Dynamics and genetic regulation of macronutrient concentrations during grain development in maize

Nitrogen(N), phosphorus(P), and potassium(K) are essential macronutrients that are crucial not only for maize growth and development, but also for crop yield and quality. The genetic basis of macronutrient dynamics and accumulation during grain filling in maize remains largely unknown. In this study, we evaluated grain N, P, and K concentrations in 206 recombinant inbred lines generated from a cross of DH1M and T877 at six time points after pollination. We then calculated conditional phenotypic values at different time intervals to explore the dynamic characteristics of the N, P, and K concentrations. Abundant phenotypic variations were observed in the concentrations and net changes of these nutrients. Unconditional quantitative trait locus(QTL) mapping revealed 41 non-redundant QTLs, including 17, 16, and 14 for the N, P, and K concentrations, respectively. Conditional QTL mapping uncovered 39 non-redundant QTLs related to net changes in the N, P, and K concentrations. By combining QTL, gene expression, co-expression analysis, and comparative genomic data, we identified 44, 36, and 44 candidate genes for the N, P, and K concentrations, respectively, including GRMZM2G371058 encoding a Doftype zinc finger DNA-binding family protein, which was associated with the N concentration, and GRMZM2G113967encoding a CBL-interacting protein kinase, which was related to the K concentration. The results deepen our understanding of the genetic factors controlling N, P, and K accumulation during maize grain development and provide valuable genes for the genetic improvement of nutrient concentrations in maize.

Rice melatonin deficiency causes premature leaf senescence via DNA methylation regulation

In a study of DNA methylation changes in melatonin-deficient rice mutants,mutant plants showed premature leaf senescence during grain-filling and reduced grain yield.Melatonin deficiency led to transcriptional reprogramming,especially of genes involved in chlorophyll and carbon metabolism,redox regulation,and transcriptional regulation,during dark-induced leaf senescence.Hypomethylation of mCG and mCHG in the melatonin-deficient rice mutants was associated with the expression change of both protein-coding genes and transposable element-related genes.Changes in gene expression and DNA methylation in the melatonin-deficient mutants were compensated by exogenous application of melatonin.A decreased S-adenosyl-L-methionine level may have contributed to the DNA methylation variations in rice mutants of melatonin deficiency under dark conditions.

Combining QTL Mapping and Multi-Omics Identify Candidate Genes for Nutritional Quality Traits during Grain Filling Stage in Maize

The nutritional composition and overall quality of maize kernels are largely determined by the key chemical com-ponents:protein,oil,and starch.Nevertheless,the genetic basis underlying these nutritional quality traits during grainfilling remains poorly understood.In this study,the concentrations of protein,oil,and starch were studied in 204 recombinant inbred lines resulting from a cross between DH1M and T877 at four different stages post-pollination.All the traits exhibited considerable phenotypic variation.During the grain-filling stage,the levels of protein and starch content generally increased,whereas oil content decreased,with significant changes observed between 30 and 40 days after pollination.Quantitative trait locus(QTL)mapping was conducted and a total of 32 QTLs,comprising 14,12,and 6 QTLs for grain protein,oil,and starch content were detected,respectively.Few QTLs were consistently detectable across different time points.By integrating QTL analysis,glo-bal gene expression profiling,and comparative genomics,we identified 157,86,and 54 differentially expressed genes harboring nonsynonymous substitutions between the parental lines for grain protein,oil,and starch con-tent,respectively.Subsequent gene function annotation prioritized 15 candidate genes potentially involved in reg-ulating grain quality traits,including those encoding transcription factors(NAC,MADS-box,bZIP,and MYB),cell wall invertase,cellulose-synthase-like protein,cell division cycle protein,trehalase,auxin-responsive factor,and phloem protein 2-A13.Our study offers significant insights into the genetic architecture of maize kernel nutritional quality and identifies promising QTLs and candidate genes,which are crucial for the genetic enhance-ment of these traits in maize breeding programs.

