Similar articles

Abstract: A cotton germplasm collection with data for 20 quantitative traits was used to investigate the effect of the scale of quantitative trait data on the representativeness of plant sub-core collections. The relationship between the representativeness of a sub-core collection and two influencing factors, the number of traits and the sampling percentage, was studied. A mixed linear model approach was used to eliminate environmental errors and predict genotypic values of accessions. sub-core collections were constructed using a least distance stepwise sampling (LDSS) method combining standardized Euclidean distance and an unweighted pair-group method with arithmetic means (UPGMA) cluster method. The mean difference percentage (MD), variance difference percentage (VD), coincidence rate of range (CR), and variable rate of coefficient of variation (VR) served as evaluation parameters. monte Carlo simulation was conducted to study the relationship among the number of traits, the sampling percentage, and the four evaluation parameters. The results showed that the representativeness of a sub-core collection was affected greatly by the number of traits and the sampling percentage, and that these two influencing factors were closely connected. Increasing the number of traits improved the representativeness of a sub-core collection when the data of genotypic values were used. The change in the genetic diversity of sub-core collections with different sampling percentages showed a linear tendency when the number of traits was small, and a logarithmic tendency when the number of traits was large. However, the change in the genetic diversity of sub-core collections with different numbers of traits always showed a strong logarithmic tendency when the sampling percentage was changing. A CR threshold method based on monte Carlo simulation is proposed to determine the rational number of traits for a relevant sampling percentage of a sub-core collection.

[1]Biabani, A., Carpenter-Boggs, L., Coyne, C.J., Taylor, L., Smith, J.L., Higgins, S., 2011. Nitrogen fixation potential in global chickpea mini-core collection. Biol. Fertil. Soils, 47(6):679-685.

[2]Brown, A.H.D., 1995. The Core Collection at the Crossroads. In: Hodgkin, T., Brown, A.H.D., van Hintum, T.H.J.L., Morales, E.A.V. (Eds.), Core Collections of Plant Genetic Resources. John Wiley and Sons, Chichester, UK, p.3-19.

[3]Campbell, B.T., Saha, S., Percy, R., Frelichowski, J., Jenkins, J.N., Park, W., Mayee, C.D., Gotmare, V., Dessauw, D., Giband, M., 2010. Status of the global cotton germplasm resources. Crop Sci., 50(4):1161-1179.

[4]Cheng, Z., Gasic, K., Wang, Z., Chen, X., 2011. Genetic diversity and genetic structure in natural populations of Prunus davidiana germplasm by SSR markers. J. Agric. Sci., 3(4):113-125.

[5]Díez, C.M., Imperato, A., Rallo, L., Barranco, D., Trujillo, I., 2012. Worldwide core collection of olive cultivars based on simple sequence repeat and morphological markers. Crop Sci., 52(1):211-221.

[6]Frankel, O.H., Brown, A.H.D., 1984. Plant Genetics Resources Today: a Critical Appraisal. In: Holden, J.H.W., Williams, J.T. (Eds.), Crop Gentic Resources: Conservation and Evaluation. George Allen and Unwin, London, UK, p.249-257.

[7]Hu, J., Zhu, J., Xu, H.M., 2000. Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops. Theor. Appl. Genet., 101(1-2):264-268.

[8]Kang, C.W., Kim, S.Y., Lee, S.W., Mathur, P.N., Hodgkin, T., Zhou, M.D., Lee, R.J., 2006. Selection of a core collection of Korean sesame germplasm by a stepwise clustering method. Breed Sci., 56(1):85-91.

[9]Kang, H.M., Sul, J.H., Zaitlen, N.A., Kong, S., Freimer, N.B., Sabatti, C., Eskin, E., 2010. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet., 42(4):348-354.

[10]Kulkarni, V.N., Khadi, B.M., Maralappanavar, M.S., Deshapande, L.A., Narayanan, S., 2009. The worldwide gene pools of Gossypium arboreum L. and G. herbaceum L., and their improvement. Genet. Genom. Cotton, 3(1):69-97.

[11]Li, C.T., Shi, C.H., Wu, J.G., Xu, H.M., Zhang, H.Z., Ren, Y.L., 2004. Methods of developing core collections based on the predicted genotypic value of rice (Oryza sativa L.). Theor. Appl. Genet., 108(6):1272-1276.

[12]Malosetti, M., Abadie, T., 2001. Sampling strategy to develop a core collection of uruguayan maize landraces based on morphological traits. Genet. Res. Crop Evol., 48(4):381-390.

