2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project.

OBJECTIVES: This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. DATA DESCRIPTION: Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

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.

Brown midrib mutant and genome-wide association analysis uncover lignin genes for disease resistance in maize.

Brown midrib (BMR) maize (Zea mays L.) harbors mutations that result in lower lignin levels and higher feed digestibility, making it a desirable silage market class for ruminant nutrition. Northern leaf blight (NLB) epidemics in upstate New York highlighted the disease susceptibility of commercially grown BMR maize hybrids. We found the bm1, bm2, bm3, and bm4 mutants in a W64A genetic background to be more susceptible to foliar fungal (NLB, gray leaf spot [GLS], and anthracnose leaf blight [ALB]) and bacterial (Stewart's wilt) diseases. The bm1, bm2, and bm3 mutants showed enhanced susceptibility to anthracnose stalk rot (ASR), and the bm1 and bm3 mutants were more susceptible to Gibberella ear rot (GER). Colocalization of quantitative trait loci (QTL) and correlations between stalk strength and disease traits in recombinant inbred line families suggest possible pleiotropies. The role of lignin in plant defense was explored using high-resolution, genome-wide association analysis for resistance to NLB in the Goodman diversity panel. Association analysis identified 100 single and clustered single-nucleotide polymorphism (SNP) associations for resistance to NLB but did not implicate natural functional variation at bm1-bm5. Strong associations implicated a suite of diverse candidate genes including lignin-related genes such as a ß-glucosidase gene cluster, hct11, knox1, knox2, zim36, lbd35, CASP-like protein 8, and xat3. The candidate genes are targets for breeding quantitative resistance to NLB in maize for use in silage and nonsilage purposes.

Nitrate reductase activity in leaves as a plant physiological indicator of in vivo biological nitrification inhibition by Brachiaria humidicola.

The tropical forage grass Brachiaria humidicola (Bh) controls soil microbial nitrification via biological nitrification inhibition (BNI). The aim of our study was to verify if nitrate reductase activity (NRA) in Bh roots or leaves reflects in vivo performance of BNI in soils. NRA was measured in roots and leaves of contrasting accessions and apomictic hybrids of Bh grown under controlled greenhouse and natural field conditions. Nitrate (NO3-) contents were measured in soil solution and in Bh stem sap to validate NRA data. Potential soil nitrification rates (NRs) and leaf δ15N values were used to verify in vivo BNI by the NRA assay in the field study. NRA was detected in Bh leaves rather than roots, regardless of NO3- availability. NRA correlated with NO3- contents in soils and stem sap of contrasting Bh genotypes substantiating its reflectance of in vivo BNI performance. Additionally, leaf NRA data from the field study significantly correlated with simultaneously collected NRs and leaf δ15N data. The leaf NRA assay facilitated a rapid screening of contrasting Bh genotypes for their differences in in vivo performance of BNI under field and greenhouse conditions, but inconsistency of the BNI potential by Bh germplasm was observed. Among Bh genotypes tested, leaf NRA was closely linked with nitrification activity, and consequently with actual BNI performance. It was concluded that NRA in leaves of Bh can serve as an indicator of in vivo BNI activity when complemented with established BNI methodologies (δ15N, NRs) under greenhouse and field conditions.

Low 15N Natural Abundance in Shoot Tissue of Brachiaria humidicola Is an Indicator of Reduced N Losses Due to Biological Nitrification Inhibition (BNI).

