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Investigating Population History Using Temporal Genetic Differentiation

Skoglund, Pontus, Sjödin, Per, Skoglund, Tobias, Lascoux, Martin, Jakobsson, Mattias·2014·Molecular Biology and Evolution
DOI 10.1093/molbev/msu192PMC PMC4137715
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Fig. 1. Additivity of genetic drift can result in equivalent genetic differentiation (FST) under temporal structure and population divergence. (A and B) Thirty individuals are sampled from two populations (15 individuals from each population) that diverged at a given time in the past. In (A), both samples are taken at the present, and in (B), one of the samples is taken at 0.5 × 2Ne generations before present and the other sample is taken at present. (C) Thirty individuals are sampled from two discrete time points (15 individuals from each time point) in the history of a continuous population. In all three scenarios, the total time that passes between each sample is T = 2Ne generations. The 15 individuals in each sample are illustrated as a series of red circles or a series of blue circles.
Fig. 1. Additivity of genetic drift can result in equivalent genetic differentiation (FST) under temporal structure and population divergence. (A and B) Thirty individuals are sampled from two populations (15 individuals from each population) that diverged at a given time in the past. In (A), both samples are taken at the present, and in (B), one of the samples is taken at 0.5 × 2Ne generations before present and the other sample is taken at present. (C) Thirty individuals are sampled from two discrete time points (15 individuals from each time point) in the history of a continuous population. In all three scenarios, the total time that passes between each sample is T = 2Ne generations. The 15 individuals in each sample are illustrated as a series of red circles or a series of blue circles.CC BY-NC · PubMed Central
Fig. 2. Dependence of FST in temporal and divergence models conditioning on the allele being polymorphic. (A) FST as a function of T, the total time that separates two populations (two times the population divergence time) or the time that separates two samples in model of samples taken at two different time points. The gray line shows the function FST = 1−e−T/2 (Nei 1973). (B) The models used for simulating population-genetic data and computing FST. The split model illustrates a population split T/2 time units in the past and the temporal model that illustrates a single population (of constant size) from which samples have been taken at two time points. Arrows point to where, in time, sites have been ascertained for variation (see main text for a full description of the procedure).
Fig. 2. Dependence of FST in temporal and divergence models conditioning on the allele being polymorphic. (A) FST as a function of T, the total time that separates two populations (two times the population divergence time) or the time that separates two samples in model of samples taken at two different time points. The gray line shows the function FST = 1−e−T/2 (Nei 1973). (B) The models used for simulating population-genetic data and computing FST. The split model illustrates a population split T/2 time units in the past and the temporal model that illustrates a single population (of constant size) from which samples have been taken at two time points. Arrows point to where, in time, sites have been ascertained for variation (see main text for a full description of the procedure).CC BY-NC · PubMed Central
Fig. 3. Dependence of FST in a simple temporal island model. The model consists of two equally sized populations with symmetric migration rate. The X axis shows the separation in (scaled) time between the samples. θ (the scaled mutation rate) is set to 0.1. The continuous lines show how FST for models with different (scaled) migration rates depends on time separation. The dotted lines show FST if the samples are not separated in time or, equivalently, if the effective population size is infinitely large. The red line is the limit when migration is infinitely fast. In this case, the model is identical to a purely temporal model with a doubled (and constant) population size (the larger population size implies that θ is twice as large). The dark blue line is the limit when there is no migration while the light blue line at log(M)=0 is included for reference.
Fig. 3. Dependence of FST in a simple temporal island model. The model consists of two equally sized populations with symmetric migration rate. The X axis shows the separation in (scaled) time between the samples. θ (the scaled mutation rate) is set to 0.1. The continuous lines show how FST for models with different (scaled) migration rates depends on time separation. The dotted lines show FST if the samples are not separated in time or, equivalently, if the effective population size is infinitely large. The red line is the limit when migration is infinitely fast. In this case, the model is identical to a purely temporal model with a doubled (and constant) population size (the larger population size implies that θ is twice as large). The dark blue line is the limit when there is no migration while the light blue line at log(M)=0 is included for reference.CC BY-NC · PubMed Central
Fig. 4. Genetic differentiation mirrors the sampling scheme in a model with both temporal and spatial structure. (A) Two-dimensional stepping-stone migration model from which ten haploid individuals were sampled at different historical time points. (B) PCA of data generated under the model. Large symbols correspond to high PC1 and PC3 values but low PC2 values.
