Inference of recombination maps from a single pair of genomes and its application to ancient samples


Autoři: Gustavo V. Barroso aff001;  Nataša Puzović aff001;  Julien Y. Dutheil aff001
Působiště autorů: Max Planck Institute for Evolutionary Biology, Department of Evolutionary Genetics, August-Thienemann-Straße , Plön–GERMANY aff001
Vyšlo v časopise: Inference of recombination maps from a single pair of genomes and its application to ancient samples. PLoS Genet 15(11): e32767. doi:10.1371/journal.pgen.1008449
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008449

Souhrn

Understanding the causes and consequences of recombination landscape evolution is a fundamental goal in genetics that requires recombination maps from across the tree of life. Such maps can be obtained from population genomic datasets, but require large sample sizes. Alternative methods are therefore necessary to research organisms where such datasets cannot be generated easily, such as non-model or ancient species. Here we extend the sequentially Markovian coalescent model to jointly infer demography and the spatial variation in recombination rate. Using extensive simulations and sequence data from humans, fruit-flies and a fungal pathogen, we demonstrate that iSMC accurately infers recombination maps under a wide range of scenarios–remarkably, even from a single pair of unphased genomes. We exploit this possibility and reconstruct the recombination maps of ancient hominins. We report that the ancient and modern maps are correlated in a manner that reflects the established phylogeny of Neanderthals, Denisovans, and modern human populations.

Klíčová slova:

Gene mapping – Hidden Markov models – Human evolution – Chromosome mapping – Introgression – Paleogenetics – Probability distribution – Markov processes


Zdroje

1. Keightley PD, Otto SP. Interference among deleterious mutations favours sex and recombination in finite populations. Nature. 2006;443: 89–92. doi: 10.1038/nature05049 16957730

2. Hill WG, Robertson A. The effect of linkage on limits to artificial selection. Genet Res. 1966;8: 269–294. 5980116

3. Smith JM, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res. 2007;89: 391–403. doi: 10.1017/S0016672308009579 18976527

4. Ellegren H, Galtier N. Determinants of genetic diversity. Nat Rev Genet. 2016;17: 422–433. doi: 10.1038/nrg.2016.58 27265362

5. Boulton a Myers RS, Redfield RJ. The hotspot conversion paradox and the evolution of meiotic recombination. Proceedings of the National Academy of Sciences of the United States of America. 1997;94: 8058–8063. doi: 10.1073/pnas.94.15.8058 9223314

6. Myers S, Bowden R, Tumian A, Bontrop RE, Freeman C, MacFie TS, et al. Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination. Science. 2010;327: 876–879. doi: 10.1126/science.1182363 20044541

7. Kong A, Thorleifsson G, Gudbjartsson DF, Masson G, Sigurdsson A, Jonasdottir A, et al. Fine-scale recombination rate differences between sexes, populations and individuals. Nature. 2010;467: 1099–1103. doi: 10.1038/nature09525 20981099

8. Kawakami T, Mugal CF, Suh A, Nater A, Burri R, Smeds L, et al. Whole-genome patterns of linkage disequilibrium across flycatcher populations clarify the causes and consequences of fine-scale recombination rate variation in birds. Mol Ecol. 2017;26: 4158–4172. doi: 10.1111/mec.14197 28597534

9. Dumont BL, Payseur BA. Genetic Analysis of Genome-Scale Recombination Rate Evolution in House Mice. PLOS Genetics. 2011;7: e1002116. doi: 10.1371/journal.pgen.1002116 21695226

10. Baudat F, Buard J, Grey C, Fledel-Alon A, Ober C, Przeworski M, et al. PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice. Science. 2010;327: 836–840. doi: 10.1126/science.1183439 20044539

11. Auton A, Fledel-Alon A, Pfeifer S, Venn O, Ségurel L, Street T, et al. A fine-scale chimpanzee genetic map from population sequencing. Science. 2012;336: 193–198. doi: 10.1126/science.1216872 22422862

12. Singhal S, Leffler EM, Sannareddy K, Turner I, Venn O, Hooper DM, et al. Stable recombination hotspots in birds. Science. 2015;350: 928–932. doi: 10.1126/science.aad0843 26586757

