Mapping Quantitative Trait Loci for Agronomic Traits in Winter Wheat under Different Soil Moisture Levels

Abstract

Due to variable moisture conditions in the U.S. Great Plains, it is important to understand genetic control of crop traits under a range of soil moisture levels. Our objective was to identify quantitative trait loci (QTL) for yield, phenology, and morphological traits in wheat (Triticum aestivum L.) under different soil moisture conditions. Field evaluation of a winter wheat doubled haploid population (n = 185) derived from a cross between CO940610 and ‘Platte’ was carried out in Fort Collins and Greeley, Colorado, USA in 2007–2008 and 2008–2009, respectively. At each location, trials were grown under moderate drought stress and fully irrigated conditions. A total of 33 QTL for 11 traits was detected in two or more environments. A cluster of QTL for nine traits was found on chromosome 2B in the vicinity of the photoperiod response gene Ppd-B1. Other stable QTL clusters were detected on chromosome 6A and near the vernalization response gene Vrn-D3 on chromosome 7D. A QTL for grain yield on chromosome 5A was detected in three environments. With minor exceptions, the large-effect QTL were detected in both the water limited and fully irrigated environments, rather than being detected only under specific moisture levels.

Genetic diversity, GWAS and prediction for drought and terminal heat stress tolerance in bread wheat (Triticum aestivum L.)

“…Spike length associated markers were clustered to chromosomes 1A, 2B, 3D and 7B, which corroborates with the findings of El-Feki et al (2018) where ten QTLs for spike length were identified, four of which are located on chromosomes 1A, 2B, 3D and 7B. Additionally, a 5-cM chromosomal region on chromosome 1A was previously found to be significantly associated with a QTL for spike length (Zhou et al 2017).…”

“…The marker RAC875_c24550_1150 that associated with PH QTL under drought stress conditions has been previously shown to be associated with PH QTLs identified under different environments (Zou et al 2017). Clustering of significant marker-trait associations on chromosome 4B coincides with the clustering of the gibberellic acid-insensitive reduced height genes (Rht1) that regulate plant height on chromosome 4B (Cabral et al 2018;El-Feki et al 2018). The marker Tdurum_contig10843_745, which has been reported as a significantly associated marker to PH on chromosome 5A (Zou et al 2017), is closely linked to the marker wsnp_Ex_c10231_16783750 which is significantly associated with PH in our study.…”

“…The plant height QTLs were mostly clustered to chromosomes 3A, 4B, 5A and 6B. Several plant height QTLs were identified on chromosome 3A (Ali et al 2011;Rustgi et al 2013;El-Feki et al 2018). The most common plant height QTLs were detected on chromosome 4B, and a major plant height QTL cluster was mapped to chromosome 4B (Zhang et al 2018).…”

Genome-Wide Association Study Reveals Genomic Regions Associated With Ten Agronomical Traits in Wheat Under Late-Sown Conditions

“…HI is an important yield associated trait which determines the performance of wheat genotypes under abiotic stress conditions such as heat and drought. Previous studies have identified QTLs for HI on Chr1A, Chr2B, Chr5B, and Chr6B (Assanga et al, 2017;El-Feki et al, 2018). In our analysis, three significantly associated QTLs for HI were identified, one QTL on Chr1DS and other two QTLs on 7BL.The significant HI associated QTLs identified in this study are novel and need further validation for use in MAS.…”

QTL mapping of agronomic traits in wheat using the UK Avalon × Cadenza reference mapping population grown in Kazakhstan

“…Despite the recent sharp rise in the importance of genome-wide association studies (GWAS) ( Sukumaran et al, 2015 ; Zanke et al, 2015 ) in wheat, including those performed in Kazakhstan ( Turuspekov et al, 2017 ; Anuarbek et al, 2020 ; Genievskaya et al, 2020 ), QTL analyses based on the use of biparental mapping populations and associated linkage maps still play an important role in the genetic dissection of complex traits associated with yield and its components ( Cuthbert et al, 2008 ; Van Eeuwijk et al, 2010 ; Zhou et al, 2017 ; El-Feki et al, 2018 ; Onyemaobi et al, 2018 ; Tura et al, 2020 ). The importance of this approach relies on the rapid construction of an appropriate mapping population (MP), an abundance of recombination, good phenotyping capability, and the availability of automated single nucleotide polymorphism (SNP) genotyping platforms.…”