Fig. 4. Genetic differentiation mirrors the sampling scheme in a model with both temporal and spatial structure. (A) Two-dimensional stepping-stone migration model from which ten haploid individuals were sampled at different historical time points. (B) PCA of data generated under the model. Large symbols correspond to high PC1 and PC3 values but low PC2 values.CC BY-NC · PubMed Central
Fig. 5. Temporal sampling distinguishes genetic drift from population structure. (A) Constant population size model. (B) Bottleneck model. (C) Replacement model. (D) PC1 stratified by sample time under the constant population size model. (E) PC1 stratified by sample time under the bottleneck model. (F) PC1 stratified by sample time under the replacement model. Each colored circle corresponds to a single-sampled individual except for the large circles at time zero which corresponds to 20 sampled individuals in A, B, and C (in D, E, and F, the 20 individuals sampled at time zero end up on top of each other). FST between samples from before and after the bottleneck/replacement events at 5,500 years ago fails to distinguish between the models (FST = 0.0154 ± 0.0003 and 0.0153 ± 0.0003, respectively, see fig. 6).
Fig. 5. Temporal sampling distinguishes genetic drift from population structure. (A) Constant population size model. (B) Bottleneck model. (C) Replacement model. (D) PC1 stratified by sample time under the constant population size model. (E) PC1 stratified by sample time under the bottleneck model. (F) PC1 stratified by sample time under the replacement model. Each colored circle corresponds to a single-sampled individual except for the large circles at time zero which corresponds to 20 sampled individuals in A, B, and C (in D, E, and F, the 20 individuals sampled at time zero end up on top of each other). FST between samples from before and after the bottleneck/replacement events at 5,500 years ago fails to distinguish between the models (FST = 0.0154 ± 0.0003 and 0.0153 ± 0.0003, respectively, see fig. 6).CC BY-NC · PubMed Central
Fig. 6. Genetic differentiation between temporal sample groups. (A) FST computed on aggregated sample groups is unable to differentiate the bottleneck and replacement models. “Moderns”: 20 samples from time 0. “Young”: 10 samples from time 0–5,000 years ago. “Old”: 10 samples from 5,500 to 10,000 years ago. (B) FST between individuals adjacent in time is able to detect a sudden increase in FST between the pair of individuals that flank the demographic event (both bottleneck and replacement), but we are unable to separate the replacement and bottleneck scenarios. Standard errors are not shown but ranged between 0.002 and 0.003. (C) FST between 20 modern individuals and each ancient individual. Standard errors are not shown but ranged between 0.0010 and 0.0014.
Fig. 6. Genetic differentiation between temporal sample groups. (A) FST computed on aggregated sample groups is unable to differentiate the bottleneck and replacement models. “Moderns”: 20 samples from time 0. “Young”: 10 samples from time 0–5,000 years ago. “Old”: 10 samples from 5,500 to 10,000 years ago. (B) FST between individuals adjacent in time is able to detect a sudden increase in FST between the pair of individuals that flank the demographic event (both bottleneck and replacement), but we are unable to separate the replacement and bottleneck scenarios. Standard errors are not shown but ranged between 0.002 and 0.003. (C) FST between 20 modern individuals and each ancient individual. Standard errors are not shown but ranged between 0.0010 and 0.0014.CC BY-NC · PubMed Central
Fig. 7. Temporal sampling can be used to detect transient genetic barriers. (A) Split model. (B) Split-admixture model. (C) PC1 stratified by time for data simulated under the split model. (D) PC1 stratified by time for data simulated under the split-admixture model. Each colored circle corresponds to a single sampled individual except for the large circles at time zero, which corresponds to 20 sampled individuals in A and B (in C and D, the 20 individuals sampled at time zero overlap). The 4 ancient individuals from 3,500, 4,500, 5,500, and 6,500 years ago (marked circles) were sampled from population B (bottom population in the model illustrations) and the remaining ancient 16 individuals were sampled from population A (top population in the model illustrations).
