Genome-wide assessment of population structure and association mapping for agronomic and grain nutritional traits in proso millet (Panicum miliaceum L.).

Proso millet is an important but under-researched and underutilized crop with the potential to become a future smart crop because of its climate-resilient features and high nutrient content. Assessing diversity and marker-trait associations are essential to support the genomics-assisted improvement of proso millet. This study aimed to assess the population structure and diversity of a proso millet diversity panel and identify marker-trait associations for agronomic and grain nutrient traits. In this study, genome-wide single nucleotide polymorphisms (SNPs) were identified by mapping raw genotyping-by-sequencing (GBS) data onto the proso millet genome, resulting in 5621 quality-filtered SNPs in 160 diverse accessions. The modified Roger's Distance assessment indicated an average distance of 0.268 among accessions, with the race miliaceum exhibiting the highest diversity and ovatum the lowest. Proso millet germplasm diversity was structured according to geographic centers of origin and domestication. Genome-wide association mapping identified 40 marker-trait associations (MTAs), including 34 MTAs for agronomic traits and 6 for grain nutrients; 20 of these MTAs were located within genes. Favourable alleles and phenotypic values were estimated for all MTAs. This study provides valuable insights into the population structure and diversity of proso millet, identified marker-trait associations, and reported favourable alleles and their phenotypic values for supporting genomics-assisted improvement efforts in proso millet.

Sampling Root-Associated Microbiome Communities of Maize (Zea mays).

The soil microbiome of maize shapes its fitness, sustainability, and productivity. Accurately sampling maize's belowground microbial communities is important for identifying and characterizing these functions. Here, we describe a protocol to sample the maize rhizosphere (including the rhizoplane and endorhizosphere) and root zone (still influential but further from the root) in a form suitable for downstream analyses like culturing and DNA extractions. Although this protocol is written with Zea mays as the focus, these methods can generally be applied to any plant with similar fibrous root systems.

Sampling the Maize (Zea mays) Leaf Microbiome.

The microbiota of maize leaves can be beneficial or detrimental to the host. Foliar diseases are the most obvious detrimental impact of the leaf microbiome, though more subtle effects of the normal (nondisease) community are an active area of research. This protocol describes two specific methodologies to sample the maize leaf microbiome: one sampling the surface (epiphyte) microbiome and one sampling the interior (endophyte) microbiome. Each method begins with collected leaf tissue and finishes with a cell suspension suitable for either isolating live microbes or extracting DNA for sequencing.

Preparation of Illumina 16s Amplicon Sequencing Libraries with Peptide Nucleic Acids (PNAs) for the Analysis of Maize-Associated Microbiomes.

One of the most common methods to survey bacterial communities is targeted amplification of the hypervariable regions of the 16s rRNA gene followed by sequencing. This protocol details Illumina library preparation of such amplicons from communities isolated from maize. We include both staggered PCR primers to improve Illumina base calling and peptide nucleic acids (PNAs) to reduce the presence of plant organelles. Primers are designed with Illumina adapter sequences for the addition of sample-specific indexes (barcodes). We also briefly discuss alternative primer sets, including ones that directly discriminate against plant organelles or that amplify different organisms (e.g., fungal internal transcribed spacer [ITS] sequences).

Sampling Maize (Zea mays) Seed Endophytes.

For most farmers, the production of maize grain is the ultimate goal of the entire field season. From the point of view of plant microbiome studies, seeds are particularly interesting in that they are the only avenue for vertical transmission of microbes from parent to offspring, though microbes can also enter maize seeds via wounds or silks. Although the presence of seed endophytes is well documented, their role, if any, in seed health and their effects on the next generation of plants are largely unknown. This protocol describes the isolation of seed endophytes. Its primary focus is properly sterilizing the seed surface, followed by grinding to release the endophytes. The end product is a cell suspension suitable for either culturing or DNA analysis.

Sampling and Analysis of the Maize Microbiome.

