Physical Dormancy Release in Medicago truncatula Seeds Is Related to Environmental Variations
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
16-21053S
Grantová Agentura České Republiky
PubMed
32295289
PubMed Central
PMC7238229
DOI
10.3390/plants9040503
PII: plants9040503
Knihovny.cz E-zdroje
- Klíčová slova
- Medicago, association mapping, climate adaptation, genomics, germination, legumes, physical dormancy, plasticity, seed dormancy,
- Publikační typ
- časopisecké články MeSH
Seed dormancy and timing of its release is an important developmental transition determining the survival of individuals, populations, and species in variable environments. Medicago truncatula was used as a model to study physical seed dormancy at the ecological and genetics level. The effect of alternating temperatures, as one of the causes releasing physical seed dormancy, was tested in 178 M. truncatula accessions over three years. Several coefficients of dormancy release were related to environmental variables. Dormancy varied greatly (4-100%) across accessions as well as year of experiment. We observed overall higher physical dormancy release under more alternating temperatures (35/15 °C) in comparison with less alternating ones (25/15 °C). Accessions from more arid climates released dormancy under higher experimental temperature alternations more than accessions originating from less arid environments. The plasticity of physical dormancy can probably distribute the germination through the year and act as a bet-hedging strategy in arid environments. On the other hand, a slight increase in physical dormancy was observed in accessions from environments with higher among-season temperature variation. Genome-wide association analysis identified 136 candidate genes related to secondary metabolite synthesis, hormone regulation, and modification of the cell wall. The activity of these genes might mediate seed coat permeability and, ultimately, imbibition and germination.
Department of Botany Palacký University Šlechtitelů 27 783 71 Olomouc Czech Republic
Department of Geoinformatics Palacký University 17 listopadu 50 771 46 Olomouc Czech Republic
Instituto Nacional de Tecnología Agropecuaria Hilario Ascasubi 8142 Argentina
Zobrazit více v PubMed
Tognetti P.M., Mazia N., Ibáñez G. Seed local adaptation and seedling plasticity account for Gleditsia triacanthos tree invasion across biomes. Ann. Bot. 2019;124:307–318. doi: 10.1093/aob/mcz077. PubMed DOI PMC
Valladares F., Sanchez-Gomez D., Zavala M.A. Quantitative estimation of phenotypic plasticity: Bridging the gap between the evolutionary concept and its ecological applications. J. Ecol. 2006;94:1103–1116. doi: 10.1111/j.1365-2745.2006.01176.x. DOI
Forsman A. Rethinking phenotypic plasticity and its consequences for individuals, populations and species. Heredity. 2015;115:276–284. doi: 10.1038/hdy.2014.92. PubMed DOI PMC
Clausen J. Stages in the Evolution of Plant Species. Cornell University Press; Ithaca, NY, USA: 1951.
Ungerer M., Johnson L., Herman M. Ecological genomics: Understanding gene and genome function in the natural environment. Heredity. 2008;100:178–183. doi: 10.1038/sj.hdy.6800992. PubMed DOI
Nicotra A.B., Atkin O.K., Bonser S.P., Davidson A.M., Finnegan E.J., Mathesius U., van Kleunen M. Plant phenotypic plasticity in a changing climate. Trends. Plant. Sci. 2010;15:684–692. doi: 10.1016/j.tplants.2010.09.008. PubMed DOI
Richards C.L., Bossdorf O., Muth N.Z., Gurevitch J., Pigliucci M. Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecol. Lett. 2006;9:981–993. doi: 10.1111/j.1461-0248.2006.00950.x. PubMed DOI
Baskin J.M., Baskin C.C. Seeds: Ecology, Biogeography and Evolution of Dormancy and Germination. 2nd ed. Academic Press; Amsterdam, The Netherlands: 2014.
