Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Plant Material
2.2. Genotyping
2.3. Diversity Analysis
2.3.1. Estimation of Genetic Diversity Parameters
2.3.2. Genetic Structure and Relationship between Landraces
- (1)
- (2)
- A Bayesian multi-locus approach, implemented in the ADMIXTURE v1.3.0 software, to assign probabilistically each landrace to K ancestral populations assumed to be in Hardy–Weinberg equilibrium [36]. Different methods were used to identify the most appropriate number of ancestral populations (K): cross-validation error or difference between successive cross-validations [36] and graphical methods [37]. Since ADMIXTURE requires multi-locus genotypes of individual plants, we simulated the genotype of five individuals for each population for a subset of 2500 independent SNPs to avoid artifacts of linkage disequilibrium (See Method S5 in [33] for more details).
- (3)
- A linear penalized regression approach, to quantitatively assign each of the 626 landraces to seven genetic groups established for 156 landraces representing European and American diversity [33]. Allelic frequencies at 23,412 SNPs of each of the seven genetic groups were estimated considering landraces with a membership superior to 0.6. Admixture coefficients associated with the new landraces were then obtained through a penalized regression approach via fitting, for each of the 626 landraces, a linear regression model using landrace allelic frequencies as response variables and group frequencies as explanatory variables, using the R package “quadprog” [38] v1.5-8. The coefficients of each regression were constrained to be positive and sum to one and were considered as estimates of the admixture coefficients.
2.4. Phenotypic Evaluations
2.5. Statistical Analysis of Phenotypic Data
3. Results
3.1. Creating the EVA Maize Collection
3.2. Genotypic Diversity of the Collection
3.3. Phenotypic Diversity of the Collection
3.4. Principal Component Analysis
3.5. Cluster Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Revilla, P.; Soengas, P.; Cartea, M.E.; Malvar, R.A.; Ordás, A. Isozyme variability among European maize populations and the introduction of maize on Europe. Maydica 2003, 48, 141–152. [Google Scholar]
- Rebourg, C.; Chastanet, M.; Gouesnard, B.; Welcker, C.; Dubreuil, P.; Charcosset, A. Maize introduction into Europe: The history reviewed in the light of molecular data. Theor. Appl. Genet. 2003, 106, 895–903. [Google Scholar] [CrossRef]
- ECPGR Plant Genetic Resources Strategy for Europe. European Cooperative Programme for Plant Genetic Resources, Rome, Italy. Available online: https://www.ecpgr.org/resources/ecpgr-publications/publication/plant-genetic-resources-strategy-for-europe-2021 (accessed on 14 April 2024).
- ECPGR: European Evaluation Network (EVA). Available online: https://www.ecpgr.cgiar.org/eva (accessed on 1 May 2024).
- Revilla, P.; Alves, M.L.; Andjelkovic, V.; Balconi, C.; Dinis, I.; Reis Mendes-Moreira, P.M.; Redaelli, R.; Ruiz De Galarreta, J.I.; Vaz Patto, M.C.; Slađana, Ž.; et al. Traditional foods from maize (Zea mays L.) in Europe. Front. Nutr. 2022, 8, 683399. [Google Scholar] [CrossRef]
- Gauthier, P.; Gouesnard, B.; Dallard, J.; Redaelli, R.; Rebourg, C.; Charcosset, A.; Boyat, A. RFLP diversity and relationships among European maize populations. Theor. Appl. Genet. 2002, 105, 91–99. [Google Scholar] [CrossRef]
- Gouesnard, B.; Dallard, J.; Bertin, P.; Boyat, A.; Charcosset, A. European maize landraces: Genetic diversity, core collection definition and methodology of use. Maydica 2005, 50, 225–234. [Google Scholar]
- Berardo, N.; Mazzinelli, G.; Valoti, P.; Redaelli, R. Characterization of maize germplasm for the chemical composition of the grain. J. Agric. Food Chem. 2009, 57, 2378–2384. [Google Scholar] [CrossRef]
- Malvar, R.A.; Butrón, A.; Álvarez, A.; Ordás, B.; Soengas, P.; Revilla, P.; Ordás, A. Evaluation of the European Union maize landrace core collection for resistancee to Sesamia nonagrioides (Lepidoptera: Noctuidae) and Ostrinia nubilalis (Lepidoptera: Crambidae). J. Econ. Entomol. 2004, 97, 628–634. [Google Scholar] [CrossRef]
- Taba, S.; Twumasi-Afriyie, S. Regeneration guidelines: Maize. In Crop Specific Regeneration Guidelines [CD-ROM]; Dulloo, M.E., Thormann, I., Jorge, M.A., Hanson, J., Eds.; CGIAR System-Wide Genetic Resource Programme: Rome, Italy, 2008; 10p, Available online: https://cropgenebank.sgrp.cgiar.org/images/file/maize/Maize_ENG.pdf (accessed on 14 April 2024).