Maize is an important plant for both global food security and genetics research. As the importance of microorganisms to plant health is becoming clearer, there is a growing interest in understanding the relationship between maize and its associated microbiome; i.e., the collection of microorganisms living on, around, and inside the plant. The ultimate goal of this research is to use these microbial communities to support more robust and sustainable maize production. Here, we provide an overview of recent progress in the field of maize microbiome research. We discuss the major microbiome compartments (rhizosphere, phyllosphere, and endosphere) and known functions of the microbiome. We also review the methods currently available to study the maize microbiome and its functions, and discuss how to carry out maize microbiome experiments, including both a general workflow (suitable for most microbiome analyses) and maize-specific experimental considerations.

Manipulating the Maize (Zea mays) Microbiome.

Maize (Zea mays) is a multifaceted cereal grass used globally for nutrition, animal feed, food processing, and biofuels, and a model system in genetics research. Studying the maize microbiome sometimes requires its manipulation to identify the contributions of specific taxa and ecological traits (i.e., diversity, richness, network structure) to maize growth and physiology. Due to regulatory constraints on applying engineered microorganisms in field settings, greenhouse-based experimentation is often the first step for understanding the contribution of root-associated microbiota-whether natural or engineered-to plant phenotypes. In this protocol, we describe methods to inoculate maize with a specific microbiome as a tool for understanding the microbiota's influence on its host plant. The protocol involves removal of the native seed microbiome followed by inoculation of new microorganisms; separate protocols are provided for inoculations from pure culture, from soil slurry, or by mixing in live soil. These protocols cover the most common methods for manipulating the maize microbiome in soil-grown plants in the greenhouse. The methods outlined will ultimately result in rhizosphere microbial assemblages with varying degrees of microbial diversity, ranging from low diversity (individual strain and synthetic community [SynCom] inoculation) to high diversity (percent live inoculation), with the slurry inoculation method representing an "intermediate diversity" treatment.

kGWASflow: a modular, flexible, and reproducible Snakemake workflow for k-mers-based GWAS.

Genome-wide association studies (GWAS) have been widely used to identify genetic variation associated with complex traits. Despite its success and popularity, the traditional GWAS approach comes with a variety of limitations. For this reason, newer methods for GWAS have been developed, including the use of pan-genomes instead of a reference genome and the utilization of markers beyond single-nucleotide polymorphisms, such as structural variations and k-mers. The k-mers-based GWAS approach has especially gained attention from researchers in recent years. However, these new methodologies can be complicated and challenging to implement. Here, we present kGWASflow, a modular, user-friendly, and scalable workflow to perform GWAS using k-mers. We adopted an existing kmersGWAS method into an easier and more accessible workflow using management tools like Snakemake and Conda and eliminated the challenges caused by missing dependencies and version conflicts. kGWASflow increases the reproducibility of the kmersGWAS method by automating each step with Snakemake and using containerization tools like Docker. The workflow encompasses supplemental components such as quality control, read-trimming procedures, and generating summary statistics. kGWASflow also offers post-GWAS analysis options to identify the genomic location and context of trait-associated k-mers. kGWASflow can be applied to any organism and requires minimal programming skills. kGWASflow is freely available on GitHub (https://github.com/akcorut/kGWASflow) and Bioconda (https://anaconda.org/bioconda/kgwasflow).

Effects of Inbreeding on Microbial Community Diversity of Zea mays.

Heterosis, also known as hybrid vigor, is the basis of modern maize production. The effect of heterosis on maize phenotypes has been studied for decades, but its effect on the maize-associated microbiome is much less characterized. To determine the effect of heterosis on the maize microbiome, we sequenced and compared the bacterial communities of inbred, open pollinated, and hybrid maize. Samples covered three tissue types (stalk, root, and rhizosphere) in two field experiments and one greenhouse experiment. Bacterial diversity was more affected by location and tissue type than genetic background for both within-sample (alpha) and between-sample (beta) diversity. PERMANOVA analysis similarly showed that tissue type and location had significant effects on the overall community structure, whereas the intraspecies genetic background and individual plant genotypes did not. Differential abundance analysis identified only 25 bacterial ASVs that significantly differed between inbred and hybrid maize. Predicted metagenome content was inferred with Picrust2, and it also showed a significantly larger effect of tissue and location than genetic background. Overall, these results indicate that the bacterial communities of inbred and hybrid maize are often more similar than they are different and that non-genetic effects are generally the largest influences on the maize microbiome.

