透過基因體預測原生地氣候來探索綠豆的適應能力

Abstract

綠豆是一種重要的豆科作物，提供優質蛋白質和其他必需營養素，廣泛種植於亞洲。然而綠豆育種面臨了許多環境壓力，因應多變的氣候環境，若能提供不同栽培種綠豆適合生長的環境資訊，將能幫助育種的選擇。地區適應性，即植物基因型與原生環境的匹配，是可以提供作物抗逆境一重要資訊。基於此，本研究旨在利用基因體預測模型，預測綠豆品系的原生氣候條件，並探討適應性如何影響其性狀表現。我們的實驗材料為來自亞洲的栽培種綠豆，這1,108 個綠豆在先前的研究中發現來自不同地區的群體，他們長期適應於當地環境，群體間的遺傳差異很大。利用部分已知氣候數據的品系來預測其他未知當地氣候的品系，我們應用多種基因體預測模型（如隨機森林、rrBLUP等）對28項氣候變數進行原生環境預測。結果顯示，隨機森林在各類模型中表現出最高的準確度和最低的誤差，且隨機森林模型在預測多數環境變數都具有最高準確性。此外，通過多個共同花園試驗，我們發現品系的果莢數量在氣候相似性高的花園中表現更佳，反映了地區適應性的影響。GWAS 分析進一步揭示了與泛素化相關的基因（如 E3泛素連接酶）可能在調控綠豆表型可塑性中發揮重要作用。本研究結果證實，基因體預測模型可用於預測綠豆的原生氣候條件，並闡明了地區適應性對作物性狀的影響。這些發現不僅為綠豆的氣候適應性育種提供了依據，還為應對氣候變遷下的農業育種提供了新思路。

Mungbean is an important leguminous crop that provides high-quality protein and other essential nutrients, widely grown in Asia. However, mungbean breeding faces numerous environmental challenges, and providing information on suitable environments for different mungbean cultivars to grow in can greatly help breeding decisions in response to changing climatic conditions. Local adaptation, which refers to the match between a plant's genotype and its native environment. It is crucial information for crop resistance. Based on this, this study aimed to use genomic prediction models to predict the native climate conditions of mungbean accessions and explore how adaptability affects trait performance. Our experimental material consisted of 1,108 cultivated mungbean accessions from Asia. Previous studies have found that these accessions represent populations from different regions that have long been locally adapted to their native environments, with significant genetic differences between populations. We used accessions with known climate data to predict the native climate for other accessions with unknown climates, and applied various genomic prediction models (such as random forest, rrBLUP, etc.) to predict 28 climate variables. The results showed that the random forest model performed better than other models in terms of accuracy and exhibited the lowest error in predicting most environmental variables. In addition, through multiple common garden experiments, we found that accession’s pod number exhibited better performance in gardens with higher climate similarity, reflecting the effect of local adaptation. GWAS analysis further revealed that genes related to ubiquitination, such as E3 ubiquitin ligase, may play important roles in regulating phenotypic plasticity in mungbean. Our findings confirm that genomic prediction models can be used to predict native climate conditions for mungbean and elucidate the effects of local adaptation on crop traits. These findings not only provide a basis for climate-adaptive breeding of mungbean but also offer new perspectives for agricultural breeding in the face of climate change.