- Simeonovska, E.; Gadžo, D.; Jovović, Z.; Murariu, D.; Kondic, D.; Mandic, D.; Fetahu, S.; Šarčević, H.; Elezi, F.; Prodanović, S.; et al. Collecting local landraces of maize and cereals in South Eastern Europe during 2009 and 2010. Rom. Agric. Res. 2013, 30, 37–43. [Google Scholar]
- Gouesnard, B.; Dallard, J.; Panouille, A.; Boyat, A. Classification of French maize populations based on morphological traits. Agronomie 1997, 17, 491–498. [Google Scholar] [CrossRef]
- Torri, A.; Lanzanova, C.; Locatelli, S.; Valoti, P.; Balconi, C. Screening of local Italian maize varieties for resistance to Fusarium verticillioides. Maydica 2015, 60, 1–8. [Google Scholar]
- Rocha, F.; Bettencourt, E.; Carlos Gaspar, C. Genetic erosion assessment through the re-collecting of crop germplasm. Counties of Arcos de Valdevez, Melgaço, Montalegre, Ponte da Barca and Terras de Bouro (Portugal). Plant Gen. Res. Newsl. 2008, 154, 6–13. [Google Scholar]
- Rocha, F.; Gaspar, C.; Barata, A.M. The legacy of collecting missions to the valorisation of agro-biodiversity. Agric. For. 2017, 63, 25–38. [Google Scholar] [CrossRef]
- Leitão, S.T.; Ferreira, E.; Bicho, M.C.; Alves, M.L.; Pintado, D.; Santos, D.; Vaz Patto, M.C. Maize open-pollinated populations physiological improvement: Validating tools for drought response participatory selection. Sustainability 2019, 11, 6081. [Google Scholar] [CrossRef]
- Murariu, D.; Murariu, M.; Placinta, D.D. Field and laboratory screening of Romanian maize landraces very resistant to low temperatures. Maydica 2015, 60, 11. [Google Scholar]
- Murariu, D.; Placinta, D.D.; Simioniuc, D. Assessing genetic diversity in Romanian maize landraces, using molecular markers. Rom. Agric. Res. 2019, 36, 3–9. [Google Scholar] [CrossRef]
- Babic, V.; Vancetovic, J.; Prodanovic, S.; Kravić, N.; Babic, M.; Andjelkovic, V. Numerical Classification of Western Balkan Drought Tolerant Maize (Zea mays L.). Landraces. J. Agric. Sci. Technol. 2015, 17, 455–468. [Google Scholar]
- Popović, A.; Kravić, N.; Prodanović, S.; Filipović, M.; Sečanski, M.; Babić, V.; Miriţescu, M. Characterisation and evaluation towards selection of maize landraces with the best per se performances. Rom. Agric. Res. 2020, 37, 49–58. [Google Scholar] [CrossRef]
- Malvar, R.A.; Butrón, A.; Alvarez, A.; Padilla, G.; Cartea, M.E.; Revilla, P.; Ordás, A. Performance of the European Union Maize Landrace Core Collection for yield under multiple corn borer infestations. Crop Protect. 2007, 26, 775–781. [Google Scholar] [CrossRef]
- Schilperoord, P. Kulturpflanzen in der Schweiz—Mais; Verein für alpine Kulturpflanzen: Alveneu, Switzerland, 2017. [Google Scholar] [CrossRef]
- Eschholz, T.W.; Peter, R.; Stamp, P.; Hund, A. Swiss maize landraces—Their diversity and genetic relationships. Acta Agron. Hung. 2006, 54, 321–328. [Google Scholar] [CrossRef]
- Freitag, N.M. The Swiss Core Set of Maize Landraces and Its Genetic and Phenotypic Diversity. Ph.D. Thesis, ETH, Zurich, Switzerland, 2011. [Google Scholar]
- Peter, R.; Eschholz, T.W.; Stamp, P.; Liedgens, M. Swiss maize landraces—Early vigour adaptation to cool conditions. Acta Agron. Hung. 2006, 54, 329–336. [Google Scholar] [CrossRef]
