Mapping QTLs Controlling Agronomic Traits in the ‘Attila’ × ‘CDC Go’ Spring Wheat Population under Organic Management using 90K SNP Array

Abstract

Our group previously reported five quantitative trait loci (QTL) associated with plant height, test weight, thousand‐kernel weight, and grain protein content in a recombinant inbred line population derived from spring wheat (Triticum aestivum L.) cultivars ‘Attila’ and ‘CDC Go’, evaluated across three environments (2008–2010) under organic management and genotyped with 579 diversity arrays technology and Rht‐B1 markers. No QTL was identified for flowering time, maturity, grain yield, and number of tillers across all three environments. In the present study, we reanalyzed the same phenotypic data with a subset of 1200 informative single‐nucleotide polymorphic (SNP) markers out of the 90K SNP array and three gene‐specific markers (Ppd‐D1, Vrn‐A1, and Rht‐B1) to investigate if high marker density improves QTL detection. Here, five moderate‐ and eleven minor‐effect QTLs were detected across all three organic environments using the new genotypic data, including 13 QTLs that were not previously detected. Up to five QTLs were detected for each trait, except grain protein content, which individually accounted for 5.5 to 18.8% of phenotypic variance. For each trait, the total phenotypic and genetic variance explained by all detected QTLs varied from 9.3 to 39.4 and from 24.6 to 96.8%, respectively, which was much greater than in our previous study. One of the moderate‐effect QTLs on 5A was coincidental for flowering time and maturity and mapped close to the Vrn‐A1 gene, while the second moderate‐effect coincidental QTL on 4B was associated with both plant height and maturity but was 27 cM from the Rht‐B1 gene. Results from this study provide additional information for wheat researchers and organic wheat breeders.

Quantitative Trait Loci (QTL) Analysis of Fruit and Agronomic Traits of Tropical Pumpkin (Cucurbita moschata) in an Organic Production System

“…The use of molecular markers has become a viable way to identify genetic loci associated with desired phenotypes in parallel with successful establishment in an organic setting [4,5]. However, most of these approaches focus on major crops such as wheat [6], sweet corn [4], tomato [7], and barley [8], and few studies are focused on smaller crops such as pumpkin (Cucurbita moschata: 2n = 2x = 40). Hawai'i presents a unique opportunity to focus on molecular characterization for plant breeding on small crops due to its fertile soils [9], year-round growing environment [10], and consumer demand for organic products [11].…”

QTLs associated with agronomic traits in the Attila × CDC Go spring wheat population evaluated under conventional management

“…Although several factors might have contributed to our failure to identify QTLs explaining most of the phenotypic variance in the ‘Attila’ × ‘CDC Go’ RIL population, low marker density and uneven marker distribution in the linkage maps are possible reasons. Currently, a total of 81,587 gene-associated SNPs (90K) is available for wheat genotyping through the Illumina iSelect SNP array [ 19 ], of which at least 5 to 13% could be polymorphic in a given bi-parental mapping population [ 20 – 23 ]. In order to investigate if an increase in marker density improves QTL detection, we reanalyzed the same phenotype data averaged over the three organically managed environments with a subset of 1200 high quality SNPs out of the 90K SNP array and three gene specific markers ( Ppd-D1 , Vrn-A1 and Rht-B1 ) [ 20 ].…”

“…Currently, a total of 81,587 gene-associated SNPs (90K) is available for wheat genotyping through the Illumina iSelect SNP array [ 19 ], of which at least 5 to 13% could be polymorphic in a given bi-parental mapping population [ 20 – 23 ]. In order to investigate if an increase in marker density improves QTL detection, we reanalyzed the same phenotype data averaged over the three organically managed environments with a subset of 1200 high quality SNPs out of the 90K SNP array and three gene specific markers ( Ppd-D1 , Vrn-A1 and Rht-B1 ) [ 20 ]. That study identified a total of 16 QTLs distributed across 10 chromosomes of which 13 QTLs were not reported using the DArT-based low-marker-density.…”

Global warming is reducing the tillering capacity and grain yield of wheat in Yaqui Valley, Mexico

“…Historically, it has been shown that between 300 and 850 cold hours per season provide a yield near the geneticproductive potential of the varieties, with the greatest contribution to grain yield seen during the first stages, until the tillering phenophase (Zou et al, 2017). Currently, the number of cold hours or heat units is the basis of research in various regions around the world under the context of climate change (Kaur and Kaur, 2017).…”