Yield prediction through integration of genetic, environment, and management data through deep learning.

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

Maize seed endophytes.

Maize is a vital global crop, and each seed (kernel) hosts an ecosystem of microbes living inside it. However, we know very little about these endophytes and what their role is in plant production and physiology. In this Microreview, I summarize the major questions around maize seed endophytes, including what organisms are present, how they get there, whether and how they transmit across generations, and how they and the plant affect each other. Although several studies touch on each of these areas, ultimately there are far more questions than answers. Future priorities for research on maize seed endophytes should include understanding what adaptations allow microbes to be seed endophytes, how the host genetics and the environment affect these communities, and how maize seed endophytes ultimately contribute to the next generation of plants.

Soybean microbiome composition and the impact of host plant resistance.

Microbial communities play an important role in the growth and development of plants, including plant immunity and the decomposition of complex substances into absorbable nutrients. Hence, utilizing beneficial microbes becomes a promising strategy for the optimization of plant growth. The objective of this research was to explore the root bacterial profile across different soybean genotypes and the change in the microbial community under soybean cyst nematode (SCN) infection in greenhouse conditions using 16S rRNA sequencing. Soybean genotypes with soybean cyst nematode (SCN) susceptible and resistant phenotypes were grown under field and greenhouse conditions. Bulked soil, rhizosphere, and root samples were collected from each replicate. Sequencing of the bacterial 16S gene indicated that the bacterial profile of soybean root and soil samples partially overlapped but also contained different communities. The bacterial phyla Proteobacteria, Actinobacteria, and Bacteroidetes dominate the soybean root-enriched microbiota. The structure of bacteria was significantly affected by sample year (field) or time point (greenhouse). In addition, the host genotype had a small but significant effect on the diversity of the root microbiome under SCN pressure in the greenhouse test. These differences may potentially represent beneficial bacteria or secondary effects related to SCN resistance.

Identification of small effect quantitative trait loci of plant architectural, flowering, and early maturity traits in reciprocal interspecific introgression population in cotton.

Plant architecture, flowering time and maturity traits are important determinants of yield and fiber quality of cotton. Genetic dissection of loci determining these yield and quality components is complicated by numerous loci with alleles conferring small differences. Therefore, mapping populations segregating for smaller numbers and sizes of introgressed segments is expected to facilitate dissection of these complex quantitative traits. At an advanced stage in the development of reciprocal advanced backcross populations from crosses between elite Gossypium hirsutum cultivar 'Acala Maxxa' (GH) and G. barbadense 'Pima S6' (GB), we undertook mapping of plant architectural traits, flowering time and maturity. A total of 284 BC4F1 and BC4F2 progeny rows, 120 in GH and 164 in GB background, were evaluated for phenotype, with only 4 and 3 (of 7) traits showing significant differences among progenies. Genotyping by sequencing yielded 3,186 and 3,026 SNPs, respectively, that revealed a total of 27 QTLs in GH background and 22 in GB, for plant height, days to flowering, residual flowering at maturity and maturity. More than of 90% QTLs identified in both backgrounds had small effects (%PV < 10), supporting the merit of this population structure to reduce background noise and small effect QTLs. Germplasm developed in this study may serve as potential pre-breeding material to develop improved cotton cultivars.

Genome-wide association mapping of resistance to the sorghum aphid in Sorghum bicolor.

