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Home / Equine Research Bank / Mol Biol Evol / Estimating Temporally Variable Selection Intensity from Anci...
Molecular biology and evolution2023; 40(3); msad008; doi: 10.1093/molbev/msad008

Estimating Temporally Variable Selection Intensity from Ancient DNA Data.

Abstract: Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage, and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat coloration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.
© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.
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Publication Date: 2023-01-21 PubMed ID: 36661852PubMed Central: PMC10063216DOI: 10.1093/molbev/msad008Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article discusses a new Bayesian framework that allows researchers to study past selection processes through ancient DNA, despite challenges such as postmortem damage and fragmentation. This method enables the reconstruction of population allele frequency trajectories through time to better understand the drivers of selection.

Introduction

  • The research revolves around the study of ancient DNA (aDNA) to explore temporal changes in allele frequencies—a direct indicator of genetic variation stemming from evolution.
  • These genetic time series could potentially improve power for the inference of selection and test hypotheses about the drivers of past selection events such as the incidence of plant and animal domestication.
  • However, studying aDNA involves several challenges including postmortem damage, high fragmentation, low coverage, and small sample sizes.

Novel Bayesian Framework

  • To overcome these challenges, the authors introduced a novel Bayesian framework.
  • This framework estimates temporally variable selection based on genotype likelihoods instead of allele frequencies. This method aims to account for uncertainties resulting from the damaged and fragmented state of aDNA molecules.
  • The Bayesian approach allows the researchers to reconstruct the underlying allele frequency trajectories of the population through time.
  • Such reconstructions could provide more insights into what may have driven selection in the past.

Evaluation and Application

  • The authors evaluated the performance of their model through extensive simulations.
  • They also demonstrated its practical utility by applying it to ancient horse samples, specifically genotyped at the loci for coat coloration.
  • The results of this study showed that the incorporation of sample uncertainties into the model could improve the inference of selection.

Conclusion

  • The authors believe their novel Bayesian framework can potentially circumvent the limitations of studying aDNA and offer a more accurate and comprehensive understanding of past selection events.
  • This approach could allow scientists to explore the genetic basis of evolution based on fossil and archaeological samples more effectively and could offer insights into the timeline of genetic changes and drivers of these changes.

Cite This Article

APA
He Z, Dai X, Lyu W, Beaumont M, Yu F. (2023). Estimating Temporally Variable Selection Intensity from Ancient DNA Data. Mol Biol Evol, 40(3), msad008. https://doi.org/10.1093/molbev/msad008

Publication

Molecular biology and evolution
ISSN: 1537-1719
NlmUniqueID: 8501455
Country: United States
Language: English
Volume: 40
Issue: 3
PII: msad008

Researcher Affiliations

He, Zhangyi
  • Cancer Research UK Beatson Institute, Glasgow, United Kingdom.
  • Department of Computer Science, University of Oxford, Oxford, United Kingdom.
Dai, Xiaoyang
  • The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
Lyu, Wenyang
  • School of Mathematics, University of Bristol, Bristol, United Kingdom.
Beaumont, Mark
  • School of Biological Sciences, University of Bristol, Bristol, United Kingdom.
Yu, Feng
  • School of Mathematics, University of Bristol, Bristol, United Kingdom.

MeSH Terms

  • Animals
  • Horses / genetics
  • DNA, Ancient
  • Bayes Theorem
  • Gene Frequency
  • DNA / genetics
  • Time Factors
  • Models, Genetic

