食用豆在中国粮食组成、人类健康、土壤改良中占有重要地位,尤其是在贫困地区作为蛋白质的主要来源。随着食用豆基因组相继被破译,推动了食用豆分子遗传基础和分子育种研究。国家食用豆产业技术体系成立以来,育成了一批高产、优质、抗病虫、耐逆、适宜机械化收获的食用豆新品种,集成了一批适合不同产区的栽培技术,病虫害绿色防控技术的研发与应用取得了良好的防治效果,生产机械与技术研究初显成效,产后加工技术提升与产品创新研究促进产业提质增效。随着食用豆新品种新技术的示范与推广,食用豆总产和单产水平显著提高,特别是在过去十年内蚕豆、豌豆由干籽粒生产转变为鲜食菜用生产,食用豆种植面积提高了21.1%,单产提高了3.9%,总产提高了36.8%。随着食用豆产业规模不断扩大,越来越多的食用豆种被列为国家农产品地理标志产品,正在形成一批农业企业品牌。在食用豆产业的带动下,品种权保护与转让数量逐渐增多,食用豆种业开始起步发展。随着人们健康意识的提高,国内外市场需求快速上升,国家乡村振兴战略的实施和农业供给侧结构性改革的推进,大力培育壮大贫困地区特色优势产业,给食用豆生产和种业带来了新的机遇;但是中国食用豆生产和种业也面临生产成本较高、生产效率低、科研平台建设有待加强、缺乏突破性大品种、品种权保护力度不够等诸多挑战。本文在总结中国食用豆产业和种业的现状与问题的基础上,讨论了其未来的发展方向。

Adzuki beans (ABs; Vigna angularis) were reported to show potential for prevention of cholesterol absorption and lowering of the blood cholesterol level. However, the main active compounds and some cellular effects remain unknown. In this study, we evaluated the potential cholesterol-lowering effects of (+)-catechin 7-O-β-D-glucopyranoside (C7G) and (+)-epicatechin 7-O-β-D-glucopyranoside (E7G), identified as abundant polyphenols in ABs.To investigate the cholesterol-lowering activity in vitro, cholesterol micelles, bile acids, and Caco-2 cells as an intestinal model were used in the study. C7G and E7G each inhibited micellar solubility in a dose-dependent manner, and their inhibitory activity was as strong as that of (+)-catechin (IC values: C7G, 0.23 ± 0.03 mg/ml; E7G, 0.22 ± 0.02 mg/ml; (+)-catechin, 0.26 ± 0.11 mg/ml). The AB polyphenols showed binding activity toward bile acids and changed them into an insoluble form. When Caco-2 cells were treated with C7G or E7G, the amount of incorporated cholesterol was significantly decreased compared with vehicle-treated control cells, and no cytotoxicity was observed under the experimental conditions used. Meanwhile, quantitative real-time PCR revealed that the mRNA level of the cholesterol transporter NPC1L1 remained unchanged in the treated cells.Taken together, the present findings suggest that C7G and E7G are the main active compounds in ABs, and have the ability to inhibit micellar solubility, bind to bile acids, and suppress cholesterol absorption. The present study supports the health benefits of ABs as a medicinal food and the application of AB polyphenols as medicinal supplements to suppress cholesterol elevation.© 2023. The Author(s), under exclusive licence to Springer Nature B.V.

Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios.The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25-0.88 for plant height, and - 0.29-0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset.Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.© 2023. The Author(s).

