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Abstract 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.

Abstract Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic‐enabled breeding tools, like marker‐assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of Miscanthus breeding. The present study conducted genome‐wide association (GWA) and genomic prediction of biomass yield and 14 yield‐components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single‐nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield‐component traits in M. sacchariflorus that may facilitate marker‐assisted breeding for biomass yield. Genomic prediction accuracy for the yield‐related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus. This study indicated that MAS and genomic prediction will likely be beneficial for conducting population‐improvement of M. sacchariflorus.

Abstract 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.

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.

Abstract Miscanthus is a genus of perennial grasses native to East Asia that shows promise as a biofuel energy source. Breeding efforts for increasing biofuel capability in this genus have focused on two species, namely M. sinensis (Msi) and M. sacchariflorus (Msa). For these efforts to succeed, it is critical that both Msi and Msa, as well as their interspecific crosses, can be grown at a wide range of latitudes. Therefore, the purpose of this study was to investigate how well existing data from Msi and Msa trials grown at locations throughout the northern hemisphere can train state‐of‐the‐art genomic selection (GS) models to predict genomic estimated breeding values (GEBVs) of dry yield for untested Msi and Msa accessions in untested environments. We found that accounting for genotype by environment interaction in the GS model did not notably improve predictive ability. Additionally, we observed that locations at lower latitudes showed higher predictive ability relative to locations at higher latitudes. These results suggest that it is crucial to increase the number of trial locations at higher latitude locations to investigate the source of this correlation. This will make it possible to train GS models using data from environments that are similar to growing conditions at the locations targeted by Msi and Msa breeders. Such an increase of trial locations in target environments could pave the way toward advancing breeding efforts for overwintering ability in Msi and Msa, and ultimately support the potential of Miscanthus as a biofuel crop.

Abstract 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.

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.

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.

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.

Abstract 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, H ind /H E , that uses the probability that two reads sampled from a genotype will belong to different alleles, instead of observed heterozygosity. The expected value of H ind /H E 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 H ind /H E 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.

Abstract 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.

Abstract 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.

Plant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Toward this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana. Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes.

Abstract Background Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models. Results We illustrate simplePHENOTYPES’ ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities. Conclusions simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.com/samuelbfernandes/simplePHENOTYPES .

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.

Abstract Background The functional genome of agronomically important plant species remains largely unexplored, yet presents a virtually untapped resource for targeted crop improvement. Functional elements of regulatory DNA revealed through profiles of chromatin accessibility can be harnessed for fine-tuning gene expression to optimal phenotypes in specific environments. Result Here, we investigate the non-coding regulatory space in the maize (Zea mays) genome during early reproductive development of pollen- and grain-bearing inflorescences. Using an assay for differential sensitivity of chromatin to micrococcal nuclease (MNase) digestion, we profile accessible chromatin and nucleosome occupancy in these largely undifferentiated tissues and classify at least 1.6% of the genome as accessible, with the majority of MNase hypersensitive sites marking proximal promoters, but also 3′ ends of maize genes. This approach maps regulatory elements to footprint-level resolution. Integration of complementary transcriptome profiles and transcription factor occupancy data are used to annotate regulatory factors, such as combinatorial transcription factor binding motifs and long non-coding RNAs, that potentially contribute to organogenesis, including tissue-specific regulation between male and female inflorescence structures. Finally, genome-wide association studies for inflorescence architecture traits based solely on functional regions delineated by MNase hypersensitivity reveals new SNP-trait associations in known regulators of inflorescence development as well as new candidates. Conclusions These analyses provide a comprehensive look into the cis-regulatory landscape during inflorescence differentiation in a major cereal crop, which ultimately shapes architecture and influences yield potential.

