiers in Ecology and Evolution, Volume 6 (2018) | DOI

[95] Williamson EG, ; Slatkin M, Using Maximum Likelihood to Estimate Population Size From Tem- poral Changes in Allele Frequencies, Genetics, Volume 152 (1999), pp. 755-761

[96] Wright S, Evolution in mendelian populations, Genetics, Volume 16 (1931), pp. 97-159

[97] Wright, S. On the Roles of Directed and Random Changes in Gene Frequency in the Genetics of Populations, Evolution, Volume 2 (1948) no. 4, pp. 279-294 | DOI

[98] Yamazaki, T. Measurement of fitness at the esterase-5 locus in Drosophila pseudoobscura, Genetics, Volume 67 (1971) no. 4, pp. 579-603 | DOI

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