Skip to main content
SpringerLink
Log in

Genetic analysis without replications: model evaluation and application in spring wheat

  • Published:
Euphytica Aims and scope Submit manuscript

Abstract

Genetic data collected from various plant breeding and genetic studies may not be replicated in field designs although field variation is always present. In this study, we addressed this problem using spring wheat (Triticum aestivum L.) trial data collected from two locations. There were no intralocation replications and an extended additive-dominance (AD) model was used to account for field variation. We numerically evaluated the data from simulations and estimated the variance components. For demonstration purposes we also analyzed three agronomic traits from the actual spring wheat data set. Results showed that these data could be effectively analyzed using an extended AD model, which was more comparable to a conventional AD model. Actual data analysis revealed that grain yield was significantly influenced by systematic field variation. Additive effects were significant for all traits and dominance effects were significant for plant height and time-to-flowering. Genetic effects were predicted and used to demonstrate that most spring wheat lines developed by the South Dakota State University breeding program (SD lines) exhibited good general combining ability effects for yield improvement. Thus, this study provides a general framework to appropriately analyze data in situations where field crop data are collected from non-replicated designs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Baird D, Mead R (1991) The empirical efficiency and validity of two neighbor models. Biometrics 47:1473–1487

    Article  Google Scholar 

  • Bartlett MS (1978) Nearest neighbor models in the analysis of field experiments (with discussion). J R Stat Soc B 40:147–174

    Google Scholar 

  • Besag J, Kempton R (1986) Statistical analysis of field experiments using neighboring plots. Biometrics 42:231–251

    Article  Google Scholar 

  • Cockerham CC (1980) Random and fixed effects in plant genetics. Theor Appl Genet 56:119–131

    Article  Google Scholar 

  • Cullis B, Gogel B, Verbyla A, Thompson R (1998) Spatial analysis of multi-environment early generation variety trials. Biometrics 54:1–18

    Article  Google Scholar 

  • Falconer DS (1989) Introduction to quantitative genetics, 3rd edn. Longman Scientific & Technical, Essex

    Google Scholar 

  • Federer WT, Nair RC, Raghavarao D (1975) Some augmented row-column designs. Biometrics 31:361–373

    Article  Google Scholar 

  • Gardner CO, Eberhart SA (1966) Analysis and interpretation of the variety cross diallel and related populations. Biometrics 22:439–452

    Article  PubMed  CAS  Google Scholar 

  • Gedge DL, Fehr WR, Walker AK (1977) Intergenotypic competition between rows and within blends of soybeans. Crop Sci 17:787–790

    Article  Google Scholar 

  • Gleeson AC, Cullis BR (1987) Residual maximum likelihood (REML) estimation of a neighbor model for field experiments. Biometrics 43:277–288

    Article  Google Scholar 

  • Griffing B (1956) Concept of general and specific combining ability in relation to diallel crossing systems. Aust J Biol Sci 9:463–493

    Google Scholar 

  • Hallauer AR, Miranda JB (1988) Quantitative genetics in maize breeding. Iowa State Univ Press, Ames, IA

    Google Scholar 

  • Hartley HO, Rao JNK (1967) Maximum-likelihood estimation for the mixed analysis of variance model. Biometrika 54:93–108

    PubMed  CAS  Google Scholar 

  • Jenkins JN, Wu J, McCarty JC, Saha S, Gutierrez OA, Hayes R, Stelly DM (2006) Genetic evaluation for thirteen chromosome substitution lines crossed with five commercial cultivars: I. yield traits. Crop Sci 46:1169–1178

    Article  Google Scholar 

  • Jenkins JN, McCarty JC, Wu J, Saha S, Gutierrez OA, Hayes R, Stelly DM (2007) Genetic effects of thirteen Gossypium barbadense L. chromosome substitution lines in topcrosses with upland cotton cultivars: II. Fiber quality traits. Crop Sci 47:561–572

    Article  Google Scholar 

  • Jenkins JN, McCarty JC, Wu J, Gutierrez OA (2009) Genetic variance components and genetic effects among eleven diverse upland cotton lines and their F2 hybrids. Euphytica 167:397–408

    Article  CAS  Google Scholar 

  • Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, Inc., Sunderland, MA

    Google Scholar 

  • Nguyen NK (1997) Construction of optimal row-column designs by computer. Comp Sci Stat 28:471–475

    Google Scholar 

  • Nguyen NK, Williams ER (1993) An algorithm for constructing optimal resolvable row-column designs. Aust J Stat 35:363–370