Fig. 7. Temporal sampling can be used to detect transient genetic barriers. (A) Split model. (B) Split-admixture model. (C) PC1 stratified by time for data simulated under the split model. (D) PC1 stratified by time for data simulated under the split-admixture model. Each colored circle corresponds to a single sampled individual except for the large circles at time zero, which corresponds to 20 sampled individuals in A and B (in C and D, the 20 individuals sampled at time zero overlap). The 4 ancient individuals from 3,500, 4,500, 5,500, and 6,500 years ago (marked circles) were sampled from population B (bottom population in the model illustrations) and the remaining ancient 16 individuals were sampled from population A (top population in the model illustrations).CC BY-NC · PubMed Central
Fig. 8. Approximate Bayesian Computation of Woolly Mammoth demographic history using PC1 loadings as summary statistics. (A) Plot of PC1 versus the age of the mammoth individuals. (B) Illustration of the three-parameter model of instantaneous size change. (C) Estimated posterior distribution for the time of size change. (D) Estimated posterior distributions of effective population size before and after the size change. Prior distributions in (C) and (D) are shown by the gray lines.
Fig. 8. Approximate Bayesian Computation of Woolly Mammoth demographic history using PC1 loadings as summary statistics. (A) Plot of PC1 versus the age of the mammoth individuals. (B) Illustration of the three-parameter model of instantaneous size change. (C) Estimated posterior distribution for the time of size change. (D) Estimated posterior distributions of effective population size before and after the size change. Prior distributions in (C) and (D) are shown by the gray lines.CC BY-NC · PubMed Central
Fig. 9. Comparing a single ancient genome to modern populations. (A) Population divergence model with constant effective population size. (B) Population divergence with a 10-fold population size reduction postdating the ancient individual. (C) PCA of 100,000 SNPs simulated under the model in (A). (D) PCA of 100,000 SNPs simulated under the model in (B). (E) Population topology inferred using C tests and D tests based on 100,000 independent SNPs simulated under the model in (A), and (F) population topology inferred using C tests and D tests based on 100,000 independent SNPs simulated under the model in (B). Values for the C-statistic are only positive for the correct topology, and absolute values of the D-statistic are lowest for the correct topology. The tree topologies displayed in (E) and (F) represent the three possible topologies tested and the larger trees represent the true topology (and also the one supported by the statistics). The gray circles in (E) and (F) represent an outgroup individual constructed from the ancestral alleles of each simulated locus. For details on these tests, see Materials and Methods.
