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Genetic characterization of sunflower breeding resources from Argentina: assessing diversity in key open-pollinated and composite populations

Published online by Cambridge University Press:  25 March 2013

M. V. Moreno ,
V. Nishinakamasu ,
M. A. Loray ,
D. Alvarez ,
J. Gieco ,
A. Vicario ,
H. E. Hopp ,
R. A. Heinz ,
N. Paniego  and
V. V. Lia
M. V. Moreno
Affiliation:
Estación Experimental Agropecuaria Manfredi, Instituto Nacional de Tecnología Agropecuaria (INTA), 5988Manfredi, Córdoba, Argentina
V. Nishinakamasu
Affiliation:
Instituto de Biotecnología, Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), Nicolás Repetto y Los Reseros s/n B1686ICG, Hurlingham, Buenos Aires, Argentina
M. A. Loray
Affiliation:
Instituto Nacional de Semillas (INASE), 1095 Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
D. Alvarez
Affiliation:
Estación Experimental Agropecuaria Manfredi, Instituto Nacional de Tecnología Agropecuaria (INTA), 5988Manfredi, Córdoba, Argentina
J. Gieco
Affiliation:
Estación Experimental Agropecuaria Manfredi, Instituto Nacional de Tecnología Agropecuaria (INTA), 5988Manfredi, Córdoba, Argentina
A. Vicario
Affiliation:
Instituto Nacional de Semillas (INASE), 1095 Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
H. E. Hopp
Affiliation:
Instituto de Biotecnología, Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), Nicolás Repetto y Los Reseros s/n B1686ICG, Hurlingham, Buenos Aires, Argentina
R. A. Heinz
Affiliation:
Instituto de Biotecnología, Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), Nicolás Repetto y Los Reseros s/n B1686ICG, Hurlingham, Buenos Aires, Argentina Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), 1033 Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
N. Paniego
Affiliation:
Instituto de Biotecnología, Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), Nicolás Repetto y Los Reseros s/n B1686ICG, Hurlingham, Buenos Aires, Argentina Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), 1033 Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
V. V. Lia*
Affiliation:
Instituto de Biotecnología, Centro de Investigación en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), Nicolás Repetto y Los Reseros s/n B1686ICG, Hurlingham, Buenos Aires, Argentina Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), 1033 Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
*
* Corresponding author. E-mail: vlia@cnia.inta.gov.ar
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Abstract

Open-pollinated (OPs) and composite populations (CPs) represent a valuable resource for sunflower breeding programmes. However, little is known about the levels and distribution of genetic variation within each of these populations. In this study, quantitative and qualitative traits along with molecular markers were used to evaluate 14 populations from the Instituto Nacional de Tecnología Agropecuaria (INTA) sunflower germplasm collection. These populations were chosen to represent historically important accessions that still play a central role within the INTA sunflower breeding programme due to their extensive variation in diverse agronomically important traits. Nine quantitative and eight qualitative agro-morphological descriptors were recorded and compared with those of a larger set of accessions representative of the phenotypic diversity of the sunflower collection. Molecular characterization was conducted on a total of 311 individuals using 16 microsatellite markers. Overall, the average gene diversity was 0.56 and the average number of alleles per locus was 6.25. No statistically significant differences in genetic diversity were detected between the OPs and CPs. Global estimates of FST revealed very high levels of differentiation among accessions (FST= 0.413, P< 0.05). Population structure analyses were consistent with the observed levels of differentiation and identified two major groups. The results of this work show that high global diversity is preserved within the accessions analysed here. Additionally, this study provides a set of reliable and discriminant markers for the cost-effective molecular characterization of sunflower accessions, along with the guidelines for the delineation of sampling strategies for OPs and CPs, thus aiding the efficient management and exploitation of sunflower germplasm collections.

