Current challenges and future of agricultural genomes to phenomes in the USA.

Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.

Inhibition of ethylene involved in resistance to E. turcicum in an exotic-derived double haploid maize population.

Northern corn leaf blight (NCLB) is an economically important disease of maize. While the genetic architecture of NCLB has been well characterized, the pathogen is known to overcome currently deployed resistance genes, and the role of hormones in resistance to NCLB is an area of active research. The objectives of the study were (i) to identify significant markers associated with resistance to NCLB, (ii) to identify metabolic pathways associated with NCLB resistance, and (iii) to examine role of ethylene in resistance to NCLB. We screened 252 lines from the exotic-derived double haploid BGEM maize population for resistance to NCLB in both field and greenhouse environments. We used a genome wide association study (GWAS) and stepwise regression to identify four markers associated with resistance, followed by a pathway association study tool (PAST) to identify important metabolic pathways associated with disease severity and incubation period. The ethylene synthesis pathway was significant for disease severity and incubation period. We conducted a greenhouse assay in which we inhibited ethylene to examine the role of ethylene in resistance to NCLB. We observed a significant increase in incubation period and a significant decrease in disease severity between plants treated with the ethylene inhibitor and mock-treated plants. Our study confirms the potential of the BGEM population as a source of novel alleles for resistance. We also confirm the role of ethylene in resistance to NCLB and contribute to the growing body of literature on ethylene and disease resistance in monocots.

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids.

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops.

BACKGROUND: We consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS), where the emphasis is on accurately predicting trait values. Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recently proposed to identify genomic loci associated with multiple traits (Yu et al. in Multitask learning using task clustering with applications to predictive modeling and GWAS of plant varieties. arXiv:1710.01788 , 2017; Yu et al in Front Big Data 2:27, 2019), but these ignore population structure and familial relatedness (Yu et al in Nat Genet 38:203-208, 2006). RESULTS: This work addresses this gap by proposing a novel class of regularized multi-trait linear mixed models along with scalable approaches for estimation in the presence of high-dimensional genotypes and a large number of traits. We evaluate the effectiveness of the proposed methods using datasets in maize and sorghum diversity panels, and demonstrate benefits in both achieving high prediction accuracy in GS and in identifying relevant marker-trait associations. CONCLUSIONS: The proposed regularized multivariate linear mixed models are relevant for both GWAS and GS. We hope that they will facilitate agronomy-related research in plant biology and crop breeding endeavors.

Importance of genetic architecture in marker selection decisions for genomic prediction.

KEY MESSAGE: We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait. Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.

A first look at the ability to use genomic prediction for improving the ratooning ability of sugarcane.

The sugarcane ratooning ability (RA) is the most important target trait for breeders seeking to enhance the profitability of sugarcane production by reducing the planting cost. Understanding the genetics governing the RA could help breeders by identifying molecular markers that could be used for genomics-assisted breeding (GAB). A replicated field trial was conducted for three crop cycles (plant cane, first ratoon, and second ratoon) using 432 sugarcane clones and used for conducting genome-wide association and genomic prediction of five sugar and yield component traits of the RA. The RA traits for economic index (EI), stalk population (SP), stalk weight (SW), tonns of cane per hectare (TCH), and tonns of sucrose per hectare (TSH) were estimated from the yield and sugar data. A total of six putative quantitative trait loci and eight nonredundant single-nucleotide polymorphism (SNP) markers were associated with all five tested RA traits and appear to be unique. Seven putative candidate genes were colocated with significant SNPs associated with the five RA traits. The genomic prediction accuracies for those tested traits were moderate and ranged from 0.21 to 0.36. However, the models fitting fixed effects for the most significant associated markers for each respective trait did not give any advantages over the standard models without fixed effects. As a result of this study, more robust markers could be used in the future for clone selection in sugarcane, potentially helping resolve the genetic control of the RA in sugarcane.

Genome-wide association analysis for emergence of deeply sown rice (Oryza sativa) reveals novel aus-specific phytohormone candidate genes for adaptation to dry-direct seeding in the field.

