TSOFIBG

This package provides users two functions.

GenerateTS( ) determine a training set for genomic selection. The aim for this training set is to train the GBLUP model to find the best genotype from large candidate set.

EstimationM( ) return the first k individuals at different training set size. The result is the average of the ranking generated from different methods.

Augmented EI presented by Tsai et al.(2021) is used as the criteria and NDCG (Normalized dicounted cumulated gain, Blondel et al, 2015) is used as the index of evaluating model prediction accuracy in this package.

For more details of methods in this package, please check the article.

Users will obtain three results from GenerateTS( ): 1. the selection index of genotypes which indicates to the priority order of genotypes being selected as a training set. The ranking is based on the augmented EI value of each genotype. 2. the evaluation result of the optimal training set 3. the relative efficiency of the optimal training set at different sizes to the whole dataset based on collected phenotype data

Users will obtain one result from EstimateM( ): 1. the first k individuals of the individuals predicted by four methods. the length of list depends on user’s setting of the training set size and k

Installation

The development version of TSOFIBG could be download from GitHub with:

# install.packages("devtools")
# library(devtools)
install_github("huining0312/TSOFIBG",dependencies=TRUE,force=TRUE)

Example

For GenerateTS( ) function, two datasets are provided for users. The tropical rice dataset is provided by Spindel et al. (2015) and the 44K rice dataset is published by Zhao et al. (2011). The kinship matrix generated by arranged SNP marker matrix are provided in this package; while the raw data are available at Data Dryad digital repository and Rice Diversity website respectively.

For EstimationM( ) function, standarized snp marker matrix and phenotyped data are provided.

#library(TSOFIBG)
# dataset without strong population structure
# data("geno_trop")
# kinship = geno_trop
# n = nrow(kinship)

# --- example code for GenerateTS --- #
#result = GenerateTS(kinship,nOpsim=1000,nEvalsim=2000,CV_gpnumber=5,h=0.5,n,sg=25,mu=100,desireH = c(0.5),desireDelta=c(1/5,1/3,2/3))

# dataset with strong population structure
#data("geno_rice44K")
#data("subpop_tag")
#result = GenerateTS(kinship,nOpsim=1000,nEvalsim=2000,CV_gpnumber=5,h=0.5,n,sg=25,mu=100,subpopTag=subpop_tag,desireH = c(0.5),desireDelta=c(1/5,1/3,2/3))

# --- example code for EstimateM --- #
# opt_trainSet = result[[1]] #opt_trainSet can be obtained from GenerateTS
# data("trop_snp")
# data("pheno_tro")
#r = EstimationM(snp_matrix = snp_data_tro,opt_trainSet,pheno_tro,n=nrow(opt_trainSet),subpopTag=F)

Authors

• Hui-Ning Tu
  • Author, maintainer
  • Email: huining0312@gmail.com
• Chen-Tuo Liao
  • Author, thesis advisor
  • Email: ctliao@ntu.edu.tw
  • Department of Agronomy, National Taiwan University, Taipei, Taiwan