The evolution and domestication of cotton is of great interest from both economic and evolutionary standpoints. Although many genetic and genomic resources have been generated for cotton, the genetic underpinnings of the transition from wild to domesticated cotton remain poorly known. Here we generated an intraspecific QTL mapping population specifically targeting domesticated cotton phenotypes. We used 466 F2 individuals derived from an intraspecific cross between the wild Gossypium hirsutum var. yucatanense (TX2094) and the elite cultivar G. hirsutum cv. Acala Maxxa, in two environments, to identify 120 QTL associated with phenotypic changes under domestication. While the number of QTL recovered in each subpopulation was similar, only 22 QTL were considered coincident (i.e., shared) between the two locations, eight of which shared peak markers. Although approximately half of QTL were located in the A-subgenome, many key fiber QTL were detected in the D-subgenome, which was derived from a species with unspinnable fiber. We found that many QTL are environment-specific, with few shared between the two environments, indicating that QTL associated with G. hirsutum domestication are genomically clustered but environmentally labile. Possible candidate genes were recovered and are discussed in the context of the phenotype. We conclude that the evolutionary forces that shape intraspecific divergence and domestication in cotton are complex, and that phenotypic transformations likely involved multiple interacting and environmentally responsive factors.

Abstract To breed improved biomass cultivars of Miscanthus ×giganteus, it will be necessary to select the highest‐yielding and best‐adapted genotypes of its parental species, Miscanthus sinensis and Miscanthus sacchariflorus. We phenotyped a diverse clonally propagated panel of 569 M. sinensis and nine natural diploid M. ×giganteus at one subtropical (Zhuji, China) and five temperate locations (Sapporo, Japan; Leamington, Ontario, Canada; Fort Collins, CO; Urbana, IL; and Chuncheon, Korea) for dry biomass yield and 14 yield‐component traits, in trials grown for 3 years. Notably, dry biomass yield of four Miscanthus accessions exceeded 80 Mg/ha in Zhuji, China, approaching the highest observed for any land plant. Additionally, six M. sinensis in Sapporo, Japan and one in Leamington, Canada also yielded more than the triploid M. ×giganteus ‘1993‐1780’ control, with values exceeding 20 Mg/ha. Diploid M. ×giganteus was the best‐yielding group at the northern sites. Genotype‐by‐environment interactions were modest among the five northern trial sites but large between Zhuji, and the northern sites. M. sinensis accessions typically yielded best at trial sites with latitudes similar to collection sites, although broad adaptation was observed for accessions from southern Japan. Genotypic heritabilities for third year yields ranged from 0.71 to 0.88 within locations. Compressed circumference was the best predictor of yield. These results establish a baseline of data for initiating selection to improve biomass yield of M. sinensis and M. ×giganteus in a diverse set of relevant geographies.

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 selection (GS) has been demonstrated to enhance the selection process in breeding programs. The objectives of this study were to experimentally evaluate different GS methods in sugarcane hybrids and to determine the prospect of GS in future breeding approaches. Using sugar and yield-related trait data from 432 sugarcane clones and 10,435 single nucleotide polymorphisms (SNPs), a study was conducted using seven different GS models. While fivefold cross-validated prediction accuracy differed by trait and by crop cycle, there were only small differences in prediction accuracy among the different models. Prediction accuracy was on average 0.20 across all traits and crop cycles for all tested models. Utilizing a trait-assisted GS model, we could effectively predict the fivefold cross-validated genomic estimated breeding value of ratoon crops using both SNPs and trait values from the plant cane crop. We found that the plateau of prediction accuracy could be achieved with 4000 to 5000 SNPs. Prediction accuracy did not decline with decreasing size of the training population until it was reduced below 60% (259) to 80% (346) of the original number of clones. Our findings suggest that GS is possibly a new direction for improving sugar and yield-related traits in sugarcane.

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.

Abstract Overwintering ability is an important selection criterion for Miscanthus breeding in temperate regions. Insufficient overwintering ability of the currently leading Miscanthus biomass cultivar, M. ×giganteus (M×g) ‘1993–1780', in regions where average annual minimum temperatures are −26.1°C (USDA hardiness zone 5) or lower poses a pressing need to develop new cultivars with superior cold tolerance. To facilitate breeding of Miscanthus, this study characterized phenotypic and genetic variation of overwintering ability in an M. sinensis germplasm panel consisting of 564 accessions, evaluated in field trials at three locations in North America and two in Asia. Genome‐wide association (GWA) and genomic prediction analyses were performed. The Korea/N China M. sinensis genetic group is a valuable gene pool for cold tolerance. The Yangtze‐Qinling, Southern Japan, and Northern Japan genetic groups were also potential sources of cold tolerance. A total of 73 marker–trait associations were detected for overwintering ability. Estimated breeding value for overwintering ability based on these 73 markers could explain 55% of the variation for first winter overwintering ability among M. sinensis. Average genomic prediction ability for overwintering ability across 50 fivefold cross‐validations was high (~0.73) after accounting for population structure. Common genomic regions for overwintering ability were detected by GWA analyses and a previous parallel QTL mapping study using three interconnected biparental F1 populations. One QTL on Miscanthus LG 8 encompassed five GWA hits and a known cold‐responsive gene, COR47. The other overwintering ability QTL on Miscanthus LG 11 contained two GWA hits and three known cold stress‐related genes, carboxylesterase 13 (CEX13), WRKY2 transcription factor, and cold shock domain (CSDP1). Miscanthus accessions collected from high latitude locations with cold winters had higher rates of overwintering, and more alleles for overwintering, than accessions collected from southern locations with mild winters.

