Genetic diversity and population structure of bermudagrass (Cynodon spp.) revealed by genotyping-by-sequencing.

Bermudagrass (Cynodon spp.) breeding and cultivar development is hampered by limited information regarding its genetic and phenotypic diversity. To explore diversity in bermudagrass, a total of 206 Cynodon accessions consisting of 193 common bermudagrass (C. dactylon var. dactylon) and 13 African bermudagrass (C. transvaalensis) accessions of worldwide origin were assembled for genetic characterization. Genotyping-by-sequencing (GBS) was employed for genetic marker development. With a minor allele frequency of 0.05 and a minimum call rate of 0.5, a total of 37,496 raw single nucleotide polymorphisms (SNPs) were called de novo and were used in the genetic diversity characterization. Population structure analysis using ADMIXTURE revealed four subpopulations in this germplasm panel, which was consistent with principal component analysis (PCA) and phylogenetic analysis results. The first three principal components explained 15.6%, 10.1%, and 3.8% of the variance in the germplasm panel, respectively. The first subpopulation consisted of C. dactylon accessions from various continents; the second subpopulation was comprised mainly of C. transvaalensis accessions; the third subpopulation contained C. dactylon accessions primarily of African origin; and the fourth subpopulation represented C. dactylon accessions obtained from the Oklahoma State University bermudagrass breeding program. Genetic diversity parameters including Nei's genetic distance, inbreeding coefficient, and Fst statistic revealed substantial genetic variation in the Cynodon accessions, demonstrating the potential of this germplasm panel for further genetic studies and cultivar development in breeding programs.

Genes and genetic mechanisms contributing to fall armyworm resistance in maize.

Maize (Zea mays L.) is a crop of major economic and food security importance globally. The fall armyworm (FAW), Spodoptera frugiperda, can devastate entire maize crops, especially in countries or markets that do not allow the use of transgenic crops. Host-plant insect resistance is an economical and environmentally benign way to control FAW, and this study sought to identify maize lines, genes, and pathways that contribute to resistance to FAW. Of the 289 maize lines phenotyped for FAW damage in artificially infested, replicated field trials over 3 years, 31 were identified with good levels of resistance that could donate FAW resistance into elite but susceptible hybrid parents. The 289 lines were genotyped by sequencing to provide single nucleotide polymorphism (SNP) markers for a genome-wide association study (GWAS), followed by a metabolic pathway analysis using the Pathway Association Study Tool (PAST). GWAS identified 15 SNPs linked to 7 genes, and PAST identified multiple pathways, associated with FAW damage. Top pathways, and thus useful resistance mechanisms for further study, include hormone signaling pathways and the biosynthesis of carotenoids (particularly zeaxanthin), chlorophyll compounds, cuticular wax, known antibiosis agents, and 1,4-dihydroxy-2-naphthoate. Targeted metabolite analysis confirmed that maize genotypes with lower levels of FAW damage tend to have higher levels of chlorophyll a than genotypes with high FAW damage, which tend to have lower levels of pheophytin, lutein, chlorophyll b and ß-carotene. The list of resistant genotypes, and the results from the genetic, pathway, and metabolic study, can all contribute to efficient creation of FAW resistant cultivars.

Comparative Analysis of Multiple GWAS Results Identifies Metabolic Pathways Associated with Resistance to A. flavus Infection and Aflatoxin Accumulation in Maize.

Aflatoxins are carcinogenic secondary metabolites produced by several species of Aspergillus, including Aspergillus flavus, an important ear rot pathogen in maize. Most commercial corn hybrids are susceptible to infection by A. flavus, and aflatoxin contaminated grain causes economic damage to farmers. The creation of inbred lines resistant to Aspergillus fungal infection or the accumulation of aflatoxins would be aided by knowing the pertinent alleles and metabolites associated with resistance in corn lines. Multiple Quantitative Trait Loci (QTL) and association mapping studies have uncovered several dozen potential genes, but each with a small effect on resistance. Metabolic pathway analysis, using the Pathway Association Study Tool (PAST), was performed on aflatoxin accumulation resistance using data from four Genome-wide Association Studies (GWAS). The present research compares the outputs of these pathway analyses and seeks common metabolic mechanisms underlying each. Genes, pathways, metabolites, and mechanisms highlighted here can contribute to improving phenotypic selection of resistant lines via measurement of more specific and highly heritable resistance-related traits and genetic gain via marker assisted or genomic selection with multiple SNPs linked to resistance-related pathways.

