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Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Review Article

Genomic Insights into the Adaptive Convergent Evolution

Author(s): Yan Hao, Yanhua Qu, Gang Song and Fumin Lei*

Volume 20, Issue 2, 2019

Page: [81 - 89] Pages: 9

DOI: 10.2174/1389202920666190313162702

Price: $65

Abstract

Adaptive convergent evolution, which refers to the same or similar phenotypes produced by species from independent lineages under similar selective pressures, has been widely examined for a long time. Accumulating studies on the adaptive convergent evolution have been reported from many different perspectives (cellular, anatomical, morphological, physiological, biochemical, and behavioral). Recent advances in the genomic technologies have demonstrated that adaptive convergence can arise from specific genetic mechanisms in different hierarchies, ranging from the same nucleotide or amino acid substitutions to the biological functions or pathways. Among these genetic mechanisms, the same amino acid changes in protein-coding genes play an important role in adaptive phenotypic convergence. Methods for detecting adaptive convergence at the protein sequence level have been constantly debated and developed. Here, we review recent progress on using genomic approaches to evaluate the genetic mechanisms of adaptive convergent evolution, summarize the research methods for identifying adaptive amino acid convergence, and discuss the future perspectives for researching adaptive convergent evolution.

Keywords: Convergent evolution, Phenotype, Genomics, Genetic mechanism, Amino acid convergence, Adaptive evolution.

Graphical Abstract

[1]
Losos, J.B. Convergence, adaptation, and constraint. Evolution, 2011, 65(7), 1827-1840.
[2]
Rosenblum, E.B.; Parent, C.E.; Brandt, E.E. The molecular basis of phenotypic convergence. Annu. Rev. Ecol. Evol. Syst., 2014, 45(1), 203-226.
[3]
Bridgham, J.T. Predicting the basis of convergent evolution. Science, 2016, 354(6310), 289-289.
[4]
Emery, N.J.; Clayton, N.S. The mentality of crows: Convergent evolution of intelligence in corvids and apes. Science, 2004, 306(5703), 1903-1907.
[5]
Dalziel, A.C.; Laporte, M.; Rougeux, C.; Guderley, H.; Bernatchez, L. Convergence in organ size but not energy metabolism enzyme activities among wild lake whitefish (Coregonus clupeaformis) species pairs. Mol. Ecol., 2017, 26(1), 225-244.
[6]
Mitterboeck, T.F.; Liu, S.; Adamowicz, S.J.; Fu, J.; Zhang, R.; Song, W.; Meusemann, K.; Zhou, X. Positive and relaxed selection associated with flight evolution and loss in insect transcriptomes. Gigascience, 2017, 6(10), 1-14.
[7]
Ben-Hamo, M.; Munoz-Garcia, A.; Larrain, P.; Pinshow, B.; Korine, C.; Williams, J.B. The cutaneous lipid composition of bat wing and tail membranes: A case of convergent evolution with birds. Proc. Biol. Sci., 2016, 283(1833), 20160636.
[http://dx.doi.org/10.1098/rspb.2016.0636]
[8]
Liu, Y.; Cotton, J.A.; Shen, B.; Han, X.; Rossiter, S.J.; Zhang, S. Convergent sequence evolution between echolocating bats and dolphins. Curr. Biol., 2010, 20(2), 53-54.
[9]
Liu, Y.; Rossiter, S.J.; Han, X.Q.; Cotton, J.A.; Zhang, S.Y. Cetaceans on a molecular fast track to ultrasonic hearing. Curr. Biol., 2010, 20(20), 1834-1839.
[10]
Rossiter, S.J.; Zhang, S.; Liu, Y. Prestin and high frequency hearing in mammals. Commun. Integr. Biol., 2011, 4(2), 236-239.
[11]
Liu, Z.; Qi, F.Y.; Zhou, X.; Ren, H.Q.; Shi, P. Parallel sites implicate functional convergence of the hearing gene Prestin among echolocating mammals. Mol. Biol. Evol., 2014, 31(9), 2415-2424.