- Arca, M.; Mary-Huard, T.; Gouesnard, B.; Berard, A.; Bauland, C.; Combes, V.; Madur, D.; Charcosset, A.; Nicolas, S.D. Deciphering the genetic diversity of landraces with high throughput SNP genotyping of DNA bulks: Methodology and application to the maize 50k array. Front. Plant Sci. 2021, 11, 568699. [Google Scholar] [CrossRef]
- Ganal, M.W.; Durstewitz, G.; Polley, A.; Berard, A.; Buckler, E.S.; Charcosset, A.; Clarke, J.D.; Graner, E.-M.; Hansen, M.; Joets, J.; et al. A large maize (Zea mays L.) SNP genotyping array: Development and germplasm genotyping, and genetic mapping to compare with the B73 Reference genome. PLoS ONE 2011, 6, e28334. [Google Scholar] [CrossRef]
- Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. USA 1973, 70, 3321–3323. [Google Scholar] [CrossRef]
- Nei, M. F-statistics and analysis of gene diversity in subdivided populations. Ann. Hum. Genet. 1977, 41, 225–233. [Google Scholar] [CrossRef]
- Nei, M.; Chesser, R.K. Estimation of fixation indices and gene diversities. Ann. Hum. Genet. 1983, 47, 253–259. [Google Scholar] [CrossRef]
- Rogers, J.S. Measures of Genetic Similarity and Genetic Distance. In Studies in Genetics VII; University of Texas Publication 7213; University of Texas: Austin, TX, USA, 1972; pp. 145–153. [Google Scholar]
- Mary-Huard, T.; Balding, D. Fast and Accurate Joint Inference of Coancestry Parameters for Populations and/or Individuals. PLoS Genet. 2023, 19, e1010054. [Google Scholar] [CrossRef]
- Arca, M.; Gouesnard, B.; Mary-Huard, T.; Le Paslier, M.C.; Bauland, C.; Combes, V.; Madur, D.; Charcosset, A.; Nicolas, S.D. Genotyping of DNA pools identifies untapped landraces and genomic regions to develop next-generation varieties. Plant Biotechnol. J. 2023, 21, 1123–1139. [Google Scholar] [CrossRef]
- Gower, J.C. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 1966, 53, 325–338. [Google Scholar] [CrossRef]
- Paradis, E.; Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 2019, 35, 526–528. [Google Scholar] [CrossRef]
- Alexander, D.H.; Novembre, J.; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009, 19, 1655–1664. [Google Scholar] [CrossRef]
- Evanno, G.; Regnaut, S.; Goudet, J. Detecting the number of clusters of individuals using the software Structure: A simulation study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef]
- Turlach, B.A. quadprog: Functions to Solve Quadratic Programming Problems. R Package Version 1.5-8. 2019. Available online: https://CRAN.R-project.org/package=quadprog (accessed on 14 April 2024).
- Yu, G.; Smith, D.K.; Zhu, H.; Guan, Y.; Lam, T.T. ggtree: An r Package for Visualization and Annotation of Phylogenetic Trees with Their Covariates and Other Associated Data. Methods Ecol. Evol. 2017, 8, 28–36. [Google Scholar] [CrossRef]
- Xu, S.; Dai, Z.; Guo, P.; Fu, X.; Liu, S.; Zhou, L.; Tang, W.; Feng, T.; Chen, M.; Zhan, L.; et al. ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data. Mol. Biol. Evol. 2021, 38, 4039–4042. [Google Scholar] [CrossRef]
- Xu, S.; Li, L.; Luo, X.; Chen, M.; Tang, W.; Zhan, L.; Dai, Z.; Lam, T.T.; Guan, Y.; Yu, G. Ggtree: A serialized data object for visualization of a phylogenetic tree and annotation data. iMeta 2022, 1, e56. [Google Scholar] [CrossRef]
- Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016. [Google Scholar]
- Yu, G. scatterpie: Scatter Pie Plot. R Package Version 0.2.1. 2023. Available online: https://CRAN.R-project.org/package=scatterpie (accessed on 14 April 2024).
