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Joint modeling of additive and non-additive genetic line effects in single field trials

Abstract

A statistical approach is presented for selection of best performing lines for commercial release and best parents for future breeding programs from standard agronomic trials. The method involves the partitioning of the genetic effect of a line into additive and non-additive effects using pedigree based inter-line relationships, in a similar manner to that used in animal breeding. A difference is the ability to estimate non-additive effects. Line performance can be assessed by an overall genetic line effect with greater accuracy than when ignoring pedigree information and the additive effects are predicted breeding values. A generalized definition of heritability is developed to account for the complex models presented.

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References

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

  2. Brown D, Tier B, Reverter A, Banks R, Graser H (2000) OVIS: a multiple trait breeding value estimation program for genetic evaluation of sheep. Wool Technol Sheep Breed 48

  3. Cooper M, Hammer GL (2005) Preface to special issue: complex traits and plant breeding—can we understand the complexities of gene-to-phenotype relationships and use such knowledge to enhance plant breeding outcomes? Aust J Agric Res 56:869–872

  4. Costa e Silva J, Borralho NMG, Potts BM (1994) Additive and non-additive genetic parameters from clonally replicated and seedling progenies of Eucalyptus globulus. Genetics 138:963–971

  5. Crepieux S, Lebreton C, Servin B, Charmet G (2004) Quantitative trait loci QTL detection in multicross inbred designs: recovering QTL identical-by-descent status information from marker data. Genetics 168:1737–1749

  6. Crianiceanu CM, Ruppert D (2004) Likelihood ratio tests in linear mixed models with one variance component. J Roy Stat Soc B66:165–185

  7. Cullis BR, Gleeson AC (1991) Spatial analysis of field experiments—an extension to two dimensions. Biometrics 47:1449–1460

  8. Cullis BR, Smith A, Coombes N (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat (in press)

  9. Davik J, Honne B (2005) Genetic variance and breeding values for resistance to wind-borne disease [Sphaeotheca macularis (wallr. exfr.)] in strawberry (Fragaria x ananassa duch.) estimated by exploring mixed models and spatial models and pedigree information. Theor Appl Genet 111:256–264

  10. Durel CE, Laurens F, Fouillet A, Lespinasse Y (1998) Utilization of pedigree information to estimate genetic parameters from large unbalanced data sets in apple. Theor Appl Genet 96:1077–1085

  11. Dutkowski GW, Costa e Silva J, Gilmour AR, Lopez GA (2002) Spatial analysis methods for forest genetic trials. Can J For Res 32:2201–2214

  12. Eckermann PJ, Verbyla AP, Cullis BR, Thompson R (2001) The analysis of quantitative traits in wheat mapping populations. Aust J Agric Res 52:1195–1206

  13. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman Group Ltd

  14. Gilmour AR, Cullis B, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293

  15. Gilmour AR, Cullis BR, Gogel B, Welham SJ, Thompson R (2005) ASReml, user guide. Release 2.0. VSN International Ltd, Hemel Hempstead

  16. Henderson CR (1976) A simple method for computing the inverse of a numerator relationship matrix used in the prediction of breeding values. Biometrics 32:69–83

  17. John J, Ruggiero K, Williams E (2002) ALPHA(n)-designs. Aust NZ J Stat 44:457–465

  18. Martin R, Eccleston J, Chan B (2004) Efficient factorial experiments when the data are spatially correlated. J Stat Plan Inference 126:377–395

  19. Meuwissen THE, Luo Z (1992) Computing inbreeding coefficients in large populations. Genet Sel Evol 24:305–313

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

  21. R Development Core Team (2005) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0

  22. Self SG, Liang K-Y (1987) Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J Am Stat Assoc 82:605–610

  23. Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143:1–14

  24. Sneller CH (1994) SAS programs for calculating coefficients of parentage. Crop Sci 34:1679–1680

  25. Stram DO, Lee JW (1994) Variance components testing in the longitudinal mixed effects model. Biometrics 50:1171–1177

  26. Topal A, Aydin C, Akgun N, Babaoglu M (2004) Diallel cross analysis in durum wheat (Triticum durum Desf.) identification of best parents for some kernel physical features. Field Crops Res 87:1–12

  27. Verbyla AP, Cullis BR, Kenward M, Welham S (1999) The analysis of designed experiments and longitudinal data using smoothing splines (with discussion). Appl Stat 48:269–311

  28. Viana JMS (2005) Dominance, epistasis, heritabilities and expected genetic gain. Genet Mol Biol 28:67–74

  29. Walsh B (2005) The struggle to exploit non-additive variation. Aust J Agric Res 56:873–881

  30. van der Werf J, de Boer IJM (1990) Estimation of additive genetic variances when base populations are selected. J Anim Sci 68:3124–3132

  31. Wright S (1922) Coefficients of inbreeding and relationship. Am Nat 56:330–338

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Acknowledgements

Thanks to the Grain Research Development Corporation (GRDC) who fund the research of Helena Oakey through a Grains Industry Research Scholarship. Arūnas Verbyla and Brian Cullis also thank GRDC. We are grateful to the breeders and technical staff of Australian Grain Technologies for access to the grain yield and pedigree data used in this study.

Author information

Correspondence to Helena Oakey.

Additional information

Communicated by M. Cooper

Appendix

Appendix

The additive relationship matrix-adjustment for self-fertilization

In plant breeding, the test lines that are included in trials are often the result of five or six generations of self-fertilization. The method of Henderson (1976) was developed for use in animal pedigrees, and as such requires for any particular line that is a result of n generations of self-fertilization that all the previous n − 1 generations of lines involved in its development are included in the pedigree. Clearly, in plant breeding trials where each test line has undergone the self-fertilization process up to n times, this would require an (unnecessarily) large pedigree to be recorded in order to obtain accurate estimates of a jt . A modification in the calculation of the inbreeding coefficient F j and therefore in the a jj value, can be incorporated into the algorithm, so that it is unnecessary to include the n − 1 generation of lines in the pedigree, just the number of generations n of self-fertilization need be recorded for each line.

