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Genetic association of yield with its component traits in a recombinant inbred line population of cotton

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Abstract

Lint yield of upland cotton (Gossypium hirsutum L.) is determined by its component traits, boll number, boll weight, and lint percentage. Selecting high yielding lines is based on the ability to manipulate component traits. In this study, 188 recombinant inbred lines and two parental lines were grown in 1999 and 2000 at Mississippi State University. Lint yield and its three component traits were measured and analyzed by an extended conditional mixed linear model approach. Boll number unit-area−1 made the largest contribution to genotypic and genotype × environment (G × E) variations for lint yield. Both boll number and lint percentage, and boll number and boll weight jointly accounted for more than 70% of the genotypic and G × E variations in lint yield. Ninety-nine percent of the genetic and phenotypic variation in lint yield could be explained by the three component traits, indicating that lint yield was mainly dependent on its three component traits. Small phenotypic variation in lint yield could be accounted for by effects of genotype, G × E interactions of boll number or boll number combined with other component trait(s) (Table 5). For boll number unit-area−1 a wider distribution of genotypic contribution effects was detected than for lint percentage and boll weight in this study. Boll number and boll weight interacted to affect lint yield, indicating that balanced selection for boll weight and boll number is needed in high-yielding line development. Comparative results with other approaches were also discussed in this study.

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References

  • Ball, R.A., R.W. McNew, E.D. Vories, T.C. Keisling & L.C. Purcel, 2001. Path analyses of population density effects on short-season soybean yield. Agron J 93: 187–195.

    Article  Google Scholar 

  • Bora, G.C., S.N. Gupta, Y.S. Tomer & S. Singh, 1998. Genetic variability, correlation and path analysis in faba bean (Vicia faba). Indian J Agric Sci 68: 212–214.

    Google Scholar 

  • Cockerham, C.C., 1980. Random and fixed effects in plant genetics. Theor Appl Genet 56: 119–131.

    Google Scholar 

  • Coyle, G.G. & C.W. Smith, 1997. Combining ability for within-boll yield components in cotton, Gossypium hirsutum L. Crop Sci 37: 1118–1122.

    Article  Google Scholar 

  • Culp, T.W. & D.C. Harrell, 1975. Influence of lint percentage, boll size, and seed size on lint yield of upland cotton with high fiber strength. Crop Sci 15: 741–746.

    Article  Google Scholar 

  • Cramer, C.S. & T.C. Wehner, 2000. Path analysis of the correlation between fruit number and plant traits of cucumber populations. Hort Sci 35: 708–711.

    Google Scholar 

  • Dilday, R.H., M.A. Mgonja, S.A. Amonsilpa, F.C. Collins & B.R. Wells, 1990. Plant height vs. mesocotyl and coleoptile elongation in rice: Linkage or pleiotropism. Crop Sci 30: 815-818.

    Article  Google Scholar 

  • Fehr, W., 1987. Principles of Cultivar Development: Volume 1, Theory and Technique, Macmillan Publishing Co., New York.

    Google Scholar 

  • Graybill, F.A., 1976. Theory and Application of Linear Model, Duxbury Press, Boston, MA.

    Google Scholar 

  • Jobson, J.D., 1991. Applied Multivariate Data Analysis Volume II: Categorical and Multivariate Methods. Springer-Verlag, New York.

    Google Scholar 

  • Kebede, H., P.K. Subudhi, D.T. Rosenow & H.T. Nguyen, 2001. Quantitative trait loci influencing drought tolerance in grain sorghum (Sorghum bicolor L. Moench). Theor Appl Genet 103: 266–276.

    CAS  Google Scholar 

  • Krzanowski, W.J., 1988. Principles of Multivariate Analysis. Oxford Science Publications, New York.

    Google Scholar 

  • McCarty, J.C. Jr., J.N. Jenkins & J. Zhu, 1998. Introgression of day-neutral genes in primitive cotton accessions: I. Genetic variances and correlations. Crop Sci 38: 1425–1428.

