| Objective:Maize is not only an important food crop,but also an important feed source for animal husbandry and breeding industry and plays an important role in food security,and high yield and stability have been the main targets of maize breeding.Flowering time traits,plant type and ear traits influenced maize yield,it is important to dissect the genetic basis of the main agronomic and yield traits and mine loci of excellent allelic variation and the key functional genes by genome-wide association analysis for breeding maize varieties with wide adaptability and high and stable yield.Methods:In this study,a natural population consisting of 580 mid-early maize inbred lines derived from a wide range of genetic diversity,were planted in a maize breeding experimental field at the Crop Research Institute of Xinjiang Academy of agriculture and reclamation Sciences,Shihezi in 2019,2020and 2021,and four flowering time traits,two plant types and eight ear traits were determined,the maize40K breeding chip based on GBTS technology was used for genotype identification.Phenotypic and genotypic data analyses were performed using the corresponding software.Use GLM model,MLM model,MLMM model,BLINK model and Farm CPU model to conduct genome-wide association analysis to find the optimal model,and co-located significant SNPs sites were mined within the whole genome.Then,according to the population LD decay distance,candidate intervals were determined within 440 kb upstream and downstream of significant SNPs,and all candidate genes were listed according to the reference genome.Candidate gene screening by combined sequence functional annotation.Results:1.Genotype data analysis showed that 31826 high-quality SNPs after QC were distributed over 10chromosomes,the 580 accessions could be divided into 2 subpopulations or 4 subpopulations,indicating high genetic diversity among the accessions.The LD coefficient decreased rapidly with increasing genetic distance and decayed at different rates per chromosome,with an average LD decaying distance of440 kb(r~2=0.1)in 10 chromosomes from 580 accessions.2.Analysis of phenotypic data showed that the 14 traits all showed abundant phenotypic variation,with high heritability,Correlation analysis showed a highly significant positive correlation between the two of the three traits at days to tasseling,days to anthesis and days to silking,at anthesis silking interval mainly correlated with the days to silking,yield mainly correlated with the traits of ear row number,kernel number per row,ear length,ear diameter,hundred kernel weight.3.The GWAS results indicated the Farm CPU model as the optimal model,and a total of 131 significantly associated SNPs were detected for the for flowering time traits,16 co-localized SNPs were identified under multiple environments(more than two environments),10 potential candidate genes were mined using colocalized SNPs.A total of 58 significantly associated SNPs were detected for the plant type traits,and 6 were Co located with markers in multiple environments,7 potential candidate genes were mined using colocalized SNPs.A total of 106 significantly associated SNPs were detected for ear traits,and 10were co localized for multiple environments,9 potential candidate genes were mined using colocalized SNPs.Conclusion:The natural populations used in this study have a rich genetic background,high phenotypic variation for major agronomic and yield traits,and high heritability.Genome wide association analysis using the Farm CPU model detected a total of 32 significantly colocalized SNPs with significant effects on phenotypic traits,and 26 potential candidate genes associated with the traits were predicted.These results provide theoretical reference for the analysis of the genetic basis of maize flowering,plant type and ear traits. |
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