Genetic Mapping of Quantitative Trait Loci Associated with Plant Height and Yield Component Traits in a Wheat (Triticum aestivum L.) Doubled Haploid Population Derived from Tugela-DN × Elands

Abstract

Grain yield is a quantitatively inherited complex trait that is strongly influenced by interacting genetic and environmental factors. The identification of major quantitative trait loci (QTL) for plant height (PH) and yield component traits (YCT) is important for improving yield potential through wheat breeding. We performed a QTL analysis for PH and YCT in the Tugela-DN × Elands doubled haploid (DH) population using a genotype-by-sequence single nucleotide polymorphism and a silicoDArT-based genetic map. Field trials were conducted under rain-fed conditions across five environments in the Free State Province of South Africa during the 2017–2018 and 2018–2019 cropping seasons. Analysis of variance revealed significant differences (p < 0.001) among DH lines and the environments. However, for G × E interactions, significant differences (p < 0.05) were only observed for spikelet number per spike. Broad-sense heritability estimates of all traits ranged between 0.44 and 0.81. Nine QTL, viz. QPh.sgi-6A.2 and QPh.sgi-4D for PH, QSl.sgi-6A.2 and QSl.sgi-7A for spike length, QGns.sgi-3B for grain number per spike (GNS), QGwps.sgi-7B for grain weight per spike (GWPS), QGw.sgi-2A and QGw.sgi-7A for grain width, and QGl.sgi-3B for grain length (GL), were identified on chromosomes 2A, 3B, 4D, 6A, 7A, and 7B, in two or more environments. Some of these QTL exhibited pleiotropic effects. The QPh.sgi-6A.2 QTL for PH and QGwps.sgi-7B for GWPS appear to be novel QTL, while the rest of the reported QTL validated previously identified QTL for PH and YCT. The study also reported a trade-off between GL and GNS. The findings of this study will be useful in elucidating the genetic architecture of yield component traits contributing to the development of new dryland wheat varieties with high and stable yield.

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

“…The information obtained in this study will be useful for manipulating the QTLs for plant breeding by marker-assisted selection. (15) for particular methods of estimation and different number of QTL-QTL-QTL triples. p-phenotypic method, g u -unweighted regression, g w1 -weighted regression: weighted triple interaction effects only, g w2 -weighted regression: weighted all effects: QTL, epistatic and triple interaction.…”

“…Most important plant traits are quantitative in nature and are influenced by multiple quantitative trait locus (QTLs). The effects of these QTLs on the expression of quantitative trait has been considered in various species such as maize [ 8 ], barley [ 9 ], rice [ 10 , 11 ], oilseed rape [ 12 , 13 , 14 ], wheat [ 15 ] and sugar beet [ 16 ]. Many loci showing cumulative small effects are often rejected in standard QTL analyses [ 17 , 18 ].…”

Genetic and Genomic Pathways to Improved Wheat (Triticum aestivum L.) Yields: A Review

“…Quantitative Trait Loci QTL mapping is a crucial method in plant genetics, particularly in wheat, for identifying regions on chromosomes associated with complex traits like yield, disease resistance, and drought tolerance [28]. The process begins with phenotyping, where traits of interest are measured across a population, followed by genotyping, where the population is screened using molecular markers distributed throughout the genome [29]. A mapping population derived from two parental lines differing in the target traits is essential for QTL mapping.…”