#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Parallel and nonparallel genomic responses contribute to herbicide resistance in Ipomoea purpurea, a common agricultural weed


Autoři: Megan Van Etten aff001;  Kristin M. Lee aff002;  Shu-Mei Chang aff003;  Regina S. Baucom aff004
Působiště autorů: Biology Department, Penn State-Scranton, Dunmore, Pennsylvania, United States of America aff001;  Department of Biological Sciences, Columbia University, New York, New York, United States of America aff002;  Plant Biology Department, University of Georgia, Athens, Georgia, United States of America aff003;  Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America aff004
Vyšlo v časopise: Parallel and nonparallel genomic responses contribute to herbicide resistance in Ipomoea purpurea, a common agricultural weed. PLoS Genet 16(2): e32767. doi:10.1371/journal.pgen.1008593
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008593

Souhrn

The repeated evolution of herbicide resistance has been cited as an example of genetic parallelism, wherein separate species or genetic lineages utilize the same genetic solution in response to selection. However, most studies that investigate the genetic basis of herbicide resistance examine the potential for changes in the protein targeted by the herbicide rather than considering genome-wide changes. We used a population genomics screen and targeted exome re-sequencing to uncover the potential genetic basis of glyphosate resistance in the common morning glory, Ipomoea purpurea, and to determine if genetic parallelism underlies the repeated evolution of resistance across replicate resistant populations. We found no evidence for changes in 5‐enolpyruvylshikimate‐3‐phosphate synthase (EPSPS), glyphosate’s target protein, that were associated with resistance, and instead identified five genomic regions that showed evidence of selection. Within these regions, genes involved in herbicide detoxification—cytochrome P450s, ABC transporters, and glycosyltransferases—are enriched and exhibit signs of selective sweeps. One region under selection shows parallel changes across all assayed resistant populations whereas other regions exhibit signs of divergence. Thus, while it appears that the physiological mechanism of resistance in this species is likely the same among resistant populations, we find patterns of both similar and divergent selection across separate resistant populations at particular loci.

Klíčová slova:

Detoxification – Evolutionary genetics – Genetic loci – Haplotypes – Herbicides – Molecular genetics – Population genetics – Sequence assembly tools


Zdroje

1. Powles SB, Yu Q. Evolution in action: plants resistant to herbicides. Annu Rev Plant Biol. 2010;61: 317–347. doi: 10.1146/annurev-arplant-042809-112119 20192743

2. Délye C, Michel S, Bérard A, Chauvel B, Brunel D, Guillemin J-P, et al. Geographical variation in resistance to acetyl-coenzyme A carboxylase-inhibiting herbicides across the range of the arable weed Alopecurus myosuroides (black-grass). New Phytol. Blackwell Publishing Ltd; 2010;186: 1005–1017.

3. Thomsen EK, Strode C, Hemmings K, Hughes AJ, Chanda E, Musapa M, et al. Underpinning sustainable vector control through informed insecticide resistance management. PLoS One. 2014;9: e99822. doi: 10.1371/journal.pone.0099822 24932861

4. Kuester A, Chang S-M, Baucom RS. The geographic mosaic of herbicide resistance evolution in the common morning glory, Ipomoea purpurea: Evidence for resistance hotspots and low genetic differentiation across the landscape. Evol Appl. 2015;8: 821–833. doi: 10.1111/eva.12290 26366199

5. Christin P-A, Weinreich DM, Besnard G. Causes and evolutionary significance of genetic convergence. Trends Genet. 2010;26: 400–405. doi: 10.1016/j.tig.2010.06.005 20685006

6. Storz JF. Causes of molecular convergence and parallelism in protein evolution. Nat Rev Genet. 2016;17: 239–250. doi: 10.1038/nrg.2016.11 26972590

7. Losos JB. Convergence, adaptation, and constraint. Evolution. 2011;65: 1827–1840. doi: 10.1111/j.1558-5646.2011.01289.x 21729041

8. Martin A, Orgogozo V. The loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation. Evolution. 2013;67: 1235–1250. doi: 10.1111/evo.12081 23617905

