Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia)
Autoři:
Tuomas Hämälä aff001; Amanda J. Gorton aff002; David A. Moeller aff001; Peter Tiffin aff001
Působiště autorů:
Department of Plant and Microbial Biology, University of Minnesota, St. Paul, Minnesota, United States of America
aff001; Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, United States of America
aff002
Vyšlo v časopise:
Pleiotropy facilitates local adaptation to distant optima in common ragweed (Ambrosia artemisiifolia). PLoS Genet 16(3): e32767. doi:10.1371/journal.pgen.1008707
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008707
Souhrn
Pleiotropy, the control of multiple phenotypes by a single locus, is expected to slow the rate of adaptation by increasing the chance that beneficial alleles also have deleterious effects. However, a prediction arising from classical theory of quantitative trait evolution states that pleiotropic alleles may have a selective advantage when phenotypes are distant from their selective optima. We examine the role of pleiotropy in regulating adaptive differentiation among populations of common ragweed (Ambrosia artemisiifolia); a species that has recently expanded its North American range due to human-mediated habitat change. We employ a phenotype-free approach by using connectivity in gene networks as a proxy for pleiotropy. First, we identify loci bearing footprints of local adaptation, and then use genotype-expression mapping and co-expression networks to infer the connectivity of the genes. Our results indicate that the putatively adaptive loci are highly pleiotropic, as they are more likely than expected to affect the expression of other genes, and they reside in central positions within the gene networks. We propose that the conditionally advantageous alleles at these loci avoid the cost of pleiotropy by having large phenotypic effects that are beneficial when populations are far from their selective optima. We further use evolutionary simulations to show that these patterns are in agreement with a model where populations face novel selective pressures, as expected during a range expansion. Overall, our results suggest that highly connected genes may be targets of positive selection during environmental change, even though they likely experience strong purifying selection in stable selective environments.
Klíčová slova:
Gene expression – Genetic loci – Genetic networks – Population genetics – principal component analysis – Quantitative trait loci – Sequence alignment – Transcriptome analysis
Zdroje
1. Fisher RA. The genetic theory of natural selection. Oxford: The Carendon Press; 1930.
2. Orr HA. Adaptation and the cost of complexity. Evolution. 2000;54: 13–20. doi: 10.1111/j.0014-3820.2000.tb00002.x 10937178
3. Welch JJ, Waxman D. Modularity and the cost of complexity. Evolution. 2003;57: 1723–1734. doi: 10.1111/j.0014-3820.2003.tb00581.x 14503615
4. Chevin LM, Martin G, Lenormand T. Fisher’s model and the genomics of adaptation: Restricted pleiotropy, heterogenous mutation, and parallel evolution. Evolution. 2010;64: 3213–3231. doi: 10.1111/j.1558-5646.2010.01058.x 20662921
5. Martin G, Lenormand T. The fitness effect of mutations across environments: Fisher’s geometrical model with multiple optima. Evolution. 2015;69: 1433–1447. doi: 10.1111/evo.12671 25908434
6. Orr HA. The population genetics of adaptation: The distribution of factors fixed during adaptive evolution. Evolution. 1998;52: 935–949. doi: 10.1111/j.1558-5646.1998.tb01823.x 28565213
7. Wagner GP, Kenney-hunt JP, Pavlicev M, Peck JR, Waxman D, Cheverud JM. Pleiotropic scaling of gene effects and the “cost of complexity.” Nature. 2008;452: 470–473. doi: 10.1038/nature06756 18368117
8. Wang Z, Liao B-Y, Zhang J. Genomic patterns of pleiotropy and the evolution of complexity. Proc Natl Acad Sci. 2010;107: 18034–18039. doi: 10.1073/pnas.1004666107 20876104
9. Taylor MA, Wilczek AM, Roe JL, Welch SM, Runcie DE, Cooper MD, et al. Large-effect flowering time mutations reveal conditionally adaptive paths through fitness landscapes in Arabidopsis thaliana. Proc Natl Acad Sci. 2019;116: 17890–17899. doi: 10.1073/pnas.1902731116 31420516
