Multi-locus Analysis of Genomic Time Series Data from Experimental Evolution


A growing number of experimental biologists are generating “evolve-and-resequence” (E&R) data in which the genomes of an experimental population are repeatedly sequenced over time. The resulting time series data provide important new insights into the dynamics of evolution. This type of analysis has only recently been made possible by next-generation sequencing, and new statistical procedures are required to analyze this novel data source. We present such a procedure here, and apply it to both simulated and real E&R data.


Vyšlo v časopise: Multi-locus Analysis of Genomic Time Series Data from Experimental Evolution. PLoS Genet 11(4): e32767. doi:10.1371/journal.pgen.1005069
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pgen.1005069

Souhrn

A growing number of experimental biologists are generating “evolve-and-resequence” (E&R) data in which the genomes of an experimental population are repeatedly sequenced over time. The resulting time series data provide important new insights into the dynamics of evolution. This type of analysis has only recently been made possible by next-generation sequencing, and new statistical procedures are required to analyze this novel data source. We present such a procedure here, and apply it to both simulated and real E&R data.


Zdroje

1. Veeramah KR, Hammer MF (2014) The impact of whole-genome sequencing on the reconstruction of human population history. Nature Reviews Genetics 15: 149–162. doi: 10.1038/nrg3625 24492235

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

3. Myers S, Bottolo L, Freeman C, McVean G, Donnelly P (2005) A fine-scale map of recombination rates and hotspots across the human genome. Science 310: 321–324. doi: 10.1126/science.1117196 16224025

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

5. Chan AH, Jenkins PA, Song YS (2012) Genome-wide fine-scale recombination rate variation in Drosophila melanogaster. PLoS Genetics 8: e1003090. doi: 10.1371/journal.pgen.1003090 23284288

6. Auton A, Li YR, Kidd J, Oliveira K, Nadel J, et al. (2013) Genetic recombination is targeted towards gene promoter regions in dogs. PLoS Genetics 9: e1003984. doi: 10.1371/journal.pgen.1003984 24348265

7. Nielsen R, Bustamante C, Clark AG, Glanowski S, Sackton TB, et al. (2005) A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biology 3: e170. doi: 10.1371/journal.pbio.0030170 15869325

8. Bustamante CD, Fledel-Alon A, Williamson S, Nielsen R, Hubisz MT, et al. (2005) Natural selection on protein-coding genes in the human genome. Nature 437: 1153–1157. doi: 10.1038/nature04240 16237444

9. Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, et al. (2006) Positive natural selection in the human lineage. Science 312: 1614–1620. doi: 10.1126/science.1124309 16778047

10. Nielsen R, Hellmann I, Hubisz M, Bustamante C, Clark AG (2007) Recent and ongoing selection in the human genome. Nature Reviews Genetics 8: 857–868. doi: 10.1038/nrg2187 17943193

11. Sella G, Petrov DA, Przeworski M, Andolfatto P (2009) Pervasive natural selection in the Drosophila genome? PLoS Genetics 5: e1000495. doi: 10.1371/journal.pgen.1000495 19503600

12. Hernandez RD, Kelley JL, Elyashiv E, Melton SC, Auton A, et al. (2011) Classic selective sweeps were rare in recent human evolution. Science 331: 920–924. doi: 10.1126/science.1198878 21330547

13. Langley CH, Stevens K, Cardeno C, Lee YCG, Schrider DR, et al. (2012) Genomic variation in natural populations of Drosophila melanogaster. Genetics 192: 533–598. doi: 10.1534/genetics.112.142018 22673804

14. Hummel S, Schmidt D, Kremeyer B, Herrmann B, Oppermann M (2005) Detection of the CCR5-Delta32 HIV resistance gene in bronze age skeletons. Genes and Immunity 6: 371–374. doi: 10.1038/sj.gene.6364172 15815693

15. Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, et al. (2010) A draft sequence of the Neandertal genome. Science 328: 710–722. doi: 10.1126/science.1188021 20448178

16. Reich D, Green RE, Kircher M, Krause J, Patterson N, et al. (2010) Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468: 1053–1060. doi: 10.1038/nature09710 21179161

