Simulation Study of Genomic Selection in Rice: Establishment of Prediction Model and Identification of Minimal Experimental Inputs for the Training Population
Date Issued
2013
Date
2013
Author(s)
Lee, Shin-Ruei
Abstract
Genomic Selection is a new strategy of marker-assisted selection that selects superior individuals based on their genomic estimated breeding values. The genomic estimated breeding values are calculated solely using individual genotypes of substantial markers through a statistical prediction model built by data collecting from a training population. Prediction accuracy of genomic estimated breeding values can be affected by several factors, including statistical methods of the prediction model, number of markers genotyped, and size of the training population. In the current study, three statistical methods – RR-BLUP, BL, and RKHS – all of which have great computing ability were chosen to establish the prediction model. 192 different sets of genotypic and phenotypic data of rice recombinant inbred populations were simulated in silico as training populations among which effective QTL numbers, population size, marker numbers, and narrow-sense heritability were assigned at different levels. In order to determine the most effective inputs of a training population for given narrow-sense heritability of a characteristics, prediction accuracy of genomic estimated breeding values was calculated and compared for all simulated training populations using the three statistical methods. At each different level of narrow-sense heritability, sets of training populations showing that genomic selection is more effective than phenotypic selection were identified, and then the set with lowest marker numbers and smallest size of the training population were selected.
Subjects
基因組選種
預測模型
規模選定
Type
thesis
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