A new strategy for using historical imbalanced yield data to conduct genome-wide association studies and develop genomic prediction models for wheat breeding.

Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over 10 years, through testing yield in 40-66 lines each year at 6-14 locations with 38-41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, was used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01287-8.

Effect of nitrogen supply on stay-green sorghum in differing post-flowering water regimes.

MAIN CONCLUSION: The expression of stay-green (SG) characteristic in sorghum under water stress was related to N supply. SG genotype performed better than a non-stay-green (NSG) genotype at medium and high N levels. The differences in physiological parameters between SG and NSG genotypes were not significant at low N level and severe water stress. Grain sorghum [Sorghum bicolor (L.) Moench] with stay-green (SG) trait has the potential to produce more biomass and use soil water and nitrogen (N) more efficiently under post-flowering water stress. Previous studies were mostly conducted without N deficiency and more information is needed for interactions among soil N availability, SG genotype, and post-flowering water stress. In this study, the differences in leaf growth and senescence, shoot and root biomass, evapotranspiration (ET), water use efficiency (WUE), leaf photosynthetic responses, and nitrogen use efficiency (NUE) between a SG genotype (BTx642) and a non-stay-green (NSG) genotype (Tx7000) were examined. The two genotypes were grown at three N levels (Low, LN; Medium, MN; High, HN) and under three post-flowering water regimes (No water deficit, ND; Moderate water deficit, MD; Severe water deficit, SD). The genotypic difference was generally significant while it frequently interacted with N levels and water regimes. At medium and high N levels, SG genotype consistently had greater green leaf area, slower senescence rate, more shoot biomass and root biomass, and greater WUE and NUE than the NSG genotype under post-flowering drought. However, differences in several variables (e.g., leaf senescence, ET, WUE and NUE) between genotypes were not significant under SD at LN. At HN and MN, photosynthetic function of SG genotype was better maintained under drought. At LN, SG genotype maintained greater green leaf area but had lower photosynthetic activity than the NSG genotype. Nonetheless, adequate N supply is important for SG genotype under drought and greater root biomass may contribute to greater NUE in SG genotype.

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.

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments.

Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F5:7recombinant inbred lines derived from the cross 'TAM 112'/'TAM 111' was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m-2 and increased test weight by 2.14 and 3.47 kg m-3 with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.