Genomic prediction relates a set of markers to variability in observed phenotypes of cultivars and allows for the prediction of phenotypes or breeding values of genotypes on unobserved individuals. Most genomic prediction approaches predict breeding values based solely on additive effects. However, the economic value of wheat lines is not only influenced by their additive component but also encompasses a non-additive part (e.g., additive × additive epistasis interaction). In this study, genomic prediction models were implemented in three target populations of environments (TPE) in South Asia. Four models that incorporate genotype × environment interaction (G × E) and genotype × genotype (GG) were tested: Factor Analytic (FA), FA with genomic relationship matrix (FA + G), FA with epistatic relationship matrix (FA + GG), and FA with both genomic and epistatic relationship matrices (FA + G + GG). Results show that the FA + G and FA + G + GG models displayed the best and a similar performance across all tests, leading us to infer that the FA + G model effectively captures certain epistatic effects. The wheat lines tested in sites in different TPE were predicted with different precisions depending on the cross-validation employed. In general, the best prediction accuracy was obtained when some lines were observed in some sites of particular TPEs and the worse genomic prediction was observed when wheat lines were never observed in any site of one TPE.

Adzuki beans are an important food legume crop in East Asia. A large number of adzuki bean accessions are maintained in the Chinese national seed genebank. A collection of 59 elite cultivars, 389 landraces, and 27 wild adzuki beans were selected and phenotyped extensively for yield and yield-related traits at two different locations (Nanning and Nanyang, China). Ten agronomic and yield-related traits were scored, and the data were subjected to analysis of variance (ANOVA), principal component analysis (PCA), correlation, and cluster analysis. Significant variation was observed for genotypes, locations, and genotype x environment interaction for most traits. Also, there were significant differences in the phenotypes among accessions of different germplasm types. The broad-sense heritability of traits studied ranged from 4.4% to 77.8%. The number of seeds per pod (77.8%), 100-seed weight (68.0%), and number of plant branches (63.9%) had a high heritability. A total of 10 traits were transformed into 3 comprehensive factors by principal component analysis, and the first three principal component factors contributed 72.31% of the total variability. Cluster analysis categorized the 475 adzuki bean accessions into five distinct groups. The results described in this study will be useful for adzuki bean breeders for the development of varieties with high end-use quality.

通过田间观测和室内分析，对来自全国各生态区的262份小豆优异种质资源在北京试验田连续两年进行了农艺性状鉴定及综合评价。结果表明：262份小豆微核心种质资源在考察的16个形态学性状中均具有丰富的遗传变异类型；不同年份间生态环境尤其是光照条件对小豆农艺性状影响较大；主成分分析确定三类影响因子，表明小豆资源的选育要集中在生长势良好（生育日数较短、株高较矮），单株荚数和单荚粒数多的地方品种；聚类分析结果显示在相似系数为0.40时可将参试材料分为5大类群，各类群间性状差异明显；群体的性状表现跟地理来源之间没有直接的关系。

为明确我国小豆主栽品种的产量潜力、适应性与稳定性, 并指导区域化应用与育种, 本研究于2022年和2023年在我国主要产区遴选8个代表性生态试验点, 对15个小豆基因型进行了多点试验。运用联合方差分析、相关性分析、加性主效应与乘性互作(AMMI)模型及基因型与环境互作效应(GGE)双标图等方法, 对产量及相关农艺性状进行了系统评价。联合方差分析结果表明, 环境是影响除百粒重外各性状变异的主要驱动因素, 百粒重主要受基因型控制。基因型与环境互作效应对产量和主茎分枝数影响极显著。相关性分析显示, 单株荚数是决定产量的关键因子, 二者呈极显著正相关。AMMI与GGE分析揭示了显著的基因型×环境互作效应, 明确了基因型的特定适应区域与稳定性差异。综合评价显示, 赤红3号(G5)表现出高产潜力, 最接近理想基因型; 保红201429-8(G8)等基因型兼具高产与稳产特性；环境评价表明, 榆林(E5)试验点因其优良的代表性和区分能力, 被鉴定为理想测试环境。本研究表明, 环境效应对小豆产量性状具有显著影响, 基因型与环境互作是品种选育和推广中充分考量的关键因素，且单株荚数可作为小豆高产育种的核心选择指标。本研究为我国小豆育种策略的优化和品种的精准推广提供了关键数据支持与科学指导。