[12]
Montealegre, Z.F.; Jonsson, T.; Robson-Brown, K.A.; Postles, M.; Robert, D. Convergent evolution between insect and mammalian audition. Science, 2012, 338(6109), 968-971.
[13]
Yokoyama, S.; Radlwimmer, F.B. The molecular genetics and evolution of red and green color vision in vertebrates. Genetics, 2001, 158(4), 1697-1710.
[14]
Christin, P.A.; Samaritani, E.; Petitpierre, B.; Salamin, N.; Besnard, G. Evolutionary insights on C4 photosynthetic subtypes in grasses from genomics and phylogenetics. Genome Biol. Evol., 2009, 1(0), 221-230.
[15]
Zhang, Z.; Xu, D.; Wang, L.; Hao, J.; Wang, J.; Zhou, X.; Wang, W.; Qiu, Q.; Huang, X.; Zhou, J.; Long, R.; Zhao, F.; Shi, P. Convergent evolution of rumen microbiomes in high-altitude mammals. Curr. Biol., 2016, 26(14), 1873-1879.
[16]
Christin, P.A.; Weinreich, D.M.; Besnard, G. Causes and evolutionary significance of genetic convergence. Trends Genet., 2010, 26(9), 400-405.
[17]
Tenaillon, O.; Rodríguez-Verdugo, A.; Gaut, R.L.; McDonald, P.; Bennett, A.F.; Long, A.D.; Gaut, B.S. The molecular diversity of adaptive convergence. Science, 2012, 335(6067), 457-461.
[18]
Schwarze, K.; Campbell, K.L.; Hankeln, T.; Storz, J.F.; Hoffmann, F.G.; Burmester, T. The globin gene repertoire of lampreys: Convergent evolution of hemoglobin and myoglobin in jawed and jawless vertebrates. Mol. Biol. Evol., 2014, 31(10), 2708-2721.
[19]
Strasser, B.; Mlitz, V.; Hermann, M.; Tschachler, E.; Eckhart, L. Convergent evolution of cysteine-rich proteins in feathers and hair. BMC Evol. Biol., 2015, 15(1)
[http://dx.doi.org/10.1186/s12862-015-0360-y]
[20]
Zhang, J.Z. Parallel adaptive origins of digestive RNases in Asian and African leaf monkeys. Nat. Genet., 2006, 38(7), 819-823.
[21]
Castoe, T.A.; de Koning, A.P.J.; Kim, H.M.; Gu, W.; Noonan, B.P.; Naylor, G.; Jiang, Z.J.; Parkinson, C.L.; Pollock, D.D. Evidence for an ancient adaptive episode of convergent molecular evolution. Proc. Natl. Acad. Sci. USA, 2009, 106(22), 8986-8991.
[22]
Parker, J.; Tsagkogeorga, G.; Cotton, J.A.; Liu, Y.; Provero, P.; Stupka, E.; Rossiter, S.J. Genome-wide signatures of convergent evolution in echolocating mammals. Nature, 2013, 502(7470), 228-231.
[23]
Zou, Z.; Zhang, J. No genome-wide protein sequence convergence for echolocation. Mol. Biol. Evol., 2015, 32(5), 1237-1241.
[24]
Thomas, G.W.C.; Hahn, M.W. Determining the null model for detecting adaptive convergence from genomic data: A case study using echolocating mammals. Mol. Biol. Evol., 2015, 32(5), 1232-1236.
[25]
Zhang, G.; Li, C.; Li, Q.; Li, B.; Larkin, D.M.; Lee, C.; Storz, J.F.; Antunes, A.; Greenwold, M.J.; Meredith, R.W. Comparative genomics reveals insights into avian genome evolution and adaptation. Science, 2014, 346(6215), 1311-1320.
[26]
Zou, Z.; Zhang, J. Are convergent and parallel amino acid substitutions in protein evolution more prevalent than neutral expectations? Mol. Biol. Evol., 2015, 32(8), 2085-2096.
