Genome-Wide Linkage Mapping of Quantitative Trait Loci for Late-Season Physiological and Agronomic Traits in Spring Wheat under Irrigated Conditions

Abstract

Many late-season physiological traits affect grain yield in wheat, either directly or indirectly. However, information on the genetic control of yield-related traits is still limited. In this study, we aimed to identify quantitative trait loci (QTL) for canopy temperature and chlorophyll content index during anthesis (CTa and CCIa, respectively), the mid grain-filling stage (CTg1 and CCIg1, respectively), and the late grain-filling stage (CTg2 and CCIg2, respectively) as well as for plant height (PH), thousand kernels weight (TKW), and grain yield (GY) using genome-wide linkage mapping. To this end, a double haploid population derived from a cross between two high yielding wheat cultivars, UI Platinum and SY Capstone, was phenotyped in four irrigated environments and genotyped using the wheat 90K iSelect platform and simple sequence repeats. The genotypic data were used to construct a high-density genetic map of 43 linkage groups (LGs) with a total length of 3594.0 cm and a marker density of 0.37 cm. A total of 116 QTL for all nine traits was detected on 33 LGs, spreading to all wheat chromosomes, except for Chr. 7D. Of these, six QTL (CTa.ui-4B.1, Q.CTg1.ui-5B-2.1, Q.CTg2.ui-6B.1, Q.PH.ui-6A-2.1, Q.TKW.ui-2D-1, and Q.GY.ui-6B) were consistently detected in more than three irrigated environments, called as stable QTL. Additionally, we identified 26 QTL clusters for more than two traits, of which the top four were located on Chromosomes 4A-1, 1B-1, 5B-2, and 2D-1. Overall, the stable QTL significantly related with grain yield, QTL clusters, and linked molecular markers identified in this study, may be useful in marker-assisted selection in early generation and early growth stage for grain yield improvement.

Deciphering the Genetics of Major End-Use Quality Traits in Wheat

“…The genetic gaps between linkage groups may represent genomic regions with a significant amount of repeat elements. Similar observations have been made in other studies using the same wheat Illumina iSelect 90K SNP assay (Kumar et al 2016; Wen et al 2017; Liu et al 2018). Second, the RIL population used in this study was derived from a cross between two elite cultivars which were developed for the Midwest region of the United States, meaning there is only limited genetic variation between them and thus low level of polymorphism markers.…”

Marker-Trait Associations for Enhancing Agronomic Performance, Disease Resistance, and Grain Quality in Synthetic and Bread Wheat Accessions in Western Siberia

“…Similarly, we identified several QTL for thousand kernel weight, which were reported earlier on chromosomes 3B (Golabadi et al 2011; Sukumaran et al 2018; Bhatta et al 2018b), 3D (Liu et al 2018), 4A (Bhatta et al 2018b), 4B (Pinto et al 2010; Bhatta et al 2018b), 5A (Sukumaran et al 2015; Guan et al 2018), 5D (Liu et al 2018), 6A (Sukumaran et al 2015; Guan et al 2018), and 6B (Assanga et al 2017), suggesting the important genomic regions governing thousand kernel weight. For harvest index, we identified three novel MTAs for increasing harvest index on chromosomes 4B and 7D.…”

QTL identification and KASP marker development for productive tiller and fertile spikelet numbers in two high-yielding hard white spring wheat cultivars

“…In the case of the DH population used in this study, using the phenotyping data collected in the same trials described in this study, correlations between PTN and fSNS with thousand kernel weight (TKW) and yield (YLD) were not significant (all P values > 0.05 and data not shown), indicating there are no direct relationships between PTN and fSNS with TKW and YLD. Furthermore, using the same genotyping data, the QTL for TKW were identified on chromosomes 2D, 3D, and 5D, while the QTL for YLD were identified on chromosomes 2A and 6B (Liu et al 2018b ). No common QTL were identified between PTN and fSNS with TKW and YLD.…”

“…The markers in each cluster were ordered using the Kosambi mapping function and the accelerated map order optimization algorithm in JMP Genomics 8.0. Groups were broken into parts if the genetic distance between adjacent markers was greater than 35 cM (Liu et al 2018b ).…”