Common gardens in teosintes reveal the establishment of a syndrome of adaptation to altitude


Autoři: Margaux-Alison Fustier aff001;  Natalia E. Martínez-Ainsworth aff001;  Jonás A. Aguirre-Liguori aff002;  Anthony Venon aff001;  Hélène Corti aff001;  Agnès Rousselet aff001;  Fabrice Dumas aff001;  Hannes Dittberner aff003;  María G. Camarena aff004;  Daniel Grimanelli aff005;  Otso Ovaskainen aff006;  Matthieu Falque aff001;  Laurence Moreau aff001;  Juliette de Meaux aff003;  Salvador Montes-Hernández aff004;  Luis E. Eguiarte aff002;  Yves Vigouroux aff005;  Domenica Manicacci aff001;  Maud I. Tenaillon aff001
Působiště autorů: Génétique Quantitative et Evolution – Le Moulon, Université Paris-Saclay, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Centre National de la Recherche Scientifique, AgroParisTech, Gif-sur-Yvette, France aff001;  Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico aff002;  Institute of Botany, University of Cologne Biocenter, Cologne, Germany aff003;  Campo Experimental Bajío, InstitutoNacional de Investigaciones Forestales, Agrícolas y Pecuarias, Celaya, Mexico aff004;  UMR Diversité, Adaptation et Développement des plantes, Université de Montpellier, Institut de Recherche pour le développement, Montpellier, France aff005;  Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland aff006;  Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway aff007
Vyšlo v časopise: Common gardens in teosintes reveal the establishment of a syndrome of adaptation to altitude. PLoS Genet 15(12): e32767. doi:10.1371/journal.pgen.1008512
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008512

Souhrn

In plants, local adaptation across species range is frequent. Yet, much has to be discovered on its environmental drivers, the underlying functional traits and their molecular determinants. Genome scans are popular to uncover outlier loci potentially involved in the genetic architecture of local adaptation, however links between outliers and phenotypic variation are rarely addressed. Here we focused on adaptation of teosinte populations along two elevation gradients in Mexico that display continuous environmental changes at a short geographical scale. We used two common gardens, and phenotyped 18 traits in 1664 plants from 11 populations of annual teosintes. In parallel, we genotyped these plants for 38 microsatellite markers as well as for 171 outlier single nucleotide polymorphisms (SNPs) that displayed excess of allele differentiation between pairs of lowland and highland populations and/or correlation with environmental variables. Our results revealed that phenotypic differentiation at 10 out of the 18 traits was driven by local selection. Trait covariation along the elevation gradient indicated that adaptation to altitude results from the assembly of multiple co-adapted traits into a complex syndrome: as elevation increases, plants flower earlier, produce less tillers, display lower stomata density and carry larger, longer and heavier grains. The proportion of outlier SNPs associating with phenotypic variation, however, largely depended on whether we considered a neutral structure with 5 genetic groups (73.7%) or 11 populations (13.5%), indicating that population stratification greatly affected our results. Finally, chromosomal inversions were enriched for both SNPs whose allele frequencies shifted along elevation as well as phenotypically-associated SNPs. Altogether, our results are consistent with the establishment of an altitudinal syndrome promoted by local selective forces in teosinte populations in spite of detectable gene flow. Because elevation mimics climate change through space, SNPs that we found underlying phenotypic variation at adaptive traits may be relevant for future maize breeding.

Klíčová slova:

Leaves – Maize – Molecular genetics – Phenotypes – Population genetics – principal component analysis – Stomata – Variant genotypes


Zdroje

1. Whitlock MC. Modern approaches to local adaptation. The American Naturalist. 2015;186.

2. Bradshaw AD. Ecological significance of genetic variation between populations. Perspectives on plant population ecology. 1984:213–28.

