| Water deficit or drought is one of the most serious abiotic stresses of plant development.In maize,drought stress during flowering causes an asynchrony between silk emergence and pollen shedding,which increases the anthesis-silking interval(ASI)and greatly reduces crop production,and the plant's response to this deficit leads to many metabolic changes.To dissect the genetic basis of these metabolic traits in maize will be helpful in breeding high drought-resistant maize varieties.In this study,we merged three marker datasets and high-density marker genotypes were imputed following a two-step approach through the identity by descent(IBD)based projection and k-nearest neighbor(KNN)algorithm.Then,we performed a genome-wide association analysis of drought-related traits detected under well watered and drought stress conditions using 156,599 SNPs in 318 maize inbred lines and evaluated the locus responding to drought stress in a hybrid population.The main results are as follows:1.Genotyping and imputation: Three marker datasets of the two maize panels(318 vs.368 inbred lines)were merged(39 inbred lines in common between the maize panels)into an integrated marker dataset.Through core markers(42,742 SNPs)that were shared between the two maize panels and following a two-step approach through the identity by descent(IBD)based projection and k-nearest neighbor(KNN)algorithm,high-density marker genotypes from the 368 inbred lines onto the 318 inbred lines were imputed,and resulted in 156,599 high-density SNPs(MAF ? 0.05).Using a principal components analysis(PCA)to illustrate the relationship between the two panels using the common SNP data of the 50 K SNP array which indicates that the majority of the haplotypes present in the panel of 318 lines should also exist in the panel of 368 lines,allowing imputation of missing haplotypes in the 318 lines from the SNP information from the 368 line panel.The imputation accuracy and proportion of missing genotypes of the 39 common inbred lines were evaluated through comparing the imputed genotype(MAF ? 0.05)and true RNA-seq genotype,indicated that the imputation accuracy and proportion of missing genotypes for each inbred line ranging from 97.17% to 99.99% and from 0.72 to 0.13.2.Genome-wide association analysis(GWAS): GWAS was performed using the imputed genotype.In total,123 significant SNP/trait associations(P ? 6.39E-6)involving 63 loci(based on local LD analysis)were identified for 168 variable related to 12 metabolic and physiological traits in three tissues and different environments under two irrigation conditions(well watered and water stress),which indicated that the imputation based GWAS provides more statistical power to identify new significant loci.3.Identified the locus responded to drought stress: 23 of the total 63 loci showed a significant interaction between locus and water regime(WW & WS)for the three tissues after using the two-way ANOVA,indicating that these QTLs of metabolites probably respond to drought stress.71 high and 68 low drought tolerance hybrids(hybrid pools)were selected and used to evaluate the potential utility of metabolite-associated loci in applied hybrid maize breeding.In the hybrid pools,a set of 10 metabolite-associated loci identified in leaf and ear were validated as responsive to drought stress,which jointly explained almost 18.4% of the variation in drought tolerance.These results provide clues to understanding the genetic basis of metabolic and physiological changes related to drought tolerance,potentially facilitating the genetic improvement of varieties with high drought tolerance in maize breeding programs.Another area interested in our research is the genetic architecture dissection of maize plant dynamic growth based on high-throughput phenotypic platform.In this study,phenotypic traits was obtained from a maize recombinant inbred line population(n=167)across 16 development stages using the automatic phenotyping platform.Combining high density genetic linkage map,including 2496 recombinant bins,a large scale QTL analysis was used to uncover the genetic basis of these complex agronomic traits.The main results are as follows:1.Trait extracting,trait variation and heritability: During the seedling stage to tasseling stage,total 167 maize recombinant lines(with 2 replicates)at 16 time points were inspected in automated phenotyping platform,yielding ~476 GB of data.With the huge amount of images and modified image analysis pipeline,106 phenotypic traits,which included 10 plant morphological traits,22 leaf architecture traits,1 plant color traits,3 biomass related traits,6 histogram texture traits,and 64 growth related traits were extracted.The RIL population manifested high diversity for most of the 106 investigated phenotypic traits at each time point ranged from 1.07 to 5.56(T14 time point)and 1.02 to 14.95-fold change(T2 time point)at minimum and maximum level at 16 time points,respectively.For all investigated phenotypic traits,approximately 3-fold change was observed on average at different time point.The investigated traits showed high heritability in general with greater than 0.5 for most traits in most of the time points and the dynamic changes was also observed for some traits.2.Evaluation of the high-throughput phenotypic platform(RPA-maize): In order to evaluate the measuring accuracy of the RPA-maize,correlation analysis were done between manual versus automatic measurements and scatter plots showed the R2 were greater than 0.97 for plant height,FW and DW.Which demonstrated that automatically measurements as good as manual measurements but with high throughput and efficiency.3.QTL mapping: With high-density genetic linkage map,we mapped the QTLs associated with variation of investigated phenotypic traits of RILs in each time point.988 QTLs were identified in total and QTL distribution across chromosomes was not random,three QTL hot spots were observed across the maize genome.These QTLs could be incorporated separately into three categories:(1)The single QTL affecting a particular trait mapped in several time points of whole development stage.It implied that the gene affecting these traits may have expressed in early stages.(2)At a particular stage,the same QTL were detected to affect several different traits indicating that they may share the same genetic control.(3)At a particular stage,most traits were controlled by a mount of QTLs with minor to mediate effects.4.Yield prediction: The variance explanation of the maize yield with maize plant phenotypic traits in the early growth stage was evaluated.And found that a combination of a few traits measured in the early stages of maize growth could be used as the good predictors of the final yield.The results shown that up 54.6% of the phenotypic variance of grain yield could been explained by combing 16 traits at 16 time points.If only 8 phenotypic traits in 4 time points(T1,T8,T9,T16)were used,29.6% of the grain yield variance was still explained.Of the 8 phenotypic triats,7 were related to plant architecture and leaf morphological traits,which indicated that the yield is closely related to these traits,especially for leaf length and leaf angle in the early development stage.At last,based on the above results,we proposed an ideotype of maize plant for breeding. |
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