    Article  Google Scholar 

  • Patterson HD, Thompson R (1971) Recovery of inter-block information when block size are unequal. Biometrika 58:545–554

    Article  Google Scholar 

  • Rao CR (1971) Estimation of variance and covariance components MINQUE theory. J Multivar Anal 1:257–275

    Article  Google Scholar 

  • Saha S, Jenkins JN, Wu J, McCarty JC, Percy RG, Cantrell RG, Stelly DM (2006) Effect of chromosome specific introgression in upland cotton on fiber and agronomic traits. Genetics 172:1927–1938

    Article  PubMed  CAS  Google Scholar 

  • Searle SR, Casella G, McCulloch CE (1992) Variance components. Wiley, New York, NY

    Book  Google Scholar 

  • Tang B, Jenkins JN, Watson CE, McCarty JC, Creech RG (1996) Evaluation of genetic variances, heritabilities, and correlations for yield and fiber traits among cotton F2 hybrid populations. Euphytica 91:315–322

    Article  Google Scholar 

  • Wilkinson GN, Eckert SR, Hancock TW, Mayo O (1983) Nearest neighbor (NN) analysis of field experiments. J R Stat Soc B 45:151–211

    Google Scholar 

  • Williams ER (1986) A neighbor model for field experiments. Biometrika 73:279–287

    Article  Google Scholar 

  • Wu J, Zhu J, Ji D, Xu F (1995) Genetic analysis of direct and maternal effects of seed traits in upland cotton. Acta Agron Sin 21:659–664

    Google Scholar 

  • Wu J, Xu H, Zhu J (1998a) An approach to eliminating systematic errors in genetic analysis. In: Chen LS, Ruan SG, Zhu J (eds) Advanced topics in biomathematics. Proceedings of international conference on mathematical biology, pp 265–270. World Scientific Publishing Co., Singapore

  • Wu T, Mather DE, Dutilleul P (1998b) Application of geostatistical and neighbor analysis to data from plant breeding trials. Crop Sci 38:1545–1553

    Article  Google Scholar 

  • Wu J, Jenkins JN, McCarty JC, Saha S, Stelly DM (2006a) An additive-dominance model to determine chromosomal effects in chromosome substitution lines and other germplasms. Theor Appl Genet 112:391–399

    Article  PubMed  CAS  Google Scholar 

  • Wu J, Jenkins JN, McCarty JC, Wu D (2006b) Variance component estimation using the ADAA model when genotypes vary across environments. Crop Sci 46:174–179

    Article  Google Scholar 

  • Wu J, Jenkins JN, McCarty JC (2008) Testing variance components by two jackknife techniques. In: Milliken G (ed) Proceedings of applied statistics in agriculture, Manhattan, KS, pp 1–17

  • Wu J, Jenkins JN, McCarty JC (2010a) A generalized approach and computer tool for quantitative genetics study. In: Gadbury G (ed) Proceedings of applied statistics in agriculture, Manhattan, KS, pp 85–106

  • Wu J, McCarty JC, Jenkins JN (2010b) Cotton chromosome substitution lines crossed with cultivars: genetic model evaluation and seed trait analyses. Theor Appl Genet 120:1473–1483

    Article  PubMed  Google Scholar 

  • Wu J, Jenkins JN, McCarty JC, Glover K (2012) Detecting epistatic effects associated with cotton traits by a modified MDR approach. Euphytica 197:289–301

    Article  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 8(1):32–44

    Google Scholar 

  • Zhu J (1998) Analytical methods for genetic models. Press of China Agriculture, Beijing, China

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Plant Science Department, South Dakota State University, Brookings, SD, 57007, USA

    Jixiang Wu, Krishna Bondalapati, Karl Glover & William Berzonsky

  2. Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, 57007, USA

    Krishna Bondalapati

  3. Crop Science Research Laboratory, USDA-ARS, Mississippi State, MS, 39762, USA

    Johnie N. Jenkins & Jack C. McCarty

Authors
  1. Jixiang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krishna Bondalapati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karl Glover
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William Berzonsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Johnie N. Jenkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jack C. McCarty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jixiang Wu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, J., Bondalapati, K., Glover, K. et al. Genetic analysis without replications: model evaluation and application in spring wheat. Euphytica 190, 447–458 (2013). https://doi.org/10.1007/s10681-012-0835-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10681-012-0835-5

Keywords

Search

Navigation