QTL Mapping And Genomic Selection For Flowering Time Of Early-maturing Maize

Maize(Zea mays L.)is one of the significant cereal crops in China.With the rise and development of industries such as brewing,chemical engineering,and biomass new energy,the market demand for maize is increasingly growing.As one of the important traits of maize production,the flowering time is an important part of maize breeding goals,which is of great significance for improving the stability and adaptability of maize and improving seed yield.Although several genes controlling the flowering time have been located and cloned,most are limited to studies on tropical or temperate late-maturing maize.In this study,early-maturing Iodent germplasm and Flint germplasm were used as parental lines to create a recombinant inbred lines(RIL)population and a doubled haploid(DH)population for mapping.Under multiple environments,maize flowering time-related traits were identified,and a genetic map was constructed using high-density molecular markers obtained from resequencing.A multi-environment combined quantitative trait loci(QTL)mapping analysis was carried out using a complete composite interval mapping method,and known flowering time genes were compared,identifying critical chromosomal regions and QTL loci controlling the flowering time.Simultaneously,genomic selection(GS)technology was used to explore the optimal prediction model,training population size,and marker number to optimize the breeding process.The main research results are as follows:(1)Maize flowering time-related traits are mainly controlled by genotypes.The flowering time-related traits in the two genetic populations showed normal distribution in multiple environments,following time typical quantitative genetic characteristics,and the heritability was high.The phenotypic variation at the flowering time stage mainly occurred between Beijing and Daqing,Mishan,there were significant interaction effects between genotype and environment.(2)The variation in maize flowering time-related traits is regulated by multiple genes.In the RIL population,a total of 38 QTL loci related to flowering time traits were identified,and in the DH population,45 QTL loci related to flowering time traits were identified.Multiple consistent QTLs exist in the two populations,possibly having the same regulatory pathways or being controlled by the same gene.Consistent QTLs were found on chromosomes 3 and 9 in the two populations,indicating that the two populations have certain genetic similarities.(3)Some QTL loci related to maize flowering time traits are consistent with previous results.Eight QTLs collocate with five known genes controlling the flowering time.The DH population’s qDTT-D-3-3 on chromosome 3 overlaps with the known flowering time gene ZmLD;the RIL population’s qDTT-R-8-1 on chromosome 8 is near the known flowering time gene ZmRAP2.7;and the DH population’s qDTT-D-9-1 and q ASI-D-9-1 on chromosome 9 overlap with the known flowering time gene GL15.qDTS-D-9-1 overlaps with the known flowering time gene CONZ1,and on chromosome 10,the DH population’s qDTT-D-10-1,qDTA-D-10-1,and qDTS-D-10-1 overlap with the known flowering time gene ZmLHY1.The above segments may be important chromosomal segments for studying the flowering time of maize,and the involved markers can serve as significant candidate markers.(4)The type of prediction model,the size of the training population,the number of markers,the heritability,and the type of genetic populationall affect the prediction accuracy of GS.The genomic best linear unbiased prediction(GBLUP)model has the highest prediction accuracy for the four flowering time-related traits in both populations.The prediction accuracy increases with the increase of the training population size and the number of markers.However,once a certain proportion is reached,model prediction contributes very little to improving prediction accuracy.Anthesis-silking interval(ASI)require a relatively large number of markers to achieve better prediction results.The prediction accuracy of various traits in the DH population is generally higher than that in the RIL population under different models,training population sizes,and marker numbers.