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.

A Kinesin-14 Motor Activates Neocentromeres to Promote Meiotic Drive in Maize.

Maize abnormal chromosome 10 (Ab10) encodes a classic example of true meiotic drive that converts heterochromatic regions called knobs into motile neocentromeres that are preferentially transmitted to egg cells. Here, we identify a cluster of eight genes on Ab10, called the Kinesin driver (Kindr) complex, that are required for both neocentromere motility and preferential transmission. Two meiotic drive mutants that lack neocentromere activity proved to be kindr epimutants with increased DNA methylation across the entire gene cluster. RNAi of Kindr induced a third epimutant and corresponding loss of meiotic drive. Kinesin gliding assays and immunolocalization revealed that KINDR is a functional minus-end-directed kinesin that localizes specifically to knobs containing 180 bp repeats. Sequence comparisons suggest that Kindr diverged from a Kinesin-14A ancestor ∼12 mya and has driven the accumulation of > 500 Mb of knob repeats and affected the segregation of thousands of genes linked to knobs on all 10 chromosomes.

Genetic Analysis of Gossypium Fiber Quality Traits in Reciprocal Advanced Backcross Populations.

In mapping populations segregating for many loci, the large amount of variation among genotypes often masks small-effect quantitative trait loci (QTL). This problem can be reduced by development of populations with fewer chromosome segments segregating. Here, we report early QTL detection in reciprocal advanced backcross populations from crosses between elite Gossypium hirsutum L. 'Acala Maxxa' (GH) and G. barbadense L. 'Pima S6' (GB). A total of 297 BCF and BCF progeny rows-127 segregating for GB chromosome segments in GH background and 170 segregating for GH chromosome segments in GB background-were evaluated in three environments. Totals of 3186 and 3026 polymorphic single-nucleotide polymorphisms (SNPs) in GH and GB backgrounds, respectively, were identified and used for trait mapping. Small-effect QTL (<10% variance explained) made up 87 and 100% of QTL in GH and GB backgrounds, respectively. In both species, favorable alleles were found with effects being masked or neutralized by unfavorable alleles, with greater scope for improvement of GH than GB by introgressive breeding. A total of three stable QTL-two in GH background for fiber elongation (ELO) and micronaire (MIC) and one in GB background for upper-half mean length (UHM)-were identified in two out of three environments. Curiously, only four QTL-three for UHM and one for ELO-showed the expected opposite effects in reciprocal backgrounds, perhaps reflecting the combined consequences of epistasis, small phenotypic effects, and low coverage of some genomic regions. Along with new information for marker-assisted breeding, this study adds to knowledge that can be used to unravel complex genetic networks governing fiber quality traits.

Novel Loci Underlie Natural Variation in Vitamin E Levels in Maize Grain.

Tocopherols, tocotrienols, and plastochromanols (collectively termed tocochromanols) are lipid-soluble antioxidants synthesized by all plants. Their dietary intake, primarily from seed oils, provides vitamin E and other health benefits. Tocochromanol biosynthesis has been dissected in the dicot Arabidopsis thaliana, which has green, photosynthetic seeds, but our understanding of tocochromanol accumulation in major crops, whose seeds are nonphotosynthetic, remains limited. To understand the genetic control of tocochromanols in grain, we conducted a joint linkage and genome-wide association study in the 5000-line U.S. maize (Zea mays) nested association mapping panel. Fifty-two quantitative trait loci for individual and total tocochromanols were identified, and of the 14 resolved to individual genes, six encode novel activities affecting tocochromanols in plants. These include two chlorophyll biosynthetic enzymes that explain the majority of tocopherol variation, which was not predicted given that, like most major cereal crops, maize grain is nonphotosynthetic. This comprehensive assessment of natural variation in vitamin E levels in maize establishes the foundation for improving tocochromanol and vitamin E content in seeds of maize and other major cereal crops.

Maize YABBY Genes drooping leaf1 and drooping leaf2 Regulate Plant Architecture.