Willis C.G., Baskin C.C., Baskin J.M., Auld J.R., Venable D.L., Cavender-Bares J., Donohue K., Rubio de Casas R. NESCent Germination Working Group. The evolution of seed dormancy: Environmental cues, evolutionary hubs, and diversification of the seed plants. New. Phytol. 2014;203:300–309. doi: 10.1111/nph.12782. PubMed DOI
Hradilová I., Duchoslav M., Brus J., Pechanec V., Hýbl M., Kopecký P., Smržová L., Štefelová N., Vaclávek T., Bariotakis M., et al. Variation in wild pea (Pisum sativum subsp. elatius) seed dormancy and its relationship to the environment and seed coat traits. PeerJ. 2019;7:e6263. PubMed PMC
Norman H.C., Cocks P.C., Galwey N.W. Hardseededness in annual clovers: Variation between populations from wet and dry environments. Aus. J. Agric. Sci. 2002;53:821–829. doi: 10.1071/AR01115. DOI
Rosenberg M.S., Anderson C.D. PASSaGE: Pattern Analysis, Spatial Statistics, and Geographic Exegesis. Version 2. Method. Ecol. Evol. 2011;2:229–232. doi: 10.1111/j.2041-210X.2010.00081.x. DOI
Smýkal P., Vernoud V., Blair M.W., Soukup A., Thompson R.D. The role of the testa during development and in establishment of dormancy of the legume seed. Front. Plant. Sci. 2014;5:351. PubMed PMC
Sultan S.E. Phenotypic plasticity for plant development, function and life history. Trends. Plant. Sci. 2000;5:537–542. doi: 10.1016/S1360-1385(00)01797-0. PubMed DOI
Templeton A.R., Levin D.A. Evolutionary consequences of seed pools. Am. Nat. 1979;114:232–249. doi: 10.1086/283471. DOI
Evans M.E.K., Ferrière R., Kane M.J., Venable D.L. Bet hedging via seed banking in desert evening primroses (Oenothera, Onagraceae): Demographic evidence from natural populations. Am. Nat. 2007;169:184–194. doi: 10.1086/510599. PubMed DOI
Venable D.L. Bet hedging in a guild of desert annuals. Ecology. 2007;88:1086–1090. doi: 10.1890/06-1495. PubMed DOI
Duncan C., Schultz N.L., Good M.K., Lewandrowski W., Cook S. The risk-takers and -avoiders: Germination sensitivity to water stress in an arid zone with unpredictable rainfall. AOB Plants. 2019 doi: 10.1093/aobpla/plz066. PubMed DOI PMC
Clauss M.J., Venable D.L. Seed germination in desert annuals: An empirical test of adaptive bet-hedging. Am. Nat. 2000;155:168–186. doi: 10.1086/303314. PubMed DOI
Van Klinken R.D., Flack L.K., Pettit W. Wet-season Dormancy Release in Seed Banks of a Tropical Leguminous Shrub is Determined by Wet Heat. Ann. Bot. 2006;98:875–883. doi: 10.1093/aob/mcl171. PubMed DOI PMC
Batlla D., Benech-Arnold R.L. A framework for the interpretation of temperature effects on dormancy and germination in seed populations showing dormancy. Seed Sci. Res. 2015;25:147–158. doi: 10.1017/S0960258514000452. DOI
Ellis R.H., Simon G., Covell S. The influence of temperature on seed germination rate in grain legumes. III. A comparison of five faba bean genotypes at constant temperatures using a new screening method. J. Exp. Bot. 1987;38:1033–1043. doi: 10.1093/jxb/38.6.1033. DOI
Renzi J.P., Chantre G.R., Cantamutto M.A. Vicia villosa ssp. villosa Roth field emergence model in a semiarid agroecosystem. Grass. Forage. Sci. 2018;73:146–158.