10. Hall MC, Basten CJ, Willis JH. Pleiotropic quantitative trait loci contribute to population divergence in traits associated with life-history variation in Mimulus guttatus. Genetics. 2006;172: 1829–1844. doi: 10.1534/genetics.105.051227 16361232
11. Latta RG, Gardner KM, Staples DA. Quantitative trait locus mapping of genes under selection across multiple years and sites in Avena barbata: Epistasis, pleiotropy, and genotype-by-environment interactions. Genetics. 2010;185: 375–385. doi: 10.1534/genetics.110.114389 20194964
12. Leinonen PH, Remington DL, Leppälä J, Savolainen O. Genetic basis of local adaptation and flowering time variation in Arabidopsis lyrata. Mol Ecol. 2013;22: 709–723. doi: 10.1111/j.1365-294X.2012.05678.x 22724431
13. Anderson JT, Lee CR, Mitchell-Olds T. Strong selection genome-wide enhances fitness trade-offs across environments and episodes of selection. Evolution. 2014;68: 16–31. doi: 10.1111/evo.12259 24102539
14. Dittmar EL, Oakley CG, Conner JK, Gould BBA, Schemske DW, Kellogg WK, et al. Factors influencing the effect size distribution of adaptive substitutions. Proc R Soc B. 2016;283: 20153065. doi: 10.1098/rspb.2015.3065 27053750
15. Ferris KG, Barnett LL, Blackman BK, Willis JH. The genetic architecture of local adaptation and reproductive isolation in sympatry within the Mimulus guttatus species complex. Mol Ecol. 2017;26: 208–224. doi: 10.1111/mec.13763 27439150
16. Smith SD. Pleiotropy and the evolution of floral integration. New Phytol. 2015;209: 80–85. doi: 10.1111/nph.13583 26224529
17. Flint J, Mackay TFC. Genetic architecture of quantitative traits in mice, flies, and humans. Genome Resaerch. 2009;19: 723–733. doi: 10.1101/gr.086660.108.19
18. Wagner GP, Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat Rev Genet. Nature Publishing Group; 2011;12: 204–213. doi: 10.1038/nrg2949 21331091
19. Hill WG, Zhang XS. On the pleiotropic structure of the genotype-phenotype map and the evolvability of complex organisms. Genetics. 2012;190: 1131–1137. doi: 10.1534/genetics.111.135681 22214609
20. Cork JM, Purugganan MD. The evolution of molecular genetic pathways and networks. BioEssays. 2004;26: 479–484. doi: 10.1002/bies.20026 15112228
21. He X, Zhang J. Toward a molecular understanding of pleiotropy. Genetics. Genetics Society of America; 2006;173: 1885–91. doi: 10.1534/genetics.106.060269 16702416
22. Wagner GP, Pavlicev M, Cheverud JM. The road to modularity. Nat Rev Genet. 2007;8: 921–931. doi: 10.1038/nrg2267 18007649
23. Erwin DH, Davidson EH. The evolution of hierarchical gene regulatory networks. Nat Rev Genet. 2009;10: 141–148. doi: 10.1038/nrg2499 19139764
24. Proulx SR, Promislow DEL, Phillips PC. Network thinking in ecology and evolution. Trends Ecol Evol. 2005;20: 345–353. doi: 10.1016/j.tree.2005.04.004 16701391
25. Rausher MD, Miller RE, Tiffin P. Patterns of evolutionary rate variation among genes of the anthocyanin biosynthetic pathway. Mol Biol Evol. 1999;16: 266–274. doi: 10.1093/oxfordjournals.molbev.a026108 10028292
26. Fraser HB, Hirsh AE, Steinmetz LM, Scharfe C, Feldman MW. Evolutionary rate in the protein interaction network. Science. 2002;296: 750–752. doi: 10.1126/science.1068696 11976460
27. Promislow DEL. Protein networks, pleiotropy and the evolution of senescence. Proc R Soc B Biol Sci. 2004;271: 1225–1234. doi: 10.1098/rspb.2004.2732 15306346
28. Hahn MW, Kern AD. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol. 2005;22: 803–806. doi: 10.1093/molbev/msi072 15616139
29. Papakostas S, Vøllestad LA, Bruneaux M, Aykanat T, Vanoverbeke J, Ning M, et al. Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions. Nat Commun. 2014;5: 1–9. doi: 10.1038/ncomms5071 24892934
30. Mähler N, Wang J, Terebieniec BK, Ingvarsson PK, Street NR, Hvidsten TR. Gene co-expression network connectivity is an important determinant of selective constraint. PLoS Genet. 2017;13: e1006402. doi: 10.1371/journal.pgen.1006402 28406900