Fig. 9. Comparing a single ancient genome to modern populations. (A) Population divergence model with constant effective population size. (B) Population divergence with a 10-fold population size reduction postdating the ancient individual. (C) PCA of 100,000 SNPs simulated under the model in (A). (D) PCA of 100,000 SNPs simulated under the model in (B). (E) Population topology inferred using C tests and D tests based on 100,000 independent SNPs simulated under the model in (A), and (F) population topology inferred using C tests and D tests based on 100,000 independent SNPs simulated under the model in (B). Values for the C-statistic are only positive for the correct topology, and absolute values of the D-statistic are lowest for the correct topology. The tree topologies displayed in (E) and (F) represent the three possible topologies tested and the larger trees represent the true topology (and also the one supported by the statistics). The gray circles in (E) and (F) represent an outgroup individual constructed from the ancestral alleles of each simulated locus. For details on these tests, see Materials and Methods.CC BY-NC · PubMed Central

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Cites

Research works printed but not linked: 61

  1. Anderson, 2005, Serial SimCoal: a population genetics model for data from multiple populations and points in time, Bioinformatics, 21, 1733, 10.1093/bioinformatics/bti154 · DOI
  2. Anderson, 2000, Monte Carlo evaluation of the likelihood for N(e) from temporally spaced samples, Genetics, 156, 2109, 10.1093/genetics/156.4.2109 · DOI
  3. Beaumont, 2003, Estimation of population growth or decline in genetically monitored populations, Genetics, 164, 1139, 10.1093/genetics/164.3.1139 · DOI
  4. Beaumont, 2002, Approximate Bayesian computation in population genetics, Genetics, 162, 2025, 10.1093/genetics/162.4.2025 · DOI · Paper page
  5. Berthier, 2002, Likelihood-based estimation of the effective population size using temporal changes in allele frequencies: a genealogical approach, Genetics, 160, 741, 10.1093/genetics/160.2.741 · DOI
  6. Briggs, 2007, Patterns of damage in genomic DNA sequences from a Neandertal, Proc Natl Acad Sci U S A., 104, 14616, 10.1073/pnas.0704665104 · DOI · Paper page
  7. Briggs, 2010, Removal of deaminated cytosines and detection of in vivo methylation in ancient DNA, Nucleic Acids Res., 38, e87, 10.1093/nar/gkp1163 · DOI · Paper page
  8. Castroviejo-Fisher, 2011, Vanishing native American dog lineages, BMC Evol Biol., 11, 73, 10.1186/1471-2148-11-73 · DOI
  9. Chan, 2006, Bayesian estimation of the timing and severity of a population bottleneck from ancient DNA, PLoS Genet., 2, 451, 10.1371/journal.pgen.0020059 · DOI
  10. Chikhi, 2010, The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes, Genetics, 186, 983, 10.1534/genetics.110.118661 · DOI
  11. DeGiorgio, 2013, Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis, Mol Biol Evol., 30, 480, 10.1093/molbev/mss233 · DOI
  12. Depaulis, 2009, Using classical population genetics tools with heterochroneous data: time matters!, PLoS One, 4, e5541, 10.1371/journal.pone.0005541 · DOI
  13. Drummond, 2007, BEAST: Bayesian evolutionary analysis by sampling trees, BMC Evol Biol., 7, 214, 10.1186/1471-2148-7-214 · DOI
  14. Drummond, 2005, Bayesian coalescent inference of past population dynamics from molecular sequences, Mol Biol Evol., 22, 1185, 10.1093/molbev/msi103 · DOI
  15. Durand, 2011, Testing for ancient admixture between closely related populations, Mol Biol Evol., 28, 2239, 10.1093/molbev/msr048 · DOI · Paper page
  16. Engelhardt, 2010, Analysis of population structure: a unifying framework and novel methods based on sparse factor analysis, PLoS Genet., 6, e1001117, 10.1371/journal.pgen.1001117 · DOI · Paper page
