Abstract
Characterizing population histories has been a major focus in evolutionary and conservation biology for decades. Driven by a desire to understand population histories, researchers have been modeling simple demographic scenarios with genetic data since the 1970s. In the last decade, the availability of genomic data and the number of demographic inference methods have dramatically increased and constitute a continuously evolving sub-discipline within population genetics. Genome sequences—both reduced representation and whole-genome sequencing and re-sequencing—contain a trove of information related to population histories and permit reconstructing complex demographic scenarios. In combination with new powerful and flexible analytical methods, population demographic inference from genomic data has revealed surprising, dynamic, and conservation-relevant histories. This chapter discusses recent advancements in demographic inference made possible by genome sequence and new analytical tools. As the theory and models of demographic inference have matured, and data sets have grown, likewise has the recognition of limitations and confounding effects. We caution that the increasing sophistication of methods should not override the critical evaluation of the researcher. Demographic inferences with genomic data offer powerful windows into the past but we encourage users to recognize inherent limitations of model assumptions, use simulations to identify potential biases, and include complementary and supporting analyses.
References
Adams AM, Hudson RR. Maximum-likelihood estimation of demographic parameters using the frequency spectrum of unlinked single-nucleotide polymorphisms. Genetics. 2004;168:1699–712.
Allentoft ME, Sikora M, Sjögren K-G, Rasmussen S, Rasmussen M, et al. Population genomics of Bronze Age Eurasia. Nature. 2015;522:167–72.
Arnold B, Kim S-T, Bomblies K. Single geographic origin of a widespread autotetraploid Arabidopsis arenosa lineage followed by interploidy admixture. Mol Biol Evol. 2015;32:1382–95.
Barnosky AD, Koch PL, Feranec RS, Wing SL, Shabel AB. Assessing the causes of late Pleistocene extinctions on the continents. Science. 2004;306:70–5.
Beaumont MA. Detecting population expansion and decline using microsatellites. Genetics. 1999;153:2013.
Beaumont MA. Estimation of population growth or decline in genetically monitored populations. Genetics. 2003;164:1139–60.
Beaumont MA. Approximate Bayesian computation in evolution and ecology. Annu Rev Ecol Evol Syst. 2010;41:379–406.
Beaumont MA, Zhang W, Balding DJ. Approximate Bayesian computation in population genetics. Genetics. 2002;162:2025–35.
Beerli P. Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol Ecol. 2004;13:827–36.
Bertorelle G, Benazzo A, Mona S. ABC as a flexible framework to estimate demography over space and time: some cons, many pros. Mol Ecol. 2010;19:2609–25.
Bhaskar A, Wang YXR, Song YS. Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data. Genome Res. 2015;25(2):268–79. doi:10.1101/gr.178756.114.
Bienvenu F, Demetrius L, Legendre S. A general formula for the generation time. ArXiv Prepr. 2013:ArXiv13076692.
Boitard S, Rodriguez W, Jay F, Mona S, Austerlitz F. Inferring population size history from large samples of genome-wide molecular data-an approximate Bayesian computation approach. PLoS Genet. 2016;12:e1005877.
Box GE, Draper NR, et al. Empirical model-building and response surfaces. New York: Wiley; 1987.
Burgarella C, Gayral P, Ballenghien M, Bernard A, David P, Jarne P, et al. Molecular evolution of freshwater snails with contrasting mating systems. Mol Biol Evol. 2015;32:2403–16.
Carneiro M, Afonso S, Geraldes A, Garreau H, Bolet G, Boucher S, Tircazes A, Queney G, Nachman MW, Ferrand N. The genetic structure of domestic rabbits. Mol Biol Evol. 2011;28:1801–16.
Carvajal-Rodríguez A. GENOMEPOP: a program to simulate genomes in populations. BMC Bioinforma. 2008;9(1):223.
