Parallelism in eco-morphology and gene expression despite variable evolutionary and genomic backgrounds in a Holarctic fish
Autoři:
Arne Jacobs aff001; Madeleine Carruthers aff001; Andrey Yurchenko aff001; Natalia V. Gordeeva aff002; Sergey S. Alekseyev aff003; Oliver Hooker aff001; Jong S. Leong aff006; David R. Minkley aff006; Eric B. Rondeau aff006; Ben F. Koop aff006; Colin E. Adams aff001; Kathryn R. Elmer aff001
Působiště autorů:
Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
aff001; Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
aff002; Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, Russia
aff003; Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia
aff004; Scottish Centre for Ecology and the Natural Environment, University of Glasgow, Rowardennan, Loch Lomond, Glasgow, United Kingdom
aff005; Biology/Centre for Biomedical Research, University of Victoria, British Columbia, Canada
aff006
Vyšlo v časopise:
Parallelism in eco-morphology and gene expression despite variable evolutionary and genomic backgrounds in a Holarctic fish. PLoS Genet 16(4): e32767. doi:10.1371/journal.pgen.1008658
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008658
Souhrn
Understanding the extent to which ecological divergence is repeatable is essential for predicting responses of biodiversity to environmental change. Here we test the predictability of evolution, from genotype to phenotype, by studying parallel evolution in a salmonid fish, Arctic charr (Salvelinus alpinus), across eleven replicate sympatric ecotype pairs (benthivorous-planktivorous and planktivorous-piscivorous) and two evolutionary lineages. We found considerable variability in eco-morphological divergence, with several traits related to foraging (eye diameter, pectoral fin length) being highly parallel even across lineages. This suggests repeated and predictable adaptation to environment. Consistent with ancestral genetic variation, hundreds of loci were associated with ecotype divergence within lineages of which eight were shared across lineages. This shared genetic variation was maintained despite variation in evolutionary histories, ranging from postglacial divergence in sympatry (ca. 10-15kya) to pre-glacial divergence (ca. 20-40kya) with postglacial secondary contact. Transcriptome-wide gene expression (44,102 genes) was highly parallel across replicates, involved biological processes characteristic of ecotype morphology and physiology, and revealed parallelism at the level of regulatory networks. This expression divergence was not only plastic but in part genetically controlled by parallel cis-eQTL. Lastly, we found that the magnitude of phenotypic divergence was largely correlated with the genetic differentiation and gene expression divergence. In contrast, the direction of phenotypic change was mostly determined by the interplay of adaptive genetic variation, gene expression, and ecosystem size. Ecosystem size further explained variation in putatively adaptive, ecotype-associated genomic patterns within and across lineages, highlighting the role of environmental variation and stochasticity in parallel evolution. Together, our findings demonstrate the parallel evolution of eco-morphology and gene expression within and across evolutionary lineages, which is controlled by the interplay of environmental stochasticity and evolutionary contingencies, largely overcoming variable evolutionary histories and genomic backgrounds.
Klíčová slova:
Evolutionary genetics – Gene expression – Genetic loci – Genetic polymorphism – Genome evolution – Lakes – Phenotypes – Parallel evolution