References

This article includes 66 references
  1. Andrieu C, Doucet A, Holenstein R. Particle Markov chain Monte Carlo methods. J R Stat Soc Ser B 72:269–342.
  2. Bank C, Ewing GB, Ferrer-Admettla A, Foll M, Jensen JD. Thinking too positive? Revisiting current methods of population genetic selection inference.. Trends Genet 2014 Dec;30(12):540-6.
    pubmed: 25438719doi: 10.1016/j.tig.2014.09.010google scholar: lookup
  3. Bellone RR, Holl H, Setaluri V, Devi S, Maddodi N, Archer S, Sandmeyer L, Ludwig A, Foerster D, Pruvost M, Reissmann M, Bortfeldt R, Adelson DL, Lim SL, Nelson J, Haase B, Engensteiner M, Leeb T, Forsyth G, Mienaltowski MJ, Mahadevan P, Hofreiter M, Paijmans JL, Gonzalez-Fortes G, Grahn B, Brooks SA. Evidence for a retroviral insertion in TRPM1 as the cause of congenital stationary night blindness and leopard complex spotting in the horse.. PLoS One 2013;8(10):e78280.
    pmc: PMC3805535pubmed: 24167615doi: 10.1371/journal.pone.0078280google scholar: lookup
  4. Bollback JP, York TL, Nielsen R. Estimation of 2Nes from temporal allele frequency data.. Genetics 2008 May;179(1):497-502.
    pmc: PMC2390626pubmed: 18493066doi: 10.1534/genetics.107.085019google scholar: lookup
  5. Bosshard L, Dupanloup I, Tenaillon O, Bruggmann R, Ackermann M, Peischl S, Excoffier L. Accumulation of Deleterious Mutations During Bacterial Range Expansions.. Genetics 2017 Oct;207(2):669-684.
    pmc: PMC5629331pubmed: 28821588doi: 10.1534/genetics.117.300144google scholar: lookup
  6. Brooks SA, Lear TL, Adelson DL, Bailey E. A chromosome inversion near the KIT gene and the Tobiano spotting pattern in horses.. Cytogenet Genome Res 2007;119(3-4):225-30.
    pubmed: 18253033doi: 10.1159/000112065google scholar: lookup
  7. Corbin LJ, Pope J, Sanson J, Antczak DF, Miller D, Sadeghi R, Brooks SA. An Independent Locus Upstream of ASIP Controls Variation in the Shade of the Bay Coat Colour in Horses.. Genes (Basel) 2020 May 30;11(6).
    pmc: PMC7349280pubmed: 32486210doi: 10.3390/genes11060606google scholar: lookup
  8. Dehasque M, Ávila-Arcos MC, Díez-Del-Molino D, Fumagalli M, Guschanski K, Lorenzen ED, Malaspinas AS, Marques-Bonet T, Martin MD, Murray GGR, Papadopulos AST, Therkildsen NO, Wegmann D, Dalén L, Foote AD. Inference of natural selection from ancient DNA.. Evol Lett 2020 Apr;4(2):94-108.
    pmc: PMC7156104pubmed: 32313686doi: 10.1002/evl3.165google scholar: lookup
  9. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ. A framework for variation discovery and genotyping using next-generation DNA sequencing data.. Nat Genet 2011 May;43(5):491-8.
    pmc: PMC3083463pubmed: 21478889doi: 10.1038/ng.806google scholar: lookup
  10. Der Sarkissian C, Ermini L, Schubert M, Yang MA, Librado P, Fumagalli M, Jónsson H, Bar-Gal GK, Albrechtsen A, Vieira FG, Petersen B, Ginolhac A, Seguin-Orlando A, Magnussen K, Fages A, Gamba C, Lorente-Galdos B, Polani S, Steiner C, Neuditschko M, Jagannathan V, Feh C, Greenblatt CL, Ludwig A, Abramson NI, Zimmermann W, Schafberg R, Tikhonov A, Sicheritz-Ponten T, Willerslev E, Marques-Bonet T, Ryder OA, McCue M, Rieder S, Leeb T, Slatkin M, Orlando L. Evolutionary Genomics and Conservation of the Endangered Przewalski's Horse.. Curr Biol 2015 Oct 5;25(19):2577-83.
    pmc: PMC5104162pubmed: 26412128doi: 10.1016/j.cub.2015.08.032google scholar: lookup
  11. Dumont BL, Payseur BA. Evolution of the genomic rate of recombination in mammals.. Evolution 2008 Feb;62(2):276-94.
    pubmed: 18067567doi: 10.1111/j.1558-5646.2007.00278.xgoogle scholar: lookup
  12. Durrett R. Probability models for DNA sequence evolution. .