Rosbakh S., Poschlod P. Initial temperature of seed germination as related to species occurrence along a temperature gradient. Funct. Ecol. 2015;29:5–14. doi: 10.1111/1365-2435.12304. DOI
Hudson A.R., Ayre D.J., Ooi M.K.J. Physical dormancy in a changing climate. Seed Sci. Res. 2015;25:66–81. doi: 10.1017/S0960258514000403. DOI
Rubio de Casas R., Willis C.G., Pearse W.D., Baskin C.C., Baskin J.M., Cavender-Bares J. Global biogeography of seed dormancy is determined by seasonality and seed size: A case study in the legumes. New. Phytol. 2017;214:1527–1536. doi: 10.1111/nph.14498. PubMed DOI
Quinlivan B.J. The effect of constant and fluctuating temperatures on the permeability of the hard seeds of some legume species. Aust. J. Agric. Res. 1961;12:1009–1022. doi: 10.1071/AR9611009. DOI
Quinlivan B.J. The relationship between temperature fluctuations and the softening of hard seeds of some legume species. Aust. J. Agric. Res. 1966;17:625–631. doi: 10.1071/AR9660625. DOI
Quinlivan B.J., Millington A.J. The effect of a Mediterranean summer environment on the permeability of hard seeds of subterranean clover. Aust. J. Agric. Res. 1962;13:377–387. doi: 10.1071/AR9620377. DOI
Berger J.D., Shrestha D., Ludwig C. Reproductive Strategies in Mediterranean Legumes: Trade-Offs between Phenology, Seed Size and Vigor within and between Wild and Domesticated Lupinus Species Collected along Aridity Gradients. Front. Plant. Sci. 2017;8:548. doi: 10.3389/fpls.2017.00548. PubMed DOI PMC
Ronfort J., Bataillon T., Santoni S., Delalande M., David J.L., Prosperi J.-M. Microsatellite diversity and broad scale geographic structure in a model legume: Building a set of nested core collection for studying naturally occurring variation in Medicago truncatula. BMC Plant Biol. 2006;6:28. doi: 10.1186/1471-2229-6-28. PubMed DOI PMC
Gallardo K., Firnhaber C., Zuber H., Héricher D., Belghazi M., Henry C., Küster H., Thompson R. A combined proteome and transcriptome analysis of developing Medicago truncatula seeds: Evidence for metabolic specialization of maternal and filial tissues. Mol. Cell. Proteom. 2007;6:2165–2179. doi: 10.1074/mcp.M700171-MCP200. PubMed DOI
Bolingue W., Vu B.L., Leprince O., Buitink J. Characterization of dormancy behaviour in seeds of the model legume Medicago truncatula. Seed. Sci. Res. 2010;20:97–107. doi: 10.1017/S0960258510000061. DOI
Faria J.M.R., Buitink J., van Lammeren A.A.M., Hilhors H.W.M. Changes in DNA and microtubules during loss and re-establishment of desiccation tolerance in germinating Medicago truncatula seeds. J. Exp. Bot. 2005;56:2119–2130. doi: 10.1093/jxb/eri210. PubMed DOI
Vu W.T., Chang P.L., Moriuchi K.S., Friesen M.L. Genetic variation of transgenerational plasticity of offspring germination in response to salinity stress and the seed transcriptome of Medicago truncatula. BMC Evol. Biol. 2015;15:59. doi: 10.1186/s12862-015-0322-4. PubMed DOI PMC
Stanton-Geddes J., Paape T., Epstein B., Briskine R., Yoder J., Mudge J., Bharti A.K., Farmer A.D., Zhou P., Denny R., et al. Candidate genes and genetic architecture of symbiotic and agronomic traits revealed by whole-genome, sequence-based association genetics in Medicago truncatula. PLoS ONE. 2013;8:e65688. doi: 10.1371/journal.pone.0065688. PubMed DOI PMC
Burgarella C., Chantret N., Gay L., Prosperi J.-M., Bonhomme M., Tiffin P., Young N.D., Ronfort J. Adaptation to climate through flowering phenology: A case study in Medicago truncatula. Mol. Ecol. 2016;25:3397–3415. doi: 10.1111/mec.13683. PubMed DOI
Julier B., Huguet T., Chardon F., Ayadi R., Pierre J.-B., Prosperi J.-M., Barre P., Huyghe C. Identification of quantitative trait loci influencing aerial morphogenesis in the model legume Medicago truncatula. Appl. Genet. 2007;114:1391–1406. doi: 10.1007/s00122-007-0525-1. PubMed DOI
Thompson J.D. Plant Evolution in the Mediterranean. Oxford University Press; Oxford, UK: 2005.