Zdroje
1. Conway Morris S. Life’s solution: inevitable humans in a lonely universe. Cambridge University Press; 2003.
2. Gould S. Wonderful life: the Burgess Shale and the nature of history. Wonderful life Burgess Shale Nat Hist. 1990.
3. Elmer KR, Meyer A. Adaptation in the age of ecological genomics: Insights from parallelism and convergence. Trends Ecol Evol. 2011;26: 298–306. doi: 10.1016/j.tree.2011.02.008 21459472
4. Kaeuffer R, Peichel CL, Bolnick DI, Hendry AP. Parallel and nonparallel aspects of ecological, phenotypic, and genetic divergence across replicate population pairs of lake and stream stickleback. Evolution. 2012;66: 402–418. doi: 10.1111/j.1558-5646.2011.01440.x 22276537
5. Mahler DL, Ingram T, Revell LJ, Losos JB. Exceptional Convergence on the Macroevolutionary Landscape in Island Lizard Radiations. Science (80-). 2013;341: 292–295. doi: 10.1126/science.1232392 23869019
6. Kowalko JE, Rohner N, Linden TA, Rompani SB, Warren WC, Borowsky R, et al. Convergence in feeding posture occurs through different genetic loci in independently evolved cave populations of Astyanax mexicanus. Proc Natl Acad Sci. 2013;110: 16933–16938. doi: 10.1073/pnas.1317192110 24085851
7. Elmer KR, Fan S, Kusche H, Luise Spreitzer M, Kautt AF, Franchini P, et al. Parallel evolution of Nicaraguan crater lake cichlid fishes via non-parallel routes. Nat Commun. 2014;5: 5168. doi: 10.1038/ncomms6168 25346277
8. Schluter D. Ecology and the origin of species. Trends Ecol Evol. 2001;16: 372–380. doi: 10.1016/s0169-5347(01)02198-x 11403870
9. Endler JA. Natural selection in the wild. Princeton University Press. 1986.
10. Oke KB, Rolshausen G, LeBlond C, Hendry AP. How Parallel Is Parallel Evolution? A Comparative Analysis in Fishes. Am Nat. 2017;190: 1–16. doi: 10.1086/691989 28617637
11. Bolnick DI, Barrett RDH, Oke KB, Rennison DJ, Stuart YE. (Non)Parallel Evolution. Annu Rev Ecol Evol Syst. 2018;12: 303–330.
12. Stuart YE, Veen T, Weber JN, Hanson D, Ravinet M, Lohman BK, et al. Contrasting effects of environment and genetics generate a continuum of parallel evolution. Nat Ecol Evol. 2017;1: 158. doi: 10.1038/s41559-017-0158 28812631
13. Conte GL, Arnegard ME, Peichel CL, Schluter D. The probability of genetic parallelism and convergence in natural populations. Proc Biol Sci. 2012;279: 5039–47. doi: 10.1098/rspb.2012.2146 23075840
14. Nosil P, Villoutreix R, de Carvalho CF, Farkas TE, Soria-Carrasco V, Feder JL, et al. Natural selection and the predictability of evolution inTimemastick insects. Science. 2018;359: 765–770. doi: 10.1126/science.aap9125 29449486
15. Langerhans RB. Predictability and Parallelism of Multitrait Adaptation. J Hered. 2017;109: 59–70. doi: 10.1093/jhered/esx043 28482006
16. Collyer ML, Adams DC. Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. Hysterix, Ital J Mammal. 2013;24: 75–83.
17. Langerhans BR, DeWitt TJ. Shared and unique features of evolutionary diversification. Am Nat. 2004;164: 335–349. doi: 10.1086/422857 15478089
18. Soria-Carrasco V, Gompert Z, Comeault A a, Farkas TE, Parchman TL, Johnston JS, et al. Stick insect genomes reveal natural selection’s role in parallel speciation. Science. 2014;344: 738–42. doi: 10.1126/science.1252136 24833390
19. Linnen CR, Kingsley EP, Jensen JD, Hoekstra HE. On the Origin and Spread of an Adaptive Allele in Deer Mice. Science (80-). 2009;325: 1095–1098. doi: 10.1126/science.1175826 19713521
20. Filteau M, Pavey S a, St-Cyr J, Bernatchez L. Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish. Mol Biol Evol. 2013;30: 1384–96. doi: 10.1093/molbev/mst053 23519315