  13. Fages A, Hanghøj K, Khan N, Gaunitz C, Seguin-Orlando A, Leonardi M, McCrory Constantz C, Gamba C, Al-Rasheid KAS, Albizuri S, Alfarhan AH, Allentoft M, Alquraishi S, Anthony D, Baimukhanov N, Barrett JH, Bayarsaikhan J, Benecke N, Bernáldez-Sánchez E, Berrocal-Rangel L, Biglari F, Boessenkool S, Boldgiv B, Brem G, Brown D, Burger J, Crubézy E, Daugnora L, Davoudi H, de Barros Damgaard P, de Los Ángeles de Chorro Y de Villa-Ceballos M, Deschler-Erb S, Detry C, Dill N, do Mar Oom M, Dohr A, Ellingvåg S, Erdenebaatar D, Fathi H, Felkel S, Fernández-Rodríguez C, García-Viñas E, Germonpré M, Granado JD, Hallsson JH, Hemmer H, Hofreiter M, Kasparov A, Khasanov M, Khazaeli R, Kosintsev P, Kristiansen K, Kubatbek T, Kuderna L, Kuznetsov P, Laleh H, Leonard JA, Lhuillier J, Liesau von Lettow-Vorbeck C, Logvin A, Lõugas L, Ludwig A, Luis C, Arruda AM, Marques-Bonet T, Matoso Silva R, Merz V, Mijiddorj E, Miller BK, Monchalov O, Mohaseb FA, Morales A, Nieto-Espinet A, Nistelberger H, Onar V, Pálsdóttir AH, Pitulko V, Pitskhelauri K, Pruvost M, Rajic Sikanjic P, Rapan Papeša A, Roslyakova N, Sardari A, Sauer E, Schafberg R, Scheu A, Schibler J, Schlumbaum A, Serrand N, Serres-Armero A, Shapiro B, Sheikhi Seno S, Shevnina I, Shidrang S, Southon J, Star B, Sykes N, Taheri K, Taylor W, Teegen WR, Trbojević Vukičević T, Trixl S, Tumen D, Undrakhbold S, Usmanova E, Vahdati A, Valenzuela-Lamas S, Viegas C, Wallner B, Weinstock J, Zaibert V, Clavel B, Lepetz S, Mashkour M, Helgason A, Stefánsson K, Barrey E, Willerslev E, Outram AK, Librado P, Orlando L. Tracking Five Millennia of Horse Management with Extensive Ancient Genome Time Series.. Cell 2019 May 30;177(6):1419-1435.e31.
    pmc: PMC6547883pubmed: 31056281doi: 10.1016/j.cell.2019.03.049google scholar: lookup
  14. Fang M, Larson G, Ribeiro HS, Li N, Andersson L. Contrasting mode of evolution at a coat color locus in wild and domestic pigs.. PLoS Genet 2009 Jan;5(1):e1000341.
    pmc: PMC2613536pubmed: 19148282doi: 10.1371/journal.pgen.1000341google scholar: lookup
  15. Feder AF, Kryazhimskiy S, Plotkin JB. Identifying signatures of selection in genetic time series.. Genetics 2014 Feb;196(2):509-22.
    pmc: PMC3914623pubmed: 24318534doi: 10.1534/genetics.113.158220google scholar: lookup
  16. Ferrer-Admetlla A, Leuenberger C, Jensen JD, Wegmann D. An Approximate Markov Model for the Wright-Fisher Diffusion and Its Application to Time Series Data.. Genetics 2016 Jun;203(2):831-46.
    pmc: PMC4896197pubmed: 27038112doi: 10.1534/genetics.115.184598google scholar: lookup
  17. Fisher RA. On the dominance ratio. Proc R Soc Edinb 42:321–341.
  18. Foll M, Poh YP, Renzette N, Ferrer-Admetlla A, Bank C, Shim H, Malaspinas AS, Ewing G, Liu P, Wegmann D, Caffrey DR, Zeldovich KB, Bolon DN, Wang JP, Kowalik TF, Schiffer CA, Finberg RW, Jensen JD. Influenza virus drug resistance: a time-sampled population genetics perspective.. PLoS Genet 2014 Feb;10(2):e1004185.
    pmc: PMC3937227pubmed: 24586206doi: 10.1371/journal.pgen.1004185google scholar: lookup
  19. Foll M, Shim H, Jensen JD. WFABC: a Wright-Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data.. Mol Ecol Resour 2015 Jan;15(1):87-98.
    pubmed: 24834845doi: 10.1111/1755-0998.12280google scholar: lookup
  20. Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM. The dynamics of molecular evolution over 60,000 generations.. Nature 2017 Nov 2;551(7678):45-50.
    pmc: PMC5788700pubmed: 29045390doi: 10.1038/nature24287google scholar: lookup
  21. Gordon NJ, Salmond DJ, Smith AFM. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc F 140:107–113.
  22. He Z, Beaumont M, Yu F. Effects of the Ordering of Natural Selection and Population Regulation Mechanisms on Wright-Fisher Models.. G3 (Bethesda) 2017 Jul 5;7(7):2095-2106.
    pmc: PMC5499119pubmed: 28500051doi: 10.1534/g3.117.041038google scholar: lookup
  23. He Z, Dai X, Beaumont M, Yu F. Detecting and Quantifying Natural Selection at Two Linked Loci from Time Series Data of Allele Frequencies with Forward-in-Time Simulations.. Genetics 2020 Oct;216(2):521-541.
    pmc: PMC7536848pubmed: 32826299doi: 10.1534/genetics.120.303463google scholar: lookup