31. Josephs EB, Wright SI, Stinchcombe JR, Schoen DJ. The relationship between selection, network connectivity, and regulatory variation within a population of Capsella grandiflora. Genome Biol Evol. 2017;9: 1099–1109. doi: 10.1093/gbe/evx068 28402527
32. Masalia RR, Bewick AJ, Burke JM. Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants. PLoS One. 2017;12: e0182289. doi: 10.1371/journal.pone.0182289 28759647
33. Flowers JM, Sezgin E, Kumagai S, Duvernell DD, Matzkin LM, Schmidt PS, et al. Adaptive evolution of metabolic pathways in Drosophila. Mol Biol Evol. 2007;24: 1347–1354. doi: 10.1093/molbev/msm057 17379620
34. Kim PM, Korbel JO, Gerstein MB. Positive selection at the protein network periphery: Evaluation in terms of structural constraints and cellular context. Proc Natl Acad Sci U S A. 2007;104: 20274–20279. doi: 10.1073/pnas.0710183104 18077332
35. Luisi P, Alvarez-Ponce D, Pybus M, Fares MA, Bertranpetit J, Laayouni H. Recent positive selection has acted on genes encoding proteins with more interactions within the whole human interactome. Genome Biol Evol. 2015;7: 1141–1154. doi: 10.1093/gbe/evv055 25840415
36. Jovelin R, Phillips PC. Evolutionary rates and centrality in the yeast gene regulatory network. Genome Biol. 2009;10. doi: 10.1186/gb-2009-10-4-r35 19358738
37. Grimm E. Trends and palaeoecological problems in the vegetation and climate history of the Northern Great Plains, USA. Proc R Irish Acad. 2001;101B: 47–64.
38. Martin MD, Zimmer EA, Olsen MT, Foote AD, Gilbert MTP, Brush GS. Herbarium specimens reveal a historical shift in phylogeographic structure of common ragweed during native range disturbance. Mol Ecol. 2014;23: 1701–1716. doi: 10.1111/mec.12675 24450363
39. Martin MD, Olsen MT, Samaniego JA, Zimmer EA, Gilbert MTP. The population genomic basis of geographic differentiation in North American common ragweed (Ambrosia artemisiifolia L.). Ecol Evol. 2016;6: 3760–3771. doi: 10.1002/ece3.2143 28725355
40. Friedman J, Barrett SCH. High outcrossing in the annual colonizing species Ambrosia artemisiifolia (Asteraceae). Ann Bot. 2008;101: 1303–1309. doi: 10.1093/aob/mcn039 18387973
41. Gorton AJ, Moeller DA, Tiffin P. Little plant, big city: A test of adaptation to urban environments in common ragweed (Ambrosia artemisiifolia). Proc R Soc B Biol Sci. 2018;285. doi: 10.1098/rspb.2018.0968 30051853
42. Gorton AJ, Tiffin P, Moeller DA. Does adaptation to historical climate shape plant responses to future rainfall patterns? A rainfall manipulation experiment with common ragweed. Oecologia. 2019;190: 941–953. doi: 10.1007/s00442-019-04463-4 31289920
43. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva E V., Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31: 3210–3212. doi: 10.1093/bioinformatics/btv351 26059717
44. Hölzer M, Marz M. De novo transcriptome assembly: A comprehensive cross-species comparison of short-read RNA-Seq assemblers. Gigascience. 2019;8: 1–16. doi: 10.1093/gigascience/giz039 31077315
45. Badouin H, Gouzy J, Grassa CJ, Murat F, Staton SE, Cottret L, et al. The sunflower genome provides insights into oil metabolism, flowering and Asterid evolution. Nature. 2017;546: 148–152. doi: 10.1038/nature22380 28538728
46. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123: 585–595. 2513255
47. Meisner J, Albrechtsen A. Inferring population structure and admixture proportions in low depth NGS data. Genetics. 2018;210: 719–731. doi: 10.1534/genetics.118.301336 30131346
48. Caye K, Jumentier B, Lepeule J, François O. LFMM 2: Fast and accurate inference of gene-environment associations in genome-wide studies. Mol Biol Evol. 2019;36: 852–860. doi: 10.1093/molbev/msz008 30657943
49. Luu K, Bazin E, Blum MGB. pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol Ecol Resour. 2017;17: 67–77. doi: 10.1111/1755-0998.12592 27601374
50. Lotterhos KE, Whitlock MC. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol Ecol. 2015;24: 1031–1046. doi: 10.1111/mec.13100 25648189
51. Lotterhos KE. The effect of neutral recombination variation on genome scans for selection. G3 Genes, Genomes, Genet. 2019;9: 1851–1867. doi: 10.1534/g3.119.400088 30971391
52. Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;37: 4302–4315. doi: 10.1002/joc.5086
53. Hämälä T, Savolainen O. Genomic patterns of local adaptation under gene flow in Arabidopsis lyrata. Mol Biol Evol. 2019;36: 2557–2571. doi: 10.1093/molbev/msz149 31236594
54. Maynard Smith J, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res. 1974;23: 23–35. doi: 10.1017/S0016672308009579 4407212
55. Fay JC, Wu C. Hitchhiking under positive Darwinian selection. Genetics. 2000;155: 1405–1413. Available: papers://cd25bd17-0cfb-4e05-8dd6-c8cc658d1c49/Paper/p2 10880498
56. Kim Y, Stephan W. Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics. 2002;160: 765–777. doi: 10.3410/f.1008369.104907 11861577
57. Nielsen R, Williamson S, Kim Y, Hubisz MJ, Clark AG, Bustamante C. Genomic scans for selective sweeps using SNP data. Genome Res. 2005;15: 1566–1575. doi: 10.1101/gr.4252305 16251466
58. Hämälä T, Guiltinan MJ, Marden JH, Maximova SN, DePamphilis CW, Tiffin P. Gene expression modularity reveals footprints of polygenic adaptation in Theobroma cacao. Mol Biol Evol. 2020;37: 110–123. doi: 10.1093/molbev/msz206 31501906
59. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9: 559. doi: 10.1186/1471-2105-9-559 19114008
60. Turesson G. The genotypical response of the plant species to the habitat. Hereditas. 1922;3: 211–350.
61. Clausen J, Keck DD, Hiesey WM. Experimental studies on the nature of species. I. Effect of varied environments on western North American plants. Carnegie Institution of Washington publication. Carnegie Institution; 1940.
62. Hedrick PW, Ginevan ME, Ewing EP. Genetic polymorphism in heterogeneous environments. Source Annu Rev Ecol Syst. 1976;7: 1–32.
63. Linhart YB, Grant MC. Evolutionary significance of local genetic differentiation in plants. Annu Rev Ecol Syst. 1996;27: 237–277. doi: 10.1146/annurev.ecolsys.27.1.237
64. Kawecki TJ, Ebert D. Conceptual issues in local adaptation. Ecol Lett. 2004;7: 1225–1241. doi: 10.1111/j.1461-0248.2004.00684.x
65. Savolainen O, Pyhäjärvi T, Knürr T. Gene flow and local adaptation in trees. Annu Rev Ecol Evol Syst. 2007;38: 595–619. doi: 10.1146/annurev.ecolsys.38.091206.095646
66. Leimu R, Fischer M. A meta-analysis of local adaptation in plants. PLoS One. 2008;3: e4010. doi: 10.1371/journal.pone.0004010 19104660
67. Hereford J. A quantitative survey of local adaptation and fitness trade-offs. Am Nat. 2009;173: 579–588. doi: 10.1086/597611 19272016
68. Anderson JT, Lee CR, Mitchell-Olds T. Life-history QTLs and natural selection on flowering time in Boechera stricta, a perennial relative of Arabidopsis. Evolution. 2011;65: 771–787. doi: 10.1111/j.1558-5646.2010.01175.x 21083662
69. Stanton-Geddes J, Shaw RG, Tiffin P. Interactions between soil habitat and geographic range location affect plant fitness. PLoS One. 2012;7: e36015. doi: 10.1371/journal.pone.0036015 22615745
70. Savolainen O, Lascoux M, Merilä J. Ecological genomics of local adaptation. Nat Rev Genet. 2013;14: 807–820. doi: 10.1038/nrg3522 24136507
71. Tiffin P, Ross-Ibarra J. Advances and limits of using population genetics to understand local adaptation. Trends Ecol Evol. 2014;29: 673–680. doi: 10.1016/j.tree.2014.10.004 25454508
72. Ågren J, Oakley CG, Lundemo S, Schemske DW. Adaptive divergence in flowering time among natural populations of Arabidopsis thaliana: Estimates of selection and QTL mapping. Evolution. 2017;71: 550–564. doi: 10.1111/evo.13126 27859214
73. Hämälä T, Mattila TM, Savolainen O. Local adaptation and ecological differentiation under selection, migration and drift in Arabidopsis lyrata. Evolution. 2018;72: 1373–1386. doi: 10.1111/evo.13502 29741234
74. Yoder JB, Stanton-Geddes J, Zhou P, Briskine R, Young ND, Tiffin P. Genomic signature of adaptation to climate in Medicago truncatula. Genetics. 2014;196: 1263–1275. doi: 10.1534/genetics.113.159319 24443444
75. Wadgymar SM, Lowry DB, Gould BA, Byron CN, Mactavish RM, Anderson JT. Identifying targets and agents of selection: innovative methods to evaluate the processes that contribute to local adaptation. Methods Ecol Evol. 2017;8: 738–749. doi: 10.1111/2041-210X.12777