21. Arendt J, Reznick D. Convergence and parallelism reconsidered: what have we learned about the genetics of adaptation?. Trends Ecol Evol. 2008;23: 26–32. doi: 10.1016/j.tree.2007.09.011 18022278
22. Zhao L, Wit J, Svetec N, Begun DJ. Parallel Gene Expression Differences between Low and High Latitude Populations of Drosophila melanogaster and D. simulans. 2015; 1–25. doi: 10.1371/journal.pgen.1005184 25950438
23. McGirr JA, Martin CH. Parallel evolution of gene expression between trophic specialists despite divergent genotypes and morphologies. Evol Lett. 2018; 62–75. doi: 10.1002/evl3.41 30283665
24. Elmer KR, Meyer A. Adaptation in the age of ecological genomics: insights from parallelism and convergence. Trends Ecol Evol. 2011;26: 298–306. doi: 10.1016/j.tree.2011.02.008 21459472
25. Schluter D. Ecological Speciation in Postglacial Fishes. Philos Trans R Soc B Biol Sci. 1996;351: 807–814. doi: 10.1098/rstb.1996.0075
26. Jonsson B, Jonsson N. Polymorphism and speciation in Arctic charr. J Fish Biol. 2001;58: 605–638. doi: 10.1006/jfbi.2000.1515
27. Alekseyev SS, Samusenok VP, Matveev AN, Yu M. Diversification, sympatric speciation, and trophic polymorphism of Arctic charr, Salvelinus alpinus complex, in Transbaikalia. Environ Biol Fishes. 2002;64: 97–114.
28. Elmer KR, Lehtonen TK, Kautt AF, Harrod C, Meyer A. Rapid sympatric ecological differentiation of crater lake cichlid fishes within historic times. BMC Biol. 2010;8: 60. doi: 10.1186/1741-7007-8-60 20459869
29. Elmer KR, Fan S, Kusche H, Spreitzer ML, Kautt AF, Franchini P, et al. Parallel evolution of Nicaraguan crater lake cichlid fishes via non-parallel routes. Nat Commun. 2014;5: 1–8. doi: 10.1038/ncomms6168 25346277
30. Bernatchez L, Renaut S, Whiteley AR, Derome N, Jeukens J, Landry L, et al. On the origin of species: insights from the ecological genomics of lake whitefish. Philos Trans R Soc Lond B Biol Sci. 2010;365: 1783–1800. doi: 10.1098/rstb.2009.0274 20439281
31. Siwertsson A, Knudsen R, Adams CE, Præbel K, Amundsen PA. Parallel and non-parallel morphological divergence among foraging specialists in European whitefish (Coregonus lavaretus). Ecol Evol. 2013;3: 1590–1602. doi: 10.1002/ece3.562 23789070
32. Saltykova E, Siwertsson A, Knudsen R. Parallel phenotypic evolution of skull-bone structures and head measurements of Arctic charr morphs in two subarctic lakes. Environ Biol Fishes. 2017;100: 137–148. doi: 10.1007/s10641-016-0564-z
33. Adams CE, Huntingford FA. Inherited differences in head allometry in polymorphic Arctic charr from Loch Rannoch, Scotland. J Fish Biol. 2002;60: 515–520. doi: 10.1006/jfbi.2002.1867
34. Garduño-Paz M V., Adams CE, Verspoor E, Knox D, Harrod C. Convergent evolutionary processes driven by foraging opportunity in two sympatric morph pairs of Arctic charr with contrasting post-glacial origins. Biol J Linn Soc. 2012;106: 794–806. doi: 10.1111/j.1095-8312.2012.01906.x
35. Hooker OE, Barry J, Van Leeuwen TE, Lyle A, Newton J, Cunningham P, et al. Morphological, ecological and behavioural differentiation of sympatric profundal and pelagic Arctic charr (Salvelinus alpinus) in Loch Dughaill Scotland. Hydrobiologia. 2016;783: 209–221. doi: 10.1007/s10750-015-2599-0
36. Lecaudey LA, Schliewen UK, Osinov AG, Taylor EB, Bernatchez L, Weiss SJ. Inferring phylogenetic structure, hybridization and divergence times within Salmoninae (Teleostei: Salmonidae) using RAD-sequencing. Mol Phylogenet Evol. 2018;124: 82–99. doi: 10.1016/j.ympev.2018.02.022 29477383