[27]
Xu, S.; He, Z.; Guo, Z.; Zhang, Z.; Wyckoff, G.J.; Greenberg, A.; Wu, C-I.; Shi, S. Genome-wide convergence during evolution of mangroves from woody plants. Mol. Biol. Evol., 2017, 34(4), 1008-1015.
[28]
Foote, A.D.; Liu, Y.; Thomas, G.W.C.; Vinař, T.; Alföldi, J.; Deng, J.; Dugan, S.; van Elk, C.E.; Hunter, M.E.; Joshi, V.; Khan, Z.; Kovar, C.; Lee, S.L.; Lindblad-Toh, K.; Mancia, A.; Nielsen, R.; Qin, X.; Qu, J.; Raney, B.J.; Vijay, N.; Wolf, J.B.W.; Hahn, M.W.; Muzny, D.M.; Worley, K.C.; Gilbert, M.T.P.; Gibbs, R.A. Convergent evolution of the genomes of marine mammals. Nat. Genet., 2015, 47(3), 272-275.
[29]
Hu, Y.; Wu, Q.; Ma, S.; Ma, T.; Shan, L.; Wang, X.; Nie, Y.; Ning, Z.; Yan, L.; Xiu, Y.; Wei, F. Comparative genomics reveals convergent evolution between the bamboo-eating giant and red pandas. Proc. Natl. Acad. Sci. USA, 2017, 114(5), 1081-1086.
[30]
Fukushima, K.; Fang, X.; Alvarez-Ponce, D.; Cai, H.; Carretero-Paulet, L.; Chen, C.; Chang, T.-H.; Farr, K.M.; Fujita, T.; Hiwatashi, Y.; Hoshi, Y.; Imai, T.; Kasahara, M.; Librado, P.; Mao, L.; Mori, H.; Nishiyama, T.; Nozawa, M.; Pálfalvi, G.; Pollard, S.T.; Rozas, J.; Sánchez-Gracia, A.; Sankoff, D.; Shibata, T.F.; Shigenobu, S.; Sumikawa, N.; Uzawa, T.; Xie, M.; Zheng, C.; Pollock, D.D.; Albert, V.A.; Li, S.; Hasebe, M. Genome of the pitcher plant Cephalotus reveals genetic changes associated with carnivory. Nat. Ecol. Evol., 2017, 1(3), 00-59.
[31]
Yu, L.; Wang, G.D.; Ruan, J.; Chen, Y.B.; Yang, C.P.; Cao, X.; Wu, H.; Liu, Y.H.; Du, Z.L.; Wang, X.P.; Yang, J.; Cheng, S.C.; Zhong, L.; Wang, L.; Wang, X.; Hu, J.Y.; Fang, L.; Bai, B.; Wang, K.L.; Yuan, N.; Wu, S.F.; Li, B.G.; Zhang, J.G.; Yang, Y.Q.; Zhang, C.L.; Long, Y.C.; Li, H.S.; Yang, J.Y.; Irwin, D.M.; Ryder, O.A.; Li, Y.; Wu, C.I.; Zhang, Y.P. Genomic analysis of snub-nosed monkeys (Rhinopithecus) identifies genes and processes related to high-altitude adaptation. Nat. Genet., 2016, 48(8), 947-952.
[32]
Liu, Z.; Qi, F.Y.; Xu, D.M.; Zhou, X.; Shi, P. Genomic and functional evidence reveals molecular insights into the origin of echolocation in whales. Sci. Adv., 2018, 4(10)
[http://dx.doi.org/10.1126/ sciadv.aat8821]
[33]
Lee, J.H.; Lewis, K.M.; Moural, T.W.; Kirilenko, B.; Borgonovo, B.; Prange, G.; Koessl, M.; Huggenberger, S.; Kang, C.; Hiller, M. Molecular parallelism in fast-twitch muscle proteins in echolocating mammals. Sci. Adv., 2018.
[http://dx.doi.org/10.1126/sciadv.aat9660]
[34]
Scotland, R.W. What is parallelism? Evol. Dev., 2011, 13(2), 214-227.
