Can genetic variability for nitrogen metabolism in the developing ear of maize be exploited to improve yield?

Quantitative trait loci (QTLs) for the main steps of nitrogen (N) metabolism in the developing ear of maize (Zea mays L.) and their co-localization with QTLs for kernel yield and putative candidate genes were searched in order to identify chromosomal regions putatively involved in the determination of yield. During the grain-filling period, the changes in physiological traits were monitored in the cob and in the developing kernels, representative of carbon and N metabolism in the developing ear. The correlations between these physiological traits and traits related to yield were examined and localized with the corresponding QTLs on a genetic map. Glycine and serine metabolism in developing kernels and the cognate genes appeared to be of major importance for kernel production. The importance of kernel glutamine synthesis in the determination of yield was also confirmed. The genetic and physiological bases of N metabolism in the developing ear can be studied in an integrated manner by means of a quantitative genetic approach using molecular markers and genomics, and combining agronomic, physiological and correlation studies. Such an approach leads to the identification of possible new regulatory metabolic and developmental networks specific to the ear that may be of major importance for maize productivity.     

Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize

To enhance our understanding of the genetic basis of nitrogen use efficiency in maize (Zea mays), we have developed a quantitative genetic approach by associating metabolic functions and agronomic traits to DNA markers. In this study, leaves of vegetative recombinant inbred lines of maize, already assessed for their agronomic performance, were analyzed for physiological traits such as nitrate content, nitrate reductase (NR), and glutamine synthetase (GS) activities. A significant genotypic variation was found for these traits and a positive correlation was observed between nitrate content, GS activity and yield, and its components. NR activity, on the other hand, was negatively correlated. These results suggest that increased productivity in maize genotypes was due to their ability to accumulate nitrate in their leaves during vegetative growth and to efficiently remobilize this stored nitrogen during grain filling. Quantitative trait loci (QTL) for various agronomic and physiological traits were searched for and located on the genetic map of maize. Coincidences of QTL for yield and its components with genes encoding cytosolic GS and the corresponding enzyme activity were detected. In particular, it appears that the GS locus on chromosome 5 is a good candidate gene that can, at least partially, explain variations in yield or kernel weight. Because at this locus coincidences of QTLs for grain yield, GS, NR activity, and nitrate content were also observed, we hypothesize that leaf nitrate accumulation and the reactions catalyzed by NR and GS are coregulated and represent key elements controlling nitrogen use efficiency in maize.     

Comparison of SSRs and SNPs in assessment of genetic relatedness in maize

Advances in high-throughput SNP genotyping and genome sequencing technologies have enabled genome-wide association mapping in dissecting the genetic basis of complex quantitative traits. In this study, 82 SSRs and 884 SNPs with minor allele frequencies (MAF) over 0.20 were used to compare their ability to assess population structure, principal component analysis (PCA) and relative kinship in a maize association panel consisting of 154 inbred lines. Compared to SNPs, SSRs provided more information on genetic diversity. The expected heterozygosity (He) of SSRs and SNPs averaged 0.65 and 0.44, and the polymorphic information content of these two markers was 0.61 and 0.34 in this panel, respectively. Additionally, SSRs performed better at clustering all lines into groups using STRUCTURE and PCA approaches, and estimating relative kinship. For both marker systems, the same clusters were observed based on PCA and the first two eigenvectors accounted for similar percentage of genetic variations in this panel. The correlation coefficients of each eigenvector from SSRs and SNPs decreased sharply when the eigenvector varied from 1 to 3, but kept around 0 when the eigenvector were over 3. The kinship estimates based on SSRs and SNPs were moderately correlated (r (2)?=?0.69). All these results suggest that SSR markers with moderate density are more informative than SNPs for assessing genetic relatedness in maize association mapping panels.     

