Abstract
BackgroundHuman demand for wheat will continue to increase together with the continuous global population growth. Agronomic traits in wheat are susceptible to environmental conditions. Therefore, in breeding practice, priority is given to QTLs of agronomic traits that can be stably detected across multiple environments and over many years.ResultsIn this study, QTL analysis was conducted for eight agronomic traits using an introgression line population across eight environments (drought stressed and well-watered) for 5 years. In total, 44 additive QTLs for the above agronomic traits were detected on 15 chromosomes. Among these, qPH-6A, qHD-1A, qSL-2A, qHD-2D and qSL-6A were detected across seven, six, five, five and four environments, respectively. The means in the phenotypic variation explained by these five QTLs were 12.26, 9.51, 7.77, 7.23, and 8.49%, respectively.ConclusionsWe identified five stable QTLs, which includes qPH-6A, qHD-1A, qSL-2A, qHD-2D and qSL-6A. They play a critical role in wheat agronomic traits. One of the dwarf genes Rht14, Rht16, Rht18 and Rht25 on chromosome 6A might be the candidate gene for qPH-6A. The qHD-1A and qHD-2D were novel stable QTLs for heading date and they differed from known vernalization genes, photoperiod genes and earliness per se genes.
Highlights
Human demand for wheat will continue to increase together with the continuous global population growth
Huang et al [5, 6] analyzed Quantitative trait loci (QTLs) related to agronomic traits with two Introgression line (IL) populations derived from ‘Prinz’/‘W-7984’ and ‘Flair’/‘XX86’, respectively, and Yan et al [7] mapped QTLs for ten agronomic traits using 160 BC3F3 ILs derived from a cross between Lumai14 and Jing411
The results revealed that Shaanhan 8675 was a high-value parent, whereas Lumai 14 was a low-value parent in terms of plant height, spike length, the number of valid tillers, fertile spikelet number per main spike, grain weight per plant, thousand-grain weight, and grain number per spike
Summary
Human demand for wheat will continue to increase together with the continuous global population growth. Agronomic traits in wheat are susceptible to environmental conditions. QTL mapping using IL populations can eliminate interference from the genetic background and improve the accuracy of gene mapping. Many QTLs for agronomic traits in wheat have been detected using IL populations. Pestsova et al [4] identified seventeen significant QTLs for agronomic traits using an IL population derived from the substitution lines ‘Chinese Spring’/ ‘Synthetic 6x’. Ibrahim et al [8] used the ‘Triso’/‘Syn084’ IL population to map QTLs and identified seven QTLs for heading date, five QTLs for days to maturity, three QTLs for number of spikes per plant, six QTLs for thousand grain weight, and seven QTLs for grain yield. In breeding practice, priority is given to QTLs of agronomic traits that can be stably detected across multiple environments and over many years
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