  17. Excoffier, 2011, Fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios, Bioinformatics, 27, 1332, 10.1093/bioinformatics/btr124 · DOI
  18. Green, 2010, A draft sequence of the neandertal genome, Science, 328, 710, 10.1126/science.1188021 · DOI · Paper page
  19. Haak, 2005, Ancient DNA from the first European farmers in 7500-year-old Neolithic sites, Science, 310, 1016, 10.1126/science.1118725 · DOI
  20. Higuchi, 1984, DNA sequence from the quagga, an extinct member of the horse family, Nature, 312, 282, 10.1038/312282a0 · DOI
  21. Ho, 2011, Skyline-plot methods for estimating demographic history from nucleotide sequences, Mol Ecol Resour., 11, 423, 10.1111/j.1755-0998.2011.02988.x · DOI
  22. Hudson, 2002, Generating samples under a Wright–Fisher neutral model of genetic variation, Bioinformatics, 18, 337, 10.1093/bioinformatics/18.2.337 · DOI
  23. Jakobsson, 2009, COMPASS: a program for generating serial samples under an infinite sites model, Bioinformatics, 25, 2845, 10.1093/bioinformatics/btp534 · DOI
  24. Kaj, 2001, Coalescent theory for seed bank models, J Appl Probab., 38, 285, 10.1017/S0021900200019860 · DOI
  25. Kingman, 1982, The coalescent, Stochastic Process Appl., 13, 235, 10.1016/0304-4149(82)90011-4 · DOI
  26. Krause, 2010, A complete mtDNA genome of an early modern human from Kostenki, Russia, Curr Biol., 20, 231, 10.1016/j.cub.2009.11.068 · DOI · Paper page
  27. Krimbas, 1971, The genetics of Dacus oleae. V. Changes of esterase polymorphism in a natural population following insecticide control-selection or drift?, Evolution, 25, 454, 10.2307/2407343 · DOI
  28. McVean, 2009, A genealogical interpretation of principal components analysis, PLoS Genet., 5, e1000686, 10.1371/journal.pgen.1000686 · DOI · Paper page
  29. Mourier, 2012, Statistical guidelines for detecting past population shifts using ancient DNA, Mol Biol Evol., 29, 2241, 10.1093/molbev/mss094 · DOI
  30. Navascues, 2010, Combining contemporary and ancient DNA in population genetic and phylogeographical studies, Mol Ecol Resour., 10, 760, 10.1111/j.1755-0998.2010.02895.x · DOI
  31. Nei, 1973, Analysis of gene diversity in subdivided populations, Proc Natl Acad Sci U S A., 70, 3321, 10.1073/pnas.70.12.3321 · DOI · Paper page
  32. Nielsen, 2001, Distinguishing migration from isolation: a Markov chain Monte Carlo approach, Genetics, 158, 885, 10.1093/genetics/158.2.885 · DOI
  33. Nordborg, 1998, On the probability of Neanderthal ancestry, Am J Hum Genet., 63, 1237, 10.1086/302052 · DOI
  34. Novembre, 2008, Interpreting principal component analyses of spatial population genetic variation, Nat Genet., 40, 646, 10.1038/ng.139 · DOI
  35. Nyström, 2010, Temporal genetic change in the last remaining population of woolly mammoth, Proc R Soc B Biol Sci., 277, 2331, 10.1098/rspb.2010.0301 · DOI
  36. Nyström, 2012, Microsatellite genotyping reveals end-Pleistocene decline in mammoth autosomal genetic variation, Mol Ecol., 21, 3391, 10.1111/j.1365-294X.2012.05525.x · DOI
  37. Pääbo, 1985, Molecular cloning of ancient Egyptian mummy DNA, Nature, 314, 644, 10.1038/314644a0 · DOI
  38. Patterson, 2006, Population structure and eigenanalysis, PLoS Genet., 2, e190, 10.1371/journal.pgen.0020190 · DOI · Paper page
  39. Pritchard, 1999, Population growth of human Y chromosomes: a study of Y chromosome microsatellites, Mol Biol Evol., 16, 1791, 10.1093/oxfordjournals.molbev.a026091 · DOI
  40. Ramakrishnan, 2005, Detecting past population bottlenecks using temporal genetic data, Mol Ecol., 14, 2915, 10.1111/j.1365-294X.2005.02586.x · DOI
  41. Ramakrishnan, 2009, Using phylochronology to reveal cryptic population histories: review and synthesis of 29 ancient DNA studies, Mol Ecol., 18, 1310, 10.1111/j.1365-294X.2009.04092.x · DOI