Since 2013, the sorghum aphid (SA), Melanaphis sorghi (Theobald), has been a serious pest that hampers all types of sorghum production in the U.S. Known sorghum aphid resistance in sorghum is limited to a few genetic regions on SBI-06. In this study, a subset of the Sorghum Association Panel (SAP) was used along with some additional lines to identify genomic regions that confer sorghum aphid resistance. SAP lines were grown in the field and visually evaluated for SA resistance during the growing seasons of 2019 and 2020 in Tifton, GA. In 2020, the SAP accessions were also evaluated for SA resistance in the field using drone-based high throughput phenotyping (HTP). Flowering time was recorded in the field to confirm that our methods were sufficient for identifying known quantitative trait loci (QTL). This study combined phenotypic data from field-based visual ratings and reflectance data to identify genome-wide associated (GWAS) marker-trait associations (MTA) using genotyping-by-sequencing (GBS) data. Several MTAs were identified for SA-related traits across the genome, with a few common markers that were consistently identified on SBI-08 and SBI-10 for aphid count and plant damage, as well as loci for reflectance-based traits on SBI-02, SBI-03, and SBI-05. Candidate genes encoding leucine-rich repeats (LRR), Avr proteins, lipoxygenases (LOXs), calmodulins (CAM) dependent protein kinase, WRKY transcription factors, flavonoid biosynthesis genes, and 12-oxo-phytodienoic acid reductase were identified near SNPs that had significant associations with different SA traits. In this study, flowering time-related genes were also identified as a positive control for the methods. The total phenotypic variation explained by significant SNPs across SA-scored traits, reflectance data, and flowering time ranged from 6 to 61%, while the heritability value ranged from 4 to 69%. This study identified three new sources of resistant lines to sorghum aphid. These results supported the existing literature, and also revealed several new loci. Markers identified in this study will support marker-assisted breeding for sorghum aphid resistance.

Genomic and Chemical Diversity of Commercially Available High-CBD Industrial Hemp Accessions.

High consumer demand for cannabidiol (CBD) has made high-CBD hemp (Cannabis sativa) an extremely high-value crop. However, high demand has resulted in the industry developing faster than the research, resulting in the sale of many hemp accessions with inconsistent performance and chemical profiles. These inconsistencies cause significant economic and legal problems for growers interested in producing high-CBD hemp. To determine the genetic and phenotypic consistency in available high-CBD hemp varieties, we obtained seed or clones from 22 different named accessions meant for commercial production. Genotypes (∼48,000 SNPs) and chemical profiles (% CBD and THC by dry weight) were determined for up to 8 plants per accession. Many accessions-including several with the same name-showed little consistency either genetically or chemically. Most seed-grown accessions also deviated significantly from their purported levels of CBD and THC based on the supplied certificates of analysis. Several also showed evidence of an active tetrahydrocannabinolic acid (THCa) synthase gene, leading to unacceptably high levels of THC in female flowers. We conclude that the current market for high-CBD hemp varieties is highly unreliable, making many purchases risky for growers. We suggest options for addressing these issues, such using unique names and developing seed and plant certification programs to ensure the availability of high-quality, verified planting materials.

Eleven biosynthetic genes explain the majority of natural variation in carotenoid levels in maize grain.

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize (Zea mays) is a food staple. Extensive natural variation exists for carotenoids in maize grain. Here, to understand its genetic basis, we conducted a joint linkage and genome-wide association study of the US maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percentage of phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide an in-depth genome-level understanding of the genetic and molecular control of carotenoids in plants. In addition, these findings provide a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.

Genome-Wide Population Structure Analyses of Three Minor Millets: Kodo Millet, Little Millet, and Proso Millet.

CORE IDEAS: Developed genome-wide SNP marker data for kodo, proso, and little millet Marker data used to analyze genetic diversity Heritability results of various traits used to validate genetic data Millets are a diverse group of small-seeded grains that are rich in nutrients but have received relatively little advanced plant breeding research. Millets are important to smallholder farmers in Africa and Asia because of their short growing season, good stress tolerance, and high nutritional content. To advance the study and use of these species, we present genome-wide marker datasets and population structure analyses for three minor millets: kodo millet (Paspalum scrobiculatum L.), little millet (Panicum sumatrense Roth), and proso millet (Panicum miliaceum L.).We generated genome-wide marker data sets for 190 accessions of each species with genotyping-by-sequencing (GBS). After filtering, we retained between 161 and 165 accessions of each species, with 3461, 2245, and 1882 single-nucleotide polymorphisms (SNPs) for kodo, little, and proso millet, respectively. Population genetic analysis revealed seven putative subpopulations of kodo millet and eight each of proso millet and little millet. To confirm the accuracy of this genetic data, we used public phenotype data on a subset of these accessions to estimate the heritability of various agronomically relevant phenotypes. Heritability values largely agree with the prior expectation for each phenotype, indicating that these SNPs provide an accurate genome-wide sample of genetic variation. These data represent one of first genome-wide population genetics analyses-and the most extensive-in these species and the first genomic analyses of any sort for little millet and kodo millet. These data will be a valuable resource for researchers and breeders trying to improve these crops for smallholder farmers.