Abstract To improve the efficiency of breeding of Miscanthus for biomass yield, there is a need to develop genomics‐assisted selection for this long‐lived perennial crop by relating genotype to phenotype and breeding value across a broad range of environments. We present the first genome‐wide association (GWA) and genomic prediction study of Miscanthus that utilizes multilocation phenotypic data. A panel of 568 Miscanthus sinensis accessions was genotyped with 46,177 single nucleotide polymorphisms (SNPs) and evaluated at one subtropical and five temperate locations over 3 years for biomass yield and 14 yield‐component traits. GWA and genomic prediction were performed separately for different years of data in order to assess reproducibility. The analyses were also performed for individual field trial locations, as well as combined phenotypic data across groups of locations. GWA analyses identified 27 significant SNPs for yield, and a total of 504 associations across 298 unique SNPs across all traits, sites, and years. For yield, the greatest number of significant SNPs was identified by combining phenotypic data across all six locations. For some of the other yield‐component traits, greater numbers of significant SNPs were obtained from single site data, although the number of significant SNPs varied greatly from site to site. Candidate genes were identified. Accounting for population structure, genomic prediction accuracies for biomass yield ranged from 0.31 to 0.35 across five northern sites and from 0.13 to 0.18 for the subtropical location, depending on the estimation method. Genomic prediction accuracies of all traits were similar for single‐location and multilocation data, suggesting that genomic selection will be useful for breeding broadly adapted M. sinensis as well as M. sinensis optimized for specific climates. All of our data, including DNA sequences flanking each SNP, are publicly available. By facilitating genomic selection in M. sinensis and Miscanthus × giganteus, our results will accelerate the breeding of these species for biomass in diverse environments.

Low or uneven read depth is a common limitation of genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), resulting in high missing data rates, heterozygotes miscalled as homozygotes, and uncertainty of allele copy number in heterozygous polyploids. Bayesian genotype calling can mitigate these issues, but previously has only been implemented in software that requires a reference genome or uses priors that may be inappropriate for the population. Here we present several novel Bayesian algorithms that estimate genotype posterior probabilities, all of which are implemented in a new R package, polyRAD. Appropriate priors can be specified for mapping populations, populations in Hardy-Weinberg equilibrium, or structured populations, and in each case can be informed by genotypes at linked markers. The polyRAD software imports read depth from several existing pipelines, and outputs continuous or discrete numerical genotypes suitable for analyses such as genome-wide association and genomic prediction.

Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.

Prediction of single-cross performance has been a major goal of plant breeders since the beginning of hybrid breeding. Recently, genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single-cross performance. Moreover, no studies have examined the potential of predicting single crosses among random inbreds derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objectives of this study were to evaluate the potential of genomic prediction for identifying superior single crosses early in the hybrid breeding pipeline and optimize its application. To accomplish these objectives, we designed and analyzed a novel population of single crosses representing the Iowa Stiff Stalk synthetic/non-Stiff Stalk heterotic pattern commonly used in the development of North American commercial maize hybrids. The performance of single crosses was predicted using parental combining ability and covariance among single crosses. Prediction accuracies were estimated using cross-validation and ranged from 0.28 to 0.77 for grain yield, 0.53 to 0.91 for plant height, and 0.49 to 0.94 for staygreen, depending on the number of tested parents of the single cross and genomic prediction method used. The genomic estimated general and specific combining abilities showed an advantage over genomic covariances among single crosses when one or both parents of the single cross were untested. Overall, our results suggest that genomic prediction of single crosses in the early stages of a hybrid breeding pipeline holds great potential to redesign hybrid breeding and increase its efficiency.