If both parents, s and d of individual j are known then, the adjustment under n generations of self-fertilization is given by

$$a_{{jj}} = 2-0.5^{{n-1}} + 0.5^{n} a_{{sd}}$$
(9)

which reduces to Henderson’s equation under no self-fertilization, i.e. n = 1, also note that a jj tends to 2 as n tends to infinity.

Under n generations of self-fertilization, when one parent is known or when no parents are known the value of a jj can be shown to be

$$a_{{jj}} = 2-0.5^{{n - 1}}. $$

The coefficient of parentage matrix-adjustment for self-fertilization

The method of Sneller (1994) does not take into consideration self-fertilization. A modification in the calculation of the inbreeding coefficient F j and therefore f jj is necessary when dealing with individuals that have been self-fertilized for n generations.

Under self-fertilization, the coefficient of parentage f jj of j in the nth generation is given by half equation Eq. 9 as follows:

$$f_{{jj}} = 1-0.5^{n} + 0.5^{n} f_{{sd}}$$
(10)

When one parent is known or when no parents are known the value of f jj is f jj  = 1 − 0.5n

ASReml code for fitting the Pedigree model 2

The following is the code for the .as ASReml file used for fitting the Pedigree model to a trial.

figurea

The stage3.giv is a file containing the inverse of the additive relationship matrix. ASReml requires a file which is just the lower triangle of this matrix. It is important to ensure that the numbering of lines in knownped factor corresponds directly to the ordering of rows and columns in the “.giv” file, so that row one and column one of the A inverse matrix contain the additive relationships of individual 1, which should correspondingly be labeled as 1 in the knownped factor. The “.giv” file can be created in ASReml if a pedigree file is supplied, and ASReml now implements the adjustment for inbred lines.

The stage3rba.asd is a text file containing the data. The knownped and filler columns have been created from the line column in which the lines are numbered from 1:253. In particular, the knownped is a column which has been defined as a factor with 129 levels. The levels correspond to the lines with known pedigree. It has “NA”s in the positions which correspond to filler lines. The filler is a column which has been defined as a factor with 124 levels, filler lines are defined as 1:124 and lines which have pedigree have “NA”s. The ped column is a factor which has two levels so that separate overall means can be fitted for filler lines and lines with known pedigree.

The additive genetic effect for each line is fitted by including the term knownped in the random part of the model specification and the epistatic genetic effect for each line is fitted by including the term ide(knownped) in the random part of the model specification, the units term is the measurement error term.

The last two lines are the predict statements to obtain the elements of the estimated prediction error variance matrix, so that the generalized heritability can be calculated. ASReml places these in the “.pvs” file. The estimated prediction error variance of the total genetic effects is used for calculating a broad sense heritability and those of the additive effects for calculating a narrow sense heritability. Calculation of generalized heritability was carried out using R (R Development Core Team 2005). The R code is available from the corresponding author.

Generalized definition of heritability

The Lagrangian given by Eq. 6 is to be optimized with respect to c. Thus, differentiating \(\L_{{{\mathbf{c}}}} \) with respect to c and setting to zero, we find

$${\mathbf{Z}}^{T}_{g} {\mathbf{P}}_{v} {\mathbf{Z}}_{g} {\mathbf{Gc}} = \lambda {\mathbf{c}}.$$
(11)

Thus, c is an eigenvector of the matrix Z g T P v Z g G with eigenvalue λ. Not only can the c that maximizes the squared correlation be found, but a complete set of eigenvectors c for Z g T P v Z g G with associated eigenvalues. Notice that from Eq. 11

$$\begin{aligned} {\mathbf{c}}^{T} {\mathbf{GZ}}^{T}_{g} {\mathbf{P}}_{v} {\mathbf{Z}}_{g} {\mathbf{Gc}}&=\lambda {\mathbf{c}}^{T} {\mathbf{Gc}}\\ &= \lambda \\ \end{aligned} $$

using the constraint. Thus, the eigenvalues provide a set of heritability components that can be used to provide an overall measure of heritability.

From results on mixed models, GZ g T P v Z g G = G − (Z g T SZ g  + G −1)−1 where S = R −1 − R −1 X(X T R −1 X)−1 X T R −1. Now (Z g T SZ g  + G −1)−1 = C ZZ is the partition of the inverse of the mixed model coefficient matrix corresponding to g. This latter term C ZZ is also equivalent to the prediction error variance matrix (i.e. \(\hbox{var} (\tilde{\mathbf{g}} - {\mathbf{g}})\)), an estimate of which is available in the software ASReml (Gilmour et al. 2005) via the predict statement. So

$${\mathbf{Z}}^{T}_{g} {\mathbf{P}}_{v} {\mathbf{Z}}_{g} {\mathbf{G}} = {\mathbf{I}}_{m} - {\mathbf{G}}^{{- 1}} {\mathbf{C}}^{{ZZ}} $$
(12)

and eigenvalues of this matrix are required to determine the generalized heritability.

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Oakey, H., Verbyla, A., Pitchford, W. et al. Joint modeling of additive and non-additive genetic line effects in single field trials. Theor Appl Genet 113, 809–819 (2006). https://doi.org/10.1007/s00122-006-0333-z

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Keywords

  • Epistatic Effect
  • Total Genetic Variation
  • Pedigree Information
  • Narrow Sense Heritability
  • Best Linear Unbiased Predictor