Finch-Savage W.E., Footitt S. Seed dormancy cycling and the regulation of dormancy mechanisms to time germination in variable field environments. J. Exp. Bot. 2017;68:843–856. doi: 10.1093/jxb/erw477. PubMed DOI
Tribouillois H., Dürr C., Demilly D., Wagner M.-H., Justes E. Determination of Germination Response to Temperature and Water Potential for a Wide Range of Cover Crop Species and Related Functional Groups. PLoS ONE. 2016;11:e0161185. doi: 10.1371/journal.pone.0161185. PubMed DOI PMC
Hu X.W., Fan Y., Baskin C.C., Baskin J.M., Wang Y.R. Comparison of the effects of temperature and water potential on seed germination of Fabaceae species from desert and subalpine grassland. Am. J. Bot. 2015;102:649–660. doi: 10.3732/ajb.1400507. PubMed DOI
Long R.L., Gorecki M.J., Renton M., Scott J.K., Colville L., Goggin D.E., Commander L.E., Westcott D.A., Cherry H., Finch-Savage W.E. The ecophysiology of seed persistence: A mechanistic view of the journey to germination or demise. Biol. Rev. Camb. Philos. Soc. 2015;90:31–59. doi: 10.1111/brv.12095. PubMed DOI
Ferreras A.E., Marcora P.I., Venier M.P., Funes G. Different strategies for breaking physical seed dormancy in field conditions in two fruit morphs of Vachellia caven (Fabaceae) Seed. Sci. Res. 2018;28:8–15. doi: 10.1017/S096025851800003X. DOI
Debieu M., Tang C., Stich B., Sikosek T., Effgen S., Josephs E., Schmitt J., Nordborg M., Koornneef M., Meaux J. Co-Variation between Seed Dormancy, Growth Rate and Flowering Time Changes with Latitude in Arabidopsis thaliana. PLoS ONE. 2013;8:e61075. doi: 10.1371/journal.pone.0061075. PubMed DOI PMC
Postma F.M., Ågren J. Maternal environment affects the genetic basis of seed dormancy in Arabidopsis thaliana. Mol. Ecol. 2015;24:785–797. doi: 10.1111/mec.13061. PubMed DOI
Lampei C., Metz J., Tielbörger K. Clinal population divergence in an adaptive parental environmental effect that adjusts seed banking. New. Phytol. 2017;214:1230–1244. doi: 10.1111/nph.14436. PubMed DOI
Wagmann K., Hautekèete N.-C., Piquot Y., Meunier C., Schmitt S.E., Van Dijk H. Seed dormancy distribution: Explanatory ecological factors. Ann. Bot. 2012;110:1205–1219. doi: 10.1093/aob/mcs194. PubMed DOI PMC
Burghardt L.T., Metcalf C.J.E., Donohue K. A cline in seed dormancy helps conserve the environment experienced during reproduction across the range of Arabidopsis thaliana. Am. J. Bot. 2016;103:47–59. doi: 10.3732/ajb.1500286. PubMed DOI
Cochrane A. Multi-year sampling provides insight into the bet-hedging capacity of the soil-stored seed reserve of a threatened Acacia species from Western Australia. Plant. Ecol. 2019;220:241–253. doi: 10.1007/s11258-019-00909-0. DOI
Ten Brink H., Gremer J.R., Kokko H. Optimal germination timing in unpredictable environments: The importance of dormancy for both among- and within-season variation. Ecol. Lett. 2020;23:620–630. doi: 10.1111/ele.13461. PubMed DOI PMC
Taylor G.B. Hardseededness in Mediterranean annual pasture legumes in Australia: A review. Aust. J. Agric. Res. 2005;56:645–661. doi: 10.1071/AR04284. DOI
Jaganathan G.K., Dalrymple S.E., Liu B. Towards an Understanding of Factors Controlling Seed Bank Composition and Longevity in the Alpine Environment. Bot. Rev. 2015;81:70–103. doi: 10.1007/s12229-014-9150-2. DOI
Tozer M.G., Ooi M.K.J. Humidity-regulated dormancy onset in the Fabaceae: A conceptual model and its ecological implications for the Australian wattle Acacia saligna. Ann. Bot. 2014;114:579–590. doi: 10.1093/aob/mcu144. PubMed DOI PMC
Penfield S., MacGregor D.R. Effects of environmental variation during seed production on seed dormancy and germination. J. Exp. Bot. 2017;68:819–825. doi: 10.1093/jxb/erw436. PubMed DOI
Stine P.A., Hunsaker C.T. Spatial Uncertainty in Ecology. Springer; New York, NY, USA: 2001. An introduction to uncertainty issues for spatial data used in ecological applications; pp. 91–107.