3. Bulmer MGG. Multiple niche polymorphism. The American Naturalist. 1972;106:254–7.

4. Endler JA. Natural selection in the wild. 1986:354.

5. Gay L, Crochet PA, Bell DA, Lenormand T. Comparing clines on molecular and phenotypic traits in hybrid zones: A window on tension zone models. Evolution. 2008;62:2789–806. doi: 10.1111/j.1558-5646.2008.00491.x 18752618

6. Lande R. Natural selection and random genetic drift in phenotypic evolution. Evolution. 1976;30:314–34. doi: 10.1111/j.1558-5646.1976.tb00911.x 28563044

7. Lenormand T. Gene flow and the limits to natural selection. Trends in Ecology and Evolution. 2002;17:183–9.

8. Whitlock MC, Gomulkiewicz R. Probability of fixation in a heterogeneous environment. Genetics. 2005:1–40.

9. Yeaman S, Otto SP. Establishment and maintenance of adaptive genetic divergence under migration, selection and drift. Evolution. 2011;67:2123–9.

10. Rundle HD, Nosil P. Ecological speciation. Ecology Letters. 2005;8:336–52.

11. Kawecki TJ, Ebert D. Conceptual issues in local adaptation. Ecology Letters. 2004;7:1225–41.

12. Hereford J. A quantitative survey of local adaptation and fitness trade-offs. The American Naturalist. 2009;173:579–88. doi: 10.1086/597611 19272016

13. Leimu R, Fischer M. A meta-analysis of local adaptation in plants. PLoS ONE. 2008;3.

14. Tiffin P, Ross-Ibarra J. Advances and limits of using population genetics to understand local adaptation. Trends in Ecology and Evolution. 2014;29:673–80. doi: 10.1016/j.tree.2014.10.004 25454508

15. Garcia-Ramos G, Kirkpatrick M. Genetic models of adaptation and gene flow in peripherial populations. Evolution. 1997;51:21–8. doi: 10.1111/j.1558-5646.1997.tb02384.x 28568782

16. Slatkin M. Rare alleles as indicators of gene flow. Evolution. 1985;39(1):53–65. doi: 10.1111/j.1558-5646.1985.tb04079.x 28563643

17. Lande R. Neutral theory of quantitative genetic variance in an island model with local extinction and colonization. Evolution. 1992;46:381–9. doi: 10.1111/j.1558-5646.1992.tb02046.x 28564025

18. Whitlock MC. Neutral additive genetic variance in a metapopulation. Genetics Research. 1999;74:215–21.

19. Spitze K. Population structure in Daphina obtusa: quantitative genetic and allozymic variation. Genetics Society of America. 1993;135:367–74.

20. Wright S. The genetical structure of populations. Annals of Eugenics. 1951;15:323–54. doi: 10.1111/j.1469-1809.1949.tb02451.x 24540312

21. Luquet E, Léna J-P, Miaud C, Plénet S. Phenotypic divergence of the common toad (Bufo bufo) along an altitudinal gradient: evidence for local adaptation. Heredity. 2015;114:69–79. doi: 10.1038/hdy.2014.71 25074572

22. Roschanski AM, Csilléry K, Liepelt S, Oddou-Muratorio S, Ziegenhagen B, Huard Fdr, et al. Evidence of divergent selection for drought and cold tolerance at landscape and local scales in Abies alba Mill. in the French Mediterranean Alps. Molecular Ecology. 2016;25:776–94. doi: 10.1111/mec.13516 26676992

23. Kawakami T, Morgan TJ, Nippert JB, Ocheltree TW, Keith R, Dhakal P, et al. Natural selection drives clinal life history patterns in the perennial sunflower species, Helianthus maximiliani. Molecular Ecology. 2011;20:2318–28. doi: 10.1111/j.1365-294X.2011.05105.x 21521394

24. Moyers BT, Rieseberg LH. Remarkable life history polymorphism may be evolving under divergent selection in the silverleaf sunflower. Molecular Ecology. 2016;25:3817–30. doi: 10.1111/mec.13723 27288664

25. Kirkpatrick M, Barton N. Chromosome inversions, local adaptation and speciation. Genetics. 2006.

26. Lowry DB, Willis JH. A widespread chromosomal inversion polymorphism contributes to a major life-history transition, local adaptation, and reproductive isolation. PLoS Biology. 2010;8.