The tropical forage grass Brachiaria humidicola (Bh) suppresses the activity of soil nitrifiers through biological nitrification inhibition (BNI). As a result, nitrate ( NO 3 - ) formation and leaching are reduced which is also expected to tighten the soil nitrogen (N) cycle. However, the beneficial relationship between reduced NO 3 - losses and enhanced N uptake due to BNI has not been experimentally demonstrated yet. Nitrification discriminates against the 15N isotope and leads to 15N depleted NO 3 - , but 15N enriched NH 4 + in soils. Leaching of 15N depleted NO 3 - enriches the residual N pool in the soil with 15N. We hypothesized that altered nitrification and NO 3 - leaching due to diverging BNI magnitudes in contrasting Bh genotypes influence soil 15N natural abundance (δ15N), which in turn is reflected in distinct δ15N in Bh shoot biomass. Consequently, high BNI was expected to be reflected in low plant δ15N of Bh. It was our objective to investigate under controlled conditions the link between shoot value of δ15N in several Bh genotypes and leached NO 3 - amounts and shoot N uptake. Additionally, plant 15N and N% was monitored among a wide range of Bh genotypes with contrasting BNI potentials in field plots for 3 years. We measured leaf δ15N of young leaves (regrown after cutback) of Bh and combined it with nitrification rates (NRs) of incubated soil to test whether there is a direct relationship between plant δ15N and BNI. Increased leached NO 3 - was positively correlated with higher δ15N in Bh, whereas the correlation between shoot N uptake and shoot δ15N was inverse. Field cultivation of a wide range of Bh genotypes over 3 years decreased NRs in incubated soil, while shoot δ15N declined and shoot N% increased over time. Leaf δ15N of Bh genotypes correlated positively with NRs of incubated soil. It was concluded that decreasing plant δ15N of Bh genotypes over time reflects the long-term effect of BNI as linked to lower NO 3 - formation and reduced NO 3 - leaching. Accordingly, a low δ15N in Bh shoot tissue verified its potential as indicator of high BNI activity of Bh genotypes.

Current issues in cereal crop biodiversity.

The exploration, conservation, and use of agricultural biodiversity are essential components of efficient transdisciplinary research for a sustainable agriculture and food sector. Most recent advances on plant biotechnology and crop genomics must be complemented with a holistic management of plant genetic resources. Plant breeding programs aimed at improving agricultural productivity and food security can benefit from the systematic exploitation and conservation of genetic diversity to meet the demands of a growing population facing climate change. The genetic diversity of staple small grains, including rice, maize, wheat, millets, and more recently quinoa, have been surveyed to encourage utilization and prioritization of areas for germplasm conservation. Geographic information system technologies and spatial analysis are now being used as powerful tools to elucidate genetic and ecological patterns in the distribution of cultivated and wild species to establish coherent programs for the management of plant genetic resources for food and agriculture.

Construction and EST sequencing of full-length, drought stress cDNA libraries for common beans (Phaseolus vulgaris L.).

BACKGROUND: Common bean is an important legume crop with only a moderate number of short expressed sequence tags (ESTs) made with traditional methods. The goal of this research was to use full-length cDNA technology to develop ESTs that would overlap with the beginning of open reading frames and therefore be useful for gene annotation of genomic sequences. The library was also constructed to represent genes expressed under drought, low soil phosphorus and high soil aluminum toxicity. We also undertook comparisons of the full-length cDNA library to two previous non-full clone EST sets for common bean. RESULTS: Two full-length cDNA libraries were constructed: one for the drought tolerant Mesoamerican genotype BAT477 and the other one for the acid-soil tolerant Andean genotype G19833 which has been selected for genome sequencing. Plants were grown in three soil types using deep rooting cylinders subjected to drought and non-drought stress and tissues were collected from both roots and above ground parts. A total of 20,000 clones were selected robotically, half from each library. Then, nearly 10,000 clones from the G19833 library were sequenced with an average read length of 850 nucleotides. A total of 4,219 unigenes were identified consisting of 2,981 contigs and 1,238 singletons. These were functionally annotated with gene ontology terms and placed into KEGG pathways. Compared to other EST sequencing efforts in common bean, about half of the sequences were novel or represented the 5' ends of known genes. CONCLUSIONS: The present full-length cDNA libraries add to the technological toolbox available for common bean and our sequencing of these clones substantially increases the number of unique EST sequences available for the common bean genome. All of this should be useful for both functional gene annotation, analysis of splice site variants and intron/exon boundary determination by comparison to soybean genes or with common bean whole-genome sequences. In addition the library has a large number of transcription factors and will be interesting for discovery and validation of drought or abiotic stress related genes in common bean.