Chen H, Hey J, Chen K. Inferring very recent population growth rate from population-scale sequencing data: using a large-sample coalescent estimator. Mol Biol Evol. 2015;32(11):2996–3011. doi:10.1093/molbev/msv158.
Chikhi L, Sousa VC, Luisi P, Goossens B, Beaumont MA. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics. 2010;186:983.
Cornuet J-M, Pudlo P, Veyssier J, Dehne-Garcia A, Gautier M, Leblois R, Marin J-M, Estoup A. DIYABC v2. 0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics. 2014;30:1187–9.
Csilléry K, Blum MG, Gaggiotti OE, François O. Approximate Bayesian computation (ABC) in practice. Trends Ecol Evol. 2010;25:410–8.
Csilléry K, François O, Blum MG. abc: an R package for approximate Bayesian computation (ABC). Methods Ecol Evol. 2012;3:475–9.
Drummond AJ, et al. Relaxed phylogenetics and dating with confidence. PLoS Biol. 2006;4(5):e88.
Evans SN, Shvets Y, Slatkin M. Non-equilibrium theory of the allele frequency spectrum. Theor Popul Biol. 2007;71:109–19.
Ewens WJ. The sampling theory of selectively neutral alleles. Theor Popul Biol. 1972;3:87–112.
Excoffier L, Foll M. fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios. Bioinformatics. 2011;27:1332–4.
Excoffier L, Dupanloup I, Huerta-Sánchez E, Sousa VC, Foll M. Robust demographic inference from genomic and SNP data. PLoS Genet. 2013;9:e1003905.
Fahrig L. Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst. 2003;34:487–515.
Fisher RA. The distribution of gene ratios for rare mutations. Proc Roy Soc Edinburgh. 1930;50:205–22.
Foote AD, Vijay N, Ávila-Arcos MC, Baird RW, Durban JW, Fumagalli M, Gibbs RA, Hanson MB, Korneliussen TS, Martin MD, et al. Genome-culture coevolution promotes rapid divergence of killer whale ecotypes. Nat Commun. 2016;7:11693.
Fu Y-X. Statistical properties of segregating sites. Theor Popul Biol. 1995;48:172–97.
Garza JC, Williamson EG. Detection of reduction in population size using data from microsatellite loci. Mol Ecol. 2001;10:305–18.
Gravel S. Population genetics models of local ancestry. Genetics. 2012;191:607–19.
Griffiths RC. The frequency spectrum of a mutation, and its age, in a general diffusion model. Theor Popul Biol. 2003;64:241–51.
Griffiths RC, Marjoram P. An ancestral recombination graph. In: Donnelly P, Tavar’e S, editors. Progress in population genetics and human evolution, IMA volumes in mathematics and its applications, vol 87. New York: Springer; 1997. p. 100–117.
Griffiths RC, Tavaré S. The age of a mutation in a general coalescent tree. Stoch Models. 1998;14:273–95.
Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 2009;5:e1000695.
Han E, Sinsheimer JS, Novembre J. Characterizing bias in population genetic inferences from low coverage sequencing data. Mol Biol Evol. 2013;31(3):723–35. doi:10.1093/molbev/mst229.
Harris K, Nielsen R. Inferring demographic history from a spectrum of shared haplotype lengths. PLoS Genet. 2013;9:e1003521.
Hein J, Schierop MH, Wiuf C. Gene genealogies, variation and evolution. A primer in coalescent theory. Oxford, UK: Oxford University Press; 2005.
Heled J, Drummond AJ. Bayesian inference of population size history from multiple loci. BMC Evol Biol. 2008;8:289.
Heller R, Bruniche-Olsen A, Siegismund HR. Cape buffalo mitogenomics reveals a Holocene shift in the African human–megafauna dynamics. Mol Ecol. 2012;21:3947–59.
Heller R, Chikhi L, Siegismund HR. The confounding effect of population structure on Bayesian skyline plot inferences of demographic history. PLoS One. 2013;8:e62992.