27. Legrand D, Larranaga N, Bertrand R, Ducatez S, Calvez O, Stevens VM, et al. Evolution of a butterfly dispersal syndrome. Proceedings of the Royal Society B. 2016; 283(1839): 20161533

28. Bierne N, Welch J, Loire E, Bonhomme F, David P. The coupling hypothesis: Why genome scans may fail to map local adaptation genes. Molecular Ecology. 2011;20:2044–72. doi: 10.1111/j.1365-294X.2011.05080.x 21476991

29. Lewontin RC, Krakauer J. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genetics. 1973;74:175–95. 4711903

30. Beaumont MA, Nichols RA. Evaluating loci for use in the genetic analysis of population structure. Proceedings of the Royal Society B. 1996;263:1619–26.

31. Vitalis R, Dawson K, Boursot P. Interpretation of variation across marker loci as evidence of selection. Genetics. 2001;158:1811–23. 11514464

32. Foll M, Gaggiotti O. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics. 2008;180:977–93. doi: 10.1534/genetics.108.092221 18780740

33. Excoffier La H T and Foll Matthieu. Detecting loci under selection in a hierarchically structured population. Heredity. 2009;103:285–98. doi: 10.1038/hdy.2009.74 19623208

34. Bonhomme M, Chevalet C, Servin B, Boitard S, Abdallah J, Blott S, et al. Detecting selection in population trees: The Lewontin and Krakauer test extended. Genetics. 2010;186:241–62. doi: 10.1534/genetics.104.117275 20855576

35. Günther T, Coop G. Robust identification of local adaptation from allele frequencies. Genetics. 2013;195:205–20. doi: 10.1534/genetics.113.152462 23821598

36. Lotterhos KE, Whitlock MC. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Molecular Ecology. 2014;23:2178–92. doi: 10.1111/mec.12725 24655127

37. Haasl RJ, Payseur BA. Fifteen years of genomewide scans for selection: trends, lessons and unaddressed genetic sources of complication. Molecular Ecology. 2016;25:5–23. doi: 10.1111/mec.13339 26224644

38. Le Corre V, Kremer A. The genetic differentiation at quantitative trait loci under local adaptation. Molecular Ecology. 2012;21:1548–66. doi: 10.1111/j.1365-294X.2012.05479.x 22332667

39. Yi X, Liang Y, Huerta-Sanchez E, Jin X, Xi Ping Cuo Z, Pool JE, et al. Sequencing of fifty human exomes reveals adaptations to high altitude. Science. 2010;329:75–8. doi: 10.1126/science.1190371 20595611

40. Coop G, Witonsky D, Di Rienzo A, Pritchard JK. Using environmental correlations to identify loci underlying local adaptation. Genetics. 2010;185:1411–23. doi: 10.1534/genetics.110.114819 20516501

41. Guillot G, Renaud S, Ledevin R, Michaux J, Claude J. A unifying model for the analysis of phenotypic, genetic, and geographic data. Systematic Biology. 2012;61:897–911. doi: 10.1093/sysbio/sys038 22398122

42. Frichot E, Schoville SD, Bouchard G, François O. Testing for associations between loci and environmental gradients using latent factor mixed models. Molecular Biology and Evolution. 2013;30:1687–99. doi: 10.1093/molbev/mst063 23543094

43. Gautier M. Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics. 2015;201:1555–79. doi: 10.1534/genetics.115.181453 26482796

44. Joost S, Bonin A, Bruford MW, Després L, Conord C, Erhardt G, et al. A spatial analysis method (SAM) to detect candidate loci for selection: Towards a landscape genomics approach to adaptation. Molecular Ecology. 2007;16:3955–69. doi: 10.1111/j.1365-294X.2007.03442.x 17850556

45. Poncet BN, Herrmann D, Gugerli F, Taberlet P, Holderegger R, Gielly L, et al. Tracking genes of ecological relevance using a genome scan in two independent regional population samples of Arabis alpina. Molecular Ecology. 2010;19:2896–907. doi: 10.1111/j.1365-294X.2010.04696.x 20609082

46. De Mita S, Thuillet AC, Gay L, Ahmadi N, Manel S, Ronfort J, et al. Detecting selection along environmental gradients: Analysis of eight methods and their effectiveness for outbreeding and selfing populations. Molecular Ecology. 2013;22:1383–99. doi: 10.1111/mec.12182 23294205