  24. He Z, Dai X, Beaumont M, Yu F. Estimation of Natural Selection and Allele Age from Time Series Allele Frequency Data Using a Novel Likelihood-Based Approach.. Genetics 2020 Oct;216(2):463-480.
    pmc: PMC7536852pubmed: 32769100doi: 10.1534/genetics.120.303400google scholar: lookup
  25. He Z, Lyu W, Beaumont MA, Yu F. Moment-based approximations for the Wright–Fisher model of population dynamics under natural selection at two linked loci. bioRxiv (p. 424882).
  26. Hunter P. The genetics of domestication: Research into the domestication of livestock and companion animals sheds light both on their "evolution" and human history.. EMBO Rep 2018 Feb;19(2):201-205.
    pmc: PMC5797965pubmed: 29335247doi: 10.15252/embr.201745664google scholar: lookup
  27. Izenman AJ. Recent developments in nonparametric density estimation. J Am Stat Assoc 86:205–224.
  28. Jewett EM, Steinrücken M, Song YS. The Effects of Population Size Histories on Estimates of Selection Coefficients from Time-Series Genetic Data.. Mol Biol Evol 2016 Nov;33(11):3002-3027.
    pmc: PMC5062326pubmed: 27550904doi: 10.1093/molbev/msw173google scholar: lookup
  29. Johri P, Aquadro CF, Beaumont M, Charlesworth B, Excoffier L, Eyre-Walker A, Keightley PD, Lynch M, McVean G, Payseur BA, Pfeifer SP, Stephan W, Jensen JD. Recommendations for improving statistical inference in population genomics.. PLoS Biol 2022 May;20(5):e3001669.
    pmc: PMC9154105pubmed: 35639797doi: 10.1371/journal.pbio.3001669google scholar: lookup
  30. Johri P, Charlesworth B, Jensen JD. Toward an Evolutionarily Appropriate Null Model: Jointly Inferring Demography and Purifying Selection.. Genetics 2020 May;215(1):173-192.
    pmc: PMC7198275pubmed: 32152045doi: 10.1534/genetics.119.303002google scholar: lookup
  31. Johri P, Eyre-Walker A, Gutenkunst RN, Lohmueller KE, Jensen JD. On the prospect of achieving accurate joint estimation of selection with population history.. Genome Biol Evol 2022 Jul 2;14(7).
    pmc: PMC9254643pubmed: 35675379doi: 10.1093/gbe/evac088google scholar: lookup
  32. Johri P, Riall K, Becher H, Excoffier L, Charlesworth B, Jensen JD. The Impact of Purifying and Background Selection on the Inference of Population History: Problems and Prospects.. Mol Biol Evol 2021 Jun 25;38(7):2986-3003.
    pmc: PMC8233493pubmed: 33591322doi: 10.1093/molbev/msab050google scholar: lookup
  33. Kim SY, Lohmueller KE, Albrechtsen A, Li Y, Korneliussen T, Tian G, Grarup N, Jiang T, Andersen G, Witte D, Jorgensen T, Hansen T, Pedersen O, Wang J, Nielsen R. Estimation of allele frequency and association mapping using next-generation sequencing data.. BMC Bioinformatics 2011 Jun 11;12:231.
    pmc: PMC3212839pubmed: 21663684doi: 10.1186/1471-2105-12-231google scholar: lookup
  34. Kojima Y, Matsumoto H, Kiryu H. Estimation of population genetic parameters using an EM algorithm and sequence data from experimental evolution populations.. Bioinformatics 2020 Jan 1;36(1):221-231.
    pubmed: 31218366doi: 10.1093/bioinformatics/btz498google scholar: lookup
  35. Lacerda M, Seoighe C. Population genetics inference for longitudinally-sampled mutants under strong selection.. Genetics 2014 Nov;198(3):1237-50.
    pmc: PMC4224163pubmed: 25213172doi: 10.1534/genetics.114.167957google scholar: lookup
  36. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools.. Bioinformatics 2009 Aug 15;25(16):2078-9.
    pmc: PMC2723002pubmed: 19505943doi: 10.1093/bioinformatics/btp352google scholar: lookup
  37. Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K, Wang J. SNP detection for massively parallel whole-genome resequencing.. Genome Res 2009 Jun;19(6):1124-32.
    pmc: PMC2694485pubmed: 19420381doi: 10.1101/gr.088013.108google scholar: lookup
