| Title: | Genetic Algorithm Assisted Genomic Best Liner Unbiased Prediction |
|---|---|
| Description: | Performs genetic algorithm (Scrucca, L (2013) <doi:10.18637/jss.v053.i04>) assisted genomic best liner unbiased prediction for genomic selection. It also provides a binning method in natural population for genomic selection under the principle of linkage disequilibrium for dimensional reduction. |
| Authors: | Yuxiang Zhang [aut, cre], Yang Xu [aut], Yanru Cui [ctb], Chenwu Xu [ctb], Shizhong Xu [ctb] |
| Maintainer: | Yuxiang Zhang <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0 |
| Built: | 2024-12-24 06:39:34 UTC |
| Source: | CRAN |
Binning the original genotypes into bins for dimensional reduction under the principle of linkage disequilibrium.
bin(genotype = genotype, binvar = 0.15)bin(genotype = genotype, binvar = 0.15)
genotype |
a matrix for genotypes in numeric format, coded as 1, 0 and -1, with individuals in rows and markers in cols. |
binvar |
a hyper-parameter between 0 and 1, the closer to 0, the fewer bins yields. Users can choose binvar based on the required number of bins, default is 0.15. |
A list with following information is returned: $bins_genotypes binned genotypes $bins_range start and stop of each bin
## load example data from GAGBLUP package data(genotype) ## binning genotypes bins <- bin(genotype,0.2)## load example data from GAGBLUP package data(genotype) ## binning genotypes bins <- bin(genotype,0.2)
Binned genotypes of 410 maize hybrids.
binsbins
A data frame with 410 rows and 810 variables.
Performs genomic selection with genetic algorithm assisted genomic best liner unbiased prediction
gagblup( genotype, phenotype, fit_fun = "HAT", maxiter = 2000, nfold = 10, nTimes = 1, seed = 123, n_core = 1 )gagblup( genotype, phenotype, fit_fun = "HAT", maxiter = 2000, nfold = 10, nTimes = 1, seed = 123, n_core = 1 )
genotype |
a matrix for genotypes in numeric format, with individuals in rows and markers in cols. |
phenotype |
a vector of phenotype, missing (NA) values are not allowed. |
fit_fun |
the fitness function. There are four options: fitness = "AIC"/"BIC"/"FIT"/"HAT", default is "HAT" |
maxiter |
max number of iterations for GAGBLUP, default is 2000 |
nfold |
the number of folds. Default is 10. |
nTimes |
the number of independent replicates for the cross-validation. Default is 1. |
seed |
the random number. Default is 123. |
n_core |
the number of CPU to be used, default is 1. |
A list with following information is returned: $R2 the squared pearson correlation coefficient between the true value and the predicted value, $predicted_value the predicted value and the true value of the phenotype, $marker_selection a vector of the selected markers, with 1 indicates selected, 0 indicates not selected.
## load example data from GAGBLUP package data(phenotype) data(bins) phenotype <- phenotype[1:200,3] result <- gagblup(bins[1:200,],phenotype,fit_fun='HAT',maxiter=1,nfold=2,nTimes=1,seed=123,n_core=1)## load example data from GAGBLUP package data(phenotype) data(bins) phenotype <- phenotype[1:200,3] result <- gagblup(bins[1:200,],phenotype,fit_fun='HAT',maxiter=1,nfold=2,nTimes=1,seed=123,n_core=1)
Genotypic data of 410 maize hybrids with 4906 SNPs.
genotypegenotype
A data frame with 410 rows and 4906 variables.
This dataset contains phenotypic data of 410 hybrids for grain yield in maize.
phenotypephenotype
A data frame with 410 rows and 3 variables:
MThe names of male parents.
FThe names of female parents.
GYThe grain yield of hybrids.