Kirk E. Jessup

Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs

Stable quantitative trait loci (QTL) are important for deployment in marker assisted selection in wheat (Triticum aestivum L.) and other crops. We reported QTL discovery in wheat using a population of 217 recombinant inbred lines and multiple statistical approach including multi-environment, multi-trait and epistatic interactions analysis. We detected nine consistent QTL linked to different traits on chromosomes 1A, 2A, 2B, 5A, 5B, 6A, 6B and 7A. Grain yield QTL were detected on chromosomes 2B.1 and 5B across three or four models of GenStat, MapQTL, and QTLNetwork while the QTL on chromosomes 5A.1, 6A.2, and 7A.1 were only significant with yield from one or two models. The phenotypic variation explained (PVE) by the QTL on 2B.1 ranged from 3.3–25.1% based on single and multi-environment models in GenStat and was pleiotropic or co-located with maturity (days to heading) and yield related traits (test weight, thousand kernel weight, harvest index). The QTL on 5B at 211 cM had PVE range of 1.8–9.3% and had no significant pleiotropic effects. Other consistent QTL detected in this study were linked to yield related traits and agronomic traits. The QTL on 1A was consistent for the number of spikes m-2 across environments and all the four analysis models with a PVE range of 5.8–8.6%. QTL for kernels spike-1 were found in chromosomes 1A, 2A.1, 2B.1, 6A.2, and 7A.1 with PVE ranged from 5.6–12.8% while QTL for thousand kernel weight were located on chromosomes 1A, 2B.1, 5A.1, 6A.2, 6B.1 and 7A.1 with PVEranged from 2.7–19.5%. Among the consistent QTL, five QTL had significant epistatic interactions (additive × additive) at least for one trait and none revealed significant additive × additive × environment interactions. Comparative analysis revealed that the region within the confidence interval of the QTL on 5B from 211.4–244.2 cM is also linked to genes for aspartate-semialdehyde dehydrogenase, splicing regulatory glutamine/lysine-rich protein 1 isoform X1, and UDP-glucose 6-dehydrogenase 1-like isoform X1. The stable QTL could be important for further validation, high throughput SNP development, and marker-assisted selection (MAS) in wheat.

Physiological Responses of Hard Red Winter Wheat to Infection by Wheat streak mosaic virus.

Wheat streak mosaic virus (WSMV) causes significant yield loss in hard red winter wheat in the U.S. Southern High Plains. Despite the prevalence of this pathogen, little is known about the physiological response of wheat to WSMV infection. A 2-year study was initiated to (i) investigate the effect of WSMV, inoculated at different development stages, on shoot and root growth, water use, water use efficiency (WUE), and photosynthesis and (ii) understand the relationships between yield and photosynthetic parameters during WSMV infection. Two greenhouse experiments were conducted with two wheat cultivars mechanically inoculated with WSMV at different developmental stages, from three-leaf to booting. WSMV inoculated early, at three- to five-leaf stage, resulted in a significant reduction in shoot biomass, root dry weight, and yield compared with wheat infected at the jointing and booting stages. However, even when inoculated as late as jointing, WSMV still reduced grain yield by at least 53%. Reduced tillers, shoot biomass, root dry weight, water use, and WUE contributed to yield loss under WSMV infection. However, infection by WSMV did not affect rooting depth and the number of seminal roots but reduced the number of nodal roots. Leaf photosynthetic parameters (chlorophyll [SPAD], net photosynthetic rate [Pn], stomatal conductance [Gs], intercellular CO2 concentration [Ci], and transpiration rate [Tr]) were reduced when infected by WSMV, and early infection reduced parameters more than late infection. Photosynthetic parameters had a linear relationship with grain yield and shoot biomass. The reduced Pn under WSMV infection was mainly in response to decreased Gs, Ci, and SPAD. The results of this study indicated that leaf chlorophyll and gas exchange parameters can be used to quantify WSMV effects on biomass and grain yield in wheat.

Spectral Reflectance Models for Characterizing Winter Wheat Genotypes

ABSTRACT Optimum wheat (Triticum aestivum L.) yield can be achieved by developing and growing the best genotypes in the most suited environments. However, exhaustive field measurements are required to characterize plants with desirable traits in breeding plots. Remote sensing tools have been shown to provide relatively accurate and simultaneous measurements of plant characteristics without destructive sampling, and at low cost. The aim of this research was to develop and evaluate spectral reflectance-based models for characterizing winter wheat genotypes in the semiarid U.S. Southern Great Plains (SGP). Field experiments were conducted at Bushland, TX, during the 2011–2012 growing season. The spectral behavior of 20 wheat genotypes with wide genetic background was analyzed in relation to leaf area index (LAI) and yield under irrigated and dryland conditions. Reflectance-based models were developed and evaluated using three approaches: the maximum correlations, the optimum multiple narrow band reflectance (OMNBR), and the vegetation indices (VIs). Results indicated that the combinations of two to four bands in OMNBR models explained most of the variability (65% to 89% and 51% to 95% for dryland and irrigated conditions, respectively). Spectral regions in visible (VIS: 350–700 nm), near-infrared (NIR: 700–1,300 nm), and mid-infrared (MIR: 1,300–2,500 nm) were sensitive to LAI and yield, most commonly the MIR region. Models developed in this study are expected to assist in developing rapid and reliable methods for germplasm screening and selection of winter wheat genotypes.