[13]Mei, Y.J., Zhou, J.P., Xu, H.M., Zhu, S.J., 2012. Development of sea island cotton (Gossypium barbadense L.) core collection using genotypic values. Austr. J. Crop Sci., 6(4):673-680.

[14]Oliveira, M.F., Nelson, R.L., Geraldi, I.O., Cruz, C.D., de Toledo, J.F.F., 2010. Establishing a soybean germplasm core collection. Field Crops Res., 119(2-3):277-289.

[15]Pino del Carpio, D., Basnet, R.K., de Vos, R.C.H., Maliepaard, C., Visser, R., Bonnema, G., 2011. The patterns of population differentiation in a Brassica rapa core collection. Theor. Appl. Genet., 122(6):1105-1118.

[16]Rao, E.S., Kadirvel, P., Symonds, R.C., Geethanjali, S., Ebert, A.W., 2011. Using SSR markers to map genetic diversity and population structure of solanum pimpinellifolium for development of a core collection. Plant Genet. Res., 10(1):38-48.

[17]Santesteban, L.G., Miranda, C., Royo, J.B., 2009. Assessment of the genetic and phenotypic diversity maintained in apple core collections constructed by using either agro-morphologic or molecular marker data. Span. J. Agric. Res., 7(3):572-584.

[18]Silvar, C., Casas, A.M., Kopahnke, D., Habekusharp, A., Schweizer, G., Gracia, M.P., Lasa, J.M., Molina-Cano, J.L., Igartua, E., Ordon, F., 2010. Screening the spanish barley core collection for disease resistance. Plant Breed., 129(1):45-52.

[19]Smýkal, P., Bačová-Kerteszová, N., Kalendar, R., Corander, J., Schulman, A., Pavelek, M., 2011. Genetic diversity of cultivated flax (Linum usitatissimum L.) germplasm assessed by retrotransposon-based markers. Theor. Appl. Genet., 122(7):1385-1397.

[20]Upadhyaya, H.D., Gowda, C.L.L., Pundir, R.P.S., Reddy, V.G., Singh, S., 2006. Development of core subset of finger millet germplasm using geographical origin and data on 14 quantitative traits. Genet. Res. Crop Evol., 53(4):679-685.

[21]Upadhyaya, H.D., Sarma, N., Ravishankar, C.R., Albrecht, T., Narasimhudu, Y., Singh, S.K., Varshney, S.K., Reddy, V.G., Singh, S., Dwivedi, S.L., 2010. Developing a mini-core collection in finger millet using multilocation data. Crop Sci., 50(5):1924-1931.

[22]Wang, C.R., Chen, S., Yu, S., 2011. Functional markers developed from multiple loci in gs3 for fine marker-assisted selection of grain length in rice. Theor. Appl. Genet., 122(5):905-913.

[23]Wang, J.C., Hu, J., Xu, H.M., Zhang, S., 2007. A strategy on constructing core collections by least distance stepwise sampling. Theor. Appl. Genet., 115(1):1-8.

[24]Wang, J.C., Hu, J., Huang, X.X., Xu, S.C., 2008. Assessment of different genetic distances in constructing cotton core subset by genotypic values. J. Zhejiang University-Sci. B, 9(5):356-362.

[25]Wulff, S.S., 2009. Evaluation of the mixed linear model with orthogonalized and studentized residuals. J. Stat. Theory Pract., 3(2):463-476.

[26]Xu, H.M., Mei, Y.J., Hu, J., Zhu, J., Gong, P., 2006. Sampling a core collection of island cotton (Gossypium barbadense L.) based on the genotypic values of fiber traits. Genet. Res. Crop Evol., 53(3):515-521.

[27]Zeng, L.H., Meredith, W.R., Boykin, D.L., 2011. Germplasm potential for continuing improvement of fiber quality in upland cotton: combining ability for lint yield and fiber quality. Crop Sci., 51(1):60-68.

[28]Zhang, J., Wang, Y., Zhang, X.Z., Li, T.Z., Wang, K., Xu, X.F., Han, Z.H., 2010. Sampling strategy to develop a primary core collection of apple cultivars based on fruit traits. Afr. J. Biotechnol., 9(2):123-127.

[29]Zhang, Z., Ersoz, E., Lai, C.Q., Todhunter, R.J., Tiwari, H.K., Gore, M.A., Bradbury, P.J., Yu, J., Arnett, D.K., Ordovas, J.M., 2010. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet., 42(4):355-360.

[30]Zhu, J., Weir, B.S., 1996. Diallel analysis for sex-linked and maternal effects. Theor. Appl. Genet., 92(1):1-9.