Keywords

cultivated sunflower germplasmgenetic diversitymolecular markersSSR
Type
Research Article
Information
Plant Genetic Resources , Volume 11 , Issue 3 , December 2013 , pp. 238 - 249
Copyright
Copyright © NIAB 2013 

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References

Alvarez D, Ludueña P, Frutos E (1992a) Correlation and causation among sunflower traits. In: Proceedings of the 13th International Sunflower Conference, 7–11 September 1992, Pisa, Italy, pp. 957–962.Google Scholar
Alvarez D, Ludueña P, Frutos E (1992b) Variability and genetic advance in sunflower. In: Proceedings of the 13th International Sunflower Conference, 7–11 September 1992, Pisa, Italy, pp. 963–968.Google Scholar
Anderson, JA, Churchill, GA, Autrique, JE, Tanksley, SD and Sorrells, ME (1993) Optimizing parental selection for genetic linkage maps. Genome 36: 181–186.Google Scholar
Balzarini M, Di Rienzo J (2003) InfoGen: Software para análisis estadístico de datos genéticos. Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba.Google Scholar
Balzarini, M, Bruno, C, Peña, A, Teich, I and Di Rienzo, J (2010) Estadística en Biotecnología. Aplicaciones en InfoGen. Córdoba: Encuentro Grupo Editor.Google Scholar
Bertero de Romano, A and Vázquez, AN (2003) Origin of the Argentine sunflower varieties. Helia 26: 127–136.Google Scholar
Belhassen, E, Augé, G, Ji, J, Billot, C, Fernandez-Martinez, J, Ruso, J and Vares, D (1994) Dynamic management of genetic resources: first generation analysis of sunflower artificial populations. Genetics, Selection, Evolution 26: 241–253.Google Scholar
Bowers, JE, Bachlava, E, Brunick, RL, Rieseberg, LH, Knapp, SJ and Burke, JM (2012) Development of a 10,000 locus genetic map of the sunflower genome based on multiple crosses. Genes-Genomes-Genetics 2: 721–729.Google Scholar
Burke, J, Tang, S, Knapp, SJ and Rieseberg, LH (2002) Genetic analysis of sunflower domestication. Genetics 161: 1257–1267.CrossRefGoogle ScholarPubMed
De la Vega, AJ, De Lacy, IH and Chapman, SC (2007) Progress over 20 years of sunflower breeding in central Argentina. Field Crops Research 100: 61–72.Google Scholar
El Mousadik, A and Petit, R (1996) High level of genetic differentiation for allelic richness among populations of the argan tree (Argania spinosa (L.) Skeels) endemic to Morocco. Theoretical and Applied Genetics 92: 832–839.Google Scholar
Evanno, G, Regnaut, S and Goudet, J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14: 2611–2620.Google Scholar
Falush, D, Stephens, M and Pritchard, JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567–1587.Google Scholar
Garayalde, AF, Poverene, M, Cantamutto, M and Carrera, AD (2011) Wild sunflower diversity in Argentina revealed by ISSR and SSR markers: an approach for conservation and breeding programmes. Annals of Applied Biology 158: 305–317.Google Scholar
Goudet J (2001) FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Available at http://www2.unil.ch/popgen/.Google Scholar
Gulya, TJ (1985) Registration of five disease resistant sunflower germplasm. Crop Science 25: 719–720.Google Scholar
Gulya, TJ and Masirevic, S (1995) Proposed methodologies for inoculation of sunflower with Puccinia helianthi and for disease assessment. In: Studies on Common Methodologies of Artificial Inoculation and Population Dynamics of Sunflower Pathogens. Rome: FAO European Research Network on Sunflower.Google Scholar
Jombart, T (2008) Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24: 1403–1405.Google Scholar
Jombart, T, Devillard, S and Balloux, F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics 11: 94.Google Scholar
Kholghi, M, Bernousi, I, Darvishzadeh Maleki, R, Pirzad, A and Hatami Maleki, H (2011) Collection, evaluation and classification of Iranian confectionary sunflower (Helianthus annuus L.) populations using multivariate statistical techniques. African Journal of Biotechnology 10: 5444–5451.Google Scholar
Legendre, P and Legendre, L (1998) Numerical Ecology. 2nd English edn.Amsterdam: Elsevier.Google Scholar
Liu, K and Muse, S (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21: 2128–2129.Google Scholar
Mandel, JR, Dechaine, JM, Marek, LF and Burke, JM (2011) Genetic diversity and population structure in cultivated sunflower and a comparison to its wild progenitor, Helianthus annuus L. Theoretical and Applied Genetics 123: 693–704.CrossRefGoogle Scholar