17. Ludwig A, Pruvost M, Reissmann M, Benecke N, Brockmann GA, et al. (2009) Coat color variation at the beginning of horse domestication. Science 324: 485. doi: 10.1126/science.1172750 19390039

18. Meyer M, Kircher M, Gansauge MT, Li H, Racimo F, et al. (2012) A high-coverage genome sequence from an archaic Denisovan individual. Science 338: 222–226. doi: 10.1126/science.1224344 22936568

19. Orlando L, Ginolhac A, Zhang G, Froese D, Albrechtsen A, et al. (2013) Recalibrating equus evolution using the genome sequence of an early middle pleistocene horse. Nature 499: 74–78. doi: 10.1038/nature12323 23803765

20. Sankararaman S, Mallick S, Dannemann M, Prüfer K, Kelso J, et al. (2014) The genomic landscape of Neanderthal ancestry in present-day humans. Nature 507: 354–357. doi: 10.1038/nature12961 24476815

21. Steinrücken M, Bhaskar A, Song YS (2014) A novel spectral method for inferring general diploid selection from time series genetic data. Annals of Applied Statistics 8: 2203–2222. doi: 10.1214/14-AOAS764 25598858

22. Wiser MJ, Ribeck N, Lenski RE (2013) Long-term dynamics of adaptation in asexual populations. Science 342: 1364–1367. doi: 10.1126/science.1243357 24231808

23. Lang GI, Rice DP, Hickman MJ, Sodergren E, Weinstock GM, et al. (2013) Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500: 571–574. doi: 10.1038/nature12344 23873039

24. Burke MK, Dunham JP, Shahrestani P, Thornton KR, Rose MR, et al. (2010) Genome-wide analysis of a long-term evolution experiment with Drosophila. Nature 467: 587–590. doi: 10.1038/nature09352 20844486

25. Orozco ter Wengel P, Kapun M, Nolte V, Kofler R, Flatt T, et al. (2012) Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles. Molecular Ecology 21: 4931–4941. doi: 10.1111/j.1365-294X.2012.05673.x

26. Tenaillon O, Rodríguez-Verdugo A, Gaut RL, McDonald P, Bennett AF, et al. (2012) The molecular diversity of adaptive convergence. Science 335: 457–461. doi: 10.1126/science.1212986 22282810

27. Shankarappa R, Margolick JB, Gange SJ, Rodrigo AG, Upchurch D, et al. (1999) Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. Journal of Virology 73: 10489–10502. 10559367

28. Burke MK (2012) How does adaptation sweep through the genome? Insights from long-term selection experiments. Proceedings of the Royal Society B: Biological Sciences 279: 5029–5038. doi: 10.1098/rspb.2012.0799 22833271

29. Parts L, Cubillos FA, Warringer J, Jain K, Salinas F, et al. (2011) Revealing the genetic structure of a trait by sequencing a population under selection. Genome Research 21: 1131–1138. doi: 10.1101/gr.116731.110 21422276

30. Feder AF, Kryazhimskiy S, Plotkin JB (2014) Identifying signatures of selection in genetic time series. Genetics 196: 509–522. doi: 10.1534/genetics.113.158220 24318534

31. Baldwin-Brown JG, Long AD, Thornton KR (2014) The power to detect quantitative trait loci using resequenced, experimentally evolved populations of diploid, sexual organisms. Molecular Biology and Evolution 31: 1040–1055. doi: 10.1093/molbev/msu048 24441104

32. Kofler R, Schlötterer C (2014) A guide for the design of evolve and resequencing studies. Molecular Biology and Evolution 31: 474–483. doi: 10.1093/molbev/mst221 24214537

33. Illingworth CJR, Parts L, Schiffels S, Liti G, Mustonen V (2012) Quantifying selection acting on a complex trait using allele frequency time series data. Molecular Biology and Evolution 29: 1187–1197. doi: 10.1093/molbev/msr289 22114362

34. Topa H, Jónás Á, Kofler R, Kosiol C, Honkela A (2014) Gaussian process test for highthroughput sequencing time series: application to experimental evolution. arXiv q-bio.PE: 1403:4086.

