Mapping Agronomic and Quality Traits in Elite Durum Wheat Lines under Differing Water Regimes

Abstract

Final grain production and quality in durum wheat are affected by biotic and abiotic stresses. The association mapping (AM) approach is useful for dissecting the genetic control of quantitative traits, with the aim of increasing final wheat production under stress conditions. In this study, we used AM analyses to detect quantitative trait loci (QTL) underlying agronomic and quality traits in a collection of 294 elite durum wheat lines from CIMMYT (International Maize and Wheat Improvement Center), grown under different water regimes over four growing seasons. Thirty-seven significant marker-trait associations (MTAs) were detected for sedimentation volume (SV) and thousand kernel weight (TKW), located on chromosomes 1B and 2A, respectively. The QTL loci found were then confirmed with several AM analyses, which revealed 12 sedimentation index (SDS) MTAs and two additional loci for SV (4A) and yellow rust (1B). A candidate gene analysis of the identified genomic regions detected a cluster of 25 genes encoding blue copper proteins in chromosome 1B, with homoeologs in the two durum wheat subgenomes, and an ubiquinone biosynthesis O-methyltransferase gene. On chromosome 2A, several genes related to photosynthetic processes and metabolic pathways were found in proximity to the markers associated with TKW. These results are of potential use for subsequent application in marker-assisted durum wheat-breeding programs.

Identification of Quantitative Trait Loci Hotspots Affecting Agronomic Traits and High-Throughput Vegetation Indices in Rainfed Wheat

“…The AM has been used to identify genomic regions related to drought and heat tolerance in durum and bread wheat (Maccaferri et al, 2016 ; Valluru et al, 2017 ). Several studies have been conducted to investigate the genetic basis of grain yield and yield-related traits in bread wheat under water-stress conditions using AM (Edae et al, 2014 ; Gizaw et al, 2018 ; Qaseem et al, 2019 ; Mérida-García et al, 2020 ). The release of genome sequences for emmer wheat (Avni et al, 2017 ), bread wheat (IWGSC, 2018 ), and durum wheat (Maccaferri et al, 2019 ) and the availability of open databases of RNA sequencing (RNA-seq) experiments (Ramírez-González et al, 2018 ) have made it possible to use a candidate gene (CG) approach to find targets within QTL intervals without performing new functional studies.…”

Genetic variability assessment of 127 Triticum turgidum L. accessions for mycorrhizal susceptibility-related traits detection

“…Close to QTLamf-6A, QTLs for epistatic effects for flour color traits (QFb.cerz-6AL.2) 61 , and for grain length (qgl6A) and weight (qtkw6A) 62 were detected. QTLamf-2A co-localize to QSPS-2A.4 63 and to the markers DArT3154, DArT3155 and DArT3156, significantly associated to yield-related trait in wheat 64 . The microsatellite Xgwm120 and the SNP 1072874, significantly and respectively associated with QTL for scab 65 and Fusarium head blight (FHB) 66 resistance, were detected in the same chromosome region AX-95019471.…”

Molecular Marker Technology for Crop Improvement

“…Genome-wide association studies (GWASs) have become a valuable tool in recent years as a complementary approach to biparental mapping, providing broader allelic coverage and higher mapping resolution. In this issue, GWASs were performed for the analysis of seminal roots in landraces of wheat and durum wheat from the Mediterranean basin [10,11], agronomic and quality traits in elite durum wheat [12], and for flowering time in maize inbred lines [13]. The studies of Roselló et al [10] and Rufo et al [11] pointed out the usefulness of the old germplasm to be used as genetic resources for improving drought-related traits in the breeding programs to broaden the genetic variability.…”

“…The studies of Roselló et al [10] and Rufo et al [11] pointed out the usefulness of the old germplasm to be used as genetic resources for improving drought-related traits in the breeding programs to broaden the genetic variability. Merida-García et al [12] combined a GWAS with a candidate gene approach to successfully identify gene clusters involved in important traits for wheat breeding. The study of Maldonado et al [13] revealed that the use of a GWAS based on haplotype blocks was more efficient than the standard approach to identify major effect loci, and the network-assisted gene prioritization used identified four genes influencing flowering time in tropical maize.…”