Genome sequencing reveals evidence of adaptive variation in the genus Zea.

Maize is a globally valuable commodity and one of the most extensively studied genetic model organisms. However, we know surprisingly little about the extent and potential utility of the genetic variation found in wild relatives of maize. Here, we characterize a high-density genomic variation map from 744 genomes encompassing maize and all wild taxa of the genus Zea, identifying over 70 million single-nucleotide polymorphisms. The variation map reveals evidence of selection within taxa displaying novel adaptations. We focus on adaptive alleles in highland teosinte and temperate maize, highlighting the key role of flowering-time-related pathways in their adaptation. To show the utility of variants in these data, we generate mutant alleles for two flowering-time candidate genes. This work provides an extensive sampling of the genetic diversity of Zea, resolving questions on evolution and identifying adaptive variants for direct use in modern breeding.

Genetic Variability Assessment of Tropical Indica Rice (Oryza sativa L.) Seedlings for Drought Stress Tolerance.

Drought stress is one of the most devastating abiotic factors limiting plant growth and development. Devising an efficient and rapid screening method at the seedling stage is vital in identifying genotypes best suited under drought conditions. An experiment was conducted to assess 74 rice genotypes for drought tolerance using specially designed mini-hoop structures. Two treatments were imposed on rice seedlings, including 100% moisture and a 50% moisture regime. Several shoot morpho-physiological traits and root traits were measured and analyzed. The genotypes exhibited a wide range of variability for the measured traits, with the leaf area showing the most significant variation, followed by plant height, tiller number, and shoot dry weight. In contrast, the drought did not significantly affect most root traits. The germplasm was classified into different categories using cumulative drought stress response indices (CDSRI); 19 genotypes (26%) were identified as drought sensitive, and 33 (45%), 15 (20%), and 7 (9%) were determined as low, moderately, and highly drought-tolerant, respectively. Genotypes IR86638 and IR49830 were the most and least drought-tolerant, respectively. Overall, a poor correlation was observed between CDSRI, total shoot traits (R2 = 0.36), and physiological parameters (R2 = 0.10). A strong linear correlation was found between CDSRI and root traits (R2 = 0.81), suggesting that root traits are more crucial and better descriptors in screening for drought tolerance. This study can help rice breeders and scientists to accelerate breeding by adopting a mini-hoop rapid screening method. The tolerant genotypes could serve as appropriate donor parents, progenies, and potential genotypes for developing drought-tolerant commercial cultivars.

Three types of genes underlying the Gametophyte factor1 locus cause unilateral cross incompatibility in maize.

Unilateral cross incompatibility (UCI) occurs between popcorn and dent corn, and represents a critical step towards speciation. It has been reported that ZmGa1P, encoding a pectin methylesterase (PME), is a male determinant of the Ga1 locus. However, the female determinant and the genetic relationship between male and female determinants at this locus are unclear. Here, we report three different types, a total of seven linked genes underlying the Ga1 locus, which control UCI phenotype by independently affecting pollen tube growth in both antagonistic and synergistic manners. These include five pollen-expressed PME genes (ZmGa1Ps-m), a silk-expressed PME gene (ZmPME3), and another silk-expressed gene (ZmPRP3), encoding a pathogenesis-related (PR) proteins. ZmGa1Ps-m confer pollen compatibility. Presence of ZmPME3 causes silk to reject incompatible pollen. ZmPRP3 promotes incompatibility pollen tube growth and thereby breaks the blocking effect of ZmPME3. In addition, evolutionary genomics analyses suggest that the divergence of the Ga1 locus existed before maize domestication and continued during breeding improvement. The knowledge gained here deepen our understanding of the complex regulation of cross incompatibility.

Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding.

Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.

Population genomics of Zea species identifies selection signatures during maize domestication and adaptation.

BACKGROUND: Maize (Zea mays L. ssp. mays) was domesticated from teosinte (Zea mays ssp. parviglumis) about 9000 years ago in southwestern Mexico and adapted to a range of environments worldwide. Researchers have depicted the maize domestication and adaptation processes over the past two decades, but efforts have been limited either in sample size or genetic diversity. To better understand these processes, we conducted a genome-wide survey of 982 maize inbred lines and 190 teosinte accessions using over 40,000 single-nucleotide polymorphism markers. RESULTS: Population structure, principal component analysis, and phylogenetic trees all confirmed the evolutionary relationship between maize and teosinte, and determined the evolutionary lineage of all species within teosinte. Shared haplotype analysis showed similar levels of ancestral alleles from Zea mays ssp. parviglumis and Zea mays ssp. mexicana in maize. Scans for selection signatures identified 394 domestication sweeps by comparing wild and cultivated maize and 360 adaptation sweeps by comparing tropical and temperate maize. Permutation tests revealed that the public association signals for flowering time were highly enriched in the domestication and adaptation sweeps. Genome-wide association study identified 125 loci significantly associated with flowering-time traits, ten of which identified candidate genes that have undergone selection during maize adaptation. CONCLUSIONS: In this study, we characterized the history of maize domestication and adaptation at the population genomic level and identified hundreds of domestication and adaptation sweeps. This study extends the molecular mechanism of maize domestication and adaptation, and provides resources for basic research and genetic improvement in maize.

Development and validation of diagnostic SNP markers for quality control genotyping in a collection of four rice (Oryza) species.

Morphological identification of closely related rice species, particularly those in the Oryza AA genome group, presents major challenges and often results in cases of misidentification. Recent work by this group identified diagnostic single nucleotide polymorphic (SNP) markers specific for several rice species and subspecies based on DArTseq next-generation sequencing technology ("DArTseq"). These SNPs can be used for quality control (QC) analysis in rice breeding and germplasm maintenance programs. Here, we present the DArTseq-based diagnostic SNPs converted into Kompetitive allele-specific PCR (KASPar or KASP) assays and validation data for a subset of them; these can be used for low-cost routine genotyping quality control (QC) analysis. Of the 224 species/subspecies' diagnostic SNPs tested, 158 of them produced working KASP assays, a conversion success rate of 70%. Two validation experiments were run with 87 of the 158 SNP markers to ensure that the assays amplified, were polymorphic, and distinguished the five species/subspecies tested. Based on these validation test results, we recommend a panel of 36 SNP markers that clearly delineate O. barthii, O. glaberrima, O. longistaminata, O. sativa spp. indica and japonica. The KASP assays provide a flexible, rapid turnaround and cost-effective tool to facilitate germplasm curation and management of these four Oryza AA genome species across multiple genebanks.

Assessment of agro-morphological, physiological and yield traits diversity among tropical rice.

Rice (Oryza sativa L.) is an essential staple food crop, but the per acre average rice yield is less than its substantial potential in many countries. Rice breeders and growers would benefit from a robust genotypes with better morpho-physiological and yield-related traits. Here, seventy-four new rice genotypes were phenotyped over two years for their gas exchange and yield potential-related traits under Mississippi rice-growing conditions. A wide range of variability was observed among genotypes for all measured traits. Detailed phenotyping of rice genotypes revealed two key relationships that function together to contribute to yield potential under the southern US climate. The first one, grain yield, grain number, and spikelet fertility, showed considerable correlation (r = 0.45 to 0.79, p < 0.001) to harvest index. Conversely, days to anthesis had a high and negative correlation with harvest index (r = -0.79, p < 0.001), which suggests that selection for short duration genotypes with efficient partitioning could improve the yields under southern US climatic conditions. Additive response index revealed a higher positive association with yield traits (R2 = 0.59) than physiological (R2 = 0.28) and morphological traits (R2 = 0.21). Compared with the commercial genotype Rex, 21.6% and 47.3% of the rice genotypes had a higher gas exchange and yield response scores. IR08A172, IR07K142 and IR07F287 were ranked as high performers in physiological and yield response indices. Our study highlights that selection for short-duration yield-related traits with efficient sink capacity traits is desirable for future breeding programs.