[35]
Stern, D.L. The genetic causes of convergent evolution. Nat. Rev. Genet., 2013, 14(11), 751-764.
[36]
Projecto-Garcia, J.; Natarajan, C.; Moriyama, H.; Weber, R.E.; Fago, A.; Cheviron, Z.A.; Dudley, R.; McGuire, J.A.; Witt, C.C.; Storz, J.F. Repeated elevational transitions in hemoglobin function during the evolution of Andean hummingbirds. Proc. Natl. Acad. Sci. USA, 2013, 110(51), 20669-20674.
[37]
Zhu, X.; Guan, Y.; Signore, A.V.; Natarajan, C.; DuBay, S.G.; Cheng, Y.; Han, N.; Song, G.; Qu, Y.; Moriyama, H.; Hoffmann, F.G.; Fago, A.; Lei, F.; Storz, J.F. Divergent and parallel routes of biochemical adaptation in high-altitude passerine birds from the Qinghai-Tibet Plateau. Proc. Natl. Acad. Sci. USA, 2018, 115(8), 1865-1870.
[38]
Zhen, Y.; Aardema, M.L.; Medina, E.M.; Schumer, M.; Andolfatto, P. Parallel molecular evolution in an herbivore community. Science, 2012, 337(6102), 1634.
[39]
Natarajan, C.; Hoffmann, F.G.; Weber, R.E.; Fago, A.; Witt, C.C.; Storz, J.F. Predictable convergence in hemoglobin function has unpredictable molecular underpinnings. Science, 2016, 354(6310), 336-339.
[40]
McCracken, K.G.; Barger, C.P.; Sorenson, M.D. Phylogenetic and structural analysis of the HbA (alphaA/betaA) and HbD (alphaD/betaA) hemoglobin genes in two high-altitude waterfowl from the Himalayas and the Andes: bar-headed goose (Anser indicus) and Andean goose (Chloephaga melanoptera). Mol. Phylogenet. Evol., 2010, 56(2), 649-658.
[41]
Begun, D.J.; Natarajan, C.; Projecto-Garcia, J.; Moriyama, H.; Weber, R.E.; Muñoz-Fuentes, V.; Green, A.J.; Kopuchian, C.; Tubaro, P.L.; Alza, L.; Bulgarella, M.; Smith, M.M.; Wilson, R.E.; Fago, A.; McCracken, K.G.; Storz, J.F. Convergent evolution of hemoglobin function in high-altitude Andean waterfowl involves limited parallelism at the molecular sequence level. PLoS Genet., 2015, 11(12), e1005681.
[42]
Yang, Y.; Wang, L.; Han, J.; Tang, X.; Ma, M.; Wang, K.; Zhang, X.; Ren, Q.; Chen, Q.; Qiu, Q. Comparative transcriptomic analysis revealed adaptation mechanism of Phrynocephalus erythrurus, the highest altitude lizard living in the Qinghai-Tibet Plateau. BMC Evol. Biol., 2015, 15, 101.
[http://dx.doi.org/10.1186/s12862-015-0371-8]
[43]
Chikina, M.; Robinson, J.D.; Clark, N.L. Hundreds of genes experienced convergent shifts in selective pressure in marine mammals. Mol. Biol. Evol., 2016, 33(9), 2182-2192.
[44]
Castiglione, G.M.; Schott, R.K.; Hauser, F.E.; Chang, B.S.W. Convergent selection pressures drive the evolution of rhodopsin kinetics at high altitudes via nonparallel mechanisms. Evolution, 2018, 72(1), 170-186.
[45]
Stern, D.L. Perspective: evolutionary developmental biology and the problem of variation. Evolution, 2000, 54(4), 1079-1091.
[46]
Vavouri, T.; Walter, K.; Gilks, W.R.; Lehner, B.; Elgar, G. Parallel evolution of conserved non-coding elements that target a common set of developmental regulatory genes from worms to humans. Genome Biol., 2007, 8(2), 15.
