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Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set’s genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.

 

Core Ideas Manual pollinations in breeding and genetics research requires pollen available when recipient silks are viable. The method collects and stores maize pollen for at least 5 days and facilitates efficient pollination. Pollen is mixed with polyetheretherketone and uses field‐collected pollen and simple storage conditions. The method can increase the number of pollinations per tassel and generates a reasonable number of viable seeds. 

Source and sink interactions play a critical but mechanistically poorly understood role in the regulation of senescence. To disentangle the genetic and molecular mechanisms underlying source–sink-regulated senescence (SSRS), we performed a phenotypic, transcriptomic, and systems genetics analysis of senescence induced by the lack of a strong sink in maize (Zea mays). Comparative analysis of genotypes with contrasting SSRS phenotypes revealed that feedback inhibition of photosynthesis, a surge in reactive oxygen species, and the resulting endoplasmic reticulum (ER) stress were the earliest outcomes of weakened sink demand. Multienvironmental evaluation of a biparental population and a diversity panel identified 12 quantitative trait loci and 24 candidate genes, respectively, underlying SSRS. Combining the natural diversity and coexpression networks analyses identified 7 high-confidence candidate genes involved in proteolysis, photosynthesis, stress response, and protein folding. The role of a cathepsin B like protease 4 (ccp4), a candidate gene supported by systems genetic analysis, was validated by analysis of natural alleles in maize and heterologous analyses in Arabidopsis (Arabidopsis thaliana). Analysis of natural alleles suggested that a 700-bp polymorphic promoter region harboring multiple ABA-responsive elements is responsible for differential transcriptional regulation of ccp4 by ABA and the resulting variation in SSRS phenotype. We propose a model for SSRS wherein feedback inhibition of photosynthesis, ABA signaling, and oxidative stress converge to induce ER stress manifested as programed cell death and senescence. These findings provide a deeper understanding of signals emerging from loss of sink strength and offer opportunities to modify these signals to alter senescence program and enhance crop productivity.

 

In biological research domains, liquid chromatography–mass spectroscopy (LC-MS) has prevailed as the preferred technique for generating high quality metabolomic data. However, even with advanced instrumentation and established data acquisition protocols, technical errors are still routinely encountered and can pose a significant challenge to unveiling biologically relevant information. In large-scale studies, signal drift and batch effects are how technical errors are most commonly manifested. We developed pseudoDrift, an R package with capabilities for data simulation and outlier detection, and a new training and testing approach that is implemented to capture and to optionally correct for technical errors in LC–MS metabolomic data. Using data simulation, we demonstrate here that our approach performs equally as well as existing methods and offers increased flexibility to the researcher. As part of our study, we generated a targeted LC–MS dataset that profiled 33 phenolic compounds from seedling stem tissue in 602 genetically diverse non-transgenic maize inbred lines. This dataset provides a unique opportunity to investigate the dynamics of specialized metabolism in plants. 

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.

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.

 

Single‐parent expression (SPE) is defined as gene expression in only one of the two parents. SPE can arise from differential expression between parental alleles, termed non‐presence/absence (non‐PAV) SPE, or from the physical absence of a gene in one parent, termed PAV SPE. We used transcriptome data of diverseZea mays(maize) inbreds and hybrids, including 401 samples from five different tissues, to test for differences between these types of SPE genes. Although commonly observed, SPE is highly genotype and tissue specific. A positive correlation was observed between the genetic distance of the two inbred parents and the number of SPE genes identified. Regulatory analysis showed that PAV SPE and non‐PAV SPE genes are mainly regulated byciseffects, with a small fraction undertransregulation. Polymorphic transposable element insertions in promoter sequences contributed to the high level ofcisregulation for PAV SPE and non‐PAV SPE genes. PAV SPE genes were more frequently expressed in hybrids than non‐PAV SPE genes. The expression of parentally silent alleles in hybrids of non‐PAV SPE genes was relatively rare but occurred in most hybrids. Non‐PAV SPE genes with expression of the silent allele in hybrids are more likely to exhibit above high parent expression level than hybrids that do not express the silent allele, leading to non‐additive expression. This study provides a comprehensive understanding of the nature of non‐PAV SPE and PAV SPE genes and their roles in gene expression complementation in maize hybrids.