Brus J., Pechanec V., Machar I. Depiction of uncertainty in the visually interpreted land cover data. Ecol. Inf. 2018;47:10–13. doi: 10.1016/j.ecoinf.2017.10.015. DOI
Buisson L., Thuiller W., Casajus N., Lek S., Grenouillet G. Uncertainty in ensemble forecasting of species distribution. Glob. Chang. Biol. 2010;16:1145–1157. doi: 10.1111/j.1365-2486.2009.02000.x. DOI
Meyer C., Weigelt P., Kreft H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 2016;19:992–1006. doi: 10.1111/ele.12624. PubMed DOI
Kerdaffrec E., Nordborg M. The maternal environment interacts with genetic variation in regulating seed dormancy in Swedish Arabidopsis thaliana. PLoS ONE. 2017;12:e0190242. doi: 10.1371/journal.pone.0190242. PubMed DOI PMC
Dias P.M.B., Brunel-Muguet S., Dürr C., Huguet T., Demilly D., Wagner M.-H., Teulat-Merah B. QTL analysis of seed germination and pre-emergence growth at extreme temperatures in Medicago truncatula. Appl. Genet. 2011;122:429–444. doi: 10.1007/s00122-010-1458-7. PubMed DOI PMC
Kang Y., Sakiroglu M., Krom N., Stanton-Geddes J., Wang M., Lee Y.-C., Young N.D., Udvardi M. Genome-wide association of drought-related and biomass traits with HapMap SNPs in Medicago truncatula. Plant Cell Environ. 2015;38:1997–2011. doi: 10.1111/pce.12520. PubMed DOI
Francoz E., Lepiniec L., North H.M. Seed coats as an alternative molecular factory: Thinking outside the box. Plant. Reprod. 2018;31:327–342. doi: 10.1007/s00497-018-0345-2. PubMed DOI
Hradilová I., Trněný O., Válková M., Cechová M., Janská A., Prokešová L., Aamir K., Krezdorn N., Rotter B., Winter P., Varshney R.K., et al. A combined comparative transcriptomic, metabolomic, and anatomical analyses of two key domestication traits: Pod dehiscence and seed dormancy in pea (Pisum. sp.) Front. Plant. Sci. 2017;8:542e. PubMed PMC
Leubner-Metzger G. Functions and regulation of β-1,3-glucanases during seed germination, dormancy release and after-ripening. Seed. Sci. Res. 2003;13:17–34. doi: 10.1079/SSR2002121. DOI
Leubner-Metzger G., Frundt C., Vogeli-Lange R., Meins F. Class I [beta]-1,3-Glucanases in the Endosperm of Tobacco during Germination. Plant. Physiol. 1995;109:751–759. doi: 10.1104/pp.109.3.751. PubMed DOI PMC
Nighojkar A., Srivastava S., Kumar A. Pectin methylesterase from germinating Vigna. sinensis. seeds. Plant. Sci. 1994;103:115–120. doi: 10.1016/0168-9452(94)90198-8. DOI
Sitrit Y., Hadfield K.A., Bennett A.B., Bradford K.J., Downie A.B. Expression of a Polygalacturonase Associated with Tomato Seed Germination. Plant. Physiol. 1999;121:419–428. doi: 10.1104/pp.121.2.419. PubMed DOI PMC
Buckeridge M.S., Hutcheon I.S., Reid J.S.G. The Role of Exo-(1→4)-β-galactanase in the Mobilization of Polysaccharides from the Cotyledon Cell Walls of Lupinus. angustifolius. Following Germination. Ann. Bot. 2005;96:435–444. doi: 10.1093/aob/mci192. PubMed DOI PMC
Hancock J.T., Neill S.J. Nitric Oxide: Its Generation and Interactions with Other Reactive Signaling Compounds. Plants. 2019;8:41. doi: 10.3390/plants8020041. PubMed DOI PMC
Müller K., Linkies A., Vreeburg R.A.M., Fry S.C., Krieger-Liszkay A., Leubner-Metzger G. In vivo cell wall loosening by hydroxyl radicals during cress seed germination and elongation growth. Plant. Physiol. 2009;150:1855–1865. doi: 10.1104/pp.109.139204. PubMed DOI PMC
Jeevan Kumar S.P., Rajendra Prasad S., Banerjee R., Thammineni C. Seed birth to death: Dual functions of reactive oxygen species in seed physiology. Ann. Bot. 2015;116:663–668. doi: 10.1093/aob/mcv098. PubMed DOI PMC
Raviv B., Godwin J., Granot G., Grafi G. The Dead Can Nurture: Novel Insights into the Function of Dead Organs Enclosing Embryos. Int. J. Mol. Sci. 2018;19:2455. doi: 10.3390/ijms19082455. PubMed DOI PMC
Bewley J.D., Bradford K.J., Hilhorst H.W.M., Nonogaki H. Seeds: Physiology of Development, Germination and Dormancy. Springer; New York, NY, USA: 2013.