Maringolo CA (2007) Regiones cromosómicas asociadas a resistencia a Podredumbre Húmeda del Capítulo de girasol (Sclerotinia sclerotiorum (Lib.) de Bary). Magister Scientiae Thesis, Universidad Nacional de Mar del Plata.Google Scholar
Nei, M (1972) Genetic distance between populations. American Naturalist 106: 283–292.Google Scholar
Nei, M (1987) Molecular Evolutionary Genetics. New York: Columbia University Press.Google Scholar
Nooryazdan, H, Serieys, H, Baciliéri, R, David, J and Bervillé, A (2010) Structure of wild annual sunflower (Helianthus annuus L.) accessions based on agro-morphological traits. Genetics Resources in Crop Evolution 57: 27–39.CrossRefGoogle Scholar
Paniego, N, Echaide, M, Muñoz, M, Fernandez, L, Torales, S, Faccio, P, Fuxan, I, Carrera, M, Zandomeni, R, Syarez, EY and Hopp, EH (2002) Microsatellite isolation and characterization in sunflower (Helianthus annuus L.). Genome 45: 34–43.Google Scholar
Peakall, R and Smouse, PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288–295.Google Scholar
Poormohammad Kiani, S, Talia, P, Maury, P, Grieu, P, Heinz, R, Perrault, A, Nishinakamasu, V, Hopp, E, Gentzbittel, L, Paniego, N and Sarrafi, A (2007) Genetic analysis of plant water status and osmotic adjustment in recombinant inbred lines of sunflower under two water treatments. Plant Science 172: 773–787.Google Scholar
Prada, D (2009) Molecular population genetics and agronomic alleles in seed banks: searching for a needle in a haystack? Journal of Experimental Botany 60: 2541–2552.Google Scholar
Pritchard, JK and Wen, W (2003) Documentation for STRUCTURE Software: Version 2. Chicago, IL: University of Chicago Press.Google Scholar
Pritchard, JK, Stephens, M and Donnelly, P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.Google Scholar
Rambaut A (2006–2009). Tree Fig. Drawing Tool Version 1.3.1. Institute of Evolutionary Biology, University of Edinburgh. Available at http://tree.bio.ed.ac.uk/.Google Scholar
Reif, JC, Zhao, Y, Würschum, T, Gowda, M and Hahn, V (2012) Genomic prediction of sunflower hybrid performance. Plant Breeding. doi:10.1111/pbr.12007 132: 107–114.CrossRefGoogle Scholar
Rohlf, JF (2004) NTSYS-pc: Numerical Taxonomy and Multivariate Analysis System, Version 2.11. Setauket, NY: Exeter.Google Scholar
R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Saitou, N and Nei, M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4: 406–425.Google Scholar
Seiler, GJ (1984) Variation in agronomic and morphologic characteristics of several populations of wild annual sunflower. Helia 7: 29–33.Google Scholar
Shannon, CE and Weaver, W (1963) The Mathematical Theory of Communication. Champaign, IL: University of Illinois Press.Google Scholar
Skoric, D (1992) Achievements and future directions of sunflower breeding. Field Crops Research 30: 231–270.Google Scholar
Talia, P, Nishinakamasu, V, Hopp, HE, Heinz, RA and Paniego, NB (2010) Genetic mapping of EST-SSR, SSR and InDel to improve saturation of genomic regions in a previously developed sunflower map. Electronic Journal of Biotechnology 13: 6.Google Scholar
Tang, S, Kishore, VK and Knapp, SJ (2003) PCR-multiplexes for a genome-wide framework of simple sequence repeat marker loci in cultivated sunflower. Theoretical and Applied Genetics 107: 6–19.CrossRefGoogle ScholarPubMed
Tanksley, SD and McCouch, SR (1997) Seed banks and molecular maps: unlocking genetic potential from the wild. Science 277: 1063–1066.Google Scholar
Waits, LP, Luikart, G and Taberlet, P (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Molecular Ecology 10: 249–256.Google Scholar
Weir, B and Cockerham, CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38: 1358–1370.Google Scholar
Wright, S (1978) Variability Within and Among Natural Populations in Evolution and the Genetics of Populations. Chicago, IL: University of Chicago Press.Google Scholar
Zhang, LS, Le Clerc, V, Li, S and Zhang, D (2005) Establishment of an effective set of simple sequence repeat markers for sunflower variety identification and diversity assessment. Canadian Journal of Botany 83: 66–72.Google Scholar
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