Hernandez RD, Williamson SH, Bustamante CD. Context dependence, ancestral misidentification, and spurious signatures of natural selection. Mol Biol Evol. 2007;24:1792–800.
Hey J, Nielsen R. Multilocus methods for estimating population sizes, migration rates and divergence time, with applications to the divergence of Drosophila pseudoobscura and D. persimilis. Genetics. 2004;167:747–60.
Hirschfeld L, Hirschfeld H. Serological differences between the blood of different races: the results of researches on the Macedonian front. Lancet. 1919;194:675–9.
Ho SY. The changing face of the molecular evolutionary clock. Trends Ecol Evol. 2014;29:496–503.
Hoban S, Arntzen JA, Bruford MW, Godoy JA, Rus Hoelzel A, Segelbacher G, Vilà C, Bertorelle G. Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion. Evol Appl. 2014;7:984–98.
Hudson RR. Properties of a neutral allele model with intragenic recombination. Theor Popul Biol. 1983;23:183–201.
Hudson RR. Gene genealogies and the coalescent process. Oxf Surv Evol Biol. 1990;7(1):44.
Hudson RR. Generating samples under a Wright–Fisher neutral model of genetic 9variation. Bioinformatics. 2002;18:337–8.
Hwang DG, Green P. Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution. Proc Natl Acad Sci U S A. 2004;101:13994–4001.
Kaj I, Krone SM, Lascoux M. Coalescent theory for seed bank models. J Appl Prob. 2001;38:285–300.
Kardos M, Luikart G, Bunch R, Dewey S, Edwards W, McWilliam S, Stephenson J, Allendorf FW, Hogg JT, Kijas J. Whole-genome resequencing uncovers molecular signatures of natural and sexual selection in wild bighorn sheep. Mol Ecol. 2015;24:5616–32.
Kardos M, Taylor HR, Ellegren H, Luikart G, Allendorf FW. Genomics advances the study of inbreeding depression in the wild. Evol Appl. 2016:n/a-n/a. doi: 10.1111/eva.12414.
Kern AD, Hey J. Exact calculation of the joint allele frequency spectrum for generalized isolation with migration models. BioRXiv. 2016. doi: http://dx.doi.org/10.1101/065003.
Kimura M. Solution of a process of random genetic drift with a continuous model. Proc Natl Acad Sci. 1955;41:144–50.
Kimura M. Diffusion models in population genetics. J Appl Probab. 1964;1:177–232.
Kingman JFC. The coalescent. Stoch Process Their Appl. 1982;13:235–48.
Kirin M, McQuillan R, Franklin CS, Campbell H, McKeigue PM, Wilson JF. Genomic runs of homozygosity record population history and consanguinity. PLoS One. 2010;5:e13996.
Korneliussen TS, Albrechtsen A, Nielsen R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics. 2014;15:356.
Krone SM, Neuhauser C. Ancestral processes with selection. Theor Popn Biol. 1997;51:210–37.
Kuhner MK. LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics. 2006;22:768–70.
Kuhner MK. Coalescent genealogy samplers: windows into population history. Trends Ecol Evol. 2009;24:86–93.
Leblois R, Estoup A, Streiff R. Genetics of recent habitat contraction and reduction in population size: does isolation by distance matter? Mol Ecol. 2006;15:3601–15.
Leblois R, Pudlo P, Néron J, Bertaux F, Beeravolu CR, Vitalis R, Rousset F. Maximum likelihood inference of population size contractions from microsatellite data. Mol Biol Evol. 2014;31(10):2805–23. doi:10.1093/molbev/msu212.
Li H, Durbin R. Inference of human population history from individual whole-genome sequences. Nature. 2011;475:493–6.
Li S, Jakobsson M. Estimating demographic parameters from large-scale population genomic data using approximate Bayesian computation. BMC Genet. 2012;13:22.
Liu X, Fu Y-X. Exploring population size changes using SNP frequency spectra. Nat Genet. 2015;47:555–9.