    Article  Google Scholar 

  • Melchinger A.E., M. Singh, W. Link, H.F. Utz & E. von Kittlitz, 1994. Heterosis and gene effects of multiplicative characters: Theoretical relationships and experimental results from Vicia faba L. Theor Appl Genet 88: 343–348.

    Google Scholar 

  • Miller, R.G., 1974. The jackknife: A review. Biometrika 61: 1-15.

    Google Scholar 

  • Myers, R.H., 1990. Classical and Modern Regression with Applications, PWS-KENT Publ. Co., Boston, MA.

    Google Scholar 

  • Piepho, H.P., 1995. A simple procedure for yield component analysis. Euphytica 84: 43–48.

    Google Scholar 

  • Samonte, S.O.P., L.T. Wilson & A.M. McClung, 1998. Path analyses of yield and yield-related traits of fifteen diverse rice genotypes. Crop Sci 38: 1130–1136.

    Article  Google Scholar 

  • Sparnaaij, L.D. & I. Bos, 1993. Component analysis of complex characters in plant breeding I. Proposed method for quantifying the relative contribution of individual components to variation of the complex character. Euphytica 70: 225–235.

    Google Scholar 

  • Tang, B., J.N. Jenkins, C.E. Watson, J.C. McCarty & R.G. Creech, 1996. Evaluation of genetic variances, heritabilities, and correlations for yield and fiber traits among cotton F2 hybrid populations Euphytica 91: 315–322.

    Google Scholar 

  • Worley, S., T.W. Culp & D.C. Harrell, 1974. The relative contributions of yield components to lint yield of upland cotton, Gossypium hirsutum L. Euphytica 23: 399–403.

    Google Scholar 

  • Worley, S., H.H. Ramey, D.C. Harrell & T.W. Culp, 1976. Ontogenetic model of cotton yield. Crop Sci 16: 30–34.

    Article  Google Scholar 

  • Wright, S., 1920. The relative importance of heredity and environment in determining the piebald patten of guinea-pigs. Proc Nat Acad Sci USA 6: 320–332.

    PubMed  CAS  Google Scholar 

  • Wu, J., 2003. Genetic variation, conditional analysis, and QTL mapping for agronomic and fiber traits in upland cotton. Ph.D. Dissertation, Mississippi State University, MS.

  • Wu, J., D. Wu, J.N. Jenkins & J.C. McCarty Jr., in press. A recursive approach to detect multivariate conditional variance components and random effects. Comput Stat Data Anal.

  • Wu, J., J. Zhu, F. Xu & D. Ji, 1995. Analysis of genetic effect × environment interactions for yield traits in upland cotton (Chinese). Heredita 17(5): 1–4.

    Google Scholar 

  • Zhu, J., 1989. Estimation of genetic variance components in the general mixed model. Ph.D. Dissertation, North Carolina State University, Raleigh, NC.

  • Zhu, J., 1993. Methods of predicting genotype value and heterosis for offspring of hybrids. J Biomath (Chinese) 8(1): 32–44.

    Google Scholar 

  • Zhu, J., 1995. Analysis of conditional effects and variance components in developmental genetics. Genetics 141: 1633–1699.

    PubMed  CAS  Google Scholar 

  • Zhu, J. & B.S. Weir, 1994. Analysis of cytoplasmic and maternal effects: I. A genetic model for diploid plant seeds and animals. Theor Appl Genet 89: 153–159.

    Google Scholar 

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Correspondence to Johnie N. Jenkins.

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Wu, J., Jenkins, J.N., McCarty, J.C. et al. Genetic association of yield with its component traits in a recombinant inbred line population of cotton. Euphytica 140, 171–179 (2004). https://doi.org/10.1007/s10681-004-2897-5

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  • DOI: https://doi.org/10.1007/s10681-004-2897-5

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