全基因組選拔訓練族群之決定

歐任翔

doi:10.6342/NTU201802290

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全基因組選拔訓練族群之決定

Training set determination for genomic selection

歐任翔 , Masters　　Advisor：廖振鐸　　

英文 DOI： 10.6342/NTU201802290

基因組育種值；基因組預測；植物育種；預測準確性；單一核?酸多型性分子標記； GEBV ； genomic prediction ； plant breeding ； prediction accuracy ； SNP marker

Abstract │ Reference (15) │ Altmetrics

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For a given candidate set of individuals which have been genotyped but not phenotyped, we develop a highly efficient algorithm to determine an optimal subset from the candidate set. The chosen subset serves as a training set to be phenotyped, and then a genomic selection (GS) model is built based on its resulting phenotype and genotype data. In this study, we typically consider
the whole-genome regression model, and adopt ridge regression estimation for marker effects in the GS model. The resulting GS model is then employed to predict genomic estimated breeding values (GEBVs) for a given test set of individuals which have been genotyped only. We propose a new optimality criterion to determine the required training set, which is directly derived from
Pearson’s correlation between the GEBVs and phenotypic values of the test set. Pearson’s correlation is the standard measure for prediction accuracy of a GS model. We implement our training set determination algorithm in R language, and illustrate it with a rice genome data set. It is shown that the training set generated from our algorithm can usually achieve a significantly
improved prediction accuracy in comparison with a randomly selected training set.

Reference ( 15 ) 〈TOP〉

Akdemir, D., Sanchez, J. I., and Jannink, J. L. (2015). Optimization of genomic selection training populations with a genetic algorithm. Genetics Selection Evolution, 47(1):38.
Endelman, J. B. (2011). Ridge regression and other kernels for genomic selection with r package rrblup. The Plant Genome, 4(3):250–255.
Heffner, E. L., Sorrells, M. E., and Jannink, J. L. (2009). Genomic selection for crop improvement. Crop Science, 49(1):1–12.
Isidro, J., Jannink, J. L., Akdemir, D., Poland, J., Heslot, N., and Sorrells, M. E. (2015). Training set optimization under population structure in genomic selection. Theoretical and Applied Genetics, 128(1):145–158.
Laloë, D. (1993). Precision and information in linear models of genetic evaluation. Genetics Selection Evolution, 25(6):557.

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