37. Adams DC, Collyer ML. A general framework for the analysis of phenotypic trajectories in evolutionary studies. Evolution (N Y). 2009;63: 1143–1154. doi: 10.1111/j.1558-5646.2009.00649.x 19210539
38. Kusche H, Elmer KR, Meyer A. Sympatric ecological divergence associated with a color polymorphism. BMC Biol. 2015;13: 82. doi: 10.1186/s12915-015-0192-7 26437665
39. Klemetsen A, Elliott J, Knudsen R, Sørensen P. Evidence for genetic dfferences in the offspring of two sympatric morphs of Arctic charr. J Fish Biol. 2002;60: 933–950. doi: 10.1006/jfbi.2002.1905
40. Gordeeva N V, Alekseyev SS, Matveev AN, Samusenok VP. Parallel evolutionary divergence in Arctic charr Salvelinus alpinus (L.) complex from Transbaikalia: variation in differentiation degree and segregation of genetic diversity between sympatric forms. Can J Fish Aquat Sci. 2015;72: 96–115. doi: 10.1139/cjfas-2014-0014
41. Wilson a J, Gíslason D, Skúlason S, Snorrason SS, Adams CE, Alexander G, et al. Population genetic structure of Arctic charr, Salvelinus alpinus, from northwest Europe on large and small spatial scales. Mol Ecol. 2004;13: 1129–42. doi: 10.1111/j.1365-294X.2004.02149.x 15078451
42. Alekseyev SS, Bajno R, Gordeeva N V., Reist JD, Power M, Kirillov a. F, et al. Phylogeography and sympatric differentiation of the Arctic charr Salvelinus alpinus (L.) complex in Siberia as revealed by mtDNA sequence analysis. J Fish Biol. 2009;75: 368–392. doi: 10.1111/j.1095-8649.2009.02331.x 20738544
43. Excoffier L, Dupanloup I, Huerta-Sánchez E, Sousa VC, Foll M. Robust Demographic Inference from Genomic and SNP Data. PLoS Genet. 2013;9. doi: 10.1371/journal.pgen.1003905 24204310
44. Coyne JA, Orr HA. Speciation. Sinauer Associates Sunderland, MA; 2004.
45. Roesti M, Gavrilets S, Hendry AP, Salzburger W, Berner D. The genomic signature of parallel adaptation from shared genetic variation. Mol Ecol. 2014; 3944–3956. doi: 10.1111/mec.12720 24635356
46. Adams CE, Huntingford FA. Incipient speciation driven by phenotypic plasticity? Evidence from sympatric populations of Arctic charr. Biol J Linn Soc. 2004;81: 611–618.
47. Ronkainen PHA, Pöllänen E, Törmäkangas T, Tiainen K, Koskenvuo M, Kaprio J, et al. Catechol-O-methyltransferase gene polymorphism is associated with skeletal muscle properties in older women alone and together with physical activity. PLoS One. 2008;3. doi: 10.1371/journal.pone.0001819 18350156
48. Recknagel H, Elmer KR, Meyer A. Crater lake habitat predicts morphological diversity in adaptive radiations of cichlid fishes. Evolution (N Y). 2014;68: 2145–2155. doi: 10.1111/evo.12412 24660780
49. Recknagel H, Hooker OE, Adams CE, Elmer KR. Ecosystem size predicts eco-morphological variability in a postglacial diversification. Ecol Evol. 2017;7: 5560–5570. doi: 10.1002/ece3.3013 28811875
50. Kautt AF, Machado-schiaffino G, Meyer A. Lessons from a natural experiment: Allopatric morphological divergence and sympatric diversification in the Midas cichlid species complex are largely influenced by ecology in a deterministic way. Evol Lett. 2018;2: 1–18. doi: 10.1002/evl3.64 30283685
51. Skoglund S, Siwertsson A, Amundsen PA, Knudsen R. Morphological divergence between three Arctic charr morphs—the significance of the deep-water environment. Ecol Evol. 2015;5: 3114–3129. doi: 10.1002/ece3.1573 26357540
52. Magalhaes IS, Whiting JR, D’Agostino D, Hohenlohe PA, Mahmud M, Bell MA, et al. Intercontinental genomic parallelism in multiple adaptive radiations. bioRxiv. 2019. http://dx.doi.org/10.1101/856344.