Reference Gene Selection for RT-qPCR Analysis in Maize Kernels Inoculated with Aspergillus flavus.

Resistance against infection by the fungus Aspergillus flavus Link in commercial maize (Zea mays L.) is the topic of many studies, but few studies have investigated the effects of A. flavus infection on gene expression levels in ear kernels. A crucial component of gene expression profiling by RT-qPCR is having a reliable set of reference genes that show relatively constant expression across the treatments and phenotypes under study. Currently, however, there is no published information on reference genes suitable for measuring changes in kernel gene expression levels after infection with A. flavus. Thus, in this study, six candidate reference genes (ACT1, ß-Tub2, eIF4A2, TATA, EFIα, and GAPDH) were evaluated and ranked according to their expression stability. The genes were amplified from first-strand cDNA samples synthesized from kernels of two susceptible and two resistant maize lines that were either inoculated with A. flavus or water or not inoculated. Three software packages were used to calculate and rank the stability of expression for these genesgeNorm, NormFinder, and BestKeeper. The analysis revealed that the most stable genes to normalize expression levels from maize kernels responding to A. flavus inoculation and wounding were ACT1, EFIα, and eIF4A2.

The genetic mechanism of heterosis utilization in maize improvement.

BACKGROUND: In maize hybrid breeding, complementary pools of parental lines with reshuffled genetic variants are established for superior hybrid performance. To comprehensively decipher the genetics of heterosis, we present a new design of multiple linked F1 populations with 42,840 F1 maize hybrids, generated by crossing a synthetic population of 1428 maternal lines with 30 elite testers from diverse genetic backgrounds and phenotyped for agronomic traits. RESULTS: We show that, although yield heterosis is correlated with the widespread, minor-effect epistatic QTLs, it may be resulted from a few major-effect additive and dominant QTLs in early developmental stages. Floral transition is probably one critical stage for heterosis formation, in which epistatic QTLs are activated by paternal contributions of alleles that counteract the recessive, deleterious maternal alleles. These deleterious alleles, while rare, epistatically repress other favorable QTLs. We demonstrate this with one example, showing that Brachytic2 represses the Ubiquitin3 locus in the maternal lines; in hybrids, the paternal allele alleviates this repression, which in turn recovers the height of the plant and enhances the weight of the ear. Finally, we propose a molecular design breeding by manipulating key genes underlying the transition from vegetative-to-reproductive growth. CONCLUSION: The new population design is used to dissect the genetic basis of heterosis which accelerates maize molecular design breeding by diminishing deleterious epistatic interactions.

Phenotypic Plasticity Contributes to Maize Adaptation and Heterosis.

Plant phenotypic plasticity describes altered phenotypic performance of an individual when grown in different environments. Exploring genetic architecture underlying plant plasticity variation may help mitigate the detrimental effects of a rapidly changing climate on agriculture, but little research has been done in this area to date. In the present study, we established a population of 976 maize F1 hybrids by crossing 488 diverse inbred lines with two elite testers. Genome-wide association study identified hundreds of quantitative trait loci associated with phenotypic plasticity variation across diverse F1 hybrids, the majority of which contributed very little variance, in accordance with the polygenic nature of these traits. We identified several quantitative trait locus regions that may have been selected during the tropical-temperate adaptation process. We also observed heterosis in terms of phenotypic plasticity, in addition to the traditional genetic value differences measured between hybrid and inbred lines, and the pattern of which was affected by genetic background. Our results demonstrate a landscape of phenotypic plasticity in maize, which will aid in the understanding of its genetic architecture, its contribution to adaptation and heterosis, and how it may be exploited for future maize breeding in a rapidly changing environment.

