TSDFGS-package {TSDFGS} | R Documentation |
Determining training set for genomic selection using a genetic algorithm (Holland J.H. (1975) <DOI:10.1145/1216504.1216510>) or simple exchange algorithm (change an individual every iteration). Three different criteria are used in both algorithms, which are r-score (Ou J.H., Liao C.T. (2018) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <DOI:10.1186/s12711-015-0116-6>) and CD-score (Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>). Phenotypic data for candidate set is not necessary for all these methods. By using it, one may readily determine a training set that can be expected to provide a better training set comparing to random sampling.
The package is used to determine the optimal training set in a highly structured, mild structured and diverse population. The function "optTrain" use a genetic algorithm or simple exchange algorithm to evaluate an optimal solution using one of the criteria (r-score (Ou J.H., Liao C.T. (2018) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <DOI:10.1186/s12711-015-0116-6>), CD-score(Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>)).
Jen-Hsiang Ou and Chen-Tuo Liao
Maintainer: Jen-Hsiang Ou<oumark.me@outlook.com>
Akdemir D., Sanchez JI., Jannink JL. (2015), Optimization of genomic selection training populations with a genetic algorithm. Genetic Selection Evolution 47:38.\
Laloe D. (1993), Precision and information in linear models of genetic evolution. Genetics Selection Evolution 25:557.\
Ou J.H., Liao C.T. (2018), Training set determination for genomic selection. National Taiwan University Master Thesis.