QTL detected for grain-filling rate in maize using a RIL population

The grain-filling rate plays an important role in determining grain yield. To elucidate the genetic basis of the grain-filling rate, a set of 203 recombinant inbred lines was evaluated at two locations over 2 years. Quantitative trait loci (QTL) for grain-filling rate were detected using conditional and unconditional QTL analysis of genetic linkage maps comprising 217 SSR markers. The results showed that the grain-filling rate increased between 15 and 35 days after pollination, then decreased at the last two sampling times. Hybrids with high grain-filling rates determined the grain yield in those areas with a short growth season for maize. A total of 23 unconditional QTL for grain-filling rate were detected using the 100-kernel weight as the input data at different sampling stages. They were distributed on 10 chromosomes (except chromosome 9), and some QTL were detected at different sampling stages. In addition, nine conditional QTL were identified using the average increase in 100-kernel weight of per day between two sampling times, and six conditional QTL were detected simultaneously using the unconditional QTL mapping strategy. The QTL mapping results demonstrated that the grain-filling rate is controlled by a complex genetic mechanism, and the QTL detected at different sampling stages might be important contributors to grain yield in maize.     

Bulk segregant analysis with molecular markers and its use for improving drought resistance in maize

The usual method to locate and compare loci regulating quantitative traits (QTLs) requires a segregating population of plants with each one genotyped with molecular markers. However, plants from such segregating populations can also be grouped according to phenotypic expression of a trait and tested for differences in allele frequency between the population bulks: bulk segregant analysis (BSA). The same probes used for making a genetic map (e.g. isozyme, RFLP, RAPD, etc) can be used for BSA. A molecular marker showing polymorphism between the parents of the population and which is closely-linked to a major QTL regulating a particular trait will mainly co-segregate with that QTL, i.e. segregate according to the phenotype if the QTL has a large effect. Thus, if plants are grouped according to expression of the trait and extreme groups tested with that polymorphic marker, the frequency of the two marker alleles present within each of the two bulks should deviate significantly from the ratio of 1 : 1 expected for most populations. As chromosomal locations of many molecular markers have now been determined in many species, the map location of closely-linked QTLs can therefore be deduced without having to genotype every individual in segregating populations. This has been used successfully with composite populations of maize to locate QTLs associated with yield under severe drought. An inbred line derived from one of the populations selected for higher drought yield has been crossed with a drought-susceptible inbred line to produce a mapping population for QTL analysis of physiological and developmental traits likely to regulate yield under drought. Future work to identify traits having QTLs with flanking markers showing significant allele frequency differences in the GSA studies will indicate those traits likely to be important in determining yield under drought.Key words: Bulk segregant analysis (BSA), drought resistance, genetic maps, maize, molecular markers, Zea mays (L.).      

Physiology of maize II: Identification of physiological markers representative of the nitrogen status of maize (Zea mays) leaves during grain filling

To illustrate the development of the source-to-sink transition in maize leaves during the grain-filling period, an integrated physiological-agronomic approach is presented in this study. The evolution of physiological markers such as total leaf nitrogen (N), chlorophyll, soluble protein, amino acid and ammonium contents was monitored from silking to a period close to maturity in different leaf stages of three maize genotypes grown at high and low levels of N fertilization. In addition, the activities of glutamine synthetase (GS) and glutamate dehydrogenase (GDH), two enzymes known to play a direct or an indirect role during leaf N remobilization, were measured. In the three genotypes examined, we found that a general decrease of most metabolic and enzyme markers occurred during leaf ageing and that this decrease was enhanced when plants were N starved. In contrast, such variations were not observed between different sections of a single leaf even at an advanced stage of leaf senescence. We found that there is a strong correlation between total N, chlorophyll, soluble protein and GS activity, which is not dependent upon the N fertilization level, which indicates the N status of the plant, either in a single leaf or during ageing. In contrast, ammonium, amino acids and GDH activity were not subject to such variations, thus suggesting that they are indicators of the metabolic activity of the whole plant in response to the level of N fertilization. The use of these markers to predict the N status of maize as a function of both plant development and N availability is discussed.     