[47]
Frankel, N.; Wang, S.; Stern, D.L. Conserved regulatory architecture underlies parallel genetic changes and convergent phenotypic evolution. Proc. Natl. Acad. Sci. USA, 2012, 109(51), 20975-20979.
[48]
Reed, R.D.; Papa, R.; Martin, A.; Hines, H.M.; Counterman, B.A.; Pardo-Diaz, C.; Jiggins, C.D.; Chamberlain, N.L.; Kronforst, M.R.; Chen, R.; Halder, G.; Nijhout, H.F.; McMillan, W.O. Optix drives the repeated convergent evolution of butterfly wing pattern mimicry. Science, 2011, 333(6046), 1137-1141.
[49]
Signor, S.A.; Liu, Y.; Rebeiz, M.; Kopp, A. Genetic convergence in the evolution of male-specific color patterns in Drosophila. Curr. Biol., 2016, 26(18), 2423-2433.
[50]
Feigin, C.Y.; Newton, A.H.; Doronina, L.; Schmitz, J.; Hipsley, C.A.; Mitchell, K.J.; Gower, G.; Llamas, B.; Soubrier, J.; Heider, T.N.; Menzies, B.R.; Cooper, A.; O’Neill, R.J.; Pask, A.J. Genome of the Tasmanian tiger provides insights into the evolution and demography of an extinct marsupial carnivore. Nat. Ecol. Evol., 2017, 2(1), 182-192.
[51]
Sackton, T.B.; Grayson, P.; Cloutier, A.; Hu, Z.; Liu, J.S.; Wheeler, N.E.; Gardner, P.P.; Clarke, J.A.; Baker, A.J.; Clamp, M.; Edwards, S.V. Convergent regulatory evolution and the origin of flightlessness in palaeognathous birds. bioRxiv, 2018, 262584
[http://dx.doi.org/10.1101/262584]
[52]
Qu, Y.; Zhao, H.; Han, N.; Zhou, G.; Song, G.; Gao, B.; Tian, S.; Zhang, J.; Zhang, R.; Meng, X.; Zhang, Y.; Zhang, Y.; Zhu, X.; Wang, W.; Lambert, D.; Ericson, P.G.; Subramanian, S.; Yeung, C.; Zhu, H.; Jiang, Z.; Li, R.; Lei, F. Ground tit genome reveals avian adaptation to living at high altitudes in the Tibetan plateau. Nat. Commun., 2013, 4, 2071.
[http://dx.doi.org/10.1038/ncomms3071]
[53]
Nagy, L.G.; Ohm, R.A.; Kovacs, G.M.; Floudas, D.; Riley, R.; Gacser, A.; Sipiczki, M.; Davis, J.M.; Doty, S.L.; De Hoog, G.S.; Lang, B.F.; Spatafora, J.W.; Martin, F.M.; Grigoriev, I.V.; Hibbett, D.S. Latent homology and convergent regulatory evolution underlies the repeated emergence of yeasts. Nat. Commun., 2014, 5(4471)
[http://dx.doi.org/10.1038/ncomms5471]
[54]
Denoeud, F.; Carretero-Paulet, L.; Dereeper, A.; Droc, G.; Guyot, R.; Pietrella, M.; Zheng, C.; Alberti, A.; Anthony, F.; Aprea, G. The coffee genome provides insight into the convergent evolution of caffeine biosynthesis. Science, 2014, 345(6201), 1181-1184.
[55]
Berens, A.J.; Hunt, J.H.; Toth, A.L. Comparative transcriptomics of convergent evolution: Different genes but conserved pathways underlie caste phenotypes across lineages of eusocial insects. Mol. Biol. Evol., 2015, 32(3), 690-703.
[56]
Roda, F.; Liu, H.; Wilkinson, M.J.; Walter, G.M.; James, M.E.; Bernal, D.M.; Melo, M.C.; Lowe, A.; Rieseberg, L.H.; Prentis, P.; Ortiz-Barrientos, D. Convergence and divergence during the adaptation to similar environments by an Australian groundsel. Evolution, 2013, 67(9), 2515-2529.