Novo-Uzal E., Fernández-Pérez F., Herrero J., Gutiérrez J., Gómez-Ros L.V., Bernal M.Á., Díaz J., Cuello J., Pomar F., Pedreño M.Á. From Zinnia to Arabidopsis: Approaching the involvement of peroxidases in lignification. J. Exp. Bot. 2013;64:3499–3518. doi: 10.1093/jxb/ert221. PubMed DOI
Pollard A.T. Seeds vs fungi: An enzymatic battle in the soil seedbank. Seed. Sci. Res. 2018;28:197–214. doi: 10.1017/S0960258518000181. DOI
Graeber K., Nakabayashi K., Miatton E., Leubner-Metzger G., Soppe W.J. Molecular mechanisms of seed dormancy. Plant. Cell. Environ. 2012;35:1769–86. doi: 10.1111/j.1365-3040.2012.02542.x. PubMed DOI
Yoder J.B., Stanton-Geddes J., Zhou P., Briskine R., Young N.D., Tiffin P. Genomic signature of adaptation to climate in Medicago truncatula. Genetics. 2014;196:1263–1275. doi: 10.1534/genetics.113.159319. PubMed DOI PMC
De Boor C. A Practical Guide to Splines. Springer; New York, NY, USA: 1978.
Machalová J. Optimal interpolating and optimal smoothing spline. J. Elect. Eng. 2002;5312:79–82.
Kader M.A. A Comparison of Seed Germination Calculation Formulae and the Associated Interpretation of Resulting Data. J. Proc. R. Soc. New. South. Wales. 2005;138:65–75.
Talská R., Machalová J., Smýkal P., Hron K. A comparison of seed germination coefficients using functional regression. Appl. Plant. Sci. 2020 (in press) PubMed PMC
ESRI. [(accessed on 5 October 2019)]; Available online: https://pro.arcgis.com.
WorldClim. [(accessed on 5 October 2019)]; Available online: http://worldclim.org.
Naimi B. Uncertainty Analysis for Species Distribution Models. Version 1.1-18. 2017. [(accessed on 5 October 2019)]; Available online: http://r-gis.net.
Fick S.E., Hijmans R.J. 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. DOI
Cobon D.H., Kouadio L., Mushtaq S., Jarvis C., Carter J., Stone G., Davis P. Evaluating the shifts in rainfall and pasture-growth variabilities across the pastoral zone of Australia during 1910–2010. Crop. Pasture. Sci. 2019;70:634–647. doi: 10.1071/CP18482. DOI
Vega G.C., Pertierra L.R., Olalla-Tarraga M.A. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data. 2018;5:170078. doi: 10.1038/sdata.2018.70. PubMed DOI PMC
ISRIC. [(accessed on 5 October 2019)]; Available online: https://www.isric.org/explore/soilgrids.
Hengl T., de Mendes J.J., Heuvelink G.B.M., Ruiperez Gonzalez M., Kilibarda M., Blagotić A., Shangguan W., Wright M.N., Geng X., Bauer-Marschallinger B., et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE. 2017;12:e0169748. doi: 10.1371/journal.pone.0169748. PubMed DOI PMC
Peterson B.G. Econometric Tools for Performance and Risk Analysis. [(accessed on 10 October 2019)];2019 Available online: https://github.com/braverock/PerformanceAnalytics.
