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.

Genome wide identification of QTL associated with yield and yield components in two popular wheat cultivars TAM 111 and TAM 112.

Two drought-tolerant wheat cultivars, 'TAM 111' and 'TAM 112', have been widely grown in the Southern Great Plains of the U.S. and used as parents in many wheat breeding programs worldwide. This study aimed to reveal genetic control of yield and yield components in the two cultivars under both dryland and irrigated conditions. A mapping population containing 124 F5:7 recombinant inbred lines (RILs) was developed from the cross of TAM 112/TAM 111. A set of 5,948 SNPs from the wheat 90K iSelect array and double digest restriction-site associated DNA sequencing was used to construct high-density genetic maps. Data for yield and yield components were obtained from 11 environments. QTL analyses were performed based on 11 individual environments, across all environments, within and across mega-environments. Thirty-six unique consistent QTL regions were distributed on 13 chromosomes including 1A, 1B, 1D, 2A, 2D, 3D, 4B, 4D, 6A, 6B, 6D, 7B, and 7D. Ten unique QTL with pleiotropic effects were identified on four chromosomes and eight were in common with the consistent QTL. These QTL increased dry biomass grain yield by 16.3 g m-2, plot yield by 28.1 g m-2, kernels spike-1 by 0.7, spikes m-2 by 14.8, thousand kernel weight by 0.9 g with favorable alleles from either parent. TAM 112 alleles mainly increased spikes m-2 and thousand kernel weight while TMA 111 alleles increased kernels spike-1, harvest index and grain yield. The saturated genetic map and markers linked to significant QTL from this study will be very useful in developing high throughput genotyping markers for tracking the desirable haplotypes of these important yield-related traits in popular parental cultivars.

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.