13. Heil S, S C, Ellison C, Dubin M, Noor MAF. Recombining without Hotspots: A Comprehensive Evolutionary Portrait of Recombination in Two Closely Related Species of Drosophila. Genome Biol Evol. 2015;7: 2829–2842. doi: 10.1093/gbe/evv182 26430062

14. Brand CL, Cattani MV, Kingan SB, Landeen EL, Presgraves DC. Molecular Evolution at a Meiosis Gene Mediates Species Differences in the Rate and Patterning of Recombination. Current Biology. 2018;28: 1289–1295.e4. doi: 10.1016/j.cub.2018.02.056 29606420

15. Kohl KP, Jones CD, Sekelsky J. Evolution of an MCM complex in flies that promotes meiotic crossovers by blocking BLM helicase. Science. 2012;338: 1363–1365. doi: 10.1126/science.1228190 23224558

16. Cutter AD, Payseur BA. Genomic signatures of selection at linked sites: unifying the disparity among species. Nature reviews Genetics. 2013;14: 262–74. doi: 10.1038/nrg3425 23478346

17. Wang J, Street NR, Scofield DG, Ingvarsson PK. Natural Selection and Recombination Rate Variation Shape Nucleotide Polymorphism Across the Genomes of Three Related Populus Species. Genetics. 2016;202: 1185–1200. doi: 10.1534/genetics.115.183152 26721855

18. Schumer M, Xu C, Powell DL, Durvasula A, Skov L, Holland C, et al. Natural selection interacts with recombination to shape the evolution of hybrid genomes. Science. 2018; eaar3684. doi: 10.1126/science.aar3684 29674434

19. Murray GGR, Soares AER, Novak BJ, Schaefer NK, Cahill JA, Baker AJ, et al. Natural selection shaped the rise and fall of passenger pigeon genomic diversity. Science. 2017;358: 951–954. doi: 10.1126/science.aao0960 29146814

20. Martin SH, Jiggins CD. Interpreting the genomic landscape of introgression. Curr Opin Genet Dev. 2017;47: 69–74. doi: 10.1016/j.gde.2017.08.007 28923541

21. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. The American Journal of Human Genetics. 2017;101: 5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856

22. Duret L, Galtier N. Biased gene conversion and the evolution of mammalian genomic landscapes. Annu Rev Genomics Hum Genet. 2009;10: 285–311. doi: 10.1146/annurev-genom-082908-150001 19630562

23. Kostka D, Hubisz MJ, Siepel A, Pollard KS. The Role of GC-Biased Gene Conversion in Shaping the Fastest Evolving Regions of the Human Genome. Mol Biol Evol. 2012;29: 1047–1057. doi: 10.1093/molbev/msr279 22075116

24. Bolívar P, Mugal CF, Nater A, Ellegren H. Recombination Rate Variation Modulates Gene Sequence Evolution Mainly via GC-Biased Gene Conversion, Not Hill–Robertson Interference, in an Avian System. Molecular Biology and Evolution. 2016;33: 216–227. doi: 10.1093/molbev/msv214 26446902

25. Glémin S, Arndt PF, Messer PW, Petrov D, Galtier N, Duret L. Quantification of GC-biased gene conversion in the human genome. Genome Res. 2015;25: 1215–1228. doi: 10.1101/gr.185488.114 25995268

26. Stumpf MPH, McVean G a. T. Estimating recombination rates from population-genetic data. Nature Reviews Genetics. 2003;4: 959–968. doi: 10.1038/nrg1227 14631356

27. Rosenberg NA, Nordborg M. Genealogical Trees, Coalescent Theory and the Analysis of Genetic Polymorphisms. Nature Reviews Genetics. 2002;3: 380–390. doi: 10.1038/nrg795 11988763

28. McVean GAT, Myers SR, Hunt S, Deloukas P, Bentley DR, Donnelly P. The fine-scale structure of recombination rate variation in the human genome. Science. 2004;304: 581–584. doi: 10.1126/science.1092500 15105499

29. Auton A, McVean G. Recombination rate estimation in the presence of hotspots. Genome Res. 2007;17: 1219–1227. doi: 10.1101/gr.6386707 17623807

30. Chan AH, Jenkins PA, Song YS. Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster. PLOS Genetics. 2012;8: e1003090. doi: 10.1371/journal.pgen.1003090 23284288