Dry direct-seeded rice (dry-DSR) is typically sown deeply to circumvent the need for irrigation, and thus seedling emergence is a crucial trait affecting plant stand and yield. To breed elite cultivars that use less water and are climate-resilient, an understanding of the genomic regions and underlying genes that confer emergence in deeply sown dry-DSR would be highly advantageous. A combined diversity panel of 470 rice accessions (RDP1 plus aus subset of 3K RGP) was evaluated with 2.9 million single nucleotide polymorphisms (SNPs) to identify associations with dry-DSR traits in the field and component traits in a controlled-environment experiment. Using genome-wide association study (GWAS) analyses, we identified 18 unique QTLs on chromosomes 1, 2, 4, 5, 6, 7, 9, 10, and 11, explaining phenotypic variance ranging from 2.6% to 17.8%. Three QTLs, namely, qSOE-1.1, qEMERG-AUS-1.2, and qEMERG-AUS-7.1, were co-located with previously reported QTLs for mesocotyl length. Among the identified QTLs, half were associated with the emergence of aus, and six were unique to the aus genetic group. Based on functional annotation, we identified eleven compelling candidate genes that primarily regulate phytohormone pathways such as cytokinin, auxin, gibberellic acid, and jasmonic acid. Prior studies indicated that these phytohormones play a critical role in mesocotyl length under deep sowing. This study provides new insight into the importance of aus and indica as desirable genetic resources to mine favorable alleles for deep-sowing tolerance in rice. The candidate genes and marker-tagged desirable alleles identified in this study should benefit rice breeding programs directly.

Linking genetic and environmental factors through marker effect networks to understand trait plasticity.

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.

Exploring the Relationship Between Pattern-Triggered Immunity and Quantitative Resistance to Xanthomonas vasicola pv. vasculorum in Maize.

Bacterial leaf streak (BLS) of maize is an emerging foliar disease of maize in the Americas. It is caused by the gram-negative nonvascular bacterium Xanthomonas vasicola pv. vasculorum. There are no chemical controls available for BLS, and thus, host resistance is crucial for managing X. vasicola pv. vasculorum. The objective of this study was to examine the genetic determinants of resistance to X. vasicola pv. vasculorum in maize, as well as the relationship between other defense-related traits and BLS resistance. Specifically, we examined the correlations among BLS severity, severity for three fungal diseases, flg-22 response, hypersensitive response, and auricle color. We conducted quantitative trait locus (QTL) mapping for X. vasicola pv. vasculorum resistance using the maize recombinant inbred line population Z003 (B73 × CML228). We detected three QTLs for BLS resistance. In addition to the disease resistance QTL, we detected a single QTL for auricle color. We observed significant, yet weak, correlations among BLS severity, levels of pattern-triggered immunity response and leaf flecking. These results will be useful for understanding resistance to X. vasicola pv. vasculorum and mitigating the impact of BLS on maize yields.

Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces.

Barley (Hordeum vulgare ssp. vulgare) is one of the first crops to be domesticated and is adapted to a wide range of environments. Worldwide barley germplasm collections possess valuable allelic variations that could further improve barley productivity. Although barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits, polymorphisms from East Asian varieties remain scarce. In this study, we analyze exome polymorphisms in a panel of 274 barley varieties collected worldwide, including 137 varieties from East Asian countries and Ethiopia. We reveal the underlying population structure and conduct genome-wide association studies for 10 agronomic traits. Moreover, we examin genome-wide associations for traits related to grain size such as awn length and glume length. Our results demonstrate the value of diverse barley germplasm panels containing Eastern varieties, highlighting their distinct genomic signatures relative to Western subpopulations.

Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean.

Improvements in genetics, technology, and agricultural intensification have increased soybean yields; however, adverse climate conditions may prevent these gains from being fully realized in the future. Higher growing season temperatures reduce soybean yields in key production regions including the US Midwest, and better understanding of the developmental and physiological mechanisms that constrain soybean yield under high temperature conditions is needed. This study tested the response of two soybean cultivars to four elevated temperature treatments (+1.7, +2.6, +3.6, and +4.8 °C) in the field over three growing seasons and identified threshold temperatures for response and linear versus non-linear trait responses to temperature. Yield declined non-linearly to temperature, with decreases apparent when canopy temperature exceeded 20.9 °C for the locally adapted cultivar and 22.7°C for a cultivar adapted to more southern locations. While stem node number increased with increasing temperature, leaf area index decreased substantially. Pod production, seed size, and harvest index significantly decreased with increasing temperature. The seasonal average temperature of even the mildest treatment exceeded the threshold temperatures for yield loss, emphasizing the importance of improving temperature tolerance in soybean germplasm with intensifying climate change.

Crop modeling suggests limited transpiration would increase yield of sorghum across drought-prone regions of the United States.