9. Baucom RS. The remarkable repeated evolution of herbicide resistance. Am J Bot. 2016;103: 181–183. doi: 10.3732/ajb.1500510 26823379

10. Wake DB. Homoplasy: the result of natural selection, or evidence of design limitations? Am Nat. 1991;138: 543–567.

11. Délye C. Unravelling the genetic bases of non-target-site-based resistance (NTSR) to herbicides: a major challenge for weed science in the forthcoming decade. Pest Manag Sci. Wiley Online Library; 2013;69: 176–187. doi: 10.1002/ps.3318 22614948

12. Brazier M, Cole DJ, Edwards R. O-Glucosyltransferase activities toward phenolic natural products and xenobiotics in wheat and herbicide-resistant and herbicide-susceptible black-grass (Alopecurus myosuroides). Phytochemistry. 2002;59: 149–156. doi: 10.1016/s0031-9422(01)00458-7 11809449

13. Yu Q, Cairns A, Powles S. Glyphosate, paraquat and ACCase multiple herbicide resistance evolved in a Lolium rigidum biotype. Planta. 2007;225: 499–513. doi: 10.1007/s00425-006-0364-3 16906433

14. Karn E, Jasieniuk M. Nucleotide diversity at site 106 of EPSPS in Lolium perenne L. ssp. multiflorum from California indicates multiple evolutionary origins of herbicide resistance. Front Plant Sci. 2017;8: 777. doi: 10.3389/fpls.2017.00777 28536598

15. Herrmann KM, Weaver LM. The shikimate pathway. Annu Rev Plant Physiol Plant Mol Biol. annualreviews.org; 1999;50: 473–503. doi: 10.1146/annurev.arplant.50.1.473 15012217

16. Cummins I, Wortley DJ, Sabbadin F, He Z, Coxon CR, Straker HE, et al. Key role for a glutathione transferase in multiple-herbicide resistance in grass weeds. Proc Natl Acad Sci U S A. 2013;110: 5812–5817. doi: 10.1073/pnas.1221179110 23530204

17. Kuester A, Wilson A, Chang S-M, Baucom RS. A resurrection experiment finds evidence of both reduced genetic diversity and potential adaptive evolution in the agricultural weed Ipomoea purpurea. Mol Ecol. 2016;25: 4508–4520. doi: 10.1111/mec.13737 27357067

18. Debban CL, Okum S, Pieper KE, Wilson A, Baucom RS. An examination of fitness costs of glyphosate resistance in the common morning glory, Ipomoea purpurea. Ecol Evol. Wiley Online Library; 2015;5: 5284–5294.

19. Baucom RS, Mauricio R. Constraints on the evolution of tolerance to herbicide in the common morning glory: resistance and tolerance are mutually exclusive. Evolution. 2008;62: 2842–2854. doi: 10.1111/j.1558-5646.2008.00514.x 18786188

20. Van Etten ML, Kuester A, Chang S-M, Baucom RS. Fitness costs of herbicide resistance across natural populations of the common morning glory, Ipomoea purpurea. Evolution. 2016;70: 2199–2210. doi: 10.1111/evo.13016 27470166

21. Gaines TA, Heap IM. Mutations in herbicide-resistant weeds to EPSP synthase inhibitors. In: International Survey of Herbicide Resistant Weeds [Internet]. [cited 8 Oct 2017]. Available: http://www.weedscience.com

22. Loutre C, Dixon DP, Brazier M, Slater M, Cole DJ, Edwards R. Isolation of a glucosyltransferase from Arabidopsis thaliana active in the metabolism of the persistent pollutant 3,4-dichloroaniline. Plant J. 2003;34: 485–493. doi: 10.1046/j.1365-313x.2003.01742.x 12753587

23. Brazier-Hicks M, Edwards R. Functional importance of the family 1 glucosyltransferase UGT72B1 in the metabolism of xenobiotics in Arabidopsis thaliana. Plant J. 2005;42: 556–566. doi: 10.1111/j.1365-313X.2005.02398.x 15860014

24. Hammerton JL. Environmental factors and susceptibility to herbicides. Weeds. Weed Science Society of America; 1967;15: 330–336.