31. Baudat F, Imai Y, de Massy B. Meiotic recombination in mammals: localization and regulation. Nature Reviews Genetics. 2013;14: 794–806. doi: 10.1038/nrg3573 24136506

32. Hudson RR, Kaplan NL. Statistical properties of the number of recombination events in the history of a sample of DNA sequences. Genetics. 1985;111: 147–164. 4029609

33. Hudson RR, Kaplan NL. The coalescent process in models with selection and recombination. Genetics. 1988;120: 831–840. 3147214

34. Wiuf C, Hein J. Recombination as a point process along sequences. Theoretical population biology. 1999;55: 248–59. doi: 10.1006/tpbi.1998.1403 10366550

35. Griffiths RC, Marjoram P. An ancestral recombination graph. Progress in population genetics and human evolution. Springer; 1997. pp. 257–270. Available: https://research.monash.edu/en/publications/an-ancestral-recombination-graph

36. Griffiths RC, Marjoram P. Ancestral inference from samples of DNA sequences with recombination. J Comput Biol. 1996;3: 479–502. doi: 10.1089/cmb.1996.3.479 9018600

37. Hein J, Schierup M, Wiuf C. Gene Genealogies, Variation and Evolution: A primer in coalescent theory. Oxford, New York: Oxford University Press; 2004.

38. McVean GAT, Cardin NJ. Approximating the coalescent with recombination. Philosophical Transactions of the Royal Society B: Biological Sciences. 2005;360: 1387–1393. doi: 10.1098/rstb.2005.1673 16048782

39. Marjoram P, Wall JD. Fast “coalescent” simulation. BMC Genetics. 2006;7: 16–16. doi: 10.1186/1471-2156-7-16 16539698

40. Wilton PR, Carmi S, Hobolth A. The SMC′ Is a Highly Accurate Approximation to the Ancestral Recombination Graph. Genetics. 2015;200: 343–355. doi: 10.1534/genetics.114.173898 25786855

41. Terhorst J, Kamm JA, Song YS. Robust and scalable inference of population history from hundreds of unphased whole-genomes. Nat Genet. 2017;49: 303–309. doi: 10.1038/ng.3748 28024154

42. Li H, Durbin R. Inference of human population history from individual whole-genome sequences. Nature. 2011;475: 493–496. doi: 10.1038/nature10231 21753753

43. Schiffels S, Durbin R. Inferring human population size and separation history from multiple genome sequences. Nature Genetics. 2014;46: 919–925. doi: 10.1038/ng.3015 24952747

44. Munch K, Schierup MH, Mailund T. Unraveling recombination rate evolution using ancestral recombination maps. Bioessays. 2014;36: 892–900. doi: 10.1002/bies.201400047 25043668

45. Munch K, Mailund T, Dutheil JY, Schierup MH. A fine-scale recombination map of the human-chimpanzee ancestor reveals faster change in humans than in chimpanzees and a strong impact of GC-biased gene conversion. Genome Res. 2014;24: 467–474. doi: 10.1101/gr.158469.113 24190946

46. Rasmussen MD, Hubisz MJ, Gronau I, Siepel A. Genome-wide inference of ancestral recombination graphs. PLoS Genet. 2014;10: e1004342. doi: 10.1371/journal.pgen.1004342 24831947

47. Dutheil JY. Hidden Markov Models in Population Genomics. Methods Mol Biol. 2017;1552: 149–164. doi: 10.1007/978-1-4939-6753-7_11 28224497

48. Durbin R, Eddy SR, Krogh A, Mitchison G. Biological sequence analysis: Probabilistic models of proteins and nucleic acids [Internet]. Cambridge: Cambridge University Press; 1998. doi: 10.1017/CBO9780511790492

49. Kamm JA, Spence JP, Chan J, Song YS. Two-Locus Likelihoods Under Variable Population Size and Fine-Scale Recombination Rate Estimation. Genetics. 2016;203: 1381–1399. doi: 10.1534/genetics.115.184820 27182948

50. Adrion JR, Galloway JG, Kern AD. Inferring the landscape of recombination using recurrent neural networks. bioRxiv. 2019; 662247. doi: 10.1101/662247

51. Slatkin M. Linkage disequilibrium—understanding the evolutionary past and mapping the medical future. Nature reviews Genetics. 2008;9: 477–85. doi: 10.1038/nrg2361 18427557

