Conditional And Unconditional Qtl Analysis Of Yield And The Three Main Yield Component Traits In Common Wheat(Triticum Aestivum L.)

Common wheat (Triticum aestivum L.) is a Poaceae plant characteristic of spike-bearingtillers.Spike number per m2, kernel number per spike and kernel weight (generally representedby thousand-kernel weight) are the “three main components” determining the yield per m2.Increase in any one component can improve wheat yield. In this research, conditional andunconditional quantitative trait locus (QTL) analysis of yield and its three main componentswas conducted using3populations. The aim was to explore the genetic mechanismsunderlying the significant positive correlations between yield and its three main componentsand the negative correlations among the three main components at single QTL/gene level.Identification of the QTLs or genes that have positive effects on increasing the spike numberper m2without reducing significantly kernel number per spike and kernel weight, orincreasing the kernel number per spikewithout reducing significantly the spike number per m2and kernel weight will contribute to combining those beneficial QTL or genes for spikenumber per m2, kernel number per spike and kernel weight using molecular marker-assistedtechniques and developing high yielding wheat cultivars with larger spike and more andheavier grains.The main results are as follows:1. An integrative genetic map consisting of802loci was obtained by integrating twogenetic maps that had been constructed based on182families (“C” population) and256families (“D” population). The new map consisted of31linkage groups contained734DArTmarkers,62SSR markers, one TaGW2-CAPS marker, two HMW-GS markers and three Wxprotein subunit markers, and covered21chromosomes of the wheat genome and a total lengthof6034.1cM. Single chromosome length varied from3.5to425.3cM, with an averagebetween marker distance of7.52cM.2. A total of92unconditional additive QTL controlling yield and the three yieldcomponents were detected using IciMapping v3.3. Of these QTLs,11were detected in eachof two environments, five in each of three environments, one in each of five environments. Therefore, these QTL were expressed stably across environments.32QTLs had contributionrates of more than10%. Among which, QTKW-D-2D-3.2, QTKW-D-2D-2.1andQKNPS-DH-7B-2.1were main effect QTLs and had contribution ratge of64.40%,31.91%and34.07%, respectively.34QTLs were located on the same segments on1A,2B,2D,3A,3B,4A,4B,6A and6D chromosomes and showed pleiotropic effects.3.181conditional QTLs were detected in the three populations when yield per m2wasconditioned on spike number per m2, kernel number per spike and thousand-kernel weight andwhen spike number per m2, kernel number per spike and thousand-kernel weight wereconditioned on each other. These QTLs accounted for4.59%-20.29%,5.14%-35.63%and3.62%-69.59%of the phenotypic variations for the DH, C and D population, respectively andwere located on19chromosomes except3D and7D.4. The genetic relationships between yield and the three main yield components andamong the three main yield components were dissected using comparative analysis ofconditional and unconditional QTL for the first time.17QTLs were detected. Of these, fiveQTLs, i.e. QSN-DH-2B, QSN-DH-3A, QSN-DH-6D, QSN-D-1A-1.1and QSN-D-3B-2.1,could increase spike number per m2without reducing kernel number per spike. Two QTLs, i.e.QKNPS-DH-2B-2.1and QKNPS-D-a5B, could increase kernel number per spike withoutreducing spike number per m2. Three QTLs, i.e. QKNPS-D H-1A、QKNPS-DH-2D-1.1、QKNPS-DH-6A, could increase kernel number per spike without reducing spike number perm2and kernel weight. Three QTLs, i.e. QTKW-DH-5B、QTKW-DH-7B and QTKW-C-4B-2.1,could increase kernel weight without reducing kernel number per spike. Two QTLs, i.e.QTKW-DH-4B and QTKW-D-2B-1.1, could increase kernel weight without reducing spikenumber per m2and kernel number per spike. Two QTLs, i.e. QY-DH-2D-1.1and QY-DH-3A,could increase yield but had no effect on spike number per m2. The afore-mentioned QTLs aresome of the important loci that contribute to overcoming the contradictions among spikenumber per m2, kernel number per spike and kernel weight5. A total of154QTLs, including52unconditional and102conditional ones, weredetected in C and D population. Map analysis on the154QTLs was conducted using the QTLprojection function of BioMercateor V3.0. As a result,149of the154QTLs were projectedonto the common genetic map of the two populations. However,33of them were located on the gaps. Finally,116QTLs were projected onto the common map.6.41pairs of unconditional epistatic and63pairs of conditional epistatic QTLs for yieldand the three main yield component traits were detected in this study. Of these,10pairs ofepistatic QTLs accounted for more than40%of the phenotypic variation. Especially, forQSN-C-2B and QSN-C-5D (located in interval wPt-8492-wPt-1454and intervalXgwm190-wPt-6429, respectively), QSN-C-3B and QSN-C-6D (located in intervalwPt-5704-wPt-667891and interval wPt-666615-wPt-666008, respectively), QSN-C-3D andQSN-C-6D (located in interval wPt-5313-Xgpw7646and interval wPt-666615-wPt-666008,respectively), the explainable phenotypic variation was59.07%,51.65%and50.77%,respectively. The additive QTL QTKW-DH-3A-2.1and the epistatic QTL QTKW-DH-3A werelocated in same interval, i.e. Xcfa2170-Xbarc51.