25. Matzrafi M, Seiwert B, Reemtsma T, Rubin B, Peleg Z. Climate change increases the risk of herbicide-resistant weeds due to enhanced detoxification. Planta. 2016;244: 1217–1227. doi: 10.1007/s00425-016-2577-4 27507240

26. Anderson DM, Swanton CJ, Hall JC, Mersey BG. The influence of temperature and relative humidity on the efficacy of glufosinate-ammonium. Weed Res. Blackwell Publishing Ltd; 1993;33: 139–147.

27. Robinson MA, Letarte J, Cowbrough MJ, Sikkema PH, Tardif FJ. Winter wheat (Triticum aestivum L.) response to herbicides as affected by application timing and temperature. Can J Plant Sci. Canadian Science Publishing; 2014;95: 325–333.

28. Leslie T, Baucom RS. De novo assembly and annotation of the transcriptome of the agricultural weed Ipomoea purpurea uncovers gene expression changes associated with herbicide resistance. G3. 2014;4: 2035–2047. doi: 10.1534/g3.114.013508 25155274

29. Hoshino A, Jayakumar V, Nitasaka E, Toyoda A, Noguchi H, Itoh T, et al. Genome sequence and analysis of the Japanese morning glory Ipomoea nil. Nat Commun. 2016;7: 13295. doi: 10.1038/ncomms13295 27824041

30. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123: 585–595. 2513255

31. Fay JC, Wu CI. Sequence divergence, functional constraint, and selection in protein evolution. Annu Rev Genomics Hum Genet. 2003;4: 213–235. doi: 10.1146/annurev.genom.4.020303.162528 14527302

32. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population-structure. Evolution. 1984;38: 1358–1370. doi: 10.1111/j.1558-5646.1984.tb05657.x 28563791

33. Lee KM, Coop G. Distinguishing Among Modes of Convergent Adaptation Using Population Genomic Data. 2017. Genetics 207: 1591–1619. doi: 10.1534/genetics.117.300417 29046403

34. Gaines TA, Zhang WL, Wang DF, Bukun B, Chisholm ST, Shaner DL, et al. Gene amplification confers glyphosate resistance in Amaranthus palmeri. Proc Natl Acad Sci U S A. 2010;107: 1029–1034. doi: 10.1073/pnas.0906649107 20018685

35. Jugulam M, Niehues K, Godar AS, Koo D-H, Danilova T, Friebe B, et al. Tandem Amplification of a Chromosomal Segment Harboring 5-Enolpyruvylshikimate-3-Phosphate Synthase Locus Confers Glyphosate Resistance in Kochia scoparia. Plant Physiol. 2014;166: 1200. doi: 10.1104/pp.114.242826 25037215

36. Nandula VK, Wright AA, Bond JA, Ray JD, Eubank TW, Molin WT. EPSPS amplification in glyphosate-resistant spiny amaranth (Amaranthus spinosus): a case of gene transfer via interspecific hybridization from glyphosate-resistant Palmer amaranth (Amaranthus palmeri). Pest Manag Sci. Wiley Online Library; 2014;70: 1902–1909. doi: 10.1002/ps.3754 24497375

37. Chatham LA, Bradley KW, Kruger GR, Martin JR, Owen MDK, Peterson DE, et al. A Multistate Study of the Association Between Glyphosate Resistance and EPSPS Gene Amplification in Waterhemp (Amaranthus tuberculatus). Weed Sci. Cambridge University Press; 2015;63: 569–577.

38. Kumar V, Jha P, Reichard N. Occurrence and Characterization of Kochia (Kochia scoparia) Accessions with Resistance to Glyphosate in Montana. Weed Technol. 2014;28: 122–130.

