结合辅助性状的玉米全基因组选择预测力评估

多性状联合全基因组选择能够有效利用性状间的遗传相关和环境相关,有望提高表型预测的准确性。本研究提出了结合辅助性状的全基因组选择策略,以来源广泛的342份玉米自交系为试验材料,对其进行基因分型测序(GBS)并分析其农艺性状,对每个目标性状均基于辅助性状及其组合进行预测,利用五倍交叉验证法评价其预测力。结果表明,利用与目标性状相关性较高的辅助性状可较大程度地提升预测力,尤其是对于低遗传力性状;随着辅助性状个数的增加,预测力也随之增加。进一步比较了5种统计模型结合辅助性状的全基因组选择的表型预测力,总体而言,再生核希尔伯特空间(RKHS)模型和贝叶斯B(BayesB)模型的预测效果较优,而极端梯度提升(XGBOOST)模型的预测效果较差。本研究结合辅助性状有效提高了玉米全基因组选择的预测准确性,为玉米的全基因组选择育种提供新的思路和参考。

Metabolic responses to combined water deficit and salt stress in maize primary roots

Soil water deficit and salt stress are major limiting factors of plant growth and agricultural productivity. The primary root is the first organ to perceive the stress signals for drought and salt stress. In this study, maize plant subjected to drought, salt and combined stresses displayed a significantly reduced primary root length relative to the control plants. GC-MS was used to determine changes in the metabolites of the primary root of maize in response to salt, drought and combined stresses. A total of 86 metabolites were measured, including 29 amino acids and amines, 21 organic acids, four fatty acids, six phosphoric acids, 10 sugars, 10 polyols, and six others. Among these, 53 metabolites with a significant change under different stresses were identified in the primary root, and the content of most metabolites showed down-accumulation. A total of four and 18 metabolites showed significant up-and down-accumulation to all three treatments, respectively. The levels of several compatible solutes, including sugars and polyols, were increased to help maintain the osmotic balance. The levels of metabolites involved in the TCA cycle, including citric acid, ketoglutaric acid, fumaric acid, and malic acid, were reduced in the primary root. The contents of metabolites in the shikimate pathway, such as quinic acid and shikimic acid, were significantly decreased. This study reveals the complex metabolic responses of the primary root to combined drought and salt stresses and extends our understanding of the mechanisms involved in root responses to abiotic tolerance in maize.

Fine Mapping and Cloning of Leafy Head Mutant Gene pla1-5 in Rice

We identified a leafy head mutant plal-5 （plastochron 1-5） from the progeny of japonica rice cultivar Taipei 309 treated with 60Co-γ ray irradiation. The plal-5 mutant has a dwarf phenotype and small leaves. Compared with its wild type, plal-5 has more leaves and fewer tillers, and it fails to produce normal panicles at the maturity stage. Genetic analysis showed that the plal-5 phenotype is controlled by a single recessive nuclear gene. Using the map-based cloning strategy, we narrowed down the location of the target gene to a 58-kb region between simple sequence repeat markers CHR1027 and CHR1030 on the long arm of chromosome 10. The target gene cosegregated with molecular markers CHR1028 and CHR1029. There were five predicted genes in the mapped region. The results from sequencing analysis revealed that there was one base deletion in the first exon of LOC_Os10g26340 encoding cytochrome P450 CYP78A11 in the plal-5 mutant, which might result in a downstream frame shift and premature termination. These results suggest that the P450 CYP78A11 gene is the candidate gene of PLA1-5.

A Rank-Sum Testing Method for Multi-Trait Comprehensive Ranking and Its Application

The rank-sum test is a nonparametric method used in variety evaluation. However, the hypothesis testing of the method hasn＇t been established for multi-trait comprehensive ranking. In this paper, under null hypothesis H0： the variety＇s ranking on each trait is random, the theoretical distribution of sum of ranks （SR） was firstly derived and further used to obtain the critical values for multi-trait comprehensive evaluation in rank-sum testing. A new C＋＋ class and its basic arithmetic were defined to deal with the miscount caused by the precision limitation of built-in data type in common statistical software under large number of varieties and traits. Finally, an application of the theoretical results was demonstrated using five starch viscosity traits of 12 glutinous maize varieties. The proposed method is so simple and convenient that it can be easily used to rank different varieties by multiple traits.

Comparison of Supervised Clustering Methods for the Analysis of DNA Microarray Expression Data

Several typical supervised clustering methods such as Gaussian mixture model-based supervised clustering （GMM）, k- nearest-neighbor （KNN）, binary support vector machines （SVMs） and multiclass support vector machines （MC-SVMs） were employed to classify the computer simulation data and two real microarray expression datasets. False positive, false negative, true positive, true negative, clustering accuracy and Matthews＇ correlation coefficient （MCC） were compared among these methods. The results are as follows： （1） In classifying thousands of gene expression data, the performances of two GMM methods have the maximal clustering accuracy and the least overall FP＋FN error numbers on the basis of the assumption that the whole set of microarray data are a finite mixture of multivariate Gaussian distributions. Furthermore, when the number of training sample is very small, the clustering accuracy of GMM-Ⅱ method has superiority over GMM- Ⅰ method. （2） In general, the superior classification performance of the MC-SVMs are more robust and more practical, which are less sensitive to the curse of dimensionality, and not only next to GMM method in clustering accuracy to thousands of gene expression data, but also more robust to a small number of high-dimensional gene expression samples than other techniques. （3） Of the MC-SVMs, OVO and DAGSVM perform better on the large sample sizes, whereas five MC-SVMs methods have very similar performance on moderate sample sizes. In other cases, OVR, WW and CS yield better results when sample sizes are small. So, it is recommended that at least two candidate methods, choosing on the basis of the real data features and experimental conditions, should be performed and compared to obtain better clustering result.

基于AMMI模型和GGE双标图对2018年江苏省水稻杂交中粳品种区域试验结果的评价分析

为合理评价水稻区试中参试品种的高产稳产性、区域适应性以及各试点的区分力和代表性,采用AMMI模型和GGE双标图对2018年江苏省水稻杂交中粳区域试验的14个品种在11个试验点的产量数据进行综合分析。结果表明,基因型效应、环境效应以及基因型与环境的互作效应均对参试品种产量产生极显著影响。在品种的高产稳产方面,高产性较好的品种有嘉优中科1号、嘉优中科1602和春优T36;稳定性较好的品种为隆嘉优77和甬优6718;在品种的适应性方面,嘉优中科1号适宜种植的试点较多,具有一定的广适性,是水稻生产上具备推广潜力的理想品种;在试验环境的功能形态方面,江苏东海县作物栽培指导站和江苏省农业科学院粮食作物研究所2个试点不仅具有较强的区分力和代表性,也是综合性最好的2个试点。综合运用AMMI模型和GGE双标图可以更加全面有效地评估品种和试点,为新品种的精准推广提供理论依据,同时为目标品种的选育提供参考。