47. Hoban S, Kelley JL, Lotterhos KE, Antolin MF, Bradburd G, Lowry DB, et al. Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. The American Naturalist. 2016;188:379–97. doi: 10.1086/688018 27622873

48. Barton N, Hermisson J, Nordborg M. Why structure matters. eLife. 2019;8.

49. Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, Wilczek AM. A map of local adaptation in Arabidopsis thaliana. Science. 2011;334:86–9. doi: 10.1126/science.1209271 21980109

50. Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, Sperone FG, et al. Adaptation to climate across the Arabidopsis thaliana genome. Science. 2011;334:83–6. doi: 10.1126/science.1209244 21980108

51. Ross-Ibarra J, Tenaillon M, Gaut BS. Historical divergence and gene flow in the genus Zea. Genetics. 2009;181:1399–413. doi: 10.1534/genetics.108.097238 19153259

52. Hufford MB, Martínez-Meyer E, Gaut BS, Eguiarte LE, Tenaillon MI. Inferences from the historical distribution of wild and domesticated maize provide ecological and evolutionary insight. PLoS ONE. 2012;7.

53. Bilinski P, Albert PS, Berg JJ, Birchler JA, Grote MN, Lorant A, et al. Parallel altitudinal clines reveal trends in adaptive evolution of genome size in Zea mays. PLoS Genetics. 2018;14.

54. Diez CM, Gaut BS, Meca E, Scheinvar E, Montes-Hernandez S, Eguiarte LE, et al. Genome size variation in wild and cultivated maize along altitudinal gradients. New Phytologist. 2013;199:264–76. doi: 10.1111/nph.12247 23550586

55. Pyhäjärvi T, Hufford MB, Mezmouk S, Ross-Ibarra J. Complex patterns of local adaptation in teosinte. Genome Biology and Evolution. 2013;5:1594–609. doi: 10.1093/gbe/evt109 23902747

56. Fang Z, Pyhäjärvi T, Weber AL, Dawe RK, Glaubitz JC, Sánchez González JdJ, et al. Megabase-scale inversion polymorphism in the wild ancestor of maize. Genetics. 2012;191:883–94. doi: 10.1534/genetics.112.138578 22542971

57. Aguirre-Liguori JA, Tenaillon MI, Vázquez-Lobo A, Gaut BS, Jaramillo-Correa JP, Montes-Hernandez S, et al. Connecting genomic patterns of local adaptation and niche suitability in teosintes. Molecular Ecology. 2017;26:4226–40. doi: 10.1111/mec.14203 28612956

58. Fustier MA, Brandenburg JT, Boitard S, Lapeyronnie J, Eguiarte LE, Vigouroux Y, et al. Signatures of local adaptation in lowland and highland teosintes from whole-genome sequencing of pooled samples. Molecular Ecology. 2017;26:2738–56. doi: 10.1111/mec.14082 28256021

59. Ovaskainen O, Karhunen M, Zheng C, Arias JMC, Merilä J. A new method to uncover signatures of divergent and stabilizing selection in quantitative traits. Genetics. 2011;189:621–32. doi: 10.1534/genetics.111.129387 21840853

60. McKinney GJ, Varian A, Scardina J, Nichols KM. Genetic and morphological divergence in three strains of brook trout Salvelinus fontinalis commonly stocked in Lake Superior. PLoS ONE. 2014;9(12):e113809. doi: 10.1371/journal.pone.0113809 25479612

61. Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR, Turchin MC, et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife. 2019;8.

62. Desrousseaux AD, Sandron F, Siberchicot A, Cierco-Ayrolles C, Mangin B. R Package ‘ LDcorSV ‘. 2017.

63. Mangin B, Siberchicot A, Nicolas S, Doligez A, This P, Cierco-Ayrolles C. Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness. Heredity. 2012;108:285–91. doi: 10.1038/hdy.2011.73 21878986

64. Savolainen O, Lascoux M, Merilä J. Ecological genomics of local adaptation. Nature reviews Genetics. 2013;14:807–20. doi: 10.1038/nrg3522 24136507

65. Anderson JT, Willis JH, Mitchell-Olds T. Evolutionary genetics of plant adaptation. Trends in Genetics. 2011;27:258–66. doi: 10.1016/j.tig.2011.04.001 21550682