39. Alvarado-Serrano DF, Van Etten ML, Chang S-M, Baucom RS. The relative contribution of natural landscapes and human-mediated factors on the connectivity of a noxious invasive weed. Heredity. 2019;122: 29–40. doi: 10.1038/s41437-018-0106-x 29967398

40. Anderson LK, Doyle GG, Brigham B, Carter J, Hooker KD, Lai A, et al. High-resolution crossover maps for each bivalent of Zea mays using recombination nodules. Genetics. 2003;165: 849–865. 14573493

41. Haupt W, Fischer TC, Winderl S, Fransz P, Torres-Ruiz RA. The centromere1 (CEN1) region of Arabidopsis thaliana: architecture and functional impact of chromatin. Plant J. 2001;27: 285–296. doi: 10.1046/j.1365-313x.2001.01087.x 11532174

42. Copenhaver GP, Browne WE, Preuss D. Assaying genome-wide recombination and centromere functions with Arabidopsis tetrads. Proc Natl Acad Sci U S A. 1998;95: 247–252. doi: 10.1073/pnas.95.1.247 9419361

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

44. Messer PW, Petrov DA. Population genomics of rapid adaptation by soft selective sweeps. Trends Ecol Evol. 2013;28: 659–669. doi: 10.1016/j.tree.2013.08.003 24075201

45. Garud NR, Messer PW, Buzbas EO, Petrov DA. Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps. PLoS Genet. 2015;11: e1005004. doi: 10.1371/journal.pgen.1005004 25706129

46. Yuan JS, Tranel PJ, Stewart CN Jr. Non-target-site herbicide resistance: a family business. Trends Plant Sci. Elsevier; 2007;12: 6–13. doi: 10.1016/j.tplants.2006.11.001 17161644

47. Nol N, Tsikou D, Eid M, Livieratos IC, Giannopolitis CN. Shikimate leaf disc assay for early detection of glyphosate resistance in Conyza canadensis and relative transcript levels of EPSPS and ABC transporter genes. Weed Res. Blackwell Publishing Ltd; 2012;52: 233–241.

48. Peng Y, Abercrombie LL, Yuan JS, Riggins CW, Sammons RD, Tranel PJ, et al. Characterization of the horseweed (Conyza canadensis) transcriptome using GS-FLX 454 pyrosequencing and its application for expression analysis of candidate non-target herbicide resistance genes. Pest Manag Sci. 2010;66: 1053–1062. doi: 10.1002/ps.2004 20715018

49. Yuan JS, Abercrombie LLG, Cao Y, Halfhill MD, Zhou X, Peng Y, et al. Functional genomics analysis of horseweed (Conyza canadensis) with special reference to the evolution of non–target-site glyphosate resistance. Weed Sci. 2010;58: 109–117.

50. Kuester A, Fall E, Chang S-M, Baucom RS. Shifts in outcrossing rates and changes to floral traits are associated with the evolution of herbicide resistance in the common morning glory. Ecol Lett. 2017;20: 41–49. doi: 10.1111/ele.12703 27905176

51. Cummins I, Bryant DN, Edwards R. Safener responsiveness and multiple herbicide resistance in the weed black-grass (Alopecurus myosuroides) [Internet]. Plant Biotechnology Journal. 2009. pp. 807–820. doi: 10.1111/j.1467-7652.2009.00445.x 19754839

52. Cummins I, Cole DJ, Edwards R. A role for glutathione transferases functioning as glutathione peroxidases in resistance to multiple herbicides in black-grass. Plant J. Wiley Online Library; 1999;18: 285–292. doi: 10.1046/j.1365-313x.1999.00452.x 10377994

53. Baucom RS. Evolutionary and ecological insights from herbicide‐resistant weeds: what have we learned about plant adaptation, and what is left to uncover? New Phytol. Wiley Online Library; 2019; Available: https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.15723

54. Délye C, Jasieniuk M, Le Corre V. Deciphering the evolution of herbicide resistance in weeds. Trends Genet. Elsevier Ltd; 2013;29: 649–658.

55. Ozias-Akins Peggy, and Jarret Robert L. Nuclear DNA Content and Ploidy Levels in the Genus Ipomoea. Journal of the American Society for Horticultural Science. American Society for Horticultural Science 119 (1); 1994; 110–15.

56. Green P, Ewing B. Phred. Version 0.020425 c. Computer program and documentation available at www.phrap.org. 2002

57. Katoh K, Misawa K, Kuma K-I, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. Oxford Univ Press; 2002;30: 3059–3066. doi: 10.1093/nar/gkf436 12136088

58. Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ. Jalview Version 2—a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009;25: 1189–1191. doi: 10.1093/bioinformatics/btp033 19151095

59. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. [Royal Statistical Society, Wiley]; 1995;57: 289–300.