Lohse K, Chmelik M, Martin SH, Barton NH. Efficient strategies for calculating blockwise likelihoods under the coalescent. Genetics. 2016;202:775–86.
Luikart G, Cornuet J-M. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv Biol. 1998;12:228–37.
MacLeod IM, Hayes BJ, Goddard ME, et al. A novel predictor of multilocus haplotype homozygosity: comparison with existing predictors. Genet Res. 2009;91:413–26.
MacLeod IM, Larkin DM, Lewin HA, Hayes BJ, Goddard ME. Inferring demography from runs of homozygosity in whole genome sequence, with correction for sequence errors. Mol Biol Evol. 2013;30(9):2209–23. doi:10.1093/molbev/mst125.
Malaspinas A-S, Westaway MC, Muller C, Sousa VC, Lao O, Alves I, et al. A genomic history of aboriginal Australia. Nature. 2016;538:207–14.
Marjoram P, Joyce P. Practical implications of coalescent theory. Chapter 5. In: Heath LS, Ramakrishnan N, editors. Problem solving handbook in computational 63 biology and bioinformatics. New York: Springer; 2010.
Marjoram P, Tavaré S. Modern computational approaches for analysing molecular genetic variation data. Nat Rev Genet. 2006;7:759–70.
Marjoram P, Wall JD. Fast “coalescent” simulation. BMC Genet. 2006;7:16.
Matsumoto T, Akashi H, Yang Z. Evaluation of ancestral sequence reconstruction methods to infer nonstationary patterns of nucleotide substitution. Genetics. 2015;200:873–90.
Mazet O, Rodríguez W, Chikhi L. Demographic inference using genetic data from a single individual: separating population size variation from population structure. Theor Popul Biol. 2015;104:46–58.
Mazet O, Rodriguez W, Grusea S, Boitard S, Chikhi L. On the importance of being structured: instantaneous coalescence rates and human evolution—lessons for ancestral population size inference. Heredity. 2016;116:362–71.
McKee JK, Sciulli PW, Fooce CD, Waite TA. Forecasting global biodiversity threats associated with human population growth. Biol Conserv. 2004;115:161–4.
McVean GAT, Cardin NJ. Approximating the coalescent with recombination. Philos Trans R Soc B. 2005;360:1387–93.
Moorjani P, Gao Z, Przeworski M. Human germline mutation and the erratic evolutionary clock. PLoS Biol. 2016;14(10):e2000744. doi:10.1371/journal.pbio.2000744.
Moran PAP. Random processes in genetics. In: Proceedings of the Cambridge Philosophical Society. 1958. p. 60.
Nadachowska-Brzyska K, Li C, Smeds L, Zhang G, Ellegren H. Temporal dynamics of avian populations during pleistocene revealed by whole-genome sequences. Curr Biol. 2015;25:1375–80.
Nadachowska-Brzyska K, Burri R, Smeds L, Ellegren H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol Ecol. 2016;25:1058–72.
Naduvilezhath L, Rose LE, Metzler D. Jaatha: a fast composite-likelihood approach to estimate demographic parameters. MolEcol. 2011;20:2709–23.
Nelson GC, Dobermann A, Nakicenovic N, O’Neill BC. Anthropogenic drivers of ecosystem change: an overview. Ecol Soc. 2006;11.
Nielsen R, Beaumont MA. Statistical inferences in phylogeography. Mol Ecol. 2009;18:1034–47.
Nielsen R, Slatkin M. An introduction to population genetics: theory and applications. Sunderland, MA: Sinauer Associates; 2013.
Nielsen R, Hubisz MJ, Hellmann I, Torgerson D, Andrés AM, Albrechtsen A, Gutenkunst R, Adams MD, Cargill M, Boyko A, Indap A, Bustamante CD, Clark AG. Darwinian and demographic forces affecting human protein coding genes. Genome Res. 2009;19:838–49.