Goss’s bacterial wilt and leaf blight is one of the most important foliar diseases of maize ( L.). To date, neither large-effect resistance genes, nor practical chemical controls exist to manage the disease. Thus, the importance of discovering durable host resistance necessitates additional genetic mapping for this disease. Unfortunately, because of the biology of the pathogen and the highly significant genotype-by-environment interaction effect observed with Goss’s wilt, consistent phenotyping across multiple years poses a hurdle for genetic studies and conventional breeding methods. The objective of this study was to perform a genome-wide association study (GWAS) to identify regions of the genome associated with Goss’s wilt resistance as well as to use genomic prediction models to evaluate the utility of genomic selection (GS) in predicting Goss’s wilt phenotypes in a panel of diverse maize lines. Using genome-wide association mapping, we were unable to identify any variants significantly associated with Goss’s wilt. However, using genomic prediction we were able to train a model with an accuracy of 0.69. Taken together, this suggests that resistance to Goss’s wilt is highly polygenic. In addition, when evaluating the accuracy of our prediction model under reduced marker density, it was shown that only 10,000 single nucleotide polymorphisms (SNPs), or ∼20% of our total marker set, was necessary to achieve prediction accuracies similar to the full marker set. This is the first report of genomic prediction for a bacterial disease of maize, and these results highlight the potential of GS for disease resistance in maize.

Variation in kernel composition across maize ( L.) germplasm is affected by a combination of the plant’s genotype, the environment in which it is grown, and the interaction between these two elements. Adapting exotic germplasm to the US Corn Belt is highly dependent on the plant’s genotype, the environment where it is grown, and the interaction between these components. Phenotypic plasticity is ill-defined when specific exotic germplasm is moved over large latitudinal distances and for the adapted variants being created. Reduced plasticity (or stability) is desired for the adapted variants, as it allows for a more rapid implementation into breeding programs throughout the Corn Belt. Here, doubled haploid lines derived from exotic maize and adapted through backcrossing exotic germplasm to elite adapted lines were used in conjunction with genome-wide association studies to explore stability in four kernel composition traits. Genotypes demonstrated a response to environments that paralleled the mean response of all genotypes used across all traits, with protein content and kernel density exhibiting the highest levels of Type II stability. Genes such as , , and were identified as potential candidates within quantitative trait locus regions. The findings within this study aid in validating previously identified genomic regions and identified novel genomic regions affecting kernel quality traits.

Certain agronomic crop traits are complex and thus governed by many small-effect loci. Statistical models typically used in a genome-wide association study (GWAS) and genomic selection (GS) quantify these signals by assessing genomic marker contributions in linkage disequilibrium (LD) with these loci to trait variation. These models have been used in separate quantitative genetics contexts until recently, when, in published studies, the predictive ability of GS models that include peak associated markers from a GWAS as fixed-effect covariates was assessed. Previous work suggests that such models could be useful for predicting traits controlled by several large-effect and many small-effect genes. We expand this work by evaluating simulated traits from diversity panels in maize ( L.) and sorghum [ (L.) Moench] using ridge-regression best linear unbiased prediction (RR-BLUP) models that include fixed-effect covariates tagging peak GWAS signals. The ability of such covariates to increase GS prediction accuracy in the RR-BLUP model under a wide variety of genetic architectures and genomic backgrounds was quantified. Of the 216 genetic architectures that we simulated, we identified 60 where the addition of fixed-effect covariates boosted prediction accuracy. However, for the majority of the simulated data, no increase or a decrease in prediction accuracy was observed. We also noted several instances where the inclusion of fixed-effect covariates increased both the variability of prediction accuracies and the bias of the genomic estimated breeding values. We therefore recommend that the performance of such a GS model be explored on a trait-by-trait basis prior to its implementation into a breeding program.