76. Price N, Moyers BT, Lopez L, Lasky JR, Monroe JG, Mullen JL, et al. Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana. Proc Natl Acad Sci. 2018;115: 5028–5033. doi: 10.1073/pnas.1719998115 29686078
77. Lotterhos KE, Yeaman S, Degner J, Aitken S, Hodgins KA. Modularity of genes involved in local adaptation to climate despite physical linkage. Genome Biol. Genome Biology; 2018;19: 1–24. doi: 10.1186/s13059-017-1381-1
78. Hermann K, Klahre U, Moser M, Sheehan H, Mandel T, Kuhlemeier C. Tight genetic linkage of prezygotic barrier loci creates a multifunctional speciation island in Petunia. Curr Biol. 2013;23: 873–877. doi: 10.1016/j.cub.2013.03.069 23602480
79. Ramsay H, Rieseberg LH, Ritland K. The correlation of evolutionary rate with pathway position in plant terpenoid biosynthesis. Mol Biol Evol. 2009;26: 1045–1053. doi: 10.1093/molbev/msp021 19188263
80. Frachon L, Libourel C, Villoutreix R, Carrère S, Glorieux C, Huard-Chauveau C, et al. Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time. Nat Ecol Evol. 2017;1: 1551–1561. doi: 10.1038/s41559-017-0297-1 29185515
81. Nielsen R. Molecular signatures of natural selection. Annu Rev Genet. 2005;39: 197–218. doi: 10.1146/annurev.genet.39.073003.112420 16285858
82. Charlesworth B, Morgan MT, Charlesworth D. The effect of deleterious mutations on neutral molecular variation. Genetics. 1993;134: 1289–1303. doi: 10.1111/j.0014-3820.2002.tb00188.x 8375663
83. Des Marais DL, Guerrero RF, Lasky JR, Scarpino S V. Topological features of a gene co-expression network predict patterns of natural diversity in environmental response. Proc R Soc B Biol Sci. 2017;284. doi: 10.1098/rspb.2017.0914 28615505
84. Essl F, Biró K, Brandes D, Broennimann O, Bullock JM, Chapman DS, et al. Biological Flora of the British Isles: Ambrosia artemisiifolia. J Ecol. 2015;103: 1069–1098. doi: 10.1111/1365-2745.12424
85. Chauvel B, Dessaint F, Cardinal-Legrand C, Bretagnolle F. The historical spread of Ambrosia artemisiifolia L. in France from herbarium records. J Biogeogr. 2006;33: 665–673. doi: 10.1111/j.1365-2699.2005.01401.x
86. Bass DJ, Delpech V, Beard J, Bass P, Walls RS. Ragweed in Australia. Aerobiologia. 2000;16: 107–111. doi: 10.1023/A:1007696112953
87. 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
88. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8: 1494–1512. doi: 10.1038/nprot.2013.084 23845962
89. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28: 3150–3152. doi: 10.1093/bioinformatics/bts565 23060610
90. Davidson NM, Hawkins ADK, Oshlack A. SuperTranscripts: A data driven reference for analysis and visualisation of transcriptomes. Genome Biol. Genome Biology; 2017;18: 1–10. doi: 10.1186/s13059-016-1139-1
91. Gopalakrishnan S, Samaniego Castruita JA, Sinding M-HS, Kuderna LFK, Räikkönen J, Petersen B, et al. The wolf reference genome sequence (Canis lupus lupus) and its implications for Canis spp. population genomics. BMC Genomics. 2017;18: 495. doi: 10.1186/s12864-017-3883-3 28662691
92. Huang X, Chen XG, Armbruster PA. Comparative performance of transcriptome assembly methods for non-model organisms. BMC Genomics. BMC Genomics; 2016;17: 1–14. doi: 10.1186/s12864-015-2294-6
93. 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
94. 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
95. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv Prepr. 2012;arXiv: 1207.3907. arXiv:1207.3907
96. 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
97. Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet. Nature Publishing Group; 2011;12: 443–451. doi: 10.1038/nrg2986 21587300
98. Korneliussen TS, Albrechtsen A, Nielsen R. ANGSD: Analysis of next generation sequencing data. BMC Bioinformatics. 2014;15: 1471–2105. doi: 10.1186/s12859-014-0356-4 25420514
99. Tajima F. Evolutionary relationship of DNA sequences in finite populations. Genetics. 1983;105: 437–60. 6628982
100. Watterson G. On the number of segregating sites in genetical models without recombination. Theor Popul Biol. 1975;7: 256–276. doi: 10.1016/0040-5809(75)90020-9 1145509