Comparisons of sampling methods for assessing intra- and inter-accession genetic diversity in three rice species using genotyping by sequencing.

To minimize the cost of sample preparation and genotyping, most genebank genomics studies in self-pollinating species are conducted on a single individual to represent an accession, which may be heterogeneous with larger than expected intra-accession genetic variation. Here, we compared various population genetics parameters among six DNA (leaf) sampling methods on 90 accessions representing a wild species (O. barthii), cultivated and landraces (O. glaberrima, O. sativa), and improved varieties derived through interspecific hybridizations. A total of 1,527 DNA samples were genotyped with 46,818 polymorphic single nucleotide polymorphisms (SNPs) using DArTseq. Various statistical analyses were performed on eleven datasets corresponding to 5 plants per accession individually and in a bulk (two sets), 10 plants individually and in a bulk (two sets), all 15 plants individually (one set), and a randomly sampled individual repeated six times (six sets). Overall, we arrived at broadly similar conclusions across 11 datasets in terms of SNP polymorphism, heterozygosity/heterogeneity, diversity indices, concordance among genetic dissimilarity matrices, population structure, and genetic differentiation; there were, however, a few discrepancies between some pairs of datasets. Detailed results of each sampling method, the concordance in their outputs, and the technical and cost implications of each method were discussed.

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information.

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

Breeding Crops for Enhanced Food Safety.

An increasing global population demands a continuous supply of nutritious and safe food. Edible products can be contaminated with biological (e.g., bacteria, virus, protozoa), chemical (e.g., heavy metals, mycotoxins), and physical hazards during production, storage, transport, processing, and/or meal preparation. The substantial impact of foodborne disease outbreaks on public health and the economy has led to multidisciplinary research aimed to understand the biology underlying the different contamination processes and how to mitigate food hazards. Here we review the knowledge, opportunities, and challenges of plant breeding as a tool to enhance the food safety of plant-based food products. First, we discuss the significant effect of plant genotypic and phenotypic variation in the contamination of plants by heavy metals, mycotoxin-producing fungi, and human pathogenic bacteria. In addition, we discuss the various factors (i.e., temperature, relative humidity, soil, microbiota, cultural practices, and plant developmental stage) that can influence the interaction between plant genetic diversity and contaminant. This exposes the necessity of a multidisciplinary approach to understand plant genotype × environment × microbe × management interactions. Moreover, we show that the numerous possibilities of crop/hazard combinations make the definition and identification of high-risk pairs, such as Salmonella-tomato and Escherichia coli-lettuce, imperative for breeding programs geared toward improving microbial safety of produce. Finally, we discuss research on developing effective assays and approaches for selecting desirable breeding germplasm. Overall, it is recognized that although breeding programs for some human pathogen/toxin systems are ongoing (e.g., Fusarium in wheat), it would be premature to start breeding when targets and testing systems are not well defined. Nevertheless, current research is paving the way toward this goal and this review highlights advances in the field and critical points for the success of this initiative that were discussed during the Breeding Crops for Enhanced Food Safety workshop held 5-6 June 2019 at University of California, Davis.

Genetic variants and underlying mechanisms influencing variance heterogeneity in maize.

Traditional genetic studies focus on identifying genetic variants associated with the mean difference in a quantitative trait. Because genetic variants also influence phenotypic variation via heterogeneity, we conducted a variance-heterogeneity genome-wide association study to examine the contribution of variance heterogeneity to oil-related quantitative traits. We identified 79 unique variance-controlling single nucleotide polymorphisms (vSNPs) from the sequences of 77 candidate variance-heterogeneity genes for 21 oil-related traits using the Levene test (P < 1.0 × 10-5 ). About 30% of the candidate genes encode enzymes that work in lipid metabolic pathways, most of which define clear expression variance quantitative trait loci. Of the vSNPs specifically associated with the genetic variance heterogeneity of oil concentration, 89% can be explained by additional linked mean-effects genetic variants. Furthermore, we demonstrated that gene × gene interactions play important roles in the formation of variance heterogeneity for fatty acid compositional traits. The interaction pattern was validated for one gene pair (GRMZM2G035341 and GRMZM2G152328) using yeast two-hybrid and bimolecular fluorescent complementation analyses. Our findings have implications for uncovering the genetic basis of hidden additive genetic effects and epistatic interaction effects, and we indicate opportunities to stabilize efficient breeding and selection of high-oil maize (Zea mays L.).