53. Doenz CJ, Krähenbühl AK, Walker J, Seehausen O, Brodersen J. Ecological opportunity shapes a large Arctic charr species radiation. Proc R Soc B Biol Sci. 2019;286. doi: 10.1098/rspb.2019.1992 31640512
54. Paccard A, Hanson D, Stuart YE, n Hippel FA, Kalbe M, Klepaker T, et al. Repeatability of adaptive radiation depends on spatial scale: regional versus global replicates of stickleback in lake versus stream habitats. J Hered. 2019; 1–14. doi: 10.1093/jhered/esz056 31690947
55. Rennison DJ, Stuart YE, Bolnick DI, Peichel CL, Rennison DJ, Peichel CL. Ecological factors and morphological traits are associated with repeated genomic differentiation between lake and stream stickleback. Philos. Trans. R. Soc. B 2019;374(17777):20180241. doi: 10.1098/rstb.2018.0241 31154970
56. Yeaman S, Aeschbacher S, Bürger R. The evolution of genomic islands by increased establishment probability of linked alleles. Mol Ecol. 2016; 2542–2558. doi: 10.1111/mec.13611 27206531
57. Foll M, Gaggiotti OE, Daub JT, Vatsiou A, Excoffier L. Widespread signals of convergent adaptation to high altitude in Asia and America. Am J Hum Genet. 2014;95: 394–407. doi: 10.1016/j.ajhg.2014.09.002 25262650
58. Zheng J, Payne JL, Wagner A. Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. Science (80-). 2019;365: 347–353. doi: 10.1126/science.aax1837 31346060
59. Barghi N, Tobler R, Nolte V, Jakšić AM, Mallard F, Otte KA, et al. Genetic redundancy fuels polygenic adaptation in Drosophila. PLoS Biology. 2019. doi: 10.1371/journal.pbio.3000128 30716062
60. Morales HE, Faria R, Johannesson K, Larsson T, Panova M, Westram AM, et al. Genomic architecture of parallel ecological divergence: beyond a single environmental contrast. Sci Adv. 2019;5: eaav996: 447854. doi: 10.1101/447854
61. Rennison DJ, Delmore KE, Samuk K, Owens GL, Miller SE. Shared patterns of genome-wide differentiation are more strongly predicted by geography than by ecology. Am Nat. 2019. https://doi.org/10.1086/706476
62. Gagnaire P-A, Pavey S a, Normandeau E, Bernatchez L. The genetic architecture of reproductive isolation during speciation-with-gene-flow in lake whitefish species pairs assessed by RAD sequencing. Evolution. 2013;67: 2483–97. doi: 10.1111/evo.12075 24033162
63. Ravinet M, Westram A, Johannesson K, Butlin R, André C, Panova M. Shared and nonshared genomic divergence in parallel ecotypes of Littorina saxatilis at a local scale. Mol Ecol. 2015; doi: 10.1111/mec.13332 26222268
64. Roda F, Liu H, Wilkinson MJ, Walter GM, James ME, Bernal DM, et al. Convergence and Divergence During the Adaptation To Similar Environments By an Australian Groundsel. Evolution (N Y). 2013;67: 2515–2529. doi: 10.1111/evo.12136 24033164
65. Rougeux C, Gagnaire PA, Praebel K, Seehausen O, Bernatchez L. Polygenic selection drives the evolution of convergent transcriptomic landscapes across continents within a Nearctic sister species complex. Mol Ecol. 2019;28: 4388–4403. doi: 10.1111/mec.15226 31482603