101. Wright S. The genetical structure of populations. Ann Eugenetics. 1951;15: 215–354.
102. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci. 2003;100: 9440–9445. doi: 10.1073/pnas.1530509100 12883005
103. Hodgins KA, Rieseberg L. Genetic differentiation in life-history traits of introduced and native common ragweed (Ambrosia artemisiifolia) populations. J Evol Biol. 2011;24: 2731–2749. doi: 10.1111/j.1420-9101.2011.02404.x 22023052
104. Bhatia G, Patterson N, Sankararaman S, Price AL. Estimating and interpreting FST: The impact of rare variants. Genome Res. Cold Spring Harbor Laboratory Press; 2013;23: 1514–1521. doi: 10.1101/gr.154831.113 23861382
105. Nei M. Molecular evolutionary genetics. Columbia university press; 1987.
106. Cruickshank TE, Hahn MW. Reanalysis suggests that genomic islands of speciation are due to reduced diversity, not reduced gene flow. Mol Ecol. 2014;23: 3133–3157. doi: 10.1111/mec.12796 24845075
107. Marçais G, Delcher AL, Phillippy AM, Coston R, Salzberg SL, Zimin A. MUMmer4: A fast and versatile genome alignment system. Darling AE, editor. PLOS Comput Biol. 2018;14: e1005944. doi: 10.1371/journal.pcbi.1005944 29373581
108. Zeng K, Fu YX, Shi S, Wu CI. Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics. 2006;174: 1431–1439. doi: 10.1534/genetics.106.061432 16951063
109. DeGiorgio M, Huber CD, Hubisz MJ, Hellmann I, Nielsen R. SweepFinder2: increased sensitivity, robustness and flexibility. Bioinformatics. 2016;32: 1895–1897. doi: 10.1093/bioinformatics/btw051 27153702
110. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28: 1353–8. doi: 10.1093/bioinformatics/bts163 22492648
111. Josephs EB, Lee YW, Stinchcombe JR, Wright SI. Association mapping reveals the role of purifying selection in the maintenance of genomic variation in gene expression. Proc Natl Acad Sci. 2015;112: 15390–15395. doi: 10.1073/pnas.1503027112 26604315
112. Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5: 101–113. doi: 10.1038/nrg1272 14735121
113. Schaefer RJ, Michno J-M, Jeffers J, Hoekenga O, Dilkes B, Baxter I, et al. Integrating coexpression networks with GWAS to prioritize causal genes in maize. Plant Cell. 2018;30: 2922–2942. doi: 10.1105/tpc.18.00299 30413654
114. Haller BC, Messer PW. SLiM 3: Forward genetic simulations beyond the Wright–Fisher model. Mol Biol Evol. 2019;36: 632–637. doi: 10.1093/molbev/msy228 30517680
115. Zhang X-S, Hill WG. Multivariate stabilizing selection and pleiotropy in the maintetanace of quantitative genetic variation. Evolution. 2003;57: 1761–1775. doi: 10.1111/j.0014-3820.2003.tb00584.x 14503618
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- The alarmones (p)ppGpp are part of the heat shock response of Bacillus subtilis
- Modeling cancer genomic data in yeast reveals selection against ATM function during tumorigenesis