66. Halbritter AH, Fior S, Keller I, Billeter R, Edwards PJ, Holderegger R, et al. Trait differentiation and adaptation of plants along elevation gradients. Journal of Evolutionary Biology. 2018;31(6):784–800. doi: 10.1111/jeb.13262 29518274

67. Körner C. The use of 'altitude' in ecological research. Trends in Ecology and Evolution. 2007;22:569–74. doi: 10.1016/j.tree.2007.09.006 17988759

68. Friend AD, Woodward FI, Switsur VR. Field measurements of photosynthesis, stomatal conductance, leaf nitrogen and δ 13 C along altitudinal gradients in Scotland. Functional Ecology. 1989;3:117.

69. Neuner G. Frost resistance in alpine woody plants. Frontiers in Plant Science. 2014;5.

70. Frohnmeyer H, Staiger D. Update on ultraviolet-B light responses ultraviolet-B radiation-mediated responses in plants. Balancing damage and protection. Plant Physiology. 2014;133:1420–8.

71. Byars SG, Papst W, Hoffmann AA. Local adaptation and cogradient selection in the alpine plant, Poa hiemata, along a narrow altitudinal gradient. Evolution. 2007;61:2925–41. doi: 10.1111/j.1558-5646.2007.00248.x 17924954

72. Luo Y, Widmer A, Karrenberg S. The roles of genetic drift and natural selection in quantitative trait divergence along an altitudinal gradient in Arabidopsis thaliana. Heredity. 2015;114:220–8. doi: 10.1038/hdy.2014.89 25293874

73. Guerin GR, Wen H, Lowe AJ, Guerin GR, Wen H. Leaf morphology shift linked to climate change. Population Ecology. 2012:882–6.

74. Kofidis G, Bosabalidis AM, Moustakas M. Contemporary seasonal and altitudinal variations of leaf structural features in oregano (Origanum vulgare L.). Annals of Botany. 2003;92(5):635–45. doi: 10.1093/aob/mcg180 12967906

75. Mendez-Vigo B, Pico FX, Ramiro M, Martinez-Zapater JM, Alonso-Blanco C. Altitudinal and climatic adaptation is mediated by flowering traits and FRI, FLC, and PHYC genes in Arabidopsis. Plant Physiology. 2011;157:1942–55. doi: 10.1104/pp.111.183426 21988878

76. Oleksyn J, Modrzynski J, Tjoelker MG, Zytkowaik R, Reich PB, Karolewski P. Growth and physiology of Picea abies populations from elevational transects: common garden evidence for altitudinal ecotypes and cold adaptation. Functional Ecology. 1998:573–90.

77. Soularue JP, Kremer A. Evolutionary responses of tree phenology to the combined effects of assortative mating, gene flow and divergent selection. Heredity. 2014;113:485–94. doi: 10.1038/hdy.2014.51 24924591

78. Doebley JF. Maize introgression into teosinte—a reappraisal. Annals of the Missouri Botanical Garden. 1984;71:1100–13.

79. Lauter N, Gustus C, Westerbergh A, Doebley J. The inheritance and evolution of leaf pigmentation and pubescence in teosinte. Genetics. 2004;167:1949–59. doi: 10.1534/genetics.104.026997 15342532

80. Smith JSC, Goodman MM, Lester RN. Variation within teosinte. I. Numerical analysis of morphological data. Economic Botany. 1981;35:187–203.

81. Raven JA. Selection pressures on stomatal evolution. New Phytologist. 2002;153:371–86.

82. Dittberner H, Korte A, Mettler-Altmann T, Weber APM, Monroe G, de Meaux J. Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana. Molecular Ecology. 2018:4052–65. doi: 10.1111/mec.14838 30118161

83. Carlson JE, Adams CA, Holsinger KE. Intraspecific variation in stomatal traits, leaf traits and physiology reflects adaptation along aridity gradients in a South African shrub. Annals of Botany. 2016;117:195–207. doi: 10.1093/aob/mcv146 26424782

84. Körner C, Mayr R. Stomatal behaviour in alpine plant communities between 600 and 2600 metres above sea level. In: Grace J, Ford ED, Jarvis PG, editors. Plants and their Atmospheric Environment: Blackwell, Oxford; 1981. p. pp 205–18.