- IBPGR. Descriptors for Maize; International Maize and Wheat Improvement Center, Mexico City/International Board for Plant Genetic Resources: Rome, Italy, 1991. [Google Scholar]
- Dubreuil, P.; Charcosset, A. Genetic diversity within and among maize populations: A comparison between isozyme and nuclear RFLP loci. Theor. Appl. Genet. 1998, 96, 577–587. [Google Scholar] [CrossRef]
- Brandenburg, J.-T.; Mary-Huard, T.; Rigaill, G.; Hearne, S.J.; Corti, H.; Joets, J.; Vitte, C.; Charcosset, A.; Nicolas, S.D.; Tenaillon, M.I. Independent Introductions and Admixtures Have Contributed to Adaptation of European Maize and Its American Counterparts. PLoS Genet. 2017, 13, e1006666. [Google Scholar] [CrossRef]
- Rebourg, C.; Gouesnard, B.; Charcosset, A. Large scale molecular analysis of traditional European maize populations. Relationships with morphological variation. Heredity 2001, 86, 574–587. [Google Scholar] [CrossRef]
- Camus-Kulandaivelu, L.; Veyrieras, J.B.; Madur, D.; Combes, V.; Fourmann, M.; Barraud, S.; Dubreuil, P.; Gouesnard, B.; Manicacci, D.; Charcosset, A. Maize adaptation to temperate climate: Relationship between population structure and polymorphism in the dwarf8 gene. Genetics 2006, 172, 2449–2463. [Google Scholar] [CrossRef]
- Mir, C.; Zerjal, T.; Combes, V.; Dumas, F.; Madur, D.; Bedoya, C.; Dreisigacker, S.; Franco, J.; Grudloyma, P.; Hao, P.X.; et al. Out of America: Tracing the genetic footprints of the global diffusion of maize. Theor. Appl. Genet. 2013, 126, 2671–2682. [Google Scholar] [CrossRef]
- Holker, A.C.; Mayer, M.; Presterl, T.; Bolduan, T.; Bauer, E.; Ordas, B.; Brauner, P.C.; Ouzunova; Melchinger, A.; Schön, C. European maize landraces made accessible for plant breeding and genome-based studies. Theor. Appl. Genet. 2019, 132, 3333–3345. [Google Scholar] [CrossRef]
- Bedoya, C.A.; Dreisigacker, S.; Hearne, S.; Franco, J.; Mir, C.; Prasanna, B.M.; Taba, S.; Charcosset, A.; Warburton, M.L. Genetic diversity and population structure of native maize populations in Latin America and the Caribbean. PLoS ONE 2017, 12, e0173488. [Google Scholar] [CrossRef] [PubMed]
- Rojas-Barrera, I.C.; Wegier, A.; De Jesús Sánchez González, J.; Owens, G.L.; Rieseberg, L.H.; Piñero, D. Contemporary evolution of maize landraces and their wild relatives influenced by gene flow with modern maize varieties. Proc. Natl. Acad. Sci. USA 2019, 116, 21302–21311. [Google Scholar] [CrossRef]
- McLean-Rodríguez, F.D.; Costich, D.E.; Camacho-Villa, T.C.; Pè, M.E.; Dell’Acqua, M. Genetic diversity and selection signatures in maize landraces compared across 50 years of in situ and ex situ conservation. Heredity 2021, 126, 913–928. [Google Scholar] [CrossRef]
- Zhang, H.; Liu, X.; Zhang, Y.; Jiang, C.; Cui, D.; Liu, H.; Li, D.; Wang, L.; Chen, T.; Lihua, N. Genetic analysis and fine mapping of the Ga1-S gene region conferring cross-incompatibility in maize. Theor. Appl. Genet. 2012, 124, 459–465. [Google Scholar] [CrossRef]