52. McVean GAT. A genealogical interpretation of linkage disequilibrium. Genetics. 2002;162: 987–991. 12399406

53. Wirtz J, Rauscher M, Wiehe T. Topological linkage disequilibrium calculated from coalescent genealogies. Theoretical Population Biology. 2018; doi: 10.1016/j.tpb.2018.09.001 30243857

54. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 1974;19: 716–723. doi: 10.1109/TAC.1974.1100705

55. Staab PR, Zhu S, Metzler D, Lunter G. Scrm: Efficiently simulating long sequences using the approximated coalescent with recombination. Bioinformatics. 2015;31: 1680–1682. doi: 10.1093/bioinformatics/btu861 25596205

56. Dapper AL, Payseur BA. Effects of Demographic History on the Detection of Recombination Hotspots from Linkage Disequilibrium. Mol Biol Evol. 2018;35: 335–353. doi: 10.1093/molbev/msx272 29045724

57. Johnston HR, Cutler DJ. Population demographic history can cause the appearance of recombination hotspots. Am J Hum Genet. 2012;90: 774–783. doi: 10.1016/j.ajhg.2012.03.011 22560089

58. Martin SH, Davey J, Salazar C, Jiggins C. Recombination rate variation shapes barriers to introgression across butterfly genomes. bioRxiv. 2018; 297531. doi: 10.1101/297531

59. Grossen C, Keller L, Biebach I, Consortium TIGG, Croll D. Introgression from Domestic Goat Generated Variation at the Major Histocompatibility Complex of Alpine Ibex. PLOS Genetics. 2014;10: e1004438. doi: 10.1371/journal.pgen.1004438 24945814

60. Francioli LC, Polak PP, Koren A, Menelaou A, Chun S, Renkens I, et al. Genome-wide patterns and properties of de novo mutations in humans. Nature Genetics. 2015;47: 822–826. doi: 10.1038/ng.3292 25985141

61. Fu Q, Li H, Moorjani P, Jay F, Slepchenko SM, Bondarev AA, et al. Genome sequence of a 45,000-year-old modern human from western Siberia. Nature. 2014;514: 445–449. doi: 10.1038/nature13810 25341783

62. Croll D, Lendenmann MH, Stewart E, McDonald BA. The impact of recombination hotspots on genome evolution of a fungal plant pathogen. Genetics. 2015;201: 1213–1228. doi: 10.1534/genetics.115.180968 26392286

63. Comeron JM, Ratnappan R, Bailin S. The Many Landscapes of Recombination in Drosophila melanogaster. Petrov DA, editor. PLoS Genet. 2012;8: e1002905. doi: 10.1371/journal.pgen.1002905 23071443

64. Stukenbrock EH, Dutheil JY. Fine-Scale Recombination Maps of Fungal Plant Pathogens Reveal Dynamic Recombination Landscapes and Intragenic Hotspots. Genetics. 2018;208: 1209–1229. doi: 10.1534/genetics.117.300502 29263029

65. Spence JP, Song YS. Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. bioRxiv. 2019; 532168. doi: 10.1101/532168

66. Mallick S, Li H, Lipson M, Mathieson I, Gymrek M, Racimo F, et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature. 2016; doi: 10.1038/nature18964 27654912

67. Slatkin M, Racimo F. Ancient DNA and human history. PNAS. 2016;113: 6380–6387. doi: 10.1073/pnas.1524306113 27274045

68. Prüfer K, Racimo F, Patterson N, Jay F, Sankararaman S, Sawyer S, et al. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature. 2013;505: 43–49. doi: 10.1038/nature12886 24352235

69. Prüfer K, Filippo C de, Grote S, Mafessoni F, Korlević P, Hajdinjak M, et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science. 2017;358: 655–658. doi: 10.1126/science.aao1887 28982794

70. Meyer M, Kircher M, Gansauge M-T, Li H, Racimo F, Mallick S, et al. A High-Coverage Genome Sequence from an Archaic Denisovan Individual. Science. 2012;338: 222–226. doi: 10.1126/science.1224344 22936568

71. Reich D, Thangaraj K, Patterson N, Price AL, Singh L. Reconstructing Indian population history. Nature. 2009;461: 489–494. doi: 10.1038/nature08365 19779445