85. Bresson CC, Vitasse Y, Kremer A, Delzon S. To what extent is altitudinal variation of functional traits driven by genetic adaptation in European oak and beech? Tree Physiology. 2011;31:1164–74. doi: 10.1093/treephys/tpr084 21908436

86. Kooyers NJ, Greenlee AB, Colicchio JM, Oh M, Blackman BK. Replicate altitudinal clines reveal that evolutionary flexibility underlies adaptation to drought stress in annual Mimulus guttatus. New Phytologist. 2015;206:152–65. doi: 10.1111/nph.13153 25407964

87. Körner C, Neumayer M, Menendez-Riedl SP, Smeets-Scheel A. Functional morphology of mountain plants. Flora. 1989;182:353–83.

88. Jakobsson A, Eriksson O. A comparative study of seed number, seed size, seedling size and recruitment in grassland plants. Oikos. 2000;88:494–502.

89. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, et al. The genetic architecture of maize flowering time. Science. 2009;325:714–8. doi: 10.1126/science.1174276 19661422

90. Moreau L, Charcosset A, Gallais A. Use of trial clustering to study QTL x environment effects for grain yield and related traits in maize. Theoretical and Applied Genetics. 2004;110:92–105. doi: 10.1007/s00122-004-1781-y 15551040

91. Durand E, Bouchet S, Bertin P, Ressayre A, Jamin P, Charcosset A, et al. Flowering time in maize: Linkage and epistasis at a major effect locus. Genetics. 2012;190:1547–62. doi: 10.1534/genetics.111.136903 22298708

92. Li D, Wang X, Zhang X, Chen Q, Xu G, Xu D, et al. The genetic architecture of leaf number and its genetic relationship to flowering time in maize. New Phytologist. 2015;210:256–68. doi: 10.1111/nph.13765 26593156

93. Yang CJ, Samayoa LF, Bradbury PJ, Olukolu BA, Xue W, York AM, et al. The genetic architecture of teosinte catalyzed and constrained maize domestication. Proceedings of the National Academy of Sciences of the United States of America. 2019; 116(12):5643–5652. doi: 10.1073/pnas.1820997116 30842282

94. Aguirre-Liguori JA, Gaut BS, Jaramillo-Correa JP, Tenaillon MI, Montes-Hernández S, García-Oliva F, et al. Divergence with gene flow is driven by local adaptation to temperature and soil phosphorus concentration in teosinte subspecies (Zea mays parviglumis and Zea mays mexicana) Molecular Ecology. 2019:2814–30. doi: 10.1111/mec.15098 30980686

95. Consortium G. 1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell. 2016;166:481–91. doi: 10.1016/j.cell.2016.05.063 27293186

96. Novembre J, Barton NH. Tread lightly interpreting polygenic tests of selection. Genetics. 2018; 208(4):1351–5. doi: 10.1534/genetics.118.300786 29618592

97. Guo J, Wu Y, Zhu Z, Zheng Z, Trzaskowski M, Zeng J, et al. Global genetic differentiation of complex traits shaped by natural selection in humans. Nature Communications. 2018;9(1):1865. doi: 10.1038/s41467-018-04191-y 29760457

98. Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife. 2019;8:1–47.

99. Whitt SR, Wilson LM, Tenaillon MI, Gaut BS, Buckler ES. Genetic diversity and selection in the maize starch pathway. Proceedings of the National Academy of Sciences of the United States of America. 2002;99:12959–62. doi: 10.1073/pnas.202476999 12244216

100. Jaenicke-Despres V, Buckler ES, Smith BD, Gilbert MTP, Cooper A, Doebley J, et al. Early allelic selection in maize as revealed by ancient DNA. Science. 2003;302:1206–9. doi: 10.1126/science.1089056 14615538

101. Weber AL, Briggs WH, Rucker J, Baltazar BM, De Jesús Sánchez-González J, Feng P, et al. The genetic architecture of complex traits in teosinte (Zea mays ssp. parviglumis): New evidence from association mapping. Genetics. 2008;180:1221–32. doi: 10.1534/genetics.108.090134 18791250

102. Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, et al. Adaptation of maize to temperate climates: Mid-density genome-wide association genetics and diversity patterns reveal key genomic regions, with a major contribution of the Vgt2 (ZCN8) locus. PLoS ONE. 2013;8.