- Gouesnard, B.; Rebourg, C.; Welcker, C.; Charcosset, A. Analysis of photoperiod sensitivity within a collection of tropical maize populations. Genet. Resour. Crop Evol. 2002, 49, 471–481. [Google Scholar] [CrossRef]
- Rashid, Z.; Sofi, M.; Harlapur, S.I.; Kachapur, R.M.; Dar, Z.A.; Singh, P.K.; Zaidi, P.H.; Vivek, B.S.; Nair, S.K. Genome-wide association studies in tropical maize germplasm reveal novel and known genomic regions for resistance to Northern corn leaf blight. Sci. Rep. 2020, 10, 21949. [Google Scholar] [CrossRef]
- Barbosa, P.A.; Fritsche-Neto, R.; Andrade, M.C.; Petroli, C.D.; Burgueño, J.; Galli, G.; Willcox, M.C.; Sonder, K.; Vidal-Martínez, V.A.; Sifuentes-Ibarra, E.; et al. Introgression of maize diversity for drought tolerance: Subtropical maize landraces as source of new positive variants. Front. Plant Sci. 2021, 12, 691211. [Google Scholar] [CrossRef]
- Brandolini, A.; Brandolini, A. Maize introduction, evolution and diffusion in Italy. Maydica 2009, 54, 233–242. [Google Scholar]
- Diaw, Y.; Tollon-Cordet, C.; Charcosset, A.; Nicolas, S.; Madur, D.; Ronfort, J.; David, J.; Gouesnard, B. Genetic diversity of maize landraces from the South-West of France. PLoS ONE 2021, 16, e0238334. [Google Scholar] [CrossRef]
- Romero Navarro, J.A.; Willcox, M.; Burgueno, J.; Romay, C.; Swarts, K.; Trachsel, S.; Preciado, E.; Terron, A.; Vallejo Delgado, H.; Vidal, V.; et al. A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat. Genet. 2017, 49, 476–480. [Google Scholar] [CrossRef]
- Wang, L.; Josephs, E.B.; Lee, K.M.; Roberts, L.M.; Rellán-Álvarez, R.; Ross-Ibarra, J.; Hufford, M.B. Molecular parallelism underlies convergent highland adaptation of maize landraces. Mol. Biol. Evol. 2021, 38, 3567–3580. [Google Scholar] [CrossRef]
- Mendes-Moreira, P.; Satovic, Z.; Mendes-Moreira, J.; Santos, J.P.; Nina Santos, J.P.; Pego, S.; Vaz Patto, M.C. Maize participatory breeding in Portugal: Comparison of farmer’s and breeder’s on-farm selection. Plant Breed. 2017, 136, 861–871. [Google Scholar] [CrossRef]
- Wambugu, P.W.; Ndjiondjop, M.N.; Henry, R.J. Role of genomics in promoting the utilization of plant genetic resources in genebanks. Brief. Funct. Genom. 2018, 17, 198–206. [Google Scholar] [CrossRef]
- Westengen, O.T.; Skarbø, K.; Mulesa, T.H.; Berg, T. Access to genes: Linkages between genebanks and farmers’ seed systems. Food Secur. 2018, 10, 9–25. [Google Scholar] [CrossRef]
- Mascher, M.; Schreiber, M.; Scholz, U.; Graner, A.; Reif, J.C.; Stein, N. Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding. Nat. Genet. 2019, 51, 1076–1081. [Google Scholar] [CrossRef]
- Wang, B.; Lin, Z.; Li, X.; Zhao, Y.; Zhao, B.; Wu, G.; Ma, X.; Wang, H.; Xie, Y.; Li, Q.; et al. Genome-wide selection and genetic improvement during modern maize breeding. Nat. Genet. 2020, 52, 565–571. [Google Scholar] [CrossRef]
- Mayer, M.; Hölker, A.C.; González-Segovia, E.; Eva Bauer, E.; Presterl, T.; Ouzunova, M.; Melchinger, A.E.; Schön, C.C. Discovery of beneficial haplotypes for complex traits in maize landraces. Nat. Commun. 2020, 11, 4954. [Google Scholar] [CrossRef]
