Characterizing the oligogenic architecture of plant growth phenotypes informs genomic selection approaches in a common wheat population

Improved genomic prediction performance with ensembles of diverse models

The improvement of selection accuracy of genomic prediction is a key factor in accelerating genetic gain for crop breeding. Traditionally, efforts have focused on developing superior individual genomic prediction models. However, this approach has limitations due to the absence of a consistently "best" individual genomic prediction model, as suggested by the No Free Lunch Theorem. The No Free Lunch Theorem states that the performance of an individual prediction model is expected to be equivalent to the others when averaged across all prediction scenarios. To address this, we explored an alternative method: combining multiple genomic prediction models into an ensemble. The investigation of ensembles of prediction models is motivated by the Diversity Prediction Theorem, which indicates the prediction error of the many-model ensemble should be less than the average error of the individual models due to the diversity of predictions among the individual models. To investigate the implications of the No Free Lunch and Diversity Prediction Theorems, we developed a naïve ensemble-average model, which equally weights the predicted phenotypes of individual models. We evaluated this model using 2 traits influencing crop yield-days to anthesis and tiller number per plant-in the teosinte nested association mapping dataset. The results show that the ensemble approach increased prediction accuracies and reduced prediction errors over individual genomic prediction models. The advantage of the ensemble was derived from the diverse predictions among the individual models, suggesting the ensemble captures a more comprehensive view of the genomic architecture of these complex traits. These results are in accordance with the expectations of the Diversity Prediction Theorem and suggest that ensemble approaches can enhance genomic prediction performance and accelerate genetic gain in crop breeding programs.

Nitrogen addition enhances seed yield by improving soil enzyme activity and nutrients

Nitrogen (N) addition is a simple and effective field management approach to enhancing plant productivity. Nonetheless, the regulatory mechanisms governing nitrogen concentrations and their effect on soil enzyme activity, nutrient levels, and seed yield in the Festuca kirilowii seed field have yet to be elucidated. Therefore, this study sought to investigate the effect of N fertilizer application on soil enzyme activities, soil nutrients, and seed yield of F. kirilowii Steud cv. Huanhu, the only domesticated variety in the Festuca genus of the Poaceae family, was investigated based on two-year field experiments in the Qinghai-Tibet Plateau (QTP). Results showed that N input significantly affected soil nutrients (potential of hydrogen, total nitrogen, organic matter, and total phosphorus). In addition, soil enzyme activities (urease, catalase, sucrase, and nitrate reductase) significantly increased in response to varying N concentrations, inducing changes in soil nutrient contents. Introducing N improved both seed yield and yield components (number of tillers and number of fertile tillers). These findings suggest that the introduction of different concentrations of N fertilizers can stimulate soil enzyme activity, thus hastening nutrient conversion and increasing seed yield. The exhaustive evaluation of the membership function showed that the optimal N fertilizer treatment was N4 (75 kg·hm-2) for both 2022 and 2023. This finding provides a practical recommendation for improving the seed production of F. kirilowii in QTP.

Wheat bZIPC1 interacts with FT2 and contributes to the regulation of spikelet number per spike

KEY MESSAGE

The wheat transcription factor bZIPC1 interacts with FT2 and affects spikelet and grain number per spike. We identified a natural allele with positive effects on these two economically important traits. Loss-of-function mutations and natural variation in the gene FLOWERING LOCUS T2 (FT2) in wheat have previously been shown to affect spikelet number per spike (SNS). However, while other FT-like wheat proteins interact with bZIP-containing transcription factors from the A-group, FT2 does not interact with any of them. In this study, we used a yeast-two-hybrid screen with FT2 as bait and identified a grass-specific bZIP-containing transcription factor from the C-group, designated here as bZIPC1. Within the C-group, we identified four clades including wheat proteins that show Y2H interactions with different sets of FT-like and CEN-like encoded proteins. bZIPC1 and FT2 expression partially overlap in the developing spike, including the inflorescence meristem. Combined loss-of-function mutations in bZIPC-A1 and bZIPC-B1 (bzipc1) in tetraploid wheat resulted in a drastic reduction in SNS with a limited effect on heading date. Analysis of natural variation in the bZIPC-B1 (TraesCS5B02G444100) region revealed three major haplotypes (H1-H3), with the H1 haplotype showing significantly higher SNS, grain number per spike and grain weight per spike than both the H2 and H3 haplotypes. The favorable effect of the H1 haplotype was also supported by its increased frequency from the ancestral cultivated tetraploids to the modern tetraploid and hexaploid wheat varieties. We developed markers for the two non-synonymous SNPs that differentiate the bZIPC-B1b allele in the H1 haplotype from the ancestral bZIPC-B1a allele present in all other haplotypes. These diagnostic markers are useful tools to accelerate the deployment of the favorable bZIPC-B1b allele in pasta and bread wheat breeding programs.

