selectiongain (version 2.0.50.1)

multistageoptimum.search: Function for optimizing three-stage selection in plant breeding with one marker-assisted selection stage and two phenotypic selection stages

Description

This function is used to calculate the maximum of $\Delta G$ based on correlation matrix, which depends on locations, testers and replicates, with a grid search algorithm. The changing correlation matrix of three-stage selection are the testcross progenies of DH lines in one marker-assisted selection (MAS) stage and two phenotypic selection (PS) stages.

Usage

multistageoptimum.search (maseff=0.4, VGCAandE, 
  VSCA, CostProd, CostTest,  Nf, Budget, N2grid, 
  N3grid, L2grid, L3grid, T2grid, T3grid, R2, R3, alg, 
  detail, fig,alpha.nursery,cost.nursery,
  t2free,parallel.search)

Arguments

maseff

is the efficiency of MAS.

VGCAandE

is the vector of variance components of genetic effect, genotype $\times$ location interaction, genotype $\times$ year interaction, genotype $\times$ location $\times$ year interaction and the plot error. When VSCA is specified, it refers to

VSCA

is the vector of variance components for specific combining ability.

CostProd

contains the initial costs of producing or identifying a candidate in each stage.

CostTest

contains a vector with length n reflecting the cost of evaluating a candidate in the tests performed at stage i, i=1,...,n. The cost might vary in different stages.

Nf

is the number of finally selected candidates.

Budget

contains the value of total budget.

N2grid

is the vector of lower and upper limits as well as the grid width of number of candidates in the first field test stage.

N3grid

is the vector of lower and upper limits as well as the grid width of number of candidates in the second field test stage.

L2grid

is the vector of lower and upper limits of number of location as well as the width in the first field test stage.

L3grid

is the vector of lower and upper limits of number of location as well as the width in the second field test stage.

T2grid

is the vector of lower and upper limits of number of tester as well as the width in the first field test stage.

T3grid

is the vector of lower and upper limits of number of tester as well as the width in the second field test stage.

R2

is the number of replications in the first field test stage. By default it is 1.

R3

is the number of replications in the second field test stage. By default it is 1.

alg

is used to switch between two algorithms. If alg = GenzBretz(), which is by default, the quasi-Monte Carlo algorithm from Genz et al. (2009, 2013), will be used. If alg = Miwa(), the program will use the Miwa algorithm (Mi et al.

detail

is the control parameter to decide if the result of all the grids will be given (=TRUE) or only the maximum (=FALSE).

fig

is the control parameter to decide if a contour plot will be saved in the default folder of R. The default value is FALSE, which means no figure will be saved.

alpha.nursery

a value that should be 0

cost.nursery

a vector of length two c([cost of producing a DH line],[cost of testing a DH in nursery]). The default value is {0,0}.

t2free

is a logical value. If =FALSE, the cost of using T3 and T2 testers will be accounted seperately. If =TRUE, the cost of using T3 and T2 testers will be accounted according to number of testers, i.e., CostProd=c(CostProd[1],CostProd[2]*T2,CostProd[3]*(T3-T2

parallel.search

is a logical variable to desided if the multiple cores can be used for computing, by default is FALSE. The users have to notice that assign cores also cost time. So this procedure can only be efficient if the dim >5.

Value

• If $\texttt{detail}$ = FALSE, the output of this function is a vector of the optimum allocation i.e., which achieves the maximum $\Delta G$. Otherwise, the result for all the grid points, which have been calculated, will be exported as a table in the Rgui.

Details

for the new added to parameters "alpha.nursery" and "cost.nursery" since v2.0.47: After producing new DH lines, breeders do NOT go directly for a selection stage in the field, neither for genomic selection. Most of the times, they prefer to make a small field experiment (called "nursery") in which all DH lines are observed and discarded for other traits as disease resistance. That means, all DH lines with poor resistance will be discarded. At the end of the nursery stage only certain amount of DH lines (alpha) advance to the first selection stage (phenotypic or genomic). Specially in maize that makes sense, because in experience around 90 percent of the new DH lines are very weak in terms of per se performance what make them not suitable as new hybrid parents. Then, budget should not be used to make genotyping on or testcrossing with them. Only the alpha fraction should be used for entering the stage 1 of the multistageoptimum.search function. More details are available in the Crop Science and Computational Statistics papers.

References

A. Genz and F. Bretz. Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics, Vol. 195, Springer-Verlag, Heidelberg, 2009. A. Genz, F. Bretz, T. Miwa, X. Mi, F. Leisch, F. Scheipl and T. Hothorn. mvtnorm: Multivariate normal and t distributions. R package version 0.9-9995, 2013. E.L. Heffner, A.J. Lorenz, J.L. Jannink, and M.E. Sorrells. Plant breeding with genomic selection: gain per unit time and cost. Crop Sci. 50: 1681-1690, 2010. X. Mi, T. Miwa and T. Hothorn. Implement of Miwa's analytical algorithm of multi-normal distribution. R Journal, 1:37-39, 2009.

See Also

selectiongain()

Examples

CostProd =c(0.5,1,1)
CostTest = c(0.5,1,1)
Budget=1021
# Budget is very small here to save time in package checking
# for the example in Heffner's paper, please change it to Budget=10021

VCGCAandError=c(0.4,0.2,0.2,0.4,2)
VCSCA=c(0.2,0.1,0.1,0.2)
Nf=10


multistageoptimum.search (maseff=0.4, VGCAandE=VCGCAandError, 
VSCA=VCSCA, CostProd = c(0.5,1,1), CostTest = c(0.5,1,1), 
Nf = 10, Budget = Budget, N2grid = c(11, 1211, 30), 
N3grid = c(11, 211, 5), L2grid=c(1,1,1), L3grid=c(6,6,1),
T2grid=c(1,2,1), T3grid=c(3,5,1), R2=1, R3=1, alg = Miwa(), 
detail=TRUE, fig=TRUE)