Abstract
The genetic manipulation of major yield components and agronomic traits is an important approach to increase wheat grain yield. Phenotyping of these traits is cost-effective but is time-consuming and the output is also confounded by environmental conditions. In the present study, we aimed to identify quantitative trait loci (QTL) and tightly linked, friendly used molecular markers to select for productive tiller number (PTN), fertile spikelet number per spike (fSNS), thousand kernel weight (TKW), grain yield (GY), height (HT), and heading date (HD). These traits were assessed in eight field trials over three years in a double haploid (DH) population that were derived from two adapted high yielding spring wheat cultivars ‘UI Platinum’ and ‘LCS Star’. The DH population of 181 lines was genotyped using the 90K iSelect SNP platform and markers for known genes (Ppd, Vrn, Rht, and FT) that affect plant adaptation. The genotypic data was used in linkage analysis and QTL analysis for yield components and agronomic data using JMP Genomics Software (V9.0). To consider spatial variation, the best linear unbiased prediction (BLUP) was calculated for each trait across all trials. QTL analyses were conducted separately for each trait in individual environments and in trait BLUP across all environments. A total of 48 linkage groups were constructed with a total length of 3892.81 cM and a marker density of 0.33 marker/cM. A total of nineteen QTL were detected, including five for fSNS on chromosomes 5D, 6A, 7B (two QTL), and 7D; two for PTN on chromosomes 4A and 6A; three QTL for TKW on chromosomes 4A, 6A, and 7D; one QTL for GY on chromosome 7D; four QTL for HD on chromosomes 4B, 6A, 7B, and 7D; and four QTL for HT on chromosomes 4A (two QTL), 5D, and 7D. The two parents have complementary and additive QTL effects in all traits evaluated, providing opportunities to improve each trait through pyramiding. However, four QTL, QPTN.uia2-6A, QfSNS.uia2-6A, QTKW.uia2-6A, and QHD.uia2-6A were clustered on chromosome 6A; five other QTL, QTKW.uia2-7D, QfSNS.uia2-7D, QHT.uia2-7D, QGY.uia2-7D, and QHD.uia2-7D were clustered in a small region on chromosome 7DS. The two QTL clusters each control traits that were negatively correlated, suggesting that the trade-off effects pose a challenge and further dissecting of the two clusters is necessary in order to use them in yield improvement. Using the exosome capture data, linkage maps of interest were saturated with additional KASP markers, which helps to dissect the identified QTL clusters. A few of QTL in the two cluster regions were further validated in an elite spring wheat panel, confirming the realty and effectiveness of the identified QTL. KASP markers developed in the present study may useful to pyramid multiple yield components to enhance yield improvement in wheat.