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.

Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures

Large-effect loci-those statistically significant loci discovered by genome-wide association studies or linkage mapping-associated with key traits segregate amidst a background of minor, often undetectable, genetic effects in wild and domesticated plants and animals. Accurately attributing mean differences and variance explained to the correct components in the linear mixed model analysis is vital for selecting superior progeny and parents in plant and animal breeding, gene therapy, and medical genetics in humans. Marker-assisted prediction and its successor, genomic prediction, have many advantages for selecting superior individuals and understanding disease risk. However, these two approaches are less often integrated to study complex traits with different genetic architectures. This simulation study demonstrates that the average semivariance can be applied to models incorporating Mendelian, oligogenic, and polygenic terms simultaneously and yields accurate estimates of the variance explained for all relevant variables. Our previous research focused on large-effect loci and polygenic variance separately. This work aims to synthesize and expand the average semivariance framework to various genetic architectures and the corresponding mixed models. This framework independently accounts for the effects of large-effect loci and the polygenic genetic background and is universally applicable to genetics studies in humans, plants, animals, and microbes.

The Effect of Different Cropping Systems on Yield, Quality, Productivity Elements, and Morphological Characters in Wheat (Triticum aestivum)

The aim of this work was to study how certain applied cropping systems (conventional systems differentiated by fertilization level or sowing season and subsistence farming) influence yield, quality, productivity elements, and morphological characters in a collection of Romanian and foreign wheat cultivars. The following indicators were evaluated: productive potential (yield), quality (test weight, protein content, wet gluten content, deformation index, sedimentation index, and gluten index), as well as other elements that determine yield (number of ears/square meter, thousand kernel weight, number of grains/ear, and weight of grains/ear) and plant height. The results show that the cropping systems influenced all the elements studied except the thousand-kernel weight. The only characteristics influenced by higher nitrogen fertilization were test weight, protein content, wet gluten content, deformation index, and gluten index. The superiority of a delayed conventional system was shown by the number of grains/wheat ear and the deformation index. Protein content was differentiated between the conventional and the subsistence system, but especially between the low-input and the conventional system. Nitrogen supply is the most important factor for determining wheat productivity and grain quality.

A Haplotype-Based GWAS Identified Trait-Improving QTL Alleles Controlling Agronomic Traits under Contrasting Nitrogen Fertilization Treatments in the MAGIC Wheat Population WM-800

The multi-parent-advanced-generation-intercross (MAGIC) population WM-800 was developed by intercrossing eight modern winter wheat cultivars to enhance the genetic diversity present in breeding populations. We cultivated WM-800 during two seasons in seven environments under two contrasting nitrogen fertilization treatments. WM-800 lines exhibited highly significant differences between treatments, as well as high heritabilities among the seven agronomic traits studied. The highest-yielding WM-line achieved an average yield increase of 4.40 dt/ha (5.2%) compared to the best founder cultivar Tobak. The subsequent genome-wide-association-study (GWAS), which was based on haplotypes, located QTL for seven agronomic traits including grain yield. In total, 40, 51, and 46 QTL were detected under low, high, and across nitrogen treatments, respectively. For example, the effect of QYLD_3A could be associated with the haplotype allele of cultivar Julius increasing yield by an average of 4.47 dt/ha (5.2%). A novel QTL on chromosome 2B exhibited pleiotropic effects, acting simultaneously on three-grain yield components (ears-per-square-meter, grains-per-ear, and thousand-grain-weight) and plant-height. These effects may be explained by a member of the nitrate-transporter-1 (NRT1)/peptide-family, TaNPF5.34, located 1.05 Mb apart. The WM-800 lines and favorable QTL haplotypes, associated with yield improvements, are currently implemented in wheat breeding programs to develop advanced nitrogen-use efficient wheat cultivars.

Effect of nitrogen fertilizer on seed yield and quality of Kengyilia melanthera (Triticeae, Poaceae)

Widely distributed in the alpine sandy grassland in east Qinghai-Tibet Plateau (QTP), Kengyilia melanthera is considered as an ideal pioneer grass for the restoration of degraded and desertification grassland in the region. Under the special ecological and climatic conditions in the northwest Sichuan plateau located in east QTP, it is of great significance to optimize the amount of nitrogen fertilizer for the seed production of this species. The impact of nitrogen (N) fertilizer application on seed yield and quality of K. melanthera 'Aba', the only domesticated variety in the Kengyilia genus of Poaceae, was investigated based on two-year field experiments in the northwestern Sichuan plateau. The results showed that with the increase of N fertilizer application, the number of tillers, number of fertile tillers, 1,000-seed weight and seed yield of this species increased likewise. The optimum N fertilizer rate deduced in the present study was 180 kg·hm^-2, where the number of fertile tillers 1,000-seed weight and seed yield reached the peak values. Interestingly, the standard germination rate, germination energy, accelerated aging germination rate, dehydrogenase and acid phosphatase activity of seeds were not affected by the increasing the input of N fertilizer. The comprehensive evaluation of membership function showed that the optimal N fertilizer treatment was 180 kg·hm^-2 both for 2016 and 2017. This study provided a certain practical suggestion for the improvement of seed production of K. melanthera in the northwest Sichuan plateau.