60. Jombart T, Ahmed I. adegenet 1.3–1: new tools for the analysis of genome-wide SNP data [Internet]. Bioinformatics. 2011. doi: 10.1093/bioinformatics/btr521 21926124

61. Paradis E. pegas: an R package for population genetics with an integrated—modular approach. Bioinformatics. 2010. pp. 419–420.

62. Notredame C, Higgins DG, Heringa J. T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol. Elsevier; 2000;302: 205–217. doi: 10.1006/jmbi.2000.4042 10964570

63. Bushnell B. BBMap short read aligner. University of California, Berkeley, California URL http://sourceforgenet/projects/bbmap. 2016;

64. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25: 2078–2079. doi: 10.1093/bioinformatics/btp352 19505943

65. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27: 2156–2158. doi: 10.1093/bioinformatics/btr330 21653522

66. Kamvar ZN, Tabima JF, Grünwald NJ. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ. 2014;2: e281. doi: 10.7717/peerj.281 24688859

67. Goudet J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Mol Ecol Resour. Wiley Online Library; 2005;5: 184–186.

68. Raj A, Stephens M, Pritchard JK. fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics. 2014;197: 573–589. doi: 10.1534/genetics.114.164350 24700103

69. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. Oxford Univ Press; 2007;23: 2633–2635.

70. Foll M, Gaggiotti O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics. Genetics Soc America; 2008;180: 977–993.

71. Coop G, Witonsky D, Di Rienzo A, Pritchard JK. Using environmental correlations to identify loci underlying local adaptation. Genetics. 2010;185: 1411–1423. doi: 10.1534/genetics.110.114819 20516501

72. Günther T, Coop G. Robust identification of local adaptation from allele frequencies. Genetics. 2013;195: 205–220. doi: 10.1534/genetics.113.152462 23821598

73. Matasci N, Hung L-H, Yan Z, Carpenter EJ, Wickett NJ, Mirarab S, et al. Data access for the 1,000 Plants (1KP) project. Gigascience. 2014;3: 17. doi: 10.1186/2047-217X-3-17 25625010

74. Benazzo A, Panziera A, Bertorelle G. 4P: fast computing of population genetics statistics from large DNA polymorphism panels. Ecol Evol. 2015;5: 172–175. doi: 10.1002/ece3.1261 25628874

75. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. Oxford Univ Press; 2014;30: 2114–2120.

76. Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJ, Birol I. ABySS: a parallel assembler for short read sequence data. Genome Res. 2009;19: 1117–1123. doi: 10.1101/gr.089532.108 19251739

77. Salmela L, Rivals E. LoRDEC: accurate and efficient long read error correction. Bioinformatics. Oxford Univ Press; 2014;30: 3506–3514. doi: 10.1093/bioinformatics/btu538 25165095

78. Ye C, Hill C, Wu S, Ruan J, Zhanshan M. DBG2OLC: Efficient assembly of large genomes using long erroneous reads of the third generation sequencing technologies. Scientific Reports. 2016; 31900.

79. Stanke M, Morgenstern B. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. Oxford Univ Press; 2005;33: W465–7. doi: 10.1093/nar/gki458 15980513

80. Solovyev V, Kosarev P, Seledsov I, Vorobyev D. Automatic annotation of eukaryotic genes, pseudogenes and promoters. Genome Biol. genomebiology.biomedcentral.com; 2006;7 Suppl 1: S10.1–12.

81. Bromberg Y, Rost B. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 2007;35: 3823–3835. doi: 10.1093/nar/gkm238 17526529

82. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. Oxford Univ Press; 1997;25: 955–964. doi: 10.1093/nar/25.5.955 9023104

83. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215: 403–410. doi: 10.1016/S0022-2836(05)80360-2 2231712

84. Hirakawa H, Okada Y, Tabuchi H, Shirasawa K, Watanabe A, Tsuruoka H, et al. Survey of genome sequences in a wild sweet potato, Ipomoea trifida (H. B. K.) G. Don. DNA Res. 2015;22: 171–179. doi: 10.1093/dnares/dsv002 25805887

85. Kircher M, Sawyer S, Meyer M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 2012;40: e3. doi: 10.1093/nar/gkr771 22021376

86. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31: 1674–1676. doi: 10.1093/bioinformatics/btv033 25609793

87. Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. Oxford University Press; 2010;26: 589–595. doi: 10.1093/bioinformatics/btp698 20080505

88. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. genome.cshlp.org; 2010;20: 1297–1303. doi: 10.1101/gr.107524.110 20644199

89. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. Wiley Online Library; 2013;43: 11.10.1–33.

90. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. Nature Research; 2011;43: 491–498.

91. Meynert AM, Bicknell LS, Hurles ME, Jackson AP, Taylor MS. Quantifying single nucleotide variant detection sensitivity in exome sequencing. BMC Bioinformatics. 2013;14: 195. doi: 10.1186/1471-2105-14-195 23773188

92. Verrier P, Theodoulou F, Murphy A. Download—TAIR 10 blastsest TAIR10_cdna_20101214_updated. In: The Arabidopsis Information Resource [Internet]. 2010 [cited 10 Oct 2016]. Available: https://www.arabidopsis.org/download_files/Sequences/TAIR10_blastsets/TAIR10_cdna_20101214_updated

93. Kent WJ. BLAT—the BLAST-like alignment tool. Genome Res. 2002;12: 656–664. doi: 10.1101/gr.229202 11932250

94. Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, Clawson H, et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 2006;34: D590–8. doi: 10.1093/nar/gkj144 16381938

95. Vergara IA, Frech C, Chen N. CooVar: co-occurring variant analyzer. BMC Res Notes. 2012;5: 615. doi: 10.1186/1756-0500-5-615 23116482

96. Weir BS. Genetic data analysis. Methods for discrete population genetic data. Sinauer Associates, Inc. Publishers; 1990.

97. Baduel P, Hunter B, Yeola S, Bomblies K. Genetic basis and evolution of rapid cycling in railway populations of tetraploid Arabidopsis arenosa. PLoS Genet. 2018;14: e1007510. doi: 10.1371/journal.pgen.1007510 29975688

98. Wang M., Huang X., Li R., Xu H., Jin L., & He Y. (2014). Detecting recent positive selection with high accuracy and reliability by conditional coalescent tree. Molecular biology and evolution, 31(11), 3068–3080. doi: 10.1093/molbev/msu244 25135945

99. Zeng K., Fu Y. X., Shi S., & Wu C. I. (2006). Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics, 174(3), 1431–1439. doi: 10.1534/genetics.106.061432 16951063

100. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. Oxford University Press; 2018;35: 526–528.

101. Team RC. R Core Team. 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL< http://www.R-project.org; 2013.

102. Bhatnagar SR, Yang Y, Khundrakpam B, Evans AC, Blanchette M, Bouchard L, et al. An analytic approach for interpretable predictive models in high-dimensional data in the presence of interactions with exposures [Internet]. Genetic Epidemiology. 2018. pp. 233–249. doi: 10.1002/gepi.22112 29423954

103. Bilton TP, McEwan JC, Clarke SM, Brauning R, van Stijn TC, Rowe SJ, et al. Linkage Disequilibrium Estimation in Low Coverage High-Throughput Sequencing Data. Genetics. 2018;209: 389–400. doi: 10.1534/genetics.118.300831 29588288


Článek vyšel v časopise

PLOS Genetics


2020 Číslo 2
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Hypertenze a hypercholesterolémie – synergický efekt léčby
nový kurz
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Multidisciplinární zkušenosti u pacientů s diabetem
Autoři: Prof. MUDr. Martin Haluzík, DrSc., prof. MUDr. Vojtěch Melenovský, CSc., prof. MUDr. Vladimír Tesař, DrSc.

Úloha kombinovaných preparátů v léčbě arteriální hypertenze
Autoři: prof. MUDr. Martin Haluzík, DrSc.

Halitóza
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Terapie roztroušené sklerózy v kostce
Autoři: MUDr. Dominika Šťastná, Ph.D.

Všechny kurzy
Přihlášení
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

#ADS_BOTTOM_SCRIPTS#