  42. Rosenberg, 2005, Clines, clusters, and the effect of study design on the inference of human population structure, PLoS Genet., 1, e70, 10.1371/journal.pgen.0010070 · DOI
  43. Sánchez-Quinto, 2012, Genomic affinities of two 7,000-year-old Iberian hunter-gatherers, Curr Biol., 22, 1494, 10.1016/j.cub.2012.06.005 · DOI · Paper page
  44. Schlebusch, 2012, Genomic variation in seven Khoe-San groups reveals adaptation and complex African history, Science, 338, 374, 10.1126/science.1227721 · DOI
  45. Serre, 2004, No evidence of Neandertal mtDNA contribution to early modern humans, PLoS Biol., 2, e57, 10.1371/journal.pbio.0020057 · DOI
  46. Serre, 2004, Evidence for gradients of human genetic diversity within and among continents, Genome Res., 14, 1679, 10.1101/gr.2529604 · DOI · Paper page
  47. Shapiro, 2014, A Paleogenomic perspective on evolution and gene function: new insights from ancient DNA, Science, 343, 1236573, 10.1126/science.1236573 · DOI · Paper page
  48. Sheehan, 2013, Estimating variable effective population sizes from multiple genomes: a sequentially Markov conditional sampling distribution approach, Genetics, 194, 647, 10.1534/genetics.112.149096 · DOI
  49. Sjödin, 2014, Assessing the maximum contribution from ancient populations, Mol Biol Evol., 31, 1248, 10.1093/molbev/msu059 · DOI
  50. Skoglund, 2011, Estimation of population divergence times from non-overlapping genomic sequences: examples from dogs and wolves, Mol Biol Evol., 28, 1505, 10.1093/molbev/msq342 · DOI
  51. Skoglund, 2012, Origins and genetic legacy of Neolithic farmers and hunter–gatherers in Europe, Science, 336, 466, 10.1126/science.1216304 · DOI · Paper page
  52. Slatkin, 1991, Inbreeding coefficients and coalescence times, Genet Res., 58, 167, 10.1017/S0016672300029827 · DOI
  53. Tavare, 1997, Inferring coalescence times from DNA sequence data, Genetics, 145, 505, 10.1093/genetics/145.2.505 · DOI
  54. Vartanyan, 1993, Holocene dwarf mammoths from Wrangel Island in the Siberian Arctic, Nature, 362, 337, 10.1038/362337a0 · DOI
  55. Voight, 2005, Interrogating multiple aspects of variation in a full resequencing data set to infer human population size changes, Proc Natl Acad Sci U S A., 102, 18508, 10.1073/pnas.0507325102 · DOI · Paper page
  56. Wang, 2001, A pseudo-likelihood method for estimating effective population size from temporally spaced samples, Genet Res., 78, 243, 10.1017/S0016672301005286 · DOI
  57. Waples, 1989, Temporal variation in allele frequencies: testing the right hypothesis, Evolution, 43, 1236, 10.2307/2409359 · DOI
  58. Weir, 1996, Genetic data analysis II
  59. Wright, 1940, Breeding structure of populations in relation to speciation, Am Nat., 74, 232, 10.1086/280891 · DOI
  60. Wright, 1949, Population structure in evolution, Proc Am Philos Soc., 93, 471
  61. Wright, 1951, The genetical structure of populations, Ann Eugen., 15, 323, 10.1111/j.1469-1809.1949.tb02451.x · DOI

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  • Ancient DNA analysis

    Orlando, L., Allaby, R., Skoglund, P., Der Sarkissian, C., Stockhammer, P. W., Ávila-Arcos, M. C., Fu, Q., Krause, J., Willerslev, E., Stone, A. C., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00011-0 · Cited by 411

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Metadata

Authors
Skoglund, P., Sjödin, P., Skoglund, T., Lascoux, M., & Jakobsson, M.
Venue
Molecular Biology and Evolution
Publisher
Oxford University Press (OUP)
Published
2014-06-17
Volume
31
Issue
9
Pages
2516-2527
ISSN
  • 1537-1719
  • 0737-4038
DOI
10.1093/molbev/msu192
PMC
PMC4137715
Cited by
60
References
14
Language
en

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