QTL Analysis And Molecular Marker Development For Yield Traits And Related Traits Of Wheat Multi-spikelet Germplasm 10-A

Wheat?Triticum aestivum L.?is one of the world's major food crops.High yield,good quality and high resistance are main objectives of wheat breeding.Wheat yield is mainly composed of three factors:the number of spike per unit area,grains per spike,and grain weight.Usually,through improving one or two yield compositive traits,the yield could be increased in high-yield or super-high-yield wheat breeding.Therefore,in order to increase the yield,it is necessary to further understand the genetic regularity of yield-related traits,and to tap and use the potential of specific germplasm resources related to yield traits.In this study,yield and related traits were investiaged in a F_2:4 pedigree population constructed from the hybridization of multiple spikelet materials 10-A and oligo-spikelet material BE89 under three different conditions,and the QTLs of all traits were analyzed usingthe high-density molecular genetic map constructed by 968 DArT markers and 8 SSR markers.The results laid the foundation for further exploration of the genetic mechanism of yield and its related traits.The main results of this study are as follows:1.Analysis of population traits showed that 10-A had great advantages in spike length,kernel number per spike,and total spikelet per spike,while BE89 had obvious advantages in awn length and thousand-kernel weight,and the offspring F_2:4 populations had both retained10-A higher spikelet per spike and kernel number per spike,but also had the characteristics of higher thousand-kernel weight of BE89.In all environments,the traits of the population located in the E2 environment were greatly influenced by the environment,and the values of all the traits were generally smaller.The yield and its related traits were all significantly different in the population,and the coefficient of variation among the traits varied widely.Among them,the maximum variation coefficient of grain weight per plant was 54.79%,and the minimum coefficient of variation of plant height was 8.6%.Through the correlation analysis of all three environmental traits,the average correlation coefficient was as high as0.543,and the variance values of the kernel number per spike and thousand-kernel weight were larger,indicating that the offspring population had a larger separation,and total spikelet per spike,kernel number per spike and thousand-kernel weight were extremely significantly positively correlated with the grain weight per plant in at least two environments.The multi-environment regression equation showed that the traits determining the grain weight per plant were the fertile tillers number,kernel number per spike,and thousand-kernel weight.The sum of the three environmental variations of the three traits accounted for 82.1%,75.5%,and 75.8%of the total yield variation,respectively.2.A total of 968 DArT markers and 8 SSR markers comprised 52 linkage groups.The total length of the genetic map was 1878.408 cM,and the average genetic distance between markers was 1.925 cM,with an average of 1.5 linkage groups per chromosome.The average density of three genomic markers was 1.79 cM,1.59 cM,and 2.84 cM.B genome was the longest with a maximum of 453 markers,and the average density between markers was a minimum of 1.59 cM.The number of D genome markers was at least 211,and the average density between markers was 2.84 cM.3.Using the composite linear model-based composite interval mapping method,226QTL loci co-localized to yield and its related traits were distributed on 19 chromosomes except 3A and 7A,and the contribution rate to the phenotypic variation was 1.62%³8.24%.Among them,10 QTLs for total spikelet per spike were mapped to five chromosomes,and the contribution rates ranged from 6.18%to 15.37%.QTss-1A.1 could be detected in three environments.The contribution rates were 13.54%,7.96%and 12.57%,respectively.The single-environment QTss-2A.2 had the largest contribution rate of 15.37%.Four kernel number per spike QTLs were located on three chromosomes,and the contribution rate ranged from 6.65%to 11.18%.All the QTLs were detected in a single environment,of which QKns-1A.2 had the highest contribution rate of 11.18%.Twenty-two thousand-kernel weight QTLs were mapped to 14 chromosomes,and the contribution rates ranged from 2.47 to 19.1%.All QTLs were located in a single environment,of which QTkw-1B.1 had the highest contribution rate of 19.1%,QTkw-1A.1 and QTkw-2B.3 also contributed more than 15%.Thirty-one grain weight per plant QTLs were mapped to 14 chromosomes,and their contribution rates ranged from 3.4%to 30.37%.QGwp-5D.1 was detected in all three environments,with contribution rates of 25.28%,11.3%,and 28.99%,respectively.The other two QGwp-4B.1 and QGwp-5D.2 were detected in two environments,with contribution rates of 14.63%and 34.77%,17.49%and 28.3%,respectively.The QGwp-1A.1 and QGwp-5D.3were identified in the E2 environment,with contribution rates as high as 30.37%and 38.24%.4.The QTLs for comprehensive yield and its related traits were mapped to 17 QTL clusters,involving 51 QTLs of 12 traits,located in 1A,1B,1D,2A,2B,3D,4B,5A,5B,5D,6A and 6B and so on a total of 12 chromosomes.The QTL cluster C1 includes QTLs for traits such as spike length,total spikelet per spike,and kernel number per spike.QTss-1A.1 of the total spikelet per spike in the C1 cluster was detectedin the three environments,and the contribution rates were 13.54%,7.96%,and 12.57%.The QTL cluster C11 located on the 5A chromosome consists of four agronomic traits including plant height,spike length,spikelet density,and thousand-kernel weight.The genetic distance of the flanking markers both sides of the QTL was 2.5 cM.5.The small population could be genotyped and significantly separated using the seven KASP markers,which were developed based on 4 QTL loci,spike length QSl-1A.1,fertile tillers number QFtn-5D.4,thousand-kernel weight QKns-5A.1 and grain weight per plant QGwp-5D.1.The T-test of the phenotyping data between two genotypes identified by the developing markers showed the extremely significant differences.These markers can lay the foundation for the next step of marking large groups of verification.