A typical plant genome-wide association study (GWAS) uses a mixed linear model (MLM) that includes a trait as the response variable, a marker as an explanatory variable, and fixed and random effect covariates accounting for population structure and relatedness. Although effective in controlling for false positive signals, this model typically fails to detect signals that are correlated with population structure or are located in high linkage disequilibrium (LD) genomic regions. This result likely arises from each tested marker being used to estimate population structure and relatedness. Previous work has demonstrated that it is possible to increase the power of the MLM by estimating relatedness (i.e., kinship) with markers that are not located on the chromosome where the tested marker resides. To quantify the amount of additional significant signals one can expect using this so-called K_chr model, we reanalyzed Mendelian, polygenic, and complex traits in two maize (Zea mays L.) diversity panels that have been previously assessed using the traditional MLM. We demonstrated that the K_chr model could find more significant associations, especially in high LD regions. This finding is underscored by our identification of novel genomic signals proximal to the tocochromanol biosynthetic pathway gene ZmVTE1 that are associated with a ratio of tocotrienols. We conclude that the K_chr model can detect more intricate sources of allelic variation underlying agronomically important traits, and should therefore become more widely used for GWAS. To facilitate the implementation of the K_chr model, we provide code written in the R programming language.

The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010–2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.

Flowering and height related traits are extensively studied in maize for three main reasons: 1) easily obtained phenotypic measurements, 2) highly heritable, and 3) importance of these traits to adaptation and grain yield. However, variation in flowering and height traits is extensive and findings from previous studies are genotype specific. Herein, a diverse panel of exotic derived doubled haploid lines, in conjunction with genome-wide association analysis, is used to further explore adaptation related trait variation of exotic germplasm for potential use in adapting exotic germplasm to the U.S. Corn-Belt. Phenotypes for the association panel were obtained from six locations across the central-U.S. and genotyping was performed using the genotyping-by-sequencing method. Nineteen flowering time candidate genes were found for three flowering traits. Eighteen candidate genes were found for four height related traits, with the majority of the candidate genes relating to plant hormones auxin and gibberellin. A single gene was discovered for ear height that also had effects on -like flowering gene expression levels. Findings will be used to inform future research efforts of the USDA Germplasm Enhancement of Maize project and eventually aid in the rapid adaptation of exotic germplasm to temperate U.S. environments.

Brassinosteroids (BRs) and Gibberellins (GAs) are two classes of plant hormones affecting plant height (PHT). Thus, manipulation of BR and GA levels or signaling enables optimization of crop grain and biomass yields. We established backcross (BC) families, selected for increased PHT, in two elite maize inbred backgrounds. Various exotic accessions used in the germplasm enhancement in maize project served as donors. BC1-derived doubled haploid lines in the same two elite maize inbred backgrounds established without selection for plant height were included for comparison. We conducted genome-wide association studies to explore the genetic control of PHT by BR and GA. In addition, we used BR and GA inhibitors to compare the relationship between PHT, BR, and GA in inbred lines and heterozygotes from a physiological and biological perspective. A total of 73 genomic loci were discovered to be associated with PHT, with seven co-localized with GA, and two co-localized with BR candidate genes. PHT determined in field trials was significantly correlated with seedling stage BR and GA inhibitor responses. However, this observation was only true for maize heterozygotes, not for inbred lines. Path analysis results suggest that heterozygosity increases GA levels, which in turn promote BR levels. Thus, at least part of heterosis for PHT in maize can be explained by increased GA and BR levels, and seedling stage hormone inhibitor response is promising to predict heterosis for PHT.

Emerging evidence suggests a link between young people’s interest in alternative food production practices and dietary quality. The primary purpose of this study was to examine the impact of a student-driven sustainable food systems education and promotion intervention on adolescent school lunch selection, consumption, and waste behaviors. Sixth grade science teachers at two middle schools (n = 268 students) implemented a standards-based curriculum on sustainable food systems, addressing the environmental impacts of food choices and food waste. The cumulating curriculum activity required the 6th grade students to share their food systems knowledge with their 7th and 8th grade counterparts (n = 426) through a cafeteria promotional campaign to discourage food waste. School-wide monthly plate waste assessments were used to evaluate changes in vegetable consumption and overall plate waste using a previously validated digital photography method. At baseline, the intervention students consumed significantly less vegetables relative to the control group (47.1% and 71.8% of vegetables selected, respectively (p = 0.006). This disparity was eliminated after the intervention with the intervention group consuming 69.4% and the control consuming 68.1% of selected vegetables (p = 0.848). At five months follow up, the intervention group wasted significantly less salad bar vegetables compared to the control group (24.2 g and 50.1 g respectively (p = 0.029). These findings suggest that food systems education can be used to promote improved dietary behaviors among adolescent youth.