Genomic selection methods for crop improvement:Current status and prospects

With marker and phenotype information from observed populations, genomic selection （GS） can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations. To overcome this issue and improve prediction accuracy, many models and algorithms, including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS. The principles and characteristics of current popular GS methods and research progress in hese methods for crop improvement are reviewed in this paper.

Genomic selection: A breakthrough technology in rice breeding

Rice(Oryza sativa)provides a staple food source for more than half the world population.However,the current pace of rice breeding in yield growth is insufficient to meet the food demand of the everincreasing global population.Genomic selection(GS)holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are measured.Previous simulation and experimental studies have demonstrated the usefulness of GS in rice breeding.However,several affecting factors and limitations require careful consideration when performing GS.In this review,we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice breeding.We also highlight effective strategies to increase the predictive ability of various models,including GS models incorporating functional markers,genotype by environment interactions,multiple traits,selection index,and multiple omic data.Finally,we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.

Genetic mapping of quantitative trait loci in crops

Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists.Two complementary approaches for genetic mapping,linkage mapping and association mapping have led to successful dissection of complex traits in many crop species.Both of these methods detect quantitative trait loci(QTL) by identifying marker–trait associations,and the only fundamental difference between them is that between mapping populations,which directly determine mapping resolution and power.Based on this difference,we first summarize in this review the advances and limitations of family-based mapping and natural population-based mapping instead of linkage mapping and association mapping.We then describe statistical methods used for improving detection power and computational speed and outline emerging areas such as large-scale meta-analysis for genetic mapping in crops.In the era of next-generation sequencing,there has arisen an urgent need for proper population design,advanced statistical strategies,and precision phenotyping to fully exploit high-throughput genotyping.

A multivariate partial least squares approach to joint association analysis for multiple correlated traits

Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.

Using genomic data to improve the estimation of general combining ability based on sparse partial diallel cross designs in maize

Evaluation of general combining ability(GCA)is crucial to hybrid breeding in maize.Although the complete diallel cross design can provide an efficient estimation,sparse partial diallel cross(SPDC)is more flexible in breeding practice.Using real and simulated data sets of partial diallel crosses between 266 maize inbred lines,this study investigated the performance of SPDC designs for estimating the GCA.With different distributions of parental lines involved in crossing(called random,balanced and unbalanced samplings),different numbers of hybrids were sampled as the training sets to estimate the GCA of the 266 inbred lines.In this process,three statistical approaches were applied.One obtained estimations through the ordinary least square(OLS)method,and the other two utilized genomic prediction(GP)to estimate the GCA.It was found that the coefficient of determination of each approach was always higher than the heritability of a target trait,showing that the GCA for maize inbred lines could be accurately predicted with SPDC designs.Both the GP approaches were more accurate than the OLS,particularly in the scenario for a low-heritability trait with a small sample size.Additionally,prediction results demonstrated that a big sample of hybrids could greatly help improve the accuracy.The random sampling of parental lines had little influence on the average accuracy.However,the prediction for lines that never or seldom involved in crossing might suffer from much lower accuracy.

Genome-wide association study identifies novel candidate loci or genes affecting stalk strength in maize

Stalk strength increases resistance to stalk lodging,which causes maize(Zea mays L.)production losses worldwide.The genetic mechanisms regulating stalk strength remain unclear.In this study,three stalk strength-related traits(rind penetrometer resistance,stalk crushing strength,and stalk bending strength)and four plant architecture traits(plant height,ear height,stem diameter,stem length)were measured in three field trials.Substantial phenotypic variation was detected for these traits.A genome-wide association study(GWAS)was conducted using general and mixed linear models and 372,331 single-nucleotide polymorphisms(SNPs).A total of 94 quantitative trait loci including 241 SNPs were detected.By combining the GWAS data with public gene expression data,56 candidate genes within 50 kb of the significant SNPs were identified,including genes encoding flavonol synthase(GRMZM2G069298,ZmFLS2),nitrate reductase(GRMZM5G878558,ZmNR2),glucose-1-phosphate adenylyltransferase(GRMZM2G027955),and laccase(GRMZM2G447271).Resequencing GRMZM2G069298 and GRMZM5G878558 in all tested lines revealed respectively 47 and 2 variants associated with RPR.Comparison of the RPR of the zmnr2EMS mutant and the wild-type plant under high-and low-nitrogen conditions verified the GRMZM5G878558 function.These findings may be useful for clarifying the genetic basis of stalk strength.The identified candidate genes and variants may be useful for the genetic improvement of maize lodging resistance.