Country | EVA Maize Genebank | Holding Institute Code (* WIEWS) | Number of Accessions | Part of ** EUMLCC | Criteria for Selection of Materials | References |
---|---|---|---|---|---|---|
Croatia | University of Zagreb, Faculty of Agriculture | HRV041 | 50 | na | Representative of national collection based on a wide geographical distribution; phenological and morphological data. | [11] |
France | INRAE—Montpellier | FRA015 | 80 | 16 | Morphological traits to maximize number of classes; representative of national collection. | [7,12] |
Italy | CREA-Cereal and Industrial Crops Bergamo | ITA386 | 65 | 19 | Representative of different geographical areas in Italy; promising sources of stress-tolerance traits. | [8,13] |
Portugal | INIAV, Braga | PRT001 | 42 | 17 | Morphological, agronomical, and molecular characterization; previous knowledge from national breeding programmes. | [14,15] |
ESAC-IPC, Coimbra | PRT053 | 6 | na | Drought tolerance; yield evaluation at different altitudes. | [16] | |
Romania | Suceava Genebank | ROM007 | 51 | na | Early and semi-early (FAO groups 200–300); collected between 1973 and 2000; cold tolerance. | [17,18] |
Serbia | Maize Research Institute Zemun Polje | SRB001 | 91 | na | Drought tolerance; stability and high grain yield; good performance in breeding programmes. | [19,20] |
Spain | Misión Biológica de Galicia, Pontevedra (MBG-CSIC) | ESP004 | 2 | na | Representative of available diversity; corn borer resistance; good for breadmaking; cold and drought tolerance. | [1,9,21] |
ESP007 | 8 | na | ||||
ESP009 | 137 | 23 | ||||
ESP016 | 4 | na | ||||
ESP119 | 4 | na | ||||
Switzerland | Agroscope, Nyon | CHE001 | 86 | na | Representative of the collection from different regions in Switzerland; phenological and morphological data. | [22,23,24,25] |
Total | 626 | 75 |
Country | Location | EVA Partner | Range of FAO Maturity Rating | # Accessions Evaluated | Years of Evaluation |
---|---|---|---|---|---|
Croatia | Zagreb | University of Zagreb | 100–600 | 187 | 2022–2023 |
France | Ploudaniel | INRAE | 100–800 | 598 | 2021–2023 |
France | Alzonne | KWS | 300–500 | 326 | 2021–2023 |
Germany | Bernburg (Saale) | KWS | 100–300 | 161 | 2021–2023 |
Italy | Bergamo | CREA—Cereal and Industrial Crops | 200–500 | 178 | 2021–2023 |
Italy | Monselice | KWS | 500–800 | 218 | 2021–2023 |
Portugal | Coimbra | ESAC | 400–600 | 53 | 2022–2023 |
Romania | Suceava | Suceava genebank | 100–300 | 108 | 2021–2022 |
Serbia | Belgrade | MRIZP | 300–800 | 299 | 2021–2023 |
Spain | Pontevedra | MBG-CSIC | 100–800 | 216 | 2021 |
Switzerland | Delley | Delley Semences et Plantes | 100–250 | 84 | 2021–2022 |
Trait Name | Trait Acronym | Trait Description | Unit | Crop Ontology |
---|---|---|---|---|
Days to tasseling (anthesis, male flowering) | DT | IPGRI descriptor 4.1.1: number of days from sowing to when 50% of the plants have shed pollen. | d | CO_322:0000030 |
Days to silking (female flowering) | DS | IPGRI descriptor 4.1.2: number of days from sowing to when silks have emerged on 50% of the plants. | d | CO_322:0000031 |