  38. Librado P, Gamba C, Gaunitz C, Der Sarkissian C, Pruvost M, Albrechtsen A, Fages A, Khan N, Schubert M, Jagannathan V, Serres-Armero A, Kuderna LFK, Povolotskaya IS, Seguin-Orlando A, Lepetz S, Neuditschko M, Thèves C, Alquraishi S, Alfarhan AH, Al-Rasheid K, Rieder S, Samashev Z, Francfort HP, Benecke N, Hofreiter M, Ludwig A, Keyser C, Marques-Bonet T, Ludes B, Crubézy E, Leeb T, Willerslev E, Orlando L. Ancient genomic changes associated with domestication of the horse.. Science 2017 Apr 28;356(6336):442-445.
    pubmed: 28450643doi: 10.1126/science.aam5298google scholar: lookup
  39. Ludwig A, Pruvost M, Reissmann M, Benecke N, Brockmann GA, Castaños P, Cieslak M, Lippold S, Llorente L, Malaspinas AS, Slatkin M, Hofreiter M. Coat color variation at the beginning of horse domestication.. Science 2009 Apr 24;324(5926):485.
    pmc: PMC5102060pubmed: 19390039doi: 10.1126/science.1172750google scholar: lookup
  40. Ludwig A, Reissmann M, Benecke N, Bellone R, Sandoval-Castellanos E, Cieslak M, Fortes GG, Morales-Muñiz A, Hofreiter M, Pruvost M. Twenty-five thousand years of fluctuating selection on leopard complex spotting and congenital night blindness in horses.. Philos Trans R Soc Lond B Biol Sci 2015 Jan 19;370(1660):20130386.
    pmc: PMC4275893pubmed: 25487337doi: 10.1098/rstb.2013.0386google scholar: lookup
  41. Luengo D, Martino L, Bugallo M, Elvira V, Särkkä S. A survey of Monte Carlo methods for parameter estimation. EURASIP J Adv Signal Process 2020:1–62.
  42. Lyu W, Dai X, Beaumont M, Yu F, He Z. Inferring the timing and strength of natural selection and gene migration in the evolution of chicken from ancient DNA data.. Mol Ecol Resour 2022 May;22(4):1362-1379.
    pubmed: 34783162doi: 10.1111/1755-0998.13553google scholar: lookup
  43. Malaspinas AS. Methods to characterize selective sweeps using time serial samples: an ancient DNA perspective.. Mol Ecol 2016 Jan;25(1):24-41.
    pubmed: 26613371doi: 10.1111/mec.13492google scholar: lookup
  44. Malaspinas AS, Malaspinas O, Evans SN, Slatkin M. Estimating allele age and selection coefficient from time-serial data.. Genetics 2012 Oct;192(2):599-607.
    pmc: PMC3454883pubmed: 22851647doi: 10.1534/genetics.112.140939google scholar: lookup
  45. Mathieson I. Estimating time-varying selection coefficients from time series data of allele frequencies. bioRxiv (p. 387761).
  46. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, Harney E, Stewardson K, Fernandes D, Novak M, Sirak K, Gamba C, Jones ER, Llamas B, Dryomov S, Pickrell J, Arsuaga JL, de Castro JM, Carbonell E, Gerritsen F, Khokhlov A, Kuznetsov P, Lozano M, Meller H, Mochalov O, Moiseyev V, Guerra MA, Roodenberg J, Vergès JM, Krause J, Cooper A, Alt KW, Brown D, Anthony D, Lalueza-Fox C, Haak W, Pinhasi R, Reich D. Genome-wide patterns of selection in 230 ancient Eurasians.. Nature 2015 Dec 24;528(7583):499-503.
    pmc: PMC4918750pubmed: 26595274doi: 10.1038/nature16152google scholar: lookup
  47. Mathieson I, McVean G. Estimating selection coefficients in spatially structured populations from time series data of allele frequencies.. Genetics 2013 Mar;193(3):973-84.
    pmc: PMC3584010pubmed: 23307902doi: 10.1534/genetics.112.147611google scholar: lookup
  48. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res 2010 Sep;20(9):1297-303.
    pmc: PMC2928508pubmed: 20644199doi: 10.1101/gr.107524.110google scholar: lookup
  49. Murphy J, Hall C, Arkins S. What horses and humans see: a comparative review. Int J Zool 2009:721798.
  50. Paris C, Servin B, Boitard S. Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.. G3 (Bethesda) 2019 Dec 3;9(12):4073-4086.
    pmc: PMC6893182pubmed: 31597676doi: 10.1534/g3.119.400778google scholar: lookup
  51. Pruvost M, Bellone R, Benecke N, Sandoval-Castellanos E, Cieslak M, Kuznetsova T, Morales-Muñiz A, O'Connor T, Reissmann M, Hofreiter M, Ludwig A. Genotypes of predomestic horses match phenotypes painted in Paleolithic works of cave art.. Proc Natl Acad Sci U S A 2011 Nov 15;108(46):18626-30.
    pmc: PMC3219153pubmed: 22065780doi: 10.1073/pnas.1108982108google scholar: lookup