103. Sheehan MJ, Kennedy LM, Costich DE, Brutnell TP. Subfunctionalization of PhyB1 and PhyB2 in the control of seedling and mature plant traits in maize. Plant Journal. 2007;49:338–53. doi: 10.1111/j.1365-313X.2006.02962.x 17181778

104. Danilevskaya ON, Meng X, Hou Z, Ananiev EV, Simmons CR. A genomic and expression compendium of the expanded PEBP gene family from maize. Plant Physiology. 2007;146:250–64. doi: 10.1104/pp.107.109538 17993543

105. Meng X, Muszynski MG, Danilevskaya ON. The FT-Like ZCN8 gene functions as a floral activator and is involved in photoperiod sensitivity in maize. The Plant Cell. 2011;23:942–60. doi: 10.1105/tpc.110.081406 21441432

106. Li YX, Li C, Bradbury PJ, Liu X, Lu F, Romay CM, et al. Identification of genetic variants associated with maize flowering time using an extremely large multi-genetic background population. The Plant journal. 2016;86:391–402. doi: 10.1111/tpj.13174 27012534

107. Yu J, Li X, Zhu C, Yeh C-T, Wu W, Takacs E, et al. Genic and non-genic contributions to natural variation of quantitative traits in maize. Genome research. 2012:2436–44. doi: 10.1101/gr.140277.112 22701078

108. Wellenreuther M, Bernatchez L. Eco-evolutionary genomics of chromosomal inversions. Trends in Ecology and Evolution. 2018;33:427–40. doi: 10.1016/j.tree.2018.04.002 29731154

109. Ayala D, Ullastres A, González J. Adaptation through chromosomal inversions in Anopheles. Frontiers in Genetics. 2014;5:1–10.

110. Barth JMI, Berg PR, Jonsson PR, Bonanomi S, Corell H, Hemmer-Hansen J, et al. Genome architecture enables local adaptation of Atlantic cod despite high connectivity. Molecular Ecology. 2017;26:4452–66. doi: 10.1111/mec.14207 28626905

111. Lundberg M, Liedvogel M, Larson K, Sigeman H, Grahn M, Wright A, et al. Genetic differences between willow warbler migratory phenotypes are few and cluster in large haplotype blocks. Evolution Letters. 2017:155–68. doi: 10.1002/evl3.15 30283646

112. Twyford AD, Friedman J. Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion. Evolution. 2015;69:1476–86. doi: 10.1111/evo.12663 25879251

113. Díez CM, Gaut BS, Meca E, Scheinvar E, Montes-Hernandez S, Eguiarte LE, et al. Genome size variation in wild and cultivated maize along altitudinal gradients. New Phytologist. 2013;199(1):264–76. doi: 10.1111/nph.12247 23550586

114. Hijmans RJ, van Etten J, Cheng J, Mattiuzzi M, Sumner M, Greenberg JA, et al. Package ‘raster ‘: geographic data analysis and modeling. 2018.

115. Cuervo-Robayo AP, Téllez-Valdés O, Gómez-Albores MA, Venegas-Barrera CS, Manjarrez J, Martínez-Meyer E. An update of high-resolution monthly climate surfaces for Mexico. International Journal of Climatology. 2014;34(7):2427–37.

116. Husson F, Josse J, Le S, Mazet J. Package ‘ FactoMineR ‘. An R package. 2016:96.

117. Andorf CM, Cannon EK, Portwood JL, Gardiner JM, Harper LC, Schaeffer ML, et al. MaizeGDB update: New tools, data and interface for the maize model organism database. Nucleic Acids Research. 2016;44:1195–201.