Breeding sorghum to withstand droughts is pivotal to secure crop production in regions vulnerable to water scarcity. Limited transpiration (LT) restricts water demand at high vapor pressure deficit, saving water for use in critical periods later in the growing season. Here we evaluated the hypothesis that LT would increase sorghum grain yield in the United States. We used a process-based crop model, APSIM, which simulates interactions of genotype, environment, and management (G × E × M). In this study, the G component includes the LT trait (GT) and maturity group (GM), the EW component entails water deficit patterns, and the MP component represents different planting dates. Simulations were conducted over 33 years (1986-2018) for representative locations across the US sorghum belt (Kansas, Texas, and Colorado) for three planting dates and maturity groups. The interaction of GT x EW indicated a higher impact of LT sorghum on grain for late drought (LD), mid-season drought (MD), and early drought (ED, 8%), than on well-watered (WW) environments (4%). Thus, significant impacts of LT can be achieved in western regions of the sorghum belt. The lack of interaction of GT × GM × MP suggested that an LT sorghum would increase yield by around 8% across maturity groups and planting dates. Otherwise, the interaction GM × MP revealed that specific combinations are better suited across geographical regions. Overall, the findings suggest that breeding for LT would increase sorghum yield in the drought-prone areas of the US without tradeoffs.

Genetic mapping of sorghum resistance to an Illinois isolate of Colletotrichum sublineola.

Anthracnose leaf blight (ALB) is an economically important disease of sorghum [Sorghum bicolor (L.) Moench] caused by the fungal pathogen Colletotrichum sublineola Henn. ex Sacc. & Trotter. Although qualitative and quantitative resistance have been identified for ALB, the usefulness of resistance loci differs depending on the pathogen pathotype. Identifying resistance effective against unique pathogen pathotypes is critical to managing ALB, as the disease is managed primarily through the deployment of host resistance. We isolated C. sublineola from ALB-infected leaves collected in Illinois and found that the strain was a novel pathotype, as it produced a unique combination of virulence against a set of differential lines. Using this isolate, we inoculated 579 temperate-adapted sorghum conversion lines in 2019 and 2020. We then conducted a genome-wide association study (GWAS) and a metabolic pathway analysis using the Pathway Associated Study Tool (PAST). We identified 47 significant markers distributed across all chromosomes except chromosome 8. We identified 32 candidate genes based on physical proximity with significant markers, some of which have a known role in host defense. We identified 47 pathways associated with ALB resistance, indicating a role for secondary metabolism in defense to ALB. Our results are important to improve the understanding of the genetic basis of ALB resistance in sorghum and highlight the importance of developing durable resistance to ALB.

Assessment of two statistical approaches for variance genome-wide association studies in plants.

Genomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance-controlling loci in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used in the variance genome-wide association study (vGWAS) paradigm are the Brown-Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that these approaches are deployed as effectively as possible, it is critical to study the factors that influence their ability to identify variance-controlling loci. We used genome-wide marker data in maize (Zea mays L.) and Arabidopsis thaliana to simulate traits controlled by epistasis, genotype by environment (GxE) interactions, and variance quantitative trait nucleotides (vQTNs). We then quantified true and false positive detection rates of the BFT and DGLM across all simulated traits. We also conducted a vGWAS using both the BFT and DGLM on plant height in a maize diversity panel. The observed true positive detection rates at the maximum sample size considered (N = 2815) suggest that both of these vGWAS approaches are capable of identifying epistasis and GxE for sufficiently large sample sizes. We also noted that the DGLM decisively outperformed the BFT for simulated traits controlled by vQTNs at sample sizes of N = 500. Although we conclude that there are still certain aspects of vGWAS approaches that need further refinement, this study suggests that the BFT and DGLM are capable of identifying variance-controlling loci in current state-of-the-art plant or agronomic data sets.

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies.

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

A population-level statistic for assessing Mendelian behavior of genotyping-by-sequencing data from highly duplicated genomes.

BACKGROUND: Given the economic and environmental importance of allopolyploids and other species with highly duplicated genomes, there is a need for methods to distinguish paralogs, i.e. duplicate sequences within a genome, from Mendelian loci, i.e. single copy sequences that pair at meiosis. The ratio of observed to expected heterozygosity is an effective tool for filtering loci but requires genotyping to be performed first at a high computational cost, whereas counting the number of sequence tags detected per genotype is computationally quick but very ineffective in inbred or polyploid populations. Therefore, new methods are needed for filtering paralogs. RESULTS: We introduce a novel statistic, Hind/HE, that uses the probability that two reads sampled from a genotype will belong to different alleles, instead of observed heterozygosity. The expected value of Hind/HE is the same across all loci in a dataset, regardless of read depth or allele frequency. In contrast to methods based on observed heterozygosity, it can be estimated and used for filtering loci prior to genotype calling. In addition to filtering paralogs, it can be used to filter loci with null alleles or high overdispersion, and identify individuals with unexpected ploidy and hybrid status. We demonstrate that the statistic is useful at read depths as low as five to 10, well below the depth needed for accurate genotype calling in polyploid and outcrossing species. CONCLUSIONS: Our methodology for estimating Hind/HE across loci and individuals, as well as determining reasonable thresholds for filtering loci, is implemented in polyRAD v1.6, available at https://github.com/lvclark/polyRAD . In large sequencing datasets, we anticipate that the ability to filter markers and identify problematic individuals prior to genotype calling will save researchers considerable computational time.