CUBIC: an atlas of genetic architecture promises directed maize improvement.

BACKGROUND: Identifying genotype-phenotype links and causative genes from quantitative trait loci (QTL) is challenging for complex agronomically important traits. To accelerate maize gene discovery and breeding, we present the Complete-diallel design plus Unbalanced Breeding-like Inter-Cross (CUBIC) population, consisting of 1404 individuals created by extensively inter-crossing 24 widely used Chinese maize founders. RESULTS: Hundreds of QTL for 23 agronomic traits are uncovered with 14 million high-quality SNPs and a high-resolution identity-by-descent map, which account for an average of 75% of the heritability for each trait. We find epistasis contributes to phenotypic variance widely. Integrative cross-population analysis and cross-omics mapping allow effective and rapid discovery of underlying genes, validated here with a case study on leaf width. CONCLUSIONS: Through the integration of experimental genetics and genomics, our study provides useful resources and gene mining strategies to explore complex quantitative traits.

PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets.

In recent years, a bioinformatics method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and successfully used to find significant pathways and mechanisms explaining phenotypic traits of interest in plants. However, the many scripts implementing this method were not straightforward to use, had to be customized for each project, required user supervision, and took more than 24 h to process data. PAST (Pathway Association Study Tool), a new implementation of this method, has been developed to address these concerns. PAST has been implemented as a package for the R language. Two user-interfaces are provided; PAST can be run by loading the package in R and calling its methods, or by using an R Shiny guided user interface. In testing, PAST completed analyses in approximately half an hour to one hour by processing data in parallel and produced the same results as the previously developed method. PAST has many user-specified options for maximum customization. Thus, to promote a powerful new pathway analysis methodology that interprets GWAS data to find biological mechanisms associated with traits of interest, we developed a more accessible, efficient, and user-friendly tool. These attributes make PAST accessible to researchers interested in associating metabolic pathways with GWAS datasets to better understand the genetic architecture and mechanisms affecting phenotypes.

Evaluating rice for salinity using pot-culture provides a systematic tolerance assessment at the seedling stage.

BACKGROUND: Rice (Oryza sativa L.) is one of the major staple food crops consumed globally. However, rice production is severely affected by high salinity levels, particularly at the seedling stage. A good solution would be the development of an efficient screening methodology to identify genotypes possessing genes for salt tolerance. RESULT: A new salinity tolerance screening technique using rice seedlings in pot-culture was tested. This method controls soil heterogeneity by using pure sand as a growth medium and minimizes unexpected extreme weather conditions with a movable shelter. Seventy-four rice genotypes were screened at three salinity treatments including high salt stress (electrical conductivity (EC) 12 dSm- 1), moderate salt stress (EC 6 dSm- 1), and control (no salt stress), imposed 1 week after emergence. Several shoot and root morpho-physiological traits were measured at 37 days after sowing. A wide range of variability was observed among genotypes for measured traits with root traits being identified as the best descriptors for tolerance to salt stress conditions. Salt stress response indices (SSRI) were used to classify the 74 rice genotypes; 7 genotypes (9.46%) were identified as salt sensitive, 27 (36.48%) each as low and moderately salt tolerant, and 13 (17.57%) as highly salt tolerant. Genotypes FED 473 and IR85427 were identified as the most salt tolerant and salt sensitive, respectively. These results were further confirmed by principal component analysis (PCA) for accuracy and reliability. CONCLUSION: Although tolerant genotypes still need to be confirmed in field studies and tolerance mechanisms identified at the molecular level, information gained from this study could help rice breeders and other scientists to accelerate breeding by selecting appropriate donor parents, progenies and potential genotypes at early growth stages necessary for salinity tolerance research.