Nielsen R, Korneliussen TS, Albrechtsen A, Wang J. SNP calling, genotype calling, and sample allele frequency estimation from new-generation sequencing data. PLoS One. 2012;7(7):e37558.
Nikolic N, Chevalet C. Detecting past changes of effective population size. Evol Appl. 2014;7:663–81.
Nordborg M. Coalescent theory. In: Balding DJ, Bishop MJ, Cannings C, editors. Handbook of statistical genetics. New York: Wiley; 2001. p. 179–208
Nordborg M, Donelly P. The coalescent process with selfing. Genetics. 1997;146(3):1185–95.
Orlando L, Ginolhac A, Zhang G, Froese D, Albrechtsen A, Stiller M, Schubert M, Cappellini E, Petersen B, Moltke I, et al. Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse. Nature. 2013;499:744–8.
Orozco-terWengel P. The devil is in the details: the effect of population structure on demographic inference. Heredity. 2016;116:349–50.
Palamara PF, Pe’er I. Inference of historical migration rates via haplotype sharing. Bioinformatics. 2013;8:i180–8.
Palamara PF, Lencz T, Darvasi A, Pe’er I. Length distributions of identity by descent reveal fine-scale demographic history. Am J Hum Genet. 2012;91:1150.
Palkopoulou E, Mallick S, Skoglund P, Enk J, Rohland N, Li H, Omrak A, Vartanyan S, Poinar H, Götherström A, Reich D, Dalén L. Complete genomes reveal signatures of demographic and genetic declines in the woolly mammoth. Curr Biol. 2015;25:1395–400.
Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 2003;421:37–42.
Paten B, Herrero J, Beal K, Fitzgerald S, Birney E. Enredo and Pecan: genome-wide mammalian consistency-based multiple alignment with paralogs. Genome Res. 2008a;18:1814–28.
Paten B, Herrero J, Fitzgerald S, Beal K, Flicek P, Holmes I, Birney E. Genome-wide nucleotide-level mammalian ancestor reconstruction. Genome Res. 2008b;18:1829–43.
Pavlidis P, Laurent S, Stephan W. msABC: a modification of Hudson’s ms to facilitate multi-locus ABC analysis. Mol Ecol Resour. 2010;10:723–7.
Peery MZ, Kirby R, Reid BN, Stoelting R, Coucet-Beer E, Robinson S, Vasquez-Carillio C, Pauli JN, Palsboll PJ. Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol. 2012;21:3403–18.
Peter BM, Wegmann D, Excoffier L. Distinguishing between population bottleneck and population subdivision by a Bayesian model choice procedure. Mol Ecol. 2010:4648–60.
Polanski A, Bobrowski A, Kimmel M. A note on distributions of times to coalescence, under time-dependent population size. Theor Popul Biol. 2003;63:33–40.
Posada D, Crandall KA. Modeltest: testing the model of DNA substitution. Bioinformatics. 1998;14:817–8.
Prado-Martinez J, Sudmant PH, Kidd JM, Li H, Kelley JL, Lorente-Galdos B, Veeramah KR, Woerner AE, O’Connor TD, Santpere G, et al. Great ape genetic diversity and population history. Nature. 2013;499:471–5.
Qiu Q, Wang L, Wang K, Yang Y, Ma T, Wang Z, Zhang X, Ni Z, Hou F, Long R, et al. Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions. Nat Commun. 2015;6:10283.
Robert CP, Cornuet J-M, Marin J-M, Pillai NS. Lack of confidence in approximate Bayesian computation model choice. Proc Natl Acad Sci. 2011;108:15112–7.
Robinson JD, Bunnefeld L, Hearn J, Stone GN, Hickerson MJ. ABC inference of multi-population divergence with admixture from unphased population genomic data. Mol Ecol. 2014;23(18):4458–71.
Schiffels S, Durbin R. Inferring human population size and separation history from multiple genome sequences. Nat Genet. 2014;46:919–25.
