[目的]明确贵州水稻品种资源的现状及DUS测试[品种特异性(可区别性,Distinctness)、一致性(Uniformity)和稳定性(Stability)测试]性状在品种评价中的作用,为培育具有优良综合特性的水稻品种时提供表型选择的指导,为高效水稻DUS测试提供理论支持.[方法]采用遗传多样性分析、相关性分析、聚类分析及主成分分析等方法,研究贵州250份水稻品种的遗传多样性、综合表现及测试性状对品种的测试性能.[结果]表型分析表明,部分目测性状分布集中,倒二叶叶舌形状均为二裂,96.8％的水稻品种具有直立、半直立的生长习性,株型紧凑.剑叶初期,有99.2％的品种表现为直立、半直立状态,而96.0％的品种其基部茎节处于包裹状态;成熟期,91.2％的穗部明显下弯,且97.2％的谷粒外颖呈浅黄色,仅有1份品种的谷粒外颖有修饰色.对14个测量性状进行表型变异及多样性分析,除结实率外,其余性状具有丰富的遗传变异,变异系数为6.51％～20.82％,其中,每穗粒数的变异系数最大,结实率的变异系数最小.相关性分析揭示,各性状间均呈现不同程度的相关性,其中8对性状间达到显著水平,另外43对性状间呈极显著水平.通过聚类分析,测试品种被分为4大主要类群,第Ⅰ类群包含68份样品,其中产量相关性状均值最高;第Ⅱ类群有25份样品,每穗粒数明显高于其他类群;第Ⅲ类群体包括67份样品,具有最矮的茎秆;第Ⅳ类群体占总品种数的36％,在穗长、谷粒(糙米)长度方面的均值最高,综合表现最为出色.隶属函数法的分析结果表明,在所测量的性状中,剑叶长度、穗长、谷粒性状等10个性状对测试品种的综合特性有显著影响.在综合评价(D值)最高的10份测试品种中,有8份恢复系、2份杂交种.通过逐步回归分析,建立品种评价的最优回归方程:Y=-0.707+0.032X13+0.003X7+0.008X10+0.003X5+0.04X1+0.02X3+0.025X11-0.003X2+0.001X4-0.1156 x 10-3X8.多元线性回归分析显示,每穗粒数分布分散,拟合情况不佳,其余13个性状拟合情况则较好.[结论]在水稻测试指南中,除倒二叶的叶舌形状外,目测性状能够有效地区分不同品种;而对于测量性状,每穗粒数表现出最大的变异系数和遗传多样性指数,是判断品种特异性的有效指标.另外,剑叶长度、穗长、谷粒特性等10个性状是影响测试品种综合评价值的关键因素.
Comprehensive analysis of rice varieties in Guizhou based on DUS testing traits
[Objective]The current status of rice varieties in Guizhou and the role of DUS(distinctness,uniformity and stability)test traits in variety evaluation were clarified,so as to provide guidance for breeders in phenotypic selection when breeding rice varieties with excellent in-tegrated traits and to provide theoretical support for testing institutions to efficiently conduct rice DUS tests.[Method]Genetic diversity,corre-lation,clustering and principal component analysis were used to investigate the genetic diversity,integrated performance and test performance of the traits tested in 250 rice varieties in Guizhou.[Result]Phenotypic analysis revealed that the partial visual traits were concentrated.The ligule shape of the reciprocal second leaves was bilobed in all cases.Additionally,96.8％of the rice varieties had an erect or semi-erect growth habit with a compact plant shape.At the initial stage of the flag leaf,99.2％of the varieties showed an erect or semi-erect state,while 96.0％of the varieties had their basal stem nodes wrapped.At maturity stage,91.2％of the spikes would be significantly recurved,and 97.2％of the grain's outer glumes showed light yellow in colour.Only one copy of the variety had a modified colour on the outer glume.The analysis of phenotypic variation and diversity to the 14 testing traits revealed that,apart from fruiting percentage,the other traits exhibited sig-nificant genetic variation.The coefficients of variation ranged from 6.51％to 20.82％,with the highest coefficient of variation observed for the number of grains per spike and the lowest for the fruiting percentage.Correlation analysis showed that all traits exhibited different levels of correlation.Eight pairs of traits reached significant levels,while another 43 pairs of traits showed highly significant levels.The tested varie-ties were classified into four main groups through cluster analysis.Group Ⅰ had the highest mean values for yield-related traits,containing 68 samples.Group Ⅱ had 25 samples with a significantly higher number of grains per spike than the other groups.Group Ⅲ contained 67 samples with the shortest stalks.Group Ⅳ,which accounted for 36％of the total number of varieties,had the highest mean values for spike length,grain(brown rice)length and the best overall performance.The analysis using the affiliation function method revealed that 10 traits,including length of flag leaf,spike length and grain length,significantly affected the comprehensive characteristics of the tested varieties.The top 10 va-rieties with the highest composite evaluation(D value)included eight restoration lines and two hybrids.The optimal regression equation for variety evaluation was established through stepwise regression analysis.The equation was Y=-0.707+0.032X13+0.003X7+0.008X10+0.003X5+0.04X1+0.02X3+0.025X11-0.003X2+0.001X4-0.1156 x 10-3X8.The results of the multiple linear regression analyses indicated that the distribution of grains per spike was scattered and did not fit well,while the remaining 13 traits fitted better.[Conclusion]The rice testing guidelines indicate that partial visual traits are effective in distinguishing different varieties,except for the ligule shape of the reciprocal second leaf.Among the measured traits,the number of grains per spike shows the largest coefficient of variation and genetic diversi-ty index,making it an effective index for judging variety specificity.Furthermore,the overall evaluation value of the tested varieties is primari-ly influenced by 10 traits,including length of flag leaf,spike length and grain characteristics.
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