2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative

OBJECTIVES

This release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available.

DATA DESCRIPTION

The datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Comparative Transcriptome Analysis Reveals Genetic Mechanisms of Sugarcane Aphid Resistance in Grain Sorghum

The sugarcane aphid, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae) (SCA), has become a major pest of grain sorghum since its appearance in the USA. Several grain sorghum parental lines are moderately resistant to the SCA. However, the molecular and genetic mechanisms underlying this resistance are poorly understood, which has constrained breeding for improved resistance. RNA-Seq was used to conduct transcriptomics analysis on a moderately resistant genotype (TAM428) and a susceptible genotype (Tx2737) to elucidate the molecular mechanisms underlying resistance. Differential expression analysis revealed differences in transcriptomic profile between the two genotypes at multiple time points after infestation by SCA. Six gene clusters had differential expression during SCA infestation. Gene ontology enrichment and cluster analysis of genes differentially expressed after SCA infestation revealed consistent upregulation of genes controlling protein and lipid binding, cellular catabolic processes, transcription initiation, and autophagy in the resistant genotype. Genes regulating responses to external stimuli and stress, cell communication, and transferase activities, were all upregulated in later stages of infestation. On the other hand, expression of genes controlling cell cycle and nuclear division were reduced after SCA infestation in the resistant genotype. These results indicate that different classes of genes, including stress response genes and transcription factors, are responsible for countering the physiological effects of SCA infestation in resistant sorghum plants.

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

The effect of artificial selection on phenotypic plasticity in maize

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements.

Breeding has increased crop productivity, but whether it has also changed phenotypic plasticity is unclear. Here, the authors find maize genomic regions selected for high productivity show reduced contribution to genotype by environment variation and provide evidence for regulatory control of phenotypic stability.

Epigenetic regulation of subgenome dominance following whole genome triplication in Brassica rapa

Subgenome dominance is an important phenomenon observed in allopolyploids after whole genome duplication, in which one subgenome retains more genes as well as contributes more to the higher expressing gene copy of paralogous genes. To dissect the mechanism of subgenome dominance, we systematically investigated the relationships of gene expression, transposable element (TE) distribution and small RNA targeting, relating to the multicopy paralogous genes generated from whole genome triplication in Brassica rapa. The subgenome dominance was found to be regulated by a relatively stable factor established previously, then inherited by and shared among B. rapa varieties. In addition, we found a biased distribution of TEs between flanking regions of paralogous genes. Furthermore, the 24-nt small RNAs target TEs and are negatively correlated to the dominant expression of individual paralogous gene pairs. The biased distribution of TEs among subgenomes and the targeting of 24-nt small RNAs together produce the dominant expression phenomenon at a subgenome scale. Based on these findings, we propose a bucket hypothesis to illustrate subgenome dominance and hybrid vigor. Our findings and hypothesis are valuable for the evolutionary study of polyploids, and may shed light on studies of hybrid vigor, which is common to most species.

Co-option of the polarity gene network shapes filament morphology in angiosperms

The molecular genetic mechanisms underlying abaxial-adaxial polarity in plants have been studied as a property of lateral and flattened organs, such as leaves. In leaves, laminar expansion occurs as a result of balanced abaxial-adaxial gene expression. Over- or under- expression of either abaxializing or adaxializing genes inhibits laminar growth, resulting in a mutant radialized phenotype. Here, we show that co-option of the abaxial-adaxial polarity gene network plays a role in the evolution of stamen filament morphology in angiosperms. RNA-Seq data from species bearing laminar (flattened) or radial (cylindrical) filaments demonstrates that species with laminar filaments exhibit balanced expression of abaxial-adaxial (ab-ad) genes, while overexpression of a YABBY gene is found in species with radial filaments. This result suggests that unbalanced expression of ab-ad genes results in inhibition of laminar outgrowth, leading to a radially symmetric structure as found in many angiosperm filaments. We anticipate that co-option of the polarity gene network is a fundamental mechanism shaping many aspects of plant morphology during angiosperm evolution.

Mapping of quantitative trait locus (QTLs) that contribute to germination and early seedling drought tolerance in the interspecific cross Setaria italica×Setaria viridis

Drought tolerance is an important breeding target for enhancing the yields of grain crop species in arid and semi-arid regions of the world. Two species of Setaria, domesticated foxtail millet (S. italica) and its wild ancestor green foxtail (S. viridis) are becoming widely adopted as models for functional genomics studies in the Panicoid grasses. In this study, the genomic regions controlling germination and early seedling drought tolerance in Setaria were identified using 190 F₇ lines derived from a cross between Yugu1, a S. italica cultivar developed in China, and a wild S. viridis genotype collected from Uzbekistan. Quantitative trait loci were identified which contribute to a number of traits including promptness index, radical root length, coleoptile length and lateral root number at germinating stage and seedling survival rate was characterized by the ability of desiccated seedlings to revive after rehydration. A genetic map with 128 SSR markers which spans 1293.9 cM with an average of 14 markers per linkage group of the 9 linkage groups was constructed. A total of eighteen QTLs were detected which included nine that explained over 10% of the phenotypic variance for a given trait. Both the wild green foxtail genotype and the foxtail millet cultivar contributed the favorite alleles for traits detected in this trial, indicating that wild Setaria viridis populations may serve as a reservoir for novel stress tolerance alleles which could be employed in foxtail millet breeding.

A near complete snapshot of the Zea mays seedling transcriptome revealed from ultra-deep sequencing

RNA-sequencing (RNA-seq) enables in-depth exploration of transcriptomes, but typical sequencing depth often limits its comprehensiveness. In this study, we generated nearly 3 billion RNA-Seq reads, totaling 341 Gb of sequence, from a Zea mays seedling sample. At this depth, a near complete snapshot of the transcriptome was observed consisting of over 90% of the annotated transcripts, including lowly expressed transcription factors. A novel hybrid strategy combining de novo and reference-based assemblies yielded a transcriptome consisting of 126,708 transcripts with 88% of expressed known genes assembled to full-length. We improved current annotations by adding 4,842 previously unannotated transcript variants and many new features, including 212 maize transcripts, 201 genes, 10 genes with undocumented potential roles in seedlings as well as maize lineage specific gene fusion events. We demonstrated the power of deep sequencing for large transcriptome studies by generating a high quality transcriptome, which provides a rich resource for the research community.