AFLP-Based Genetic Diversity and Its Comparison with Diversity Based on SSR, SAMPL, and Phenotypic Traits in Bread Wheat

Data on AFLP (eight primer pairs) and 14 phenotypic traits, collected on 55 elite and exotic bread wheat genotypes, were utilized for estimations of genetic diversity. We earlier used these 55 genotypes for a similar study using SSRs and SAMPL. As many as 615 scorable AFLP bands visualized included 287 (46.6%) polymorphic bands. The phenotypic traits included yield and its component traits, as well as physiomorphological traits like flag leaf area. Dendrograms were prepared using cluster analysis based on Jaccard's similarity coefficients in case of AFLP and on squared Euclidean distances in case of phenotypic traits. PCA was conducted using AFLP data and a PCA plot was prepared, which was compared with clustering patterns in two dendrograms, one each for AFLP and phenotypic traits. The results were also compared with published results that included studies conducted elsewhere using entirely different wheat germplasm and our own SSR and SAMPL studies based on the same 55 genotypes used in the present study. It was shown that molecular markers are superior to phenotypic traits and that AFLP and SAMPL are superior to other molecular markers for estimation of genetic diversity. On the basis of AFLP analysis and keeping in view the yield performance and stability, a pair of genotypes (E3876 and E677) was recommended for hybridization in order to develop superior cultivars.     

利用分子标记和数量性状基因型值构建作物核心种质库的研究

提出了一种基于分子标记数据及数量性状基因型值构建作物种质资源核心种质库的方法.采用包括基因型与环境互作的遗传模型及相应的混合线性模型统计分析方法,无偏预测各材料的基因型值,分别用基因型值和分子标记数据计算个体间的相似系数,加权得到最终的相似距离.采用不加权类平均法（UPGMA）进行系统聚类,用多次聚类随机取样法构建核心种质库.以水稻DH群体111个基因型8个农艺性状、175个分子标记位点的数据为实例,按四种抽样比率（25%,20%,15%,10%）构建了四个核心种质库,比较了核心种质库与整个群体的分子标记多样性及数量性状的遗传变异,评价了所用方法的有效性。     

中国17个优良玉米自交系的分子标记杂合性及其与杂交种性状的关系研究

在玉米单交种育种中 ,鉴定高产杂交种和具有优良特性的自交系是一个重要的问题。研究以 1 7个优良玉米自交系为亲本 ,按照双列杂交配组合 ,利用 RAPD技术分析了 1 7个自交系的多态性以及 RAPD标记与 9个重要农艺性状 (包括产量 )的关系。基于 RAPD标记计算的相似系数聚类将 1 7个自交系分为 5个类群 ,经分析与系谱亲缘关系基本一致。杂交种性状及其特殊配合力与亲本间的遗传距离是高度相关的 ,与聚类前比较 ,聚类后平均遗传距离与平均产量、平均特殊配合力的相关系数显著提高 ,类间平均产量高于类内平均产量。RAPD技术可揭示优良玉米自交系的系谱亲缘关系 ,将自交系划分成不同的类群 ,从而为选择类间自交系杂交 ,进行亲本选配和分子标记辅助育种提供一种方法。     

Genomic regions underlying agronomic traits in linseed (Linum usitatissimum L.) as revealed by association mapping OA简

The extreme climate of the Canadian Prairies poses a major challenge to improve yield. Although it is possible to breed for yield per se, focusing on yield‐related traits could be advantageous because of their simpler genetic architecture. The Canadian flax core collection of 390 accessions was genotyped with 464 simple sequence repeat markers, and phenotypic data for nine agronomic traits including yield, bolls per area, 1,000 seed weight, seeds per boll, start of flowering, end of flowering, plant height, plant branching, and lodging collected from up to eight environments was used for association mapping. Based on a mixed model (principal component analysis (PCA) + kinship matrix (K)), 12 significant marker‐trait associations for six agronomic traits were identified. Most of the associations were stable across environments as revealed by multivariate analyses. Statistical simulation for five markers associated with 1000 seed weight indicated that the favorable alleles have additive effects. None of the modern cultivars carried the five favorable alleles and the maximum number of four observed in any accessions was mostly in breeding lines. Our results confirmed the complex genetic architecture of yield‐related traits and the inherent difficulties associated with their identification while illustrating the potential for improvement through marker‐assisted selection.     