Leaf architecture directly influences canopy structure, consequentially affecting yield. We discovered a maize (Zea mays) mutant with aberrant leaf architecture, which we named drooping leaf1 (drl1). Pleiotropic mutations in drl1 affect leaf length and width, leaf angle, and internode length and diameter. These phenotypes are enhanced by natural variation at the drl2 enhancer locus, including reduced expression of the drl2-Mo17 allele in the Mo17 inbred. A second drl2 allele, produced by transposon mutagenesis, interacted synergistically with drl1 mutants and reduced drl2 transcript levels. The drl genes are required for proper leaf patterning, development and cell proliferation of leaf support tissues, and for restricting auricle expansion at the midrib. The paralogous loci encode maize CRABS CLAW co-orthologs in the YABBY family of transcriptional regulators. The drl genes are coexpressed in incipient and emergent leaf primordia at the shoot apex, but not in the vegetative meristem or stem. Genome-wide association studies using maize NAM-RIL (nested association mapping-recombinant inbred line) populations indicated that the drl loci reside within quantitative trait locus regions for leaf angle, leaf width, and internode length and identified rare single nucleotide polymorphisms with large phenotypic effects for the latter two traits. This study demonstrates that drl genes control the development of key agronomic traits in maize.

Genotyping-by-Sequencing.

Genotyping-by-sequencing (GBS) refers to a suite of related methods that obtain genotype data from samples by using restriction enzyme digestion followed by high-throughput sequencing. GBS is a refinement of restriction site-associated DNA sequencing (RADseq) methods, with a goal of being able to perform library preparations quickly, cost-effectively, and in a high-throughput manner. This protocol contains the steps necessary to go from purified DNA to Illumina-ready libraries. It also covers the considerations that go into planning a GBS experiment. © 2017 by John Wiley & Sons, Inc.

Development of a High-Density Linkage Map and Tagging Leaf Spot Resistance in Pearl Millet Using Genotyping-by-Sequencing Markers.

Pearl millet [ (L.) R. Br; also (L.) Morrone] is an important crop throughout the world but better genomic resources for this species are needed to facilitate crop improvement. Genome mapping studies are a prerequisite for tagging agronomically important traits. Genotyping-by-sequencing (GBS) markers can be used to build high-density linkage maps, even in species lacking a reference genome. A recombinant inbred line (RIL) mapping population was developed from a cross between the lines 'Tift 99DB' and 'Tift 454'. DNA from 186 RILs, the parents, and the F was used for 96-plex KI GBS library development, which was further used for sequencing. The sequencing results showed that the average number of good reads per individual was 2.2 million, the pass filter rate was 88%, and the CV was 43%. High-quality GBS markers were developed with stringent filtering on sequence data from 179 RILs. The reference genetic map developed using 150 RILs contained 16,650 single-nucleotide polymorphisms (SNPs) and 333,567 sequence tags spread across all seven chromosomes. The overall average density of SNP markers was 23.23 SNP/cM in the final map and 1.66 unique linkage bins per cM covering a total genetic distance of 716.7 cM. The linkage map was further validated for its utility by using it in mapping quantitative trait loci (QTLs) for flowering time and resistance to leaf spot [ (Cke.) Sacc.]. This map is the densest yet reported for this crop and will be a valuable resource for the pearl millet community.

Fast-Flowering Mini-Maize: Seed to Seed in 60 Days.

Two lines of Zea mays were developed as a short-generation model for maize. The Fast-Flowering Mini-Maize (FFMM) lines A and B are robust inbred lines with a significantly shorter generation time, much smaller stature, and better greenhouse adaptation than traditional maize varieties. Five generations a year are typical. FFMM is the result of a modified double-cross hybrid between four fast-flowering lines: Neuffer's Early ACR (full color), Alexander's Early Early Synthetic, Tom Thumb Popcorn, and Gaspe Flint, followed by selection for early flowering and desirable morphology throughout an 11-generation selfing regime. Lines A and B were derived from different progeny of the initial hybrid, and crosses between Mini-Maize A and B exhibit heterosis. The ancestry of each genomic region of Mini-Maize A and B was inferred from the four founder populations using genotyping by sequencing. Other genetic and genomic tools for these lines include karyotypes for both lines A and B, kernel genetic markers y1 (white endosperm) and R1-scm2 (purple endosperm and embryo) introgressed into Mini-Maize A, and ∼24× whole-genome resequencing data for Mini-Maize A.