Schubert M, Jónsson H, Chang D, Der Sarkissian C, Ermini L, Ginolhac A, Albrechtsen A, Dupanloup I, Foucal A, Petersen B, et al. Prehistoric genomes reveal the genetic foundation and cost of horse domestication. Proc Natl Acad Sci. 2014;111:E5661–9.
Shafer ABA, Gattepaille LM, Stewart REA, Wolf JBW. Demographic inferences using short-read genomic data in an approximate Bayesian computation framework: in silico evaluation of power, biases and proof of concept in Atlantic walrus. Mol Ecol. 2015;24:328–45.
Shafer ABA, Miller JM, Kardos M. Cross-species application of SNP chips is not suitable for identifying runs of homozygosity. J Hered. 2016;107:193–5.
Shafer ABA, Peart CR, Tusso S, Maayan I, Brelsford A, Wheat CW, Wolf JBW. Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods Ecol Evol. 2017. doi: 10.1111/2041-210X.12700.
Sheehan S, Harris K, Song YS. Estimating variable effective population sizes from multiple genomes: a sequentially Markov conditional sampling distribution approach. Genetics. 2013;194:647–62.
Sousa VM, Fritz M, Beaumont MA, Chikhi L. Approximate Bayesian computation (ABC) without summary statistics: the case of admixture. Genetics. 2009;181(4):1507–19.
Städler T, Haubold B, Merino C, Stephan W, Pfaffelhuber P. The impact of sampling schemes on the site frequency spectrum in nonequilibrium subdivided populations. Genetics. 2009;182:205–16.
Storz JF, Beaumont MA. Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution. 2002;56:154–66.
Tajima F. Evolutionary relationship of DNA sequences in finite populations. Genetics. 1983;105:437–60.
Tajima F. The effect of change in population size on DNA polymorphism. Genetics. 1989;123:597.
Thuiller W. Biodiversity: climate change and the ecologist. Nature. 2007;448:550–2.
Veeramah KR, Woerner AE, Johnstone L, Gut I, Gut M, Marques-Bonet T, Carbone L, Wall JD, Hammer MF. Examining phylogenetic relationships among gibbon genera using whole genome sequence data using an approximate bayesian computation approach. Genetics. 2015;200:295–308.
Vitousek PM, Mooney HA, Lubchenco J, Melillo JM. Human domination of earth’s ecosystems. Science. 1997;277:494–9.
Wakeley J. Nonequilibrium migration in human history. Genetics. 1999;153:1863.
Wakeley J. Coalescent theory: an introduction. San Francisco: W.H. Freeman; 2008.
Wakeley J, Hey J. Estimating ancestral population parameters. Genetics. 1997;145:847–55.
Wang J, Street NR, Scofield DG, Ingvarsson PK. Variation in linked selection and recombination drive genomic divergence during allopatric speciation of European and American aspens. Mol Biol Evol. 2016;33(7):1754–67. doi:10.1093/molbev/msw051.
Waples RK, Larson WA, Waples RS. Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci. Heredity. 2016;117(4):233–40.
Warren MJ, Thomas GWC, Hahn MW, Raney BJ, Aken B, Nag R, Schmitz J, Churakov G, Noll A, Stanyon R, Webb D, Thibaud-Nissen F, Nordborg M, Marques-Bonet T, Dewar K, Weinstock GM, Wilson RK, Freimer NB. The genome of the vervet (Chlorocebus aethiops sabaeus). Genome Res. 2015;25:1921–33.
Watterson GA. The sampling theory of selectively neutral alleles. Adv Appl Probab. 1974:463–88.
Wegmann D, Leuenberger C, Neuenschwander S, Excoffier L. ABCtoolbox: a versatile toolkit for approximate Bayesian computations. BMC Bioinformatics. 2010;11:116.
Whitlock MC, McCauley DE. Indirect measures of gene flow and migration: FST|[ne]|1/(4Nm+1). Heredity. 1999;82:117–25.
Wiuf C, Hein J. Recombination as a point process along sequences. Theor Popul Biol. 1999;(55):248–59.