66. Losos JB. Improbable destinies: Fate, chance, and the future of evolution. New York, NY: Riverhead Books.; 2017.
67. Blount ZD, Lenski RE, Losos JB. Contingency and determinism in evolution: Replaying life’s tape. Science (80-). 2018;362. doi: 10.1126/science.aam5979 30409860
68. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9. doi: 10.1038/nmeth.2089 22930834
69. Adams CE, Huntingford FA. The functional significance of inherited differences in feeding morphology in a sympatric polymorphic population of Arctic charr. Evol Ecol. 2002;16: 15–25.
70. Præbel K, Knudsen R, Siwertsson A, Karhunen M, Kahilainen KK, Ovaskainen O, et al. Ecological speciation in postglacial European whitefish: Rapid adaptive radiations into the littoral, pelagic, and profundal lake habitats. Ecol Evol. 2013;3: 4970–4986. doi: 10.1002/ece3.867 24455129
71. Nugent CM, Easton AA, Norman JD, Ferguson MM, Danzmann RG. A SNP Based Linkage Map of the Arctic Charr (Salvelinus alpinus) Genome Provides Insights into the Diploidization Process After Whole Genome Duplication. G3 Genes, Genomes, Genet. 2016;7: 543–556. doi: 10.1534/g3.116.038026 27986793
72. Lien S, Koop BF, Sandve SR, Miller JR, Kent MP, Nome T, et al. The Atlantic salmon genome provides insights into rediploidization. Nature. 2016;533: 200–205. doi: 10.1038/nature17164 27088604
73. Tamazian G, Dobrynin P, Krasheninnikova K, Komissarov A, Koepfli K-P, O’Brien SJ. Chromosomer: a reference-based genome arrangement tool for producing draft chromosome sequences. Gigascience. 2016;5: 1–11. doi: 10.1186/s13742-016-0141-6 27549770
74. Tang H, Zhang X, Miao C, Zhang J, Ming R, Schnable JC, et al. ALLMAPS: robust scaffold ordering based on multiple maps. Genome Biol. 2015;16: 3. doi: 10.1186/s13059-014-0573-1 25583564
75. Keilwagen J, Wenk M, Erickson JL, Schattat MH, Grau J, Hartung F. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 2016;44. doi: 10.1093/nar/gkw092 26893356
76. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31: 3210–3212. doi: 10.1093/bioinformatics/btv351 26059717
77. Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015;16: 157. doi: 10.1186/s13059-015-0721-2 26243257
78. Christensen KA, Rondeau EB, Minkley DR, Leong JS, Nugent CM, Danzmann RG, et al. The Arctic charr (salvelinus alpinus) genome and transcriptome assembly. PLoS One. 2018;13: 1–30. doi: 10.1371/journal.pone.0204076 30212580
79. Recknagel H, Jacobs A, Herzyk P, Elmer KR. Double-digest RAD sequencing using Ion Proton semiconductor platform (ddRADseq-ion) with nonmodel organisms. Mol Ecol Resour. 2015;15: 1316–1329. doi: 10.1111/1755-0998.12406 25808755
80. Schenekar T, Lerceteau-Köhler E, Weiss S. Fine-scale phylogeographic contact zone in Austrian brown trout Salmo trutta reveals multiple waves of post-glacial colonization and a pre-dominance of natural versus anthropogenic admixture. Conserv Genet. 2014;15: 561–572. doi: 10.1007/s10592-013-0561-0
81. Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol Biol Evol. 2016;33: 1870–1874. doi: 10.1093/molbev/msw054 27004904
82. Leigh JW, Bryant D. popart: full‐feature software for haplotype network construction. Methods Ecol Evol. 2015;6: 1110–1116. doi: 10.1111/2041-210x.12410
83. Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH, De Koning D-J. Stacks: Building and Genotyping Loci De Novo From Short-Read Sequences. G3 Genes, Genomes, Genet. 2011;1: 171–182. doi: 10.1534/g3.111.000240 22384329
84. Alexander DH, Novembre J. Fast Model-Based Estimation of Ancestry in Unrelated Individuals. Genome Res. 2009;19: 1655–1664. doi: 10.1101/gr.094052.109 19648217
85. Meirmans PG, Tienderen PH. genotype and genodive: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes. 2004;4: 792–794. doi: 10.1111/j.1471-8286.2004.00770.x
86. Huson DH, Bryant D. Estimating phylogenetic trees and networks using SplitsTree 4. Manuscr Prep Softw www.Split.org. 2005.
