Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.

BACKGROUND: Recently genomic selection (GS) has emerged as an important tool for plant breeders to select superior genotypes. Multi-trait (MT) prediction model provides an opportunity to improve the predictive ability of expensive and labor-intensive traits. In this study, we assessed the potential use of a MT genomic prediction model by incorporating two physiological traits (canopy temperature, CT and normalized difference vegetation index, NDVI) to predict 5 complex primary traits (harvest index, HI; grain yield, GY; grain number, GN; spike partitioning index, SPI; fruiting efiiciency, FE) using two cross-validation schemes CV1 and CV2. RESULTS: In this study, we evaluated 236 wheat genotypes in two locations in 2 years. The wheat genotypes were genotyped with genotyping by sequencing approach which generated 27,466 SNPs. MT-CV2 (multi-trait cross validation 2) model improved predictive ability by 4.8 to 138.5% compared to ST-CV1(single-trait cross validation 1). However, the predictive ability of MT-CV1 was not significantly different compared to the ST-CV1 model. CONCLUSIONS: The study showed that the genomic prediction of complex traits such as HI, GN, and GY can be improved when correlated secondary traits (cheaper and easier phenotyping) are used. MT genomic selection could accelerate breeding cycles and improve genetic gain for complex traits in wheat and other crops.

Gene-rich UV sex chromosomes harbor conserved regulators of sexual development.

Nonrecombining sex chromosomes, like the mammalian Y, often lose genes and accumulate transposable elements, a process termed degeneration. The correlation between suppressed recombination and degeneration is clear in animal XY systems, but the absence of recombination is confounded with other asymmetries between the X and Y. In contrast, UV sex chromosomes, like those found in bryophytes, experience symmetrical population genetic conditions. Here, we generate nearly gapless female and male chromosome-scale reference genomes of the moss Ceratodon purpureus to test for degeneration in the bryophyte UV sex chromosomes. We show that the moss sex chromosomes evolved over 300 million years ago and expanded via two chromosomal fusions. Although the sex chromosomes exhibit weaker purifying selection than autosomes, we find that suppressed recombination alone is insufficient to drive degeneration. Instead, the U and V sex chromosomes harbor thousands of broadly expressed genes, including numerous key regulators of sexual development across land plants.

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticumaestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments.

An integration of field-based phenotypic and genomic data can potentially increase the genetic gain in wheat breeding for complex traits such as grain and biomass yield. To validate this hypothesis in empirical field experiments, we compared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yield (GY) using a soft wheat population that was evaluated in four environments. The physiological data including canopy temperature (CT), SPAD chlorophyll content (SPAD), membrane thermostability (MT), rate of senescence (RS), stay green trait (SGT), and NDVI values were collected at four environments (2016, 2017, and 2018 at Citra, FL; 2017 at Quincy, FL). Using a genotyping-by-sequencing (GBS) approach, a total of 19,353 SNPs were generated and used to estimate prediction model accuracy. Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data. The proposed multi-kernel models that combined physiological and genomic data showed 35 to 169% increase in prediction accuracy compared to models with only genomic data included when heading date was used as a covariate. In general, higher response to selection was captured by the model combing effects of physiological and genotype × environment interaction compared to other models. The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context.

Genetic dissection of heat-responsive physiological traits to improve adaptation and increase yield potential in soft winter wheat.

BACKGROUND: Climate change, including higher temperatures (HT) has a detrimental impact on wheat productivity and modeling studies predict more frequent heat waves in the future. Wheat growth can be impaired by high daytime and nighttime temperature at any developmental stage, especially during the grain filling stage. Leaf chlorophyll content, leaf greenness, cell membrane thermostability, and canopy temperature have been proposed as candidate traits to improve crop adaptation and yield potential of wheat under HT. Nonetheless, a significant gap exists in knowledge of genetic backgrounds associated with these physiological traits. Identifying genetic loci associated with these traits can facilitate physiological breeding for increased yield potential under high temperature stress condition in wheat. RESULTS: We conducted genome-wide association study (GWAS) on a 236 elite soft wheat association mapping panel using 27,466 high quality single nucleotide polymorphism markers. The panel was phenotyped for three years in two locations where heat shock was common. GWAS identified 500 significant marker-trait associations (MTAs) (p ≤ 9.99 × 10- 4). Ten MTAs with pleiotropic effects detected on chromosomes 1D, 2B, 3A, 3B, 6A, 7B, and 7D are potentially important targets for selection. Five MTAs associated with physiological traits had pleiotropic effects on grain yield and yield-related traits. Seventy-five MTAs were consistently expressed over several environments indicating stability and more than half of these stable MTAs were found in genes encoding different types of proteins associated with heat stress. CONCLUSIONS: We identified 500 significant MTAs in soft winter wheat under HT stress. We found several stable loci across environments and pleiotropic markers controlling physiological and agronomic traits. After further validation, these MTAs can be used in marker-assisted selection and breeding to develop varieties with high stability for grain yield under high temperature.