A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits.

The ability to accurately quantify the simultaneous effect of multiple genomic loci on multiple traits is now possible due to current and emerging high-throughput genotyping and phenotyping technologies. To date, most efforts to quantify these genotype-to-phenotype relationships have focused on either multi-trait models that test a single marker at a time or multi-locus models that quantify associations with a single trait. Therefore, the purpose of this study was to compare the performance of a multi-trait, multi-locus stepwise (MSTEP) model selection procedure we developed to (a) a commonly used multi-trait single-locus model and (b) a univariate multi-locus model. We used real marker data in maize (Zea mays L.) and soybean (Glycine max L.) to simulate multiple traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). In general, we found that both multi-trait models outperformed the univariate multi-locus model, especially when analyzing a trait of low heritability. For traits controlled by either a combination of pleiotropic and nonpleiotropic QTNs or a large number of QTNs (i.e., 50), our MSTEP model often outperformed at least one of the two alternative models. When applied to the analysis of two tocochromanol-related traits in maize grain, MSTEP identified the same peak-associated marker that has been reported in a previous study. We therefore conclude that MSTEP is a useful addition to the suite of statistical models that are commonly used to gain insight into the genetic architecture of agronomically important traits.

Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition.

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments.

The effects of climate change create formidable challenges for breeders striving to produce sufficient food quantities in rapidly changing environments. It is therefore critical to investigate the ability of multi-environment genomic prediction (GP) models to predict genomic estimated breeding values (GEBVs) in extreme environments. Exploration of the impact of training set composition on the accuracy of such GEBVs is also essential. Accordingly, we examined the influence of the number of training environments and the use of environmental covariates (ECs) in GS models on four subsets of n = 500 lines of the soybean nested association mapping (SoyNAM) panel grown in nine environments in the US-North Central Region. The ensuing analyses provided insights into the influence of both of these factors for predicting grain yield in the most and the least extreme of these environments. We found that only a subset of the available environments was needed to obtain the highest observed prediction accuracies. The inclusion of ECs in the GP model did not substantially increase prediction accuracies relative to competing models, and instead more often resulted in negative prediction accuracies. Combined with the overall low prediction accuracies for grain yield in the most extreme environment, our findings highlight weaknesses in current GP approaches for prediction in extreme environments, and point to specific areas on which to focus future research efforts.

Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice (Oryza sativa L.).

Hybrid rice varieties can outyield the best inbred varieties by 15 - 30% with appropriate management. However, hybrid rice requires more inputs and management than inbred rice to realize a yield advantage in high-yielding environments. The development of stress-tolerant hybrid rice with lowered input requirements could increase hybrid rice yield relative to production costs. We used genomic prediction to evaluate the combining abilities of 564 stress-tolerant lines used to develop Green Super Rice with 13 male sterile lines of the International Rice Research Institute for yield-related traits. We also evaluated the performance of their F1 hybrids. We identified male sterile lines with good combining ability as well as F1 hybrids with potential further use in product development. For yield per plant, accuracies of genomic predictions of hybrid genetic values ranged from 0.490 to 0.822 in cross-validation if neither parent or up to both parents were included in the training set, and both general and specific combining abilities were modeled. The accuracy of phenotypic selection for hybrid yield per plant was 0.682. The accuracy of genomic predictions of male GCA for yield per plant was 0.241, while the accuracy of phenotypic selection was 0.562. At the observed accuracies, genomic prediction of hybrid genetic value could allow improved identification of high-performing single crosses. In a reciprocal recurrent genomic selection program with an accelerated breeding cycle, observed male GCA genomic prediction accuracies would lead to similar rates of genetic gain as phenotypic selection. It is likely that prediction accuracies of male GCA could be improved further by targeted expansion of the training set. Additionally, we tested the correlation of parental genetic distance with mid-parent heterosis in the phenotyped hybrids. We found the average mid-parent heterosis for yield per plant to be consistent with existing literature values at 32.0%. In the overall population of study, parental genetic distance was significantly negatively correlated with mid-parent heterosis for yield per plant (r = -0.131) and potential yield (r = -0.092), but within female families the correlations were non-significant and near zero. As such, positive parental genetic distance was not reliably associated with positive mid-parent heterosis.