87. Malinsky M, Trucchi E, Lawson DJ, Falush D. RADpainter and fineRADstructure: Population Inference from RADseq Data. Mol Biol Evol. 2018. doi: 10.1093/molbev/msy023 29474601
88. Pickrell JK, Pritchard JK. Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data. PLoS Genet. 2012;8. doi: 10.1371/journal.pgen.1002967 23166502
89. Malinsky M. Dsuite—fast D-statistics and related ad- mixture evidence from VCF files. bioRxiv. 2019. http://dx.doi.org/10.1101/634477.
90. 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. doi: 10.1371/journal.pgen.1000695 19851460
91. Jacobs A, Hughes MR, Robinson PC, Adams CE, Elmer KR. The Genetic Architecture Underlying the Evolution of a Rare Piscivorous Life History Form in Brown Trout after Secondary Contact and Strong Introgression. Genes (Basel). 2018;9. doi: 10.3390/genes9060280 29857499
92. Kautt AF, Machado-Schiaffino G, Meyer A. Multispecies Outcomes of Sympatric Speciation after Admixture with the Source Population in Two Radiations of Nicaraguan Crater Lake Cichlids. PLoS Genet. 2016;12. doi: 10.1371/journal.pgen.1006157 27362536
93. Rougeux C, Bernatchez L, Gagnaire P-AA. Modeling the Multiple Facets of Speciation-with-Gene-Flow toward Inferring the Divergence History of Lake Whitefish Species Pairs (Coregonus clupeaformis). Genome Biol Evol. 2017;9: 2057–2074. doi: 10.1093/gbe/evx150 28903535
94. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23: 2633–2635. doi: 10.1093/bioinformatics/btm308 17586829
95. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404
96. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29: 15–21. doi: 10.1093/bioinformatics/bts635 23104886
97. Anders S, Pyl P, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31: 166–169. doi: 10.1093/bioinformatics/btu638 25260700
98. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15: 550. doi: 10.1186/s13059-014-0550-8 25516281
99. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9: 1–13.
100. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28: 1353–1358. doi: 10.1093/bioinformatics/bts163 22492648
101. Wang J, Vasaikar S, Shi Z, Greer M, Zhang B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 2017;45. doi: 10.1093/nar/gkx356 28472511
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 4
- Může hubnutí souviset s vyšším rizikem nádorových onemocnění?
- Raději si zajděte na oční! Jak souvisí citlivost zraku s rozvojem demence?
- Co způsobuje pooperační infekce? Na vině může být i naše vlastní mikrobiota
- Čeká nás průlom v diagnostice karcinomu pankreatu?
- Polibek, který mi „vzal nohy“ aneb vzácný výskyt EBV u 70leté ženy – kazuistika
Nejčtenější v tomto čísle
- Analysis of genes within the schizophrenia-linked 22q11.2 deletion identifies interaction of night owl/LZTR1 and NF1 in GABAergic sleep control
- High expression in maize pollen correlates with genetic contributions to pollen fitness as well as with coordinated transcription from neighboring transposable elements
- Molecular genetics of maternally-controlled cell divisions
- Spastin mutations impair coordination between lipid droplet dispersion and reticulum