72. Palamara PF, Terhorst J, Song YS, Price AL. High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability. Nature Genetics. 2018;50: 1311–1317. doi: 10.1038/s41588-018-0177-x 30104759

73. Brandvain Y, Kenney AM, Flagel L, Coop G, Sweigart AL. Speciation and Introgression between Mimulus nasutus and Mimulus guttatus. PLOS Genetics. 2014;10: e1004410. doi: 10.1371/journal.pgen.1004410 24967630

74. Teng H, Zhang Y, Shi C, Mao F, Cai W, Lu L, et al. Population Genomics Reveals Speciation and Introgression between Brown Norway Rats and Their Sibling Species. Mol Biol Evol. 2017;34: 2214–2228. doi: 10.1093/molbev/msx157 28482038

75. Van Belleghem SM, Baquero M, Papa R, Salazar C, McMillan WO, Counterman BA, et al. Patterns of Z chromosome divergence among Heliconius species highlight the importance of historical demography. Mol Ecol. 2018; doi: 10.1111/mec.14560 29569384

76. Delmore KE, Lugo Ramos JS, Van Doren BM, Lundberg M, Bensch S, Irwin DE, et al. Comparative analysis examining patterns of genomic differentiation across multiple episodes of population divergence in birds. Evolution Letters. 2018;2: 76–87. doi: 10.1002/evl3.46 30283666

77. Stapley J, Feulner PGD, Johnston SE, Santure AW, Smadja CM. Variation in recombination frequency and distribution across eukaryotes: patterns and processes. Phil Trans R Soc B. 2017;372: 20160455. doi: 10.1098/rstb.2016.0455 29109219

78. Librado P, Gamba C, Gaunitz C, Sarkissian CD, Pruvost M, Albrechtsen A, et al. Ancient genomic changes associated with domestication of the horse. Science. 2017;356: 442–445. doi: 10.1126/science.aam5298 28450643

79. Moorjani P, Sankararaman S, Fu Q, Przeworski M, Patterson N, Reich D. A genetic method for dating ancient genomes provides a direct estimate of human generation interval in the last 45,000 years. PNAS. 2016;113: 5652–5657. doi: 10.1073/pnas.1514696113 27140627

80. Guéguen L, Gaillard S, Boussau B, Gouy M, Groussin M, Rochette NC, et al. Bio++: efficient extensible libraries and tools for computational molecular evolution. Mol Biol Evol. 2013;30: 1745–1750. doi: 10.1093/molbev/mst097 23699471

81. Sand A, Kristiansen M, Pedersen CN, Mailund T. zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm. BMC Bioinformatics. 2013;14: 339. doi: 10.1186/1471-2105-14-339 24266924

82. Lack JB, Cardeno CM, Crepeau MW, Taylor W, Corbett-Detig RB, Stevens KA, et al. The Drosophila Genome Nexus: A Population Genomic Resource of 623 Drosophila melanogaster Genomes, Including 197 from a Single Ancestral Range Population. Genetics. 2015;199: 1229–1241. doi: 10.1534/genetics.115.174664 25631317

Štítky
Genetika Reprodukční medicína

Článek vyšel v časopise

PLOS Genetics


2019 Číslo 11

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Farmaceutická péče o pacienta s inhalační terapií
nový kurz
Autoři: Mgr. Ondřej Šimandl

Revmatoidní artritida: včas a k cíli
Autoři: MUDr. Heřman Mann

Jistoty a nástrahy antikoagulační léčby aneb kardiolog - neurolog - farmakolog - nefrolog - právník diskutují
Autoři: doc. MUDr. Štěpán Havránek, Ph.D., prof. MUDr. Roman Herzig, Ph.D., doc. MUDr. Karel Urbánek, Ph.D., prim. MUDr. Jan Vachek, MUDr. et Mgr. Jolana Těšínová, Ph.D.

Léčba akutní pooperační bolesti
Autoři: doc. MUDr. Jiří Málek, CSc.

Nové antipsychotikum kariprazin v léčbě schizofrenie
Autoři: prof. MUDr. Cyril Höschl, DrSc., FRCPsych.

Všechny kurzy
Přihlášení
Zapomenuté heslo

Nemáte účet?  Registrujte se

Zapomenuté heslo

Zadejte e-mailovou adresu se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se