Wright S. The distribution of gene frequencies under irreversible mutation. Proc Natl Acad Sci. 1938;24:253–9.
Wu C-H, Drummond AJ. Joint inference of microsatellite mutation models, population history and genealogies using transdimensional Markov Chain Monte Carlo. Genetics. 2011;188:151–64.
Xue AT, Hickerson MJ. The aggregate site frequency spectrum (aSFS) for comparative population genomic inference. Mol Ecol. 2015;24:6223–40.
Zhao S, Zheng P, Dong S, Zhan X, Wu Q, Guo X, Hu Y, He W, Zhang S, Fan W, et al. Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation. Nat Genet. 2013;45:67–71.
Živković D, Stephan W. Analytical results on the neutral non-equilibrium allele frequency spectrum based on diffusion theory. Theor Popul Biol. 2011;79:184–91.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Glossary
- Approximate Bayesian computation (ABC)
-
compares summary statistics from observed and simulated data to make demographic and statistical inferences. ABC does not rely on computing a likelihood-function.
- Bottleneck
-
a massive and temporary reduction in (effective) population size that results in an associated reduction of genetic diversity.
- Genetic drift
-
changes in the frequency of alleles due to random mating (and allele segregation in diploids). Changes are more pronounced in small populations.
- Coalescent theory
-
mathematical model governing the expected distribution of coalescence times back to a common ancestor in a population sample.
- Diffusion approximation
-
approximation of the Wright-Fisher (WF) model that leads to a continuous time stochastic process that is easier to study mathematically. It is used to derive useful formulas such as the expected time to fixation of a mutation.
- Divergence time (T)
-
estimated divergence time between two populations measured as the number of generations, typically divided by 2N e.
- Effective population size (N e)
-
the size of an idealized (Wright-Fisher) population with the same amount of genetic drift as the given real population. In most organisms, effective size is less than census size because of factors such as overlapping generations, reproductive inequality, and sex bias.
- Genealogy
-
the ancestral relationship, for a particular segment of the genome, among sampled chromosomes. This takes the form of a branching tree for non-recombining data, but becomes a tangled graph (the “ancestral recombination graph”) with recombination.
- Generation time
-
is the average interval between identical life history stages across successive generations. Generation time is often expressed in years.
- Migration (M)
-
is the average number of migrants entering each population per generation defined as 4N e m where m is the proportion of individuals per generation in each population that are immigrants.
- Recombination
-
the process of exchanging genetic material between homologous chromosomes during meiosis resulting in new combinations of alleles in the resulting gametes.
- Rho (ρ)
-
is the population-scaled recombination rate defined as 4N e r in diploid organisms.
- Panmictic population
-
a population in which all pairs of individuals are equally likely to mate.
- Site frequency spectrum (SFS)
-
also called the allele frequency spectrum, is the distribution of the allele frequencies of a given set of loci in a sample, and is often visualized as a histogram.
- Tajima’s D
-
a summary statistic that compares two estimators of the population-scaled mutation rate Θ to detect departures from the standard coalescent model. Departures can reflect demography or selection.
- Theta (Θ)
-
is the population-scaled mutation rate equal to 4N e μ in diploid organisms. It is the product of the N e and mutation rate μ and measures the capacity of a population to maintain genetic variability. Among organisms of similar μ, it functions as a measure of relative effective population size.
- Wright-Fisher model
-
is a discrete-time model of stochastic reproduction (see also genetic drift) that assumes a population of size N, random mating, and non-overlapping generations.
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Salmona, J., Heller, R., Lascoux, M., Shafer, A. (2017). Inferring Demographic History Using Genomic Data. In: Rajora, O. (eds) Population Genomics. Population Genomics. Springer, Cham. https://doi.org/10.1007/13836_2017_1
Download citation
DOI: https://doi.org/10.1007/13836_2017_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04587-6
Online ISBN: 978-3-030-04589-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)