[57]
Sun, Y.B.; Fu, T.T.; Jin, J.Q.; Murphy, R.W.; Hillis, D.M.; Zhang, Y.P.; Che, J. Species groups distributed across elevational gradients reveal convergent and continuous genetic adaptation to high elevations. Proc. Natl. Acad. Sci. USA, 2018, 115(45), 10634-10641.
[58]
Bickler, P.E.; Buck, L.T. Hypoxia tolerance in reptiles, amphibians, and fishes: life with variable oxygen availability. . Annu. Rev. Physiol., 2007, 69(1), 145-170.
[59]
Ramirez, J.M.; Folkow, L.P.; Blix, A.S. Hypoxia tolerance in mammals and birds: From the wilderness to the clinic. Annu. Rev. Physiol., 2007, 69(1), 113-143.
[60]
Moore, L.G.; Niermeyer, S.; Zamudio, S. Human adaptation to high altitude: Regional and life-cycle perspectives. Yearb. Phys. Anthropol., 1998(Suppl. 27), 25-64.
[61]
Zhang, Q.; Gou, W.; Wang, X.; Zhang, Y.; Ma, J.; Zhang, H.; Zhang, Y.; Zhang, H. Genome resequencing identifies unique adaptations of tibetan chickens to hypoxia and high-dose ultraviolet radiation in high-altitude environments. Genome Biol. Evol., 2016, 8(3), 765-776.
[62]
Beall, C.M.; Jablonski, N.G.; Steegmann, A.T. Human adaptation to climate: Temperature, ultraviolet radiation, and altitude. In: Human biology: An evolutionary and biocultural perspective, 2nd ed; John Wiley & Sons, Inc.: New York, 2012; Vol. 6, pp. 177-250.
[63]
Gnerre, S.; MacCallum, I.; Przybylski, D.; Ribeiro, F.J.; Burton, J.N.; Walker, B.J.; Sharpe, T.; Hall, G.; Shea, T.P.; Sykes, S.; Berlin, A.M.; Aird, D.; Costello, M.; Daza, R.; Williams, L.; Nicol, R.; Gnirke, A.; Nusbaum, C.; Lander, E.S.; Jaffe, D.B. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc. Natl. Acad. Sci. USA, 2011, 108(4), 1513-1518.
[64]
Luo, R.B.; Liu, B.H.; Xie, Y.L.; Li, Z.Y.; Huang, W.H.; Yuan, J.Y.; He, G.Z.; Chen, Y.X.; Pan, Q.; Liu, Y.J.; Tang, J.B.; Wu, G.X.; Zhang, H.; Shi, Y.J.; Liu, Y.; Yu, C.; Wang, B.; Lu, Y.; Han, C.L.; Cheung, D.W.; Yiu, S.M.; Peng, S.L.; Zhu, X.Q.; Liu, G.M.; Liao, X.K.; Li, Y.R.; Yang, H.M.; Wang, J.; Lam, T.W.; Wang, J. SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler. Gigascience, 2012, 1(1), 18.
[65]
Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M.; MacManes, M.D.; Ott, M.; Orvis, J.; Pochet, N.; Strozzi, F.; Weeks, N.; Westerman, R.; William, T.; Dewey, C.N.; Henschel, R.; LeDuc, R.D.; Friedman, N.; Regev, A. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc., 2013, 8(8), 1494-1512.
[66]
Schulz, M.H.; Zerbino, D.R.; Vingron, M.; Birney, E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics, 2012, 28(8), 1086-1092.
[67]
Li, L.; Stoeckert, C.J.; Roos, D.S. OrthoMCL: Identification of ortholog groups for eukaryotic genomes. Genome Res., 2003, 13(9), 2178-2189.
[68]
Lechner, M.; Findeiß, S.; Steiner, L.; Marz, M.; Stadler, P.F.; Prohaska, S.J. Proteinortho: Detection of (co-) orthologs in large-scale analysis. BMC Bioinformatics, 2011, 12(1), 1.