Accounting for heading date gene effects allows detection of small-effect QTL associated with resistance to Septoria nodorum blotch in wheat

In humid and temperate areas, Septoria nodorum blotch (SNB) is a major fungal disease of common wheat (Triticum aestivum L.) in which grain yield is reduced when the pathogen, Parastagonospora nodorum, infects leaves and glumes during grain filling. Foliar SNB susceptibility may be associated with sensitivity to P. nodorum necrotrophic effectors (NEs). Both foliar and glume susceptibility are quantitative, and the underlying genetics are not understood in detail. We genetically mapped resistance quantitative trait loci (QTL) to leaf and glume blotch using a double haploid (DH) population derived from the cross between the moderately susceptible cultivar AGS2033 and the resistant breeding line GA03185-12LE29. The population was evaluated for SNB resistance in the field in four successive years (2018–2021). We identified major heading date (HD) and plant height (PH) variants on chromosomes 2A and 2D, co-located with SNB escape mechanisms. Five QTL with small effects associated with adult plant resistance to SNB leaf and glume blotch were detected on 1A, 1B, and 6B linkage groups. These QTL explained a relatively small proportion of the total phenotypic variation, ranging from 5.6 to 11.8%. The small-effect QTL detected in this study did not overlap with QTL associated with morphological and developmental traits, and thus are sources of resistance to SNB.

A network modeling approach provides insights into the environment-specific yield architecture of wheat

Wheat (Triticum aestivum) yield is impacted by a diversity of developmental processes which interact with the environment during plant growth. This complex genetic architecture complicates identifying quantitative trait loci (QTL) that can be used to improve yield. Trait data collected on individual processes or components of yield have simpler genetic bases and can be used to model how QTL generate yield variation. The objectives of this experiment were to identify QTL affecting spike yield, evaluate how their effects on spike yield proceed from effects on component phenotypes, and to understand how the genetic basis of spike yield variation changes between environments. A 358 F5:6 RIL population developed from the cross of LA-95135 and SS-MPV-57 was evaluated in two replications at five locations over the 2018 and 2019 seasons. The parents were two soft red winter wheat cultivars differing in flowering, plant height, and yield component characters. Data on yield components and plant growth were used to assemble a structural equation model (SEM) to characterize the relationships between QTL, yield components and overall spike yield. The effects of major QTL on spike yield varied by environment, and their effects on total spike yield were proportionally smaller than their effects on component traits. This typically resulted from contrasting effects on component traits, where an increase in traits associated with kernel number was generally associated with a decrease in traits related to kernel size. In all, the complete set of identified QTL was sufficient to explain most of the spike yield variation observed within each environment. Still, the relative importance of individual QTL varied dramatically. Path analysis based on coefficients estimated through SEM demonstrated that these variations in effects resulted from both different effects of QTL on phenotypes and environment-by-environment differences in the effects of phenotypes on one another, providing a conceptual model for yield genotype-by-environment interactions in wheat.

Identification and characterization of a natural polymorphism in FT-A2 associated with increased number of grains per spike in wheat

We discovered a natural FT-A2 allele that increases grain number per spike in both pasta and bread wheat with limited effect on heading time. Increases in wheat grain yield are necessary to meet future global food demands. A previous study showed that loss-of-function mutations in FLOWERING LOCUS T2 (FT2) increase spikelet number per spike (SNS), an important grain yield component. However, these mutations were also associated with reduced fertility, offsetting the beneficial effect of the increases in SNS on grain number. Here, we report a natural mutation resulting in an aspartic acid to alanine change at position 10 (D10A) associated with significant increases in SNS and no negative effects on fertility. Using a high-density genetic map, we delimited the SNS candidate region to a 5.2-Mb region on chromosome 3AS including 28 genes. Among them, only FT-A2 showed a non-synonymous polymorphism (D10A) present in two different populations segregating for the SNS QTL on chromosome arm 3AS. These results, together with the known effect of the ft-A2 mutations on SNS, suggest that variation in FT-A2 is the most likely cause of the observed differences in SNS. We validated the positive effects of the A10 allele on SNS, grain number, and grain yield per spike in near-isogenic tetraploid wheat lines and in an hexaploid winter wheat population. The A10 allele is present at very low frequency in durum wheat and at much higher frequency in hexaploid wheat, particularly in winter and fall-planted spring varieties. These results suggest that the FT-A2 A10 allele may be particularly useful for improving grain yield in durum wheat and fall-planted common wheat varieties.