區域試驗資料中參試地點生態群劃分研究

Study on Grouping Test Locations into Mege-environments

陳丘原(Qiu-Yuan Chen)

指導教授 ： 劉力瑜

國立臺灣大學/生物資源暨農學院/農藝學研究所/碩士(2016年)

https://doi.org/10.6342/NTU201601477

摘要

GGE 雙標圖分析，可同時將區域試驗資料中基因型、環境以及基因型與環 境的交互作用資訊標示在二維平面圖上，提供育種學家判斷品種優劣以及定義試驗區域之生態群的依據。生態群的定義是數個栽培地點的集合，同生態群中的栽培地點其品系之外表型的反應相似。由於基因型與環境之交互作用的存在，少有品種能在所有環境下都表現良好，因此在進行品種評估前，應先了解目標區域的生態群組成。決定生態群亦可從各生態群中挑選少數具有代表性的栽培地點進行未來的區域試驗，以節省試驗所需花費的成本與時間。
本研究透過生態群的概念模擬一筆具有 100 個地點與 15 個基因型的區域試驗資料，討論是否將試驗區域的生態群分類後，挑選較少的試驗地點數，仍能維持試驗區域生態群之特性，期望能同時維持品種與環境之交互作用的特性與降低區域試驗所需之花費。模擬結果發現，在了解各個地點之生態群劃分後，隨機挑選較少的地點數雖然會降低地點生態群正確劃分的機率，但從 100 個環境中只取 5 個環境重複 1000 次，仍能維持 89.7% 的正確率。我們也對臺灣 77~79 年之水稻區域試驗資料進行生態群分析，顯示臺灣地區的生態群劃分年度的影響高於地理區域的影響，因此臺灣區域試驗地點的生態群劃分仍需要更進一步的探討。

關鍵字

區域試驗 ； GGE雙軸圖 ； 奇異值分解 ； 基因與環境之交互作用 ； 生態群

並列摘要

GGE biplot illustrates the effects of genotype, environment and their interactions estimated from a multiple environment trial (MET) into a two-dimensional scatter plot. It allows breeders to justify the genotype, and to identify the mega-environments in which the tested genotype perform similarly. Due to the genotype and environment interaction, it is difficult to identify a single line which can perform the best in all test locations. Therefore, after dividing the target region into homogeneous mega-environments, one can to pick the most appropriate line in each mega-environment for future promotion. After determining the mega-environments, it can be expected that fewer sites well representing the mega-environment can be selected for future MET to save the money and manpower expenses.
This study aims to find out the minimal number of test locations for consolidated mega-environment analysis. We simulated a MET data with 100 locations and 15 genotypes, as well as a predefined mega-environment structure. Subsample of different numbers of locations were randomly drawn from the simulated data to perform mega-environment analysis. Comparing with the outcomes of whole simulated data, we found the results of subsamples remained the correct rate equal to or higher than 88.24%. In real data, we performed mega-environment analysis on three sets of four-seasonal rice multiple environment trials in Taiwan. However, we found that there is no obvious mega-environment pattern in Taiwan from the 77-79 rice regional trial datasets.

並列關鍵字

multiple environment trial ； GGE biplot ； singular value decomposition ； genotype by environment interaction ； mega-environments

參考文獻

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