On the Road to Breeding 4.0: Unraveling the Good, the Bad, and the Boring of Crop Quantitative Genomics.

Understanding the quantitative genetics of crops has been and will continue to be central to maintaining and improving global food security. We outline four stages that plant breeding either has already achieved or will probably soon achieve. Top-of-the-line breeding programs are currently in Breeding 3.0, where inexpensive, genome-wide data coupled with powerful algorithms allow us to start breeding on predicted instead of measured phenotypes. We focus on three major questions that must be answered to move from current Breeding 3.0 practices to Breeding 4.0: ( a) How do we adapt crops to better fit agricultural environments? ( b) What is the nature of the diversity upon which breeding can act? ( c) How do we deal with deleterious variants? Answering these questions and then translating them to actual gains for farmers will be a significant part of achieving global food security in the twenty-first century.

Large-scale replicated field study of maize rhizosphere identifies heritable microbes.

Soil microbes that colonize plant roots and are responsive to differences in plant genotype remain to be ascertained for agronomically important crops. From a very large-scale longitudinal field study of 27 maize inbred lines planted in three fields, with partial replication 5 y later, we identify root-associated microbiota exhibiting reproducible associations with plant genotype. Analysis of 4,866 samples identified 143 operational taxonomic units (OTUs) whose variation in relative abundances across the samples was significantly regulated by plant genotype, and included five of seven core OTUs present in all samples. Plant genetic effects were significant amid the large effects of plant age on the rhizosphere microbiome, regardless of the specific community of each field, and despite microbiome responses to climate events. Seasonal patterns showed that the plant root microbiome is locally seeded, changes with plant growth, and responds to weather events. However, against this background of variation, specific taxa responded to differences in host genotype. If shown to have beneficial functions, microbes may be considered candidate traits for selective breeding.

Mapping a male-fertility restoration locus for the A4 cytoplasmic-genic male-sterility system in pearl millet using a genotyping-by-sequencing-based linkage map.

BACKGROUND: Pearl millet (Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.) R. Br) is an important cereal and fodder crop in hot and arid environments. There is great potential to improve pearl millet production through hybrid breeding. Cytoplasmic male sterility (CMS) and the corresponding nuclear fertility restoration / sterility maintenance genes (Rfs) are essential tools for economic hybrid seed production in pearl millet. Mapping the Rf genes of the A4 CMS system in pearl millet would enable more efficient introgression of both dominant male-fertility restoration alleles (Rf) and their recessive male-sterility maintenance counterparts (rf). RESULTS: A high density linkage map based on single nucleotide polymorphism (SNP) markers was generated using an F2 mapping population and genotyping-by-sequencing (GBS). The parents of this cross were 'ICMA 02777' and 'ICMR 08888', which segregate for the A4 Rf locus. The linkage map consists of 460 SNP markers distributed mostly evenly and has a total length of 462 cM. The segregation ratio of male-fertile and male-sterile plants (3:1) based on pollen production (presence/absence) indicated monogenic dominant inheritance of male-fertility restoration. Correspondingly, a major quantitative trait locus (QTL) for pollen production was found on linkage group 2, with cross-validation showing a very high QTL occurrence (97%). The major QTL was confirmed using selfed seed set as phenotypic trait, though with a lower precision. However, these QTL explained only 14.5% and 9.9% of the phenotypic variance of pollen production and selfed seed set, respectively, which was below expectation. Two functional KASP markers were developed for the identified locus. CONCLUSION: This study identified a major QTL for male-fertility restoration using a GBS-based linkage map and developed KASP markers which support high-throughput screening of the haploblock. This is a first step toward marker-assisted selection of A4 male-fertility restoration and male-sterility maintenance in pearl millet.