An approach to the genetics of nitrogen use efficiency in maize

To study the genetic variability and the genetic basis of nitrogen (N) use efficiency in maize, a set of recombinant inbred lines crossed with a tester was studied at low input (N-) and high input (N+) for grain yield and its components, grain protein content, and post-anthesis nitrogen uptake and remobilization. Other physiological traits, such as nitrate content, nitrate reductase, glutamine synthetase (GS), and glutamate dehydrogenase activities were studied at the level of the lines. Genotypexnitrogen (GxN) interaction was significant for yield and explained by variation in kernel number. In N-, N-uptake, the nitrogen nutrition index, and GS activity in the vegetative stage were positively correlated with grain yield, whereas leaf senescence was negatively correlated. Whatever N-input, post-anthesis N-uptake was highly negatively related to N-remobilization. As a whole, genetic variability was expressed differently in N+ and N-. This was confirmed by the detection of QTLs. More QTLs were detected in N+ than in N- for traits of vegetative development, N-uptake, and grain yield and its components, whereas it was the reverse for grain protein content and N-utilization efficiency. Several coincidences between genes encoding for enzymes of N metabolism and QTLs for the traits studied were observed. In particular, coincidences in three chromosome regions of QTLs for yield and N-remobilization, QTLs for GS activity and a gene encoding cytosolic GS were observed. This may have a physiological meaning. The GS locus on chromosome 5 appears to be a good candidate gene which can, at least partially, explain the variation in nitrogen use efficiency.     

The challenge of remobilisation in plant nitrogen economy. A survey of physio-agronomic and molecular approaches

In this article, we discuss the ways in which our understanding of the controls of nitrogen remobilisation in model species and crop plants have been increased through classical physiological studies and the use of transgenic plants or mutants with modified capacities for nitrogen or carbon assimilation and recycling. An improved understanding of the transition between nitrogen assimilation and nitrogen recycling will be vital, if improvements in crop nitrogen use efficiency are to reduce the need for excessive input of fertilisers and improve or stabilise yield. In this review, we present an overall view of past work and more recent studies on this topic, using different plants systems and models depicting the biochemical and molecular events occurring during the transition between sink leaves and source leaves. These models may provide a way to identify the nature of the metabolic or developmental signals triggering in a coordinate manner nitrogen and carbon recycling during leaf senescence. Another way of developing crop varieties with improved nitrogen use efficiency, and identifying key elements controlling the process of nitrogen remobilisation, is the use of quantitative genetics. We present and discuss recent findings on the genetic variability and basis of nitrogen use efficiency in crops in general and in maize in particular. A genetic approach using maize recombinant inbred lines was undertaken allowing the detection of Quantitative Trait Loci (QTLs) for morphological traits, grain yield and its components under high nitrogen or low nitrogen input. Co‐mapping was observed between genes encoding enzymes involved in nitrogen assimilation (nitrate reductase, glutamine synthetase) and these Quantitative Trait Loci. All coincidences were consistent with the expected physiological function of the corresponding enzyme activities. This work strongly suggests that in maize, nitrogen use efficiency can be improved both by marker‐assisted selection and genetic engineering.     

Usefulness of marker‐assisted selection to improve maize for increased resistance to Sesamia nonagrioides attack with no detrimental effect on yield

Two decades of investigations on maize resistance to Mediterranean corn borer (Sesamia nonagrioides Lefebvre; MCB) have shown that breeding for increased resistance to stem tunnelling by MCB often resulted in reduced yield because significant genetic correlation between both traits exists in some backgrounds. Unlike phenotypic selection, marker‐assisted selection (MAS) could differentiate markers linked only to one trait from those linked simultaneously to yield potential and susceptibility to the pest. In the current study, the suitability of MAS for improving resistance to stem tunnelling without adverse effects on yield has been tested. The unfavourable genetic relationship between yield potential and susceptibility could be overcome using MAS. Gains obtained using MAS were weak, because genetic variance explained by the quantitative trait loci (QTL) was low but results encourage us to persevere in using marker information for simultaneous improvement of resistance and yield especially if genome‐wide approaches are applied. Approaches to detect QTL are widely used, but studies on the suitability of markers linked to QTL for performing MAS have been mostly neglected.     