Anthesis–silking interval | ASI | Time difference [in days] between anthesis and silking, calculated as (ASI = DS − DT). | d | CO_322:0000001 |
Plant height | PH | IPGRI descriptor 4.1.4: from ground level to the base of the tassel after milk stage, measured in cm.; observed value recorded from an average of 10 plants per plot. | cm | CO_322:0000994 |
Ear height | EH | IPGRI descriptor 4.1.5: from ground level to the node bearing the uppermost ear after milk stage, measured in cm.; observed value recorded from an average of 10 plants per plot. | cm | CO_322:0000017 |
Relative ear height | EPHR | Ear to plant height ratio calculated as (EH/PH) × 100. | % |
Whole Panel | K1 | K2 | K3 | K4 | K5 | K6 | K7 | K8 | K9 | |
---|---|---|---|---|---|---|---|---|---|---|
Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | Mean ± S.D. | |
Size of the panel/group | 626 | 14 | 29 | 31 | 40 | 32 | 14 | 43 | 75 | 39 |
Number of monomorphic SNPs | 4 | 956 | 73 | 192 | 81 | 161 | 573 | 122 | 229 | 590 |
Allele number (A) at group level | 2.00 ± 0.01 | 1.96 ± 0.2 | 2.00 ± 0.06 | 1.99 ± 0.09 | 2.00 ± 0.06 | 1.99 ± 0.08 | 1.98 ± 0.15 | 1.99 ± 0.07 | 1.99 ± 0.1 | 1.97 ± 0.16 |
Allele number (A) average within landraces | 1.71 ± 0.13 | 1.57 ± 0.13 | 1.67 ± 0.09 | 1.69 ± 0.06 | 1.79 ± 0.08 | 1.65 ± 0.11 | 1.75 ± 0.05 | 1.72 ± 0.07 | 1.52 ± 0.11 | 1.67 ± 0.09 |
Minor allele frequency (MAF) at group level | 0.25 ± 0.14 | 0.19 ± 0.15 | 0.21 ± 0.15 | 0.19 ± 0.15 | 0.25 ± 0.14 | 0.2 ± 0.15 | 0.21 ± 0.15 | 0.19 ± 0.15 | 0.16 ± 0.15 | 0.20 ± 0.16 |
Minor allele frequency (MAF) average within landraces | 0.15 ± 0.03 | 0.13 ± 0.03 | 0.13 ± 0.02 | 0.14 ± 0.02 | 0.18 ± 0.02 | 0.14 ± 0.03 | 0.17 ± 0.01 | 0.16 ± 0.02 | 0.10 ± 0.02 | 0.15 ± 0.03 |
Total expected heterozygosity (Ht) at group level | 0.33 ± 0.15 | 0.27 ± 0.17 | 0.29 ± 0.16 | 0.26 ± 0.17 | 0.33 ± 0.15 | 0.27 ± 0.17 | 0.28 ± 0.17 | 0.27 ± 0.17 | 0.22 ± 0.17 | 0.27 ± 0.18 |
Expected heterozygosity (Hs) average within landraces | 0.21 ± 0.05 | 0.17 ± 0.04 | 0.19 ± 0.03 | 0.19 ± 0.02 | 0.24 ± 0.03 | 0.19 ± 0.04 | 0.23 ± 0.02 | 0.22 ± 0.03 | 0.14 ± 0.03 | 0.20 ± 0.03 |
Differentiation (Gst) between landraces (within group Ht) | 0.35 ± 0.14 | 0.35 ± 0.14 | 0.36 ± 0.10 | 0.25 ± 0.09 | 0.26 ± 0.10 | 0.30 ± 0.13 | 0.17 ± 0.07 | 0.18 ± 0.10 | 0.36 ± 0.15 | 0.25 ± 0.13 |
Modified Roger’s distance (MRD) between landraces | 0.24 ± 0.04 | 0.22 ± 0.04 | 0.23 ± 0.03 | 0.18 ± 0.03 | 0.21 ± 0.03 | 0.20 ± 0.03 | 0.16 ± 0.02 | 0.16 ± 0.02 | 0.20 ± 0.04 | 0.18 ± 0.04 |
* DT | * DS | * ASI | * PH | * EH | * EPHR | |
---|---|---|---|---|---|---|
Landraces | ||||||
Mean | 72.3 | 74.8 | 2.9 | 169 | 76 | 44.3 |
Minimum | 54.2 | 51.8 | −2.2 | 101 | 21 | 22.9 |
Q25 | 67.5 | 69.6 | 2.0 | 152 | 59 | 39.2 |
Median (Q50) | 72.2 | 75.1 | 2.8 | 169 | 74 | 44.2 |
Q75 | 76.2 | 79.5 | 3.9 | 185 | 90 | 48.9 |
Maximum | 94.6 | 95.6 | 7.6 | 238 | 158 | 72.1 |
Check hybrids | ||||||
Mean | 78.0 | 77.8 | 0.3 | 207 | 91 | 44.0 |
Check1 FAO210 | 68.2 | 68.6 | 0.9 | 199 | 74 | 37.4 |