Legendre P., Legendre L. Numerical Ecology. Elsevier; Amsterdam, The Netherlands: 2012. p. 990.
Ter Braak C.J.F., Šmilauer P. Canoco Reference Manual and User’s Guide: Software for Ordination. Microcomputer Power; Ithaca, NY, USA: 2012. p. 496. version 5.0.
Dutilleul P., Clifford P., Richardson S., Hemon D. Modifying the t test for assessing the correlation between two spatial processes. Biometrics. 1993;49:305–314. doi: 10.2307/2532625. PubMed DOI
Rangel T.F., Diniz-Filho J.A.F., Bini L.M. SAM: A comprehensive application for Spatial Analysis in Macroecology. Ecography. 2010;33:46–50. doi: 10.1111/j.1600-0587.2009.06299.x. DOI
Di Rienzo J.A., Casanoves F., Balzarini M.G., Gonzalez L., Tablada M., Robledo C.W. InfoStat Version 2013. Universidad Nacional de Córdoba; Córdoba, Argentina: 2013. Grupo InfoStat, FCA.
Yu J., Buckler E.S. Genetic association mapping and genome organization of maize. Curr. Opin. Biotech. 2006;17:155–160. doi: 10.1016/j.copbio.2006.02.003. PubMed DOI
Liu X., Huang M., Fan B., Buckler E.S., Zhang Z. Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLoS Genet. 2016;16:e1005767. doi: 10.1371/journal.pgen.1005767. PubMed DOI PMC
Pecrix Y., Staton S.E., Sallet E., Lelandais-Brière C., Moreau S., Carrère S., Zahm M., Kreplak J., Mayjonade B., Carine Satgé C., et al. Whole-genome landscape of Medicago. truncatula. symbiotic genes. Nat. Plants. 2018;4:1017–1025. doi: 10.1038/s41477-018-0286-7. PubMed DOI
Bonhomme M., André O., Badis Y., Ronfort J., Burgarella C., Chantret N., Miteul H., Hajri A., Baranger A., Tiffin P., et al. High-density genome-wide association mapping implicates an F-box encoding gene in Medicago truncatula resistance to Aphanomyces euteiches. New. Phytol. 2014;201:1328–1342. PubMed
Medicago Truncatula A17 r5.0 Genome Portal. [(accessed on 15 December 2019)];2019 Available online: https://medicago.toulouse.inra.fr/MtrunA17r5.0-ANR.
Branca A., Paape T.D., Zhou P., Briskine R., Farmer A.D., Mudge J., Ben C., Denny R., Sadowsky M.J., Ronfort J., et al. Whole-genome nucleotide diversity, recombination, and linkage disequilibrium in the model legume Medicago truncatula. Proc. Nat. Acad. Sci. USA. 2011;108:E864–E870. PubMed PMC
Fu F., Zhang W., Li Y.Y., Wang H.L. Establishment of the model system between phytochemicals and gene expression profiles in Macrosclereid cells of Medicago truncatula. Sci. Rep. 2017;7:2580. doi: 10.1038/s41598-017-02827-5. PubMed DOI PMC
Verdier J., Dessaint F., Schneider C., Abirached-Darmency M. A combined histology and transcriptome analysis unravels novel questions on Medicago truncatula seed coat. J. Exp. Bot. 2013;64:459–470. doi: 10.1093/jxb/ers304. PubMed DOI PMC
Bar University of Toronto. [(accessed on 15 December 2019)]; Available online: http://bar.utoronto.ca/efpmedicago/cgi-bin/efpWeb.cgi.
Gentzbittel L., Ben C., Mazurier M., Shin M.-G., Lorenz T., Rickauer M., Marjoram P., Nuzhdin S.V., Tatarinova T.V. WhoGEM: An admixture-based prediction machine accurately predicts quantitative functional traits in plants. Genome. Biol. 2019;20:106. doi: 10.1186/s13059-019-1697-0. PubMed DOI PMC