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

Kernel metabolites depict the diversity of relationship between maize hybrids and their parental lines

As the end products of cellular regulatory processes,metabolites provide the link between genotypes and phenotypes.Althoughmetabolites have been widely applied for functional gene detection and phenotype prediction in maize,there is little research focusing on the genetic information of metabolites per se.Here,we performed genetic analyses for the kernel metabolites of 11 parental inbred lines of six representative maize varieties,including Zhongdan 2,Danyu 13,Yedan 13,Zhengdan 958,Xianyu 355,and Suyu 16,as well as their 26 reciprocal hybrids.We identified a total of 208 metabolites in maize kernels using untargeted metabolite profiling technology.Both cluster analysis and principal component analysis indicated that kernel metabolites could distinguish hybrids from their parents.Analysis of variance further revealed that 163 metabolites exhibited significant differences between parents and hybrids,and 40 metabolites showed significant differences between reciprocal crosses.We also investigated the genetic effects and heterosis for each metabolite.By taking all hybrids into consideration,about two-thirds of all metabolites displayed overdominant with 36.8%and 31%of them displaying positive overdominant and negative overdominant,respectively.Besides,27.5%and 20.4%of all hybrid combinations showed significant mid-parent heterosis and over-parent heterosis,respectively.Our findings revealed that kernel metabolites exhibited the diversity of relationship between maize hybrids and their parental lines.Additionally,we identified 25 significant metabolicmarkers related to 11 agronomic traits using the LASSO method.Seven metabolic markers were associated with more than one trait simultaneously.These results provide a genetic basis for further utilization of metabolites in the genetic improvement of maize.

Multi-environment QTL mapping of crown root traits in a maize RIL population

Crown root traits,including crown root angle(CRA),diameter(CRD),and number(CRN),are major determining factors of root system architecture,which influences crop production.In maize,the genetic mechanisms determining crown root traits in the field are largely unknown.CRA,CRD,and CRN were evaluated in a recombinant inbred line population in three field trials.High phenotypic variation was observed for crown root traits,and all measured traits showed significant genotype–environment interactions.Singleenvironment(SEA)and multi-environment(MEA)quantitative trait locus(QTL)analyses were conducted for CRA,CRD,and CRN.Of 46 QTL detected by SEA,most explained less than 10%of the phenotypic variation,indicating that a large number of minor-effect QTL contributed to the genetic component of these traits.MEA detected 25 QTL associated with CRA,CRD,and CRN,and 2 and 1 QTL were identified with significant QTL-by-environment interaction effects for CRA and CRD,respectively.A total of 26.1%(12/46)of the QTL identified by SEA were also detected by MEA,with many being detected in more than one environment.These findings contribute to our understanding of the phenotypic and genotypic patterns of crown root traits in different environments.The identified environment-specific QTL and stable QTL may be used to improve root traits in maize breeding.

Development and validation of simple sequence repeat markers from Arachis hypogaea transcript sequences

Simple sequence repeats(SSRs) are important molecular markers for assessing genetic diversity in Arachis hypogaea L. and many other crops and constructing genetic linkage maps for important agricultural traits. In this study, 29,357 potential SSRs were identified in 22,806 unigenes assembled from A. hypogaea transcript sequences. Of these unigenes, 1883 and 4103 were annotated and assigned in Kyoto Encyclopedia of Genes and Genomes Orthology and Eukaryotic Orthologous Groups databases, respectively. Among the SSR motifs, mono-(19,065; 64.94%) and trinucleotide(5033; 17.14%) repeats were the most common, and the three most dominant motifs were A/T(18,358; 62.54%), AG/CT(2804;9.55%), and AAG/CTT(1396; 4.76%). Polymerase chain reaction(PCR) primer pairs were designed for 4340 novel SSR markers and 210 new SSRs were validated using 24 A. hypogaea varieties. Of the 210, 191(91%) yielded PCR products, with 37(18%) identifying polymorphisms. The 37 polymorphic SSR markers detected 146 alleles(2–10 alleles per locus), and the average polymorphic information content was 0.403(with a range of 0.077 to0.819). The new SSRs enrich the current marker resources for A. hypogaea and may also be useful for genetic diversity analysis, functional genomics research, and molecular breeding.