Genetic resistance to brown stem rot (BSR) of soybean [ (L.) Merr.], caused by (Allington & D.W. Chamb.) T.C. Harr. & McNew, has been identified and mapped with biparental populations. Although nearly 400 accessions have been identified with BSR resistance, this trait has been mapped in only 12 sources, and just two, PI84946-2 and PI88788, have been used to develop BSR resistant cultivars. Thus, there is a serious need to improve our knowledge of the genetic basis of BSR resistance in soybean so that resistance genes in cultivars can be diversified and markers close to resistance genes can be identified and used in marker-assisted selection (MAS). To this end, we conducted a genome-wide association study (GWAS) to identify novel genomic loci associated with BSR resistance and to gain further insight into a previously reported chromosome 16 region containing BSR resistance () genes. A total of 52,041 single-nucleotide polymorphisms (SNPs) were tested for association with BSR in a set of 4735 accessions from four diversity panels evaluated for resistance from 1989 to 2003. Using a unified mixed linear model and stepwise model selection, we refined the signals within the interval on chromosome 16 by finding associations that explain a substantial proportion of the total variation of BSR resistance. In combination with significant GWAS signals found elsewhere in the genome, our study will aid efforts to improve BSR resistance by providing new targets for MAS.

Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.

Genomic selection (GS) is a breeding method that uses marker–trait models to predict unobserved phenotypes. This study developed GS models for predicting traits associated with resistance to head blight (FHB) in wheat ( L.). We used genotyping-by-sequencing (GBS) to identify 5054 single-nucleotide polymorphisms (SNPs), which were then treated as predictor variables in GS analysis. We compared how the prediction accuracy of the genomic-estimated breeding values (GEBVs) was affected by (i) five genotypic imputation methods (random forest imputation [RFI], expectation maximization imputation [EMI], -nearest neighbor imputation [kNNI], singular value decomposition imputation [SVDI], and the mean imputation [MNI]); (ii) three statistical models (ridge-regression best linear unbiased predictor [RR-BLUP], least absolute shrinkage and operator selector [LASSO], and elastic net); (iii) marker density ( = 500, 1500, 3000, and 4500 SNPs); (iv) training population (TP) size ( = 96, 144, 192, and 218); (v) marker-based and pedigree-based relationship matrices; and (vi) control for relatedness in TPs and validation populations (VPs). No discernable differences in prediction accuracy were observed among imputation methods. The RR-BLUP outperformed other models in nearly all scenarios. Accuracies decreased substantially when marker number decreased to 3000 or 1500 SNPs, depending on the trait; when sample size of the training set was less than 192; when using pedigree-based instead of marker-based matrix; or when no control for relatedness was implemented. Overall, moderate to high prediction accuracies were observed in this study, suggesting that GS is a very promising breeding strategy for FHB resistance in wheat.

The superior fiber properties of L. serve as a source of novel variation for improving fiber quality in Upland cotton ( L.), but introgression from has been largely unsuccessful due to hybrid breakdown and a lack of genetic and genomic resources. In an effort to overcome these limitations, we constructed a linkage map and conducted a quantitative trait locus (QTL) analysis of 10 agronomic and fiber quality traits in a recombinant inbred mapping population derived from a cross between TM-1, an Upland cotton line, and NM24016, an elite line with stabilized introgression from . The linkage map consisted of 429 simple-sequence repeat (SSR) and 412 genotyping-by-sequencing (GBS)-based single-nucleotide polymorphism (SNP) marker loci that covered half of the tetraploid cotton genome. Notably, the 841 marker loci were unevenly distributed among the 26 chromosomes of tetraploid cotton. The 10 traits evaluated on the TM-1 × NM24016 population in a multienvironment trial were highly heritable, and most of the fiber traits showed considerable transgressive variation. Through the QTL analysis, we identified a total of 28 QTLs associated with the 10 traits. Our study provides a novel resource that can be used by breeders and geneticists for the genetic improvement of agronomic and fiber quality traits in Upland cotton.