Check2 FAO300 | 75.4 | 75.5 | 0.5 | 212 | 98 | 45.9 |
Check3 FAO400 | 78.6 | 78.4 | 0.3 | 205 | 92 | 45.0 |
Check4 FAO500 | 82.4 | 82.3 | 0.3 | 209 | 94 | 44.8 |
Check5 FAO600 | 85.4 | 84.3 | −0.7 | 209 | 98 | 46.8 |
* Traits | PRIN1 | PRIN2 | PRIN3 |
---|---|---|---|
DT | 0.450 | −0.017 | −0.445 |
DS | 0.452 | 0.187 | −0.370 |
ASI | 0.180 | 0.924 | 0.176 |
PH | 0.412 | −0.124 | 0.730 |
EH | 0.458 | −0.211 | 0.254 |
EPHR | 0.426 | −0.224 | −0.192 |
Eigenvalue | 4.331 | 0.9723 | 0.3976 |
% variability explained | 72 | 16 | 7 |
Cluster | Sub- Cluster | N | * DT | * DS | * ASI | * PH | * EH | * EPHR |
---|---|---|---|---|---|---|---|---|
A | 2 | 92.4 | 93.9 | 1.95 | 210 | 153 | 71.08 | |
1 | 2 | 92.4 | 93.9 | 1.95 | 210 | 153 | 71.08 | |
B | 312 | 74.9 | 77.8 | 3.34 | 179 | 85 | 47.3 | |
2 | 44 | 74.1 | 78.9 | 5.21 | 163 | 70 | 43.2 | |
3 | 86 | 73.6 | 75.1 | 3.28 | 182 | 86 | 47.0 | |
11 | 66 | 72.1 | 73.8 | 2.11 | 169 | 76 | 44.8 | |
14 | 19 | 74.9 | 77.8 | 3.34 | 179 | 85 | 47.3 | |
5 | 48 | 77.2 | 81.5 | 4.77 | 192 | 97 | 50.1 | |
7 | 49 | 77.6 | 79.5 | 2.31 | 195 | 99 | 50.6 | |
C | 44 | 84.0 | 86.9 | 3.28 | 204 | 117 | 56.9 | |
4 | 15 | 85.4 | 87.7 | 2.65 | 177 | 105 | 58.4 | |
8 | 29 | 86.9 | 90.9 | 4.44 | 227 | 136 | 57.8 | |
D | 8 | 57.1 | 55.4 | −1.32 | 121 | 34 | 31.2 | |
6 | 8 | 57.1 | 55.4 | −1.32 | 121 | 34 | 31.2 | |
E | 227 | 66.7 | 68.7 | 2.50 | 150 | 56 | 37.8 | |
9 | 47 | 66.4 | 67.0 | 0.95 | 156 | 62 | 40.0 | |
10 | 76 | 68.2 | 70.8 | 2.95 | 161 | 65 | 40.5 | |
12 | 46 | 66.6 | 68.6 | 2.46 | 139 | 49 | 36.4 | |
15 | 26 | 62.3 | 63.0 | 1.07 | 130 | 43 | 34.1 | |
13 | 32 | 66.9 | 71.3 | 4.86 | 147 | 46 | 33.3 | |
Total | 593 | 72.3 | 74.8 | 2.94 | 169 | 76 | 44.3 |
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Balconi, C.; Galaretto, A.; Malvar, R.A.; Nicolas, S.D.; Redaelli, R.; Andjelkovic, V.; Revilla, P.; Bauland, C.; Gouesnard, B.; Butron, A.; et al. Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity. Biology 2024, 13, 454. https://doi.org/10.3390/biology13060454
Balconi C, Galaretto A, Malvar RA, Nicolas SD, Redaelli R, Andjelkovic V, Revilla P, Bauland C, Gouesnard B, Butron A, et al. Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity. Biology. 2024; 13(6):454. https://doi.org/10.3390/biology13060454
Chicago/Turabian StyleBalconi, Carlotta, Agustin Galaretto, Rosa Ana Malvar, Stéphane D. Nicolas, Rita Redaelli, Violeta Andjelkovic, Pedro Revilla, Cyril Bauland, Brigitte Gouesnard, Ana Butron, and et al. 2024. "Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity" Biology 13, no. 6: 454. https://doi.org/10.3390/biology13060454
APA StyleBalconi, C., Galaretto, A., Malvar, R. A., Nicolas, S. D., Redaelli, R., Andjelkovic, V., Revilla, P., Bauland, C., Gouesnard, B., Butron, A., Torri, A., Barata, A. M., Kravic, N., Combes, V., Mendes-Moreira, P., Murariu, D., Šarčević, H., Schierscher-Viret, B., Vincent, M., ... Goritschnig, S. (2024). Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity. Biology, 13(6), 454. https://doi.org/10.3390/biology13060454