Genetic analysis of the seed dehydration process in maize based on a logistic model

Seed moisture at harvest is a critical trait affecting maize quality and mechanized production,and is directly determined by the dehydration process after physiological maturity.However,the dynamic nature of seed dehydration leads to inaccurate evaluation of the dehydration process by conventional determination methods.Seed dry weight and fresh weight were recorded at 14 time points after pollination in a recombinant inbred line(RIL)population derived from two inbred lines with contrasting seed dehydration dynamics.The dehydration curves of RILs were determined by fitting trajectories of dry weight accumulation and dry weight/fresh weight ratio change based on a logistic model,allowing the estimation of eight characteristic parameters that can be used to describe dehydration features.Quantitative trait locus(QTL)mapping,taking these parameters as traits,was performed using multiple methods.Single-trait QTL mapping revealed 76 QTL associated with dehydration characteristic parameters,of which the phenotypic variation explained(PVE)was 1.03%to 15.24%.Multipleenvironment QTL analysis revealed 21 related QTL with PVE ranging from 4.23%to 11.83%.Multiple-trait QTL analysis revealed 58 QTL,including 51 pleiotropic QTL.Combining these mapping results revealed 12 co-located QTL and the dehydration process of RILs was divided into three patterns with clear differences in dehydration features.These results not only deepen general understanding of the genetic characteristics of seed dehydration but also suggest that this approach can efficiently identify associated genetic loci in maize.

Comparative Analysis and Comprehensive Evaluation of Fishery Water Quality of the Major Lakes in Jiangsu Province Based on Long-term Monitoring Data

The variance analysis of fishery water quality data of five lakes from 2001 to 2011( except 2004) was performed to compare the difference of the monitoring indicators among the five above-mentioned lakes in Jiangsu Province. And TOPSIS method was employed to give comprehensive comparison of water quality of the five lakes. The results indicated that the difference of 14 major water quality indicators was very significant among lakes except copper. In addition,transparency,total nitrogen,total phosphorus had very significant difference among stations for each lake; p H,chemical oxygen demand,oil,total phosphorus,lead,cadmium,mercury had significant or very significant difference among years for each station. The TOPSIS results showed that the fishery water quality of Gaobaoshaobo Lake was the best,and Luoma Lake was just second to it,followed by Hongze Lake,Taihu Lake and Gehu Lake. In combination with the geographic position of each lake,it showed that fishery water quality of the five investigated lakes was basically increasingly better from the south to the north in Jiangsu Province,and the trend revealed high association with the developed industrial economy.

Molecular evolution and functional divergence of HAK potassium transporter gene family in rice(Oryza sativa L.)

The high-affinity K＋ （HAK） transporter gene family is the largest family in plant that functions as potassium transporter and is important for various aspects of plant life. In the present study, we identified 27 members of this family in rice genome. The phylogenetic tree divided the land plant HAK transporter proteins into 6 distinct groups. Although the main characteristic of this family was established before the origin of seed plants, they also showed some differences between the members of non-seed and seed plants. The HAK genes in rice were found to have expanded in lineage-specific manner after the split of monocots and dicots, and both segmental duplication events and tandem duplication events contributed to the expansion of this family. Functional divergence analysis for this family provided statistical evidence for shifted evolutionary rate after gene duplication. Further analysis indicated that both point mutant with positive selection and gene conversion events contributed to the evolution of this family in rice.