  52. Rebhun WC, Loew ER, Riis RC, Laratta LJ. Clinical manifestations of night blindness in the Appaloosa horse. Compend Contin Educ Pract Vet 6:S103–S106.
  53. Rieder S, Taourit S, Mariat D, Langlois B, Guérin G. Mutations in the agouti (ASIP), the extension (MC1R), and the brown (TYRP1) loci and their association to coat color phenotypes in horses (Equus caballus).. Mamm Genome 2001 Jun;12(6):450-5.
    pubmed: 11353392doi: 10.1007/s003350020017google scholar: lookup
  54. Sandoval-Castellanos E, Wutke S, Gonzalez-Salazar C, Ludwig A. Coat colour adaptation of post-glacial horses to increasing forest vegetation.. Nat Ecol Evol 2017 Dec;1(12):1816-1819.
    pubmed: 29085065doi: 10.1038/s41559-017-0358-5google scholar: lookup
  55. Schraiber JG, Evans SN, Slatkin M. Bayesian Inference of Natural Selection from Allele Frequency Time Series.. Genetics 2016 May;203(1):493-511.
    pmc: PMC4858794pubmed: 27010022doi: 10.1534/genetics.116.187278google scholar: lookup
  56. Shim H, Laurent S, Matuszewski S, Foll M, Jensen JD. Detecting and Quantifying Changing Selection Intensities from Time-Sampled Polymorphism Data.. G3 (Bethesda) 2016 Apr 7;6(4):893-904.
    pmc: PMC4825659pubmed: 26869618doi: 10.1534/g3.115.023200google scholar: lookup
  57. Skoglund P, Sjödin P, Skoglund T, Lascoux M, Jakobsson M. Investigating population history using temporal genetic differentiation.. Mol Biol Evol 2014 Sep;31(9):2516-27.
    pmc: PMC4137715pubmed: 24939468doi: 10.1093/molbev/msu192google scholar: lookup
  58. Steinrücken M, Bhaskar A, Song YS. A NOVEL SPECTRAL METHOD FOR INFERRING GENERAL DIPLOID SELECTION FROM TIME SERIES GENETIC DATA.. Ann Appl Stat 2014 Dec;8(4):2203-2222.
    pmc: PMC4295721pubmed: 25598858doi: 10.1214/14-aoas764google scholar: lookup
  59. Terhorst J, Schlötterer C, Song YS. Multi-locus analysis of genomic time series data from experimental evolution.. PLoS Genet 2015 Apr;11(4):e1005069.
    pmc: PMC4388667pubmed: 25849855doi: 10.1371/journal.pgen.1005069google scholar: lookup
  60. Terry RB, Archer S, Brooks S, Bernoco D, Bailey E. Assignment of the appaloosa coat colour gene (LP) to equine chromosome 1.. Anim Genet 2004 Apr;35(2):134-7.
    pubmed: 15025575doi: 10.1111/j.1365-2052.2004.01113.xgoogle scholar: lookup
  61. Turner TL, Miller PM. Investigating natural variation in Drosophila courtship song by the evolve and resequence approach.. Genetics 2012 Jun;191(2):633-42.
    pmc: PMC3374323pubmed: 22466043doi: 10.1534/genetics.112.139337google scholar: lookup
  62. Williamson EG, Slatkin M. Using maximum likelihood to estimate population size from temporal changes in allele frequencies.. Genetics 1999 Jun;152(2):755-61.
    pmc: PMC1460624pubmed: 10353915doi: 10.1093/genetics/152.2.755google scholar: lookup
  63. Wright S. Evolution in Mendelian Populations.. Genetics 1931 Mar;16(2):97-159.
    pmc: PMC1201091pubmed: 17246615doi: 10.1093/genetics/16.2.97google scholar: lookup
  64. Wutke S, Benecke N, Sandoval-Castellanos E, Döhle HJ, Friederich S, Gonzalez J, Hallsson JH, Hofreiter M, Lõugas L, Magnell O, Morales-Muniz A, Orlando L, Pálsdóttir AH, Reissmann M, Ruttkay M, Trinks A, Ludwig A. Spotted phenotypes in horses lost attractiveness in the Middle Ages.. Sci Rep 2016 Dec 7;6:38548.
    pmc: PMC5141471pubmed: 27924839doi: 10.1038/srep38548google scholar: lookup
  65. Wutke S, Sandoval-Castellanos E, Benecke N, Döhle HJ, Friederich S, Gonzalez J, Hofreiter M, Lõugas L, Magnell O, Malaspinas AS, Morales-Muñiz A, Orlando L, Reissmann M, Trinks A, Ludwig A. Decline of genetic diversity in ancient domestic stallions in Europe.. Sci Adv 2018 Apr;4(4):eaap9691.
    pmc: PMC5906072pubmed: 29675468doi: 10.1126/sciadv.aap9691google scholar: lookup
  66. Ye K, Gao F, Wang D, Bar-Yosef O, Keinan A. Dietary adaptation of FADS genes in Europe varied across time and geography.. Nat Ecol Evol 2017 May 26;1:167.
    pmc: PMC5672832pubmed: 29094686doi: 10.1038/s41559-017-0167google scholar: lookup

