| As an important food crop,feed crop and energy crop in China,maize has an important impact on food security,livestock and poultry products and energy supply security in China.Corn yield is affected by many factors,among which Northern Corn Leaf Blight(NCLB)is a common disease affecting corn yield.Northern corn Leaf blight is a quantitative trait,and resistance genes or genetic loci obtained by QTL positioning and GWAS(Genome-wide association study,genome-wide association analysis)have usually been included in the existing maize backbone selbred lines,with limited effect on continuing to improve breed resistance.Therefore,to improve the resistance of maize germplasm in China,more microgenes need to be aggregated.Genomic selection(GS)uses molecular markers covering the whole genome,which is more suitable for predicting complex quantitative traits controlled by microactive polygenes.In this study,the DTMA population consisting of 300 inbred lines and the combined population consisting of three DH(doubled haploid)parental populations are used as materials to identify the incidence of NCLB disease at 4 and 2 sites,3repetitions for each location.All materials are genotyped on the GBS(genotyping-by-sequencing,simplified genome sequencing)platform to obtain SNP markers.Using these SNP markers with different associations with NCLB disease,the classical RR-BLUP model is used for genome-wide prediction.The main results of this study are as follows:1.The environmental mean BLUP values of DTMA and DH combined populations are 2.36 and 1.94 and the heritability is 0.56 and 0.62.The phenotypic values of the two populations showed continuous variation,consistent with the genetic characteristics of the quantitative traits.2.After quality control of GBS genotyping data of DTMA population and DH combined population,42197 and 73459 SNP markers are obtained respectively.The SNP of the two populations is distributed on 10 chromosomes in maize,with higher density than the middle marker density.In the DTMA population,the chromosome 4and 10 are higher than the short end marker density,while in the DH combined population,the long end of chromosomes 9 and 10 is more marker dense than the short end.3.GWAS analysis identified 4 SNPs closely related to resistance to NCLB disease in the DTMA population,which are distributed on chromosomes 2(2),8 and6,and the phenotypic contribution rate ranged from 3.218% to 11.889%.Seven SNPs are located in the DH combined population,distributed on chromosomes 1(2),2,4,5(2)and 8,and the phenotypic contribution rates ranged from 2.778% to 23.135%.Both populations mapped significant SNPs on chromosomes 2 and 8,but their physical locations were not consistent and not the same QTL.In addition,the SNPs located on chromosomes 1 and 8 in the DH combined population and the SNPs located on chromosome 2 in the DTMA population were consistent with those located in the previous study,although the previous study found that SNPs or genes related to the resistance of chromosomes 4,5 and 6 to the NCLB disease.However,these genes are not consistent with the SNP physical distance mapped in this test,and they are speculated to be new micro-effective genes related to large plaque resistance.4.DTMA and DH combined populations obtained consistent results in the ratio analysis of modeling population and predicted population,when the ratio of modeling population and verification population is 6:4,the DTMA and DH combined populations have the highest average prediction accuracy and relatively low standard deviation.5.The prediction accuracy of SNP markers in both populations is higher than that of all SNP markers.For the DTMA population,when the ratio of the modeling population to the validation prediction population is 6:4,using all 42,197 SNP markers for genome-wide prediction,the prediction accuracy is 0.655,and the prediction accuracy of the 600 SNPs with P≤0.01 is higher than that using prediction accuracy of all SNP markers,among which 5052 SNPs with P≤0.1 have the highest prediction accuracy,which is 34.3% higher than the prediction accuracy of all SNP markers;For the DH combined population,when the ratio of the modeling population to the validation prediction population is 6:4,using all 73459 SNP markers to perform genome-wide prediction,the prediction accuracy is 0.679,and the prediction accuracy of the 396 SNP markers used with P≤0.001 is all higher than that predicted using all of the SNP markers.For the prediction using all SNP markers,6814 SNPs with P≤0.05 have the highest prediction accuracy,which is 12.2% higher than the prediction accuracy using all SNP markers.The results of this study show that GWAS analysis locates different SNP markers that are significantly associated with resistance to large spot disease from the DTMA population and the DH combined population,among them in chromosomes 4,5 and 6localized to macroplaque disease resistant QTL different from known QTL,this QTL may correspond to a new physiological species that needs further investigation;On the other hand,new QTLs may be involved in the response and response mechanism of complex physiological race-induced large spot disease,which are helpful for further study.Genome-wide prediction has different prediction accuracy for NCLB resistance in natural population DTMA and DH parental populations,and the prediction accuracy of DH population is higher than that of DTMA population;Whether the DH joint population controlling for population structure or DTMA population,the prediction accuracy of using SNP markers with a certain degree of association with NCLB disease is higher than that of using all SNP markers.The results of this study provide a new QTL for maize disease breeding,providing the basis for the next fine mapping and map cloning.SNP markers that are closely related to traits have shown great potential in improving the accuracy of genome-wide prediction.It is recommended to integrate this step in the genome-wide prediction of NCLB and similar diseases to simplify markers,improve computational efficiency and increase prediction accuracy. |
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