水稻基因組選種之模擬研究─訓練族群預測模型之建立與最低投入試驗規模之確立

Simulation Study of Genomic Selection in Rice: Establishment of Prediction Model and Identification of Minimal Experimental Inputs for a Training Population

李欣叡(Shin-Ruei Lee) ； 陳凱儀(Kai-Yi Chen) ； 蔡政安(Chen-An Tsai)

《作物、環境與生物資訊》 10卷2期 (2013/06) Pp. 143-164

https://doi.org/10.30061/CEB.201306_10(2).0006

摘要

基因組選種（genomic selection）是新興的分子標記輔助選種策略。其藉由訓練族群之基因型與外表型觀測值以建構統計模型，接著在訓練族群衍生之後代遺傳重組族群中使用此統計模型，由個體的分子標記基因型估算其目標性狀的育種價，然後使用個體育種價估計值作為選拔依據。已知預測模型的統計方法、分子標記數量、訓練族群大小、以及目標性狀的遺傳性質，皆會影響個體育種價估計值的預測準確度。本研究給予有效基因座數目、訓練族群大小、分子標記數量、及性狀狹義遺傳率等四種參數的不同設定，模擬192 種不同水稻重組自交系訓練族群的基因型與外表型的資料。接著， 使用RR-BLUP（ridge regression best linear unbiased prediction）、BL（Bayesian LASSO）與RKHS（reproducing kernel Hilbert space）等三種方法所建構的預測模型，計算與比較這192種組合模擬資料之三種統計模型的預測準確度。最後，在基因組選種預測準確度大於0.5且優於外表型選種的前提下，本研究依據不同遺傳率的設定，決定最有效的訓練族群試驗規模（即訓練族群大小與分子標記數量）。

關鍵字

基因組選種 ； 預測模型 ； 規模選定

並列摘要

Genomic selection is a new strategy of marker-assisted selection. The statistical model built by genotypic and phenotypic data of a training population is used to estimate breeding values of targeted traits from marker genotypes of individual plants in a genetic recombinant population derived from the training population, and then genomic selection is conducted based on individual genomic estimated breeding values. The prediction accuracy of genomic estimated breeding values can be affected by several factors, including statistical methods of the prediction model, numbers of genotyped markers, size of training population, and genetic nature of targeted traits. The present study simulated 192 sets of genotypic and phenotypic data similar to rice recombinant inbred populations as in silico training populations, among which effective QTL numbers, population size, marker number, and narrow-sense heritability were assigned at different levels. The prediction accuracy of each 192 sets of data was then calculated and compared, using prediction models built by the RR-BLUP (ridge regression best linear unbiased prediction), BL (Bayesian LASSO), and RKHS (reproducing kernel Hilbert space) methods respectively. Finally, the most effective inputs (population size and marker number) for model-training trials were determined at different levels of heritability, given that the expected prediction accuracy of genomic selection is greater than 0.5 and its prediction accuracy is greater than that of phenotypic selection.

並列關鍵字

Genomic selection ； Statistical model of prediction ； Decision of experimental inputs