[69]
Löytynoja, A.; Goldman, N. Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science, 2008, 320(5883), 1632-1635.
[70]
Katoh, K.; Standley, D.M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol., 2013, 30(4), 772-780.
[71]
Edgar, R.C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res., 2004, 32(5), 1792-1797.
[72]
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol., 2000, 17(4), 540-552.
[73]
Capella-Gutierrez, S.; Silla-Martinez, J.M.; Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics, 2009, 25(15), 1972-1973.
[74]
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol., 2007, 24(8), 1586-1591.
[75]
Benjamini, Y.; Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B, 1995, 289-300.
[76]
Storey, J.D.; Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA, 2003, 100(16), 9440-9445.
[77]
Zhang, J.; Kumar, S. Detection of convergent and parallel evolution at the amino acid sequence level. Mol. Biol. Evol., 1997, 14(5), 527-536.
[78]
Gallant, J.R.; Traeger, L.L.; Volkening, J.D.; Moffett, H.; Chen, P-H.; Novina, C.D.; Phillips, G.N.; Anand, R.; Wells, G.B.; Pinch, M. Genomic basis for the convergent evolution of electric organs. Science, 2014, 344(6191), 1522-1525.
[79]
Pfenning, A.R.; Hara, E.; Whitney, O.; Rivas, M.V.; Wang, R.; Roulhac, P.L.; Howard, J.T.; Wirthlin, M.; Lovell, P.V.; Ganapathy, G.; Mouncastle, J.; Moseley, M.A.; Thompson, J.W.; Soderblom, E.J.; Iriki, A.; Kato, M.; Gilbert, M.T.P.; Zhang, G.; Bakken, T.; Bongaarts, A.; Bernard, A.; Lein, E.; Mello, C.V.; Hartemink, A.J.; Jarvis, E.D. Convergent transcriptional specializations in the brains of humans and song-learning birds. Science, 2014, 346(6215), 1256846.
[80]
Brawand, D.; Soumillon, M.; Necsulea, A.; Julien, P.; Csardi, G.; Harrigan, P.; Weier, M.; Liechti, A.; Aximu-Petri, A.; Kircher, M.; Albert, F.W.; Zeller, U.; Khaitovich, P.; Grutzner, F.; Bergmann, S.; Nielsen, R.; Paabo, S.; Kaessmann, H. The evolution of gene expression levels in mammalian organs. Nature, 2011, 478(7369), 343-348.
[81]
Barbosa-Morais, N.L.; Irimia, M.; Pan, Q.; Xiong, H.Y.; Gueroussov, S.; Lee, L.J.; Slobodeniuc, V.; Kutter, C.; Watt, S.; Colak, R.; Kim, T.; Misquitta-Ali, C.M.; Wilson, M.D.; Kim, P.M.; Odom, D.T.; Frey, B.J.; Blencowe, B.J. The evolutionary landscape of alternative splicing in vertebrate species. Science, 2012, 338(6114), 1587-1593.
[82]
Merkin, J.; Russell, C.; Chen, P.; Burge, C.B. Evolutionary dynamics of gene and isoform regulation in mammalian tissues. Science, 2012, 338(6114), 1593-1599.
[83]
Simonson, T.S. Altitude adaptation: A glimpse through various lenses. High Alt. Med. Biol., 2015, 16(2), 125-137.
[84]
Cheviron, Z.A.; Whitehead, A.; Brumfield, R.T. Transcriptomic variation and plasticity in rufous-collared sparrows (Zonotrichia capensis) along an altitudinal gradient. Mol. Ecol., 2008, 17(20), 4556-4569.
[85]
Savolainen, O.; Lascoux, M.; Merila, J. Ecological genomics of local adaptation. Nat. Rev. Genet., 2013, 14(11), 807-820.
[86]
Storz, J.F.; Scott, G.R.; Cheviron, Z.A. Phenotypic plasticity and genetic adaptation to high-altitude hypoxia in vertebrates. J. Exp. Biol., 2010, 213(24), 4125-4136.

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