Evaluation of maize genotypes for fodder quality traits and SSR diversity

Maize is an important fodder resource for ruminants. The yield and quality of fodder is governed by genetic variability and interaction of genotypes with environment. Therefore, it is prerequisite to evaluate the available variability for fodder traits in maize. For this, 75 genotypes were evaluated in three replications for 2 years. Data were recorded on eight morphological traits including green fodder yield and dried fodder samples were evaluated for eight fodder quality traits. Wide range of estimates for fodder quality and productivity characters except DFF and LSR reflects the existence of significant variations among genotypes. ANOVA revealed significant variety × year interaction for seven traits. Plant height, stem girth, leaf-length and -width demonstrated positive correlation with green fodder yield per plant. Inverse association was observed between crude protein and cell wall component (NDF, hemicellulose). First three principle components explained 44.28% of the total variation. Two-way clustering grouped the genotypes and traits into five and three major clusters, respectively. During SSR analysis, a total of 133 alleles from 21 primers were generated with mean PIC value was 0.58. The genetic distance between genotypes was ranging between 0.16 and 0.75 with an average of 0.49. All genotypes were clustered in three main groups and clustering was consistent with genotype origin. A weak correlation was identified between morphological and molecular distance. Eventually results suggested that both morphotypes and molecular markers should exploit simultaneously to reveal the true genetic diversity to get maximum heterosis through hybridization.     

Uncovering the genetic diversity of yams (Dioscorea spp.) in China by combining phenotypic trait and molecular marker analyses

Yam is an important edible tuber and root plant worldwide; China as one of the native places of yams has many diverse local resources. The goal of this study was to clarify the genetic diversity of the commonly cultivated yam landraces and the genetic relationship between the main yam species in China. In this study, 26 phenotypic traits of 112 yam accessions from 21 provinces in China were evaluated, and 24 simple sequence repeat (SSR) and 29 sequence‐related amplified polymorphism (SRAP) markers were used for the genetic diversity analysis. Phenotypic traits revealed that Dioscorea opposita had the highest genetic diversity, followed by D. alata, D. persimilis, D. fordii, and D. esculenta. Among the 26 phenotypic traits, the Shannon diversity indexes of leaf shape, petiole color, and stem color were high, and the range in the variation of tuber‐related traits in the underground part was higher than that in the aboveground part. All accessions were divided into six groups by phenotypic trait clustering, which was also supported by principal component analysis (PCA). Molecular marker analysis showed that SSR and SRAP markers had good amplification effects and could effectively and accurately evaluate the genetic variation of yam. The unweighted pair‐group method with arithmetic means analysis based on SSR‐SRAP marker data showed that the 112 accessions were also divided into six groups, similar to the phenotypic trait results. The results of PCA and population structure analysis based on SSR‐SRAP data also produced similar results. In addition, the analysis of the origin and genetic relationship of yam indicated that the species D. opposita may have originated from China. These results demonstrate the genetic diversity and distinctness among the widely cultivated species of Chinese yam and provide a theoretical reference for the classification, breeding, germplasm innovation, utilization, and variety protection of Chinese yam resources.     

Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture

Prediction is an attempt to accurately forecast the outcome of a specific situation while using input information obtained from a set of variables that potentially describe the situation. They can be used to project physiological and agronomic processes; regarding this fact, agronomic traits such as yield can be affected by a large number of variables. In this study, we analyzed a large number of physiological and agronomic traits by screening, clustering, and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree models (with nearly the same performance evaluation) were the most useful tools in understanding the underlying relationships in physiological and agronomic features for selecting the most important and relevant traits (sowing date-location, kernel number per ear, maximum water content, kernel weight, and season duration) corresponding to the maize grain yield. In particular, decision tree generated by C&RT algorithm was the best model for yield prediction based on physiological and agronomical traits which can be extensively employed in future breeding programs. No significant differences in the decision tree models were found when feature selection filtering on data were used, but positive feature selection effect observed in clustering models. Finally, the results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field. In particular, decision tree models are method of choice with the capability of illustrating different pathways of yield increase in breeding programs, governed by their hierarchy structure of feature ranking as well as pattern discovery via various combinations of features.