Citations

This article has been cited 9 times.
  1. Simon A, Coop G. The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change. bioRxiv 2023 Jul 11;.
    doi: 10.1101/2023.07.11.548607pubmed: 37503227google scholar: lookup
  2. Shimagaki KS, Lynch RM, Barton JP. Parallel HIV-1 fitness landscapes shape viral dynamics in humans and macaques that develop broadly neutralizing antibodies. Elife 2025 Nov 10;14.
    doi: 10.7554/eLife.105466pubmed: 41212071google scholar: lookup
  3. Kreiner JM. The genetic architecture and spatiotemporal dynamics of adaptation across human-modified landscapes. New Phytol 2026 Jan;249(2):744-750.
    doi: 10.1111/nph.70520pubmed: 40919705google scholar: lookup
  4. Fine AG, Steinrücken M. A novel expectation-maximization approach to infer general diploid selection from time-series genetic data. PLoS Genet 2025 Jul;21(7):e1011769.
    doi: 10.1371/journal.pgen.1011769pubmed: 40694589google scholar: lookup
  5. Gao Y, Lee B, Barton JP. Inferring fitness seascapes from evolutionary histories. bioRxiv 2025 Jun 8;.
    doi: 10.1101/2025.06.08.658500pubmed: 40661643google scholar: lookup
  6. Shimagaki KS, Lynch RM, Barton JP. Parallel HIV-1 fitness landscapes shape viral dynamics in humans and macaques that develop broadly neutralizing antibodies. bioRxiv 2025 Jul 24;.
    doi: 10.1101/2024.07.12.603090pubmed: 39071321google scholar: lookup
  7. Fine AG, Steinrücken M. A novel expectation-maximization approach to infer general diploid selection from time-series genetic data. bioRxiv 2025 May 26;.
    doi: 10.1101/2024.05.10.593575pubmed: 38798346google scholar: lookup
  8. Simon A, Coop G. The contribution of gene flow, selection, and genetic drift to five thousand years of human allele frequency change. Proc Natl Acad Sci U S A 2024 Feb 27;121(9):e2312377121.
    doi: 10.1073/pnas.2312377121pubmed: 38363870google scholar: lookup
  9. McFadden A, Vierra M, Martin K, Brooks SA, Everts RE, Lafayette C. Spotting the Pattern: A Review on White Coat Color in the Domestic Horse. Animals (Basel) 2024 Jan 30;14(3).
    doi: 10.3390/ani14030451pubmed: 38338094google scholar: lookup
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