118. Camus-Kulandaivelu L, Veyrieras JB, Madur D, Combes V, Fourmann M, Barraud S, et al. Maize adaptation to temperate climate: Relationship between population structure and polymorphism in the Dwarf8 gene. Genetics. 2006;172:2449–63. doi: 10.1534/genetics.105.048603 16415370

119. Guichoux E, Lagache S, Wagner S, Chaumeil P, Léger P, Lepais O, et al. Current trends in microsatellite genotyping. Molecular Ecology Resources. 2011;11:591–611. doi: 10.1111/j.1755-0998.2011.03014.x 21565126

120. Jakobsson M, Rosenberg NA. CLUster Matching and Permutation Program Version 1.1.2. 2007.

121. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Molecular Ecology. 2005;14:2611–20. doi: 10.1111/j.1365-294X.2005.02553.x 15969739

122. Hardy OJ, Vekemans X. spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes. 2002;2:618–20.

123. Loiselle BA, Sork VL, Nason JD, Graham C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany. 1995;82:1420–5.

124. Pickrell JK, Pritchard JK. Inference of population splits and mixtures from genome-wide allele frequency data. PLOS Genetics. 2012;8(11):e1002967. doi: 10.1371/journal.pgen.1002967 23166502

125. Fitak RRs. optM: an R package to optimize the number of migration edges using threshold models. Journal of Heredity. 2019.

126. Sanchez JdJ, Kato Yamakake TA, Aguilar Sanmiguel M, Hernandez Casillas JM, Lopez Rodriguez A, Ruiz Corral JA. Distribución y caracterización del teocintle. 1998:165.

127. Butler D, Cullis BR, Gilmour AR, Gogel BJ. ASReml-R reference manual. Technical Report. 2007.

128. Holsinger KE, Weir BS. Genetics in geographically structured populations: defining, estimating and interpreting FST. Nature reviews Genetics. 2009;10:639–50. doi: 10.1038/nrg2611 19687804

129. Gilbert KJ, Whitlock MC. QST-FST comparisons with unbalanced half-sib designs. Molecular Ecology Resources. 2015;15:262–7. doi: 10.1111/1755-0998.12303 25042150

130. Karhunen M, Merilä J, Leinonen T, Cano JM, Ovaskainen O. driftsel: An R package for detecting signals of natural selection in quantitative traits. Molecular Ecology Resources. 2013;13:746–54. doi: 10.1111/1755-0998.12111 23656704

131. Hadfield JD. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. Journal of Statistical Software. 2010;33.

132. Semagn K, Babu R, Hearne S, Olsen M. Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): Overview of the technology and its application in crop improvement. Molecular Breeding. 2014;33:1–14.

133. Weir BS, Hill WG. Estimating F-statistics. Annu Rev Genet. 2002;36:721–50. doi: 10.1146/annurev.genet.36.050802.093940 12359738

134. Günther T, Coop G. A Short Manual for Bayenv2.0. 2016.

135. De Villemereuil P, Gaggiotti OE. A new FST-based method to uncover local adaptation using environmental variables. Methods in Ecology and Evolution. 2015.

136. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics. 2006;38(2):203–8. doi: 10.1038/ng1702 16380716

137. Rincent R, Moreau L, Monod H, Kuhn E, Melchinger AE, Malvar RA, et al. Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics. 2014;197:375–87. doi: 10.1534/genetics.113.159731 24532779

138. Bradburd GS, Ralph PL, Coop GM. Disentangling the effects of geographic and ecological isolation on genetic differentiation. Evolution. 2013;67:3258–73. doi: 10.1111/evo.12193 24102455

139. Lichstein JW. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology. 2007;188:117–31.

140. Goslee SC, Urban DL. The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software. 2007.

Štítky
Genetika Reprodukční medicína

Článek vyšel v časopise

PLOS Genetics


2019 Číslo 12

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Nová éra v léčbě migrény
nový kurz
Autoři: MUDr. Eva Medová, MUDr. Tomáš Nežádal, Ph.D.

Význam nutraceutik u kardiovaskulárních onemocnění
Autoři:

Pneumowebinář
Autoři:

White paper - jak vidíme optimální péči o zubní náhrady
Autoři: MUDr. Jindřich Charvát, CSc.

Faktory ovlivňující léčbu levotyroxinem

Všechny kurzy
Přihlášení
Zapomenuté heslo

Nemáte účet?  Registrujte se

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