35. Lynch M, Bost D, Wilson S, Maruki T, Harrison S (2014) Population-genetic inference from pooled-sequencing data. Genome Biology and Evolution 6: 1210–1218. doi: 10.1093/gbe/evu085 24787620

36. Franssen SU, Nolte V, Tobler R, Schlötterer C (2015) Patterns of linkage disequilibrium and long range hitchhiking in evolving experimental Drosophila melanogaster populations. Molecular Biology and Evolution, 32: 495–509. doi: 10.1093/molbev/msu320

37. Futschik A, Schlötterer C (2010) The next generation of molecular markers from massively parallel sequencing of pooled DNA samples. Genetics 186: 207–218. doi: 10.1534/genetics.110.114397 20457880

38. Schlötterer C, Tobler R, Kofler R, Nolte V (2014) Sequencing pools of individuals—mining genome-wide polymorphism data without big funding. Nature Reviews Genetics 15: 749–763. doi: 10.1038/nrg3803 25246196

39. Ewens WJ (1979) Mathematical Population Genetics. Springer Verlag.

40. Hazel JR (1995) Thermal adaptation in biological membranes: is homeoviscous adaptation the explanation? Annual Review of Physiology 57: 19–42. doi: 10.1146/annurev.ph.57.030195.000315 7778864

41. Comeron JM, Ratnappan R, Bailin S (2012) The many landscapes of recombination in Drosophila melanogaster. PLoS Genetics 8: e1002905. doi: 10.1371/journal.pgen.1002905 23071443

42. Singh ND, Stone EA, Aquadro CF, Clark AG (2013) Fine-scale heterogeneity in crossover rate in the garnet-scalloped region of the Drosophila melanogaster X chromosome. Genetics 194: 375–387. doi: 10.1534/genetics.112.146746 23410829

43. Cutler DJ, Jensen JD (2010) To pool, or not to pool? Genetics 186: 41–43. doi: 10.1534/genetics.110.121012 20855575

44. Gautier M, Foucaud J, Gharbi K, Cézard T, Galan M, et al. (2013) Estimation of population allele frequencies from next-generation sequencing data: pool-versus individual-based genotyping. Molecular Ecology 22: 3766–3779. doi: 10.1111/mec.12360 23730833

45. Lynch M, Bost D, Wilson S, Maruki T, Harrison S (2014) Population-genetic inference from pooled-sequencing data. Genome Biology and Evolution 6: 1210–1218. doi: 10.1093/gbe/evu085 24787620

46. Kirkpatrick M, Johnson T, Barton N (2002) General models of multilocus evolution. Genetics 161: 1727. 12196414

47. Barton NH, Otto SP (2005) Evolution of recombination due to random drift. Genetics 169: 2353–2370. doi: 10.1534/genetics.104.032821 15687279

48. Stephan W, Song YS, Langley CH (2006) The hitchhiking effect on linkage disequilibrium between linked neutral loci. Genetics 172: 2647–2663. doi: 10.1534/genetics.105.050179 16452153

49. Hudson RR (2002) Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics 18: 337–338. doi: 10.1093/bioinformatics/18.2.337 11847089

50. Li H, Stephan W (2006) Inferring the demographic history and rate of adaptive substitution in Drosophila. PLoS Genetics 2: e166. doi: 10.1371/journal.pgen.0020166 17040129

51. Peng B, Kimmel M (2005) simuPOP: a forward-time population genetics simulation environment. Bioinformatics 21: 3686–3687. doi: 10.1093/bioinformatics/bti584 16020469

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2015 Číslo 4
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Eozinofilní granulomatóza s polyangiitidou
nový kurz

Betablokátory a Ca antagonisté z jiného úhlu
Autori: prof. MUDr. Michal Vrablík, Ph.D., MUDr. Petr Janský

Autori: doc. MUDr. Petr Čáp, Ph.D.

Farmakoterapie akutní a chronické bolesti

Získaná hemofilie - Povědomí o nemoci a její diagnostika

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Nemáte účet?  Registrujte sa

Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa