An a-maize-ing set of genomes: Maize is an important crop cultivated worldwide. As maize spread across the world, selection for local environments resulted in variation, but the impact on differences between the genome has not been quantified. By producing high-quality genomic sequences of the 26 lines used in the maize nested association mapping panel, Hufford et al . map important traits and demonstrate the diversity of maize. Examining RNA and methylation of genes across accessions, the authors identified a core set of maize genes. Beyond this core set, comparative analysis across lines identified high levels of variation in the total set of genes, the maize pan-genome. The value of this resource was further exemplified by mapping quantitative traits of interest, including those related to pathogen resistance. —LMZ

Observed phenotypic variation in living organisms is shaped by genomes, environment, and their interactions. Flowering time under natural conditions can showcase the diverse outcome of the gene–environment interplay. However, identifying hidden patterns and specific factors underlying phenotypic plasticity under natural field conditions remains challenging. With a genetic population showing dynamic changes in flowering time, here we show that the integrated analyses of genomic responses to diverse environments is powerful to reveal the underlying genetic architecture. Specifically, the effect continuum of individual genes (Ma₁, Ma₆, FT, and ELF3) was found to vary in size and in direction along an environmental gradient that was quantified by photothermal time, a combination of two environmental factors (photoperiod and temperature). Gene–gene interaction was also contributing to the observed phenotypic plasticity. With the identified environmental index to quantitatively connect environments, a systematic genome-wide performance prediction framework was established through either genotype-specific reaction-norm parameters or genome-wide marker-effect continua. These parallel genome-wide approaches were demonstrated for in-season and on-target performance prediction by simultaneously exploiting genomics, environment profiling, and performance information. Improved understanding of mechanisms for phenotypic plasticity enables a concerted exploration that turns challenge into opportunity. Significance Integrated analysis of genotype by environment can reveal the pattern and mechanistic interplay underlying the observed phenotype dynamics. A critical question needs to be answered to enhance our ability to conduct genomic and environmental analysis of varied phenotypic plasticity observed in natural field conditions: How to uncover patterns at different levels to facilitate complex trait dissection and performance prediction. In this study, we first uncovered the pattern of genotype response to different environments. We then uncovered the pattern generated by the combination of environmental factors and the pattern of genetic effects at the individual gene level across environments. Finally, we demonstrated that trait dissection to individual genes and genome-wide performance prediction can be conducted through a joint genomic regression analysis framework.

The phenotypic variation of living organisms is shaped by genetics, environment, and their interaction. Understanding phenotypic plasticity under natural conditions is hindered by the apparently complex environment and the interacting genes and pathways. Herein, we report findings from the dissection of rice flowering-time plasticity in a genetic mapping population grown in natural long-day field environments. Genetic loci harboring four genes originally discovered for their photoperiodic effects (Hd1, Hd2, Hd5, and Hd6) were found to differentially respond to temperature at the early growth stage to jointly determine flowering time. The effects of these plasticity genes were revealed with multiple reaction norms along the temperature gradient. By coupling genomic selection and the environmental index, accurate performance predictions were obtained. Next, we examined the allelic variation in the four flowering-time genes across the diverse accessions from the 3000 Rice Genomes Project and constructed haplotypes at both individual-gene and multigene levels. The geographic distribution of haplotypes revealed their preferential adaptation to different temperature zones. Regions with lower temperatures were dominated by haplotypes sensitive to temperature changes, whereas the equatorial region had a majority of haplotypes that are less responsive to temperature. By integrating knowledge from genomics, gene cloning and functional characterization, and environment quantification, we propose a conceptual model with multiple levels of reaction norms to help bridge the gaps among individual gene discovery, field-level phenotypic plasticity, and genomic diversity and adaptation.

Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype–phenotype relationship in quantitative genetics. GS is a breeding strategy to predict the genotypic values of individuals for selection using their genotypic data and a trained model. It includes four major steps: training population design, model building, prediction, and selection. GS revises the traditional breeding process by assigning phenotyping a new role of generating data for the building of prediction models. The increased capacity of GS to evaluate more individuals, in combination with shorter breeding cycle times, has led to wide adoption in plant breeding. Research studies have been conducted to implement GS with different emphases in crop‐ and trait‐specific applications, prediction models, design of training populations, and identifying factors influencing prediction accuracy. GS plays different roles in plant breeding such as turbocharging of gene banks, parental selection, and candidate selection at different stages of the breeding cycle. It can be enhanced by additional data types such as phenomics, transcriptomics, metabolomics, and enviromics. In light of the rapid development of artificial intelligence, GS can be further improved by either upgrading the entire framework or individual components. Technological advances, research innovations, and emerging challenges in agriculture will continue to shape the role of GS in plant breeding.

Stalk lodging, which is generally determined by stalk strength, results in considerable yield loss and has become a primary threat to maize (Zea mays) yield under high-density planting. However, the molecular genetic basis of maize stalk strength remains unclear, and improvement methods remain inefficient. Here, we combined map-based cloning and association mapping and identified the gene stiff1 underlying a major quantitative trait locus for stalk strength in maize. A 27.2-kb transposable element insertion was present in the promoter of the stiff1 gene, which encodes an F-box domain protein. This transposable element insertion repressed the transcription of stiff1, leading to the increased cellulose and lignin contents in the cell wall and consequently greater stalk strength. Furthermore, a precisely edited allele of stiff1 generated through the CRISPR/Cas9 system resulted in plants with a stronger stalk than the unedited control. Nucleotide diversity analysis revealed that the promoter of stiff1 was under strong selection in the maize stiff-stalk group. Our cloning of stiff1 reveals a case in which a transposable element played an important role in maize improvement. The identification of stiff1 and our edited stiff1 allele pave the way for efficient improvement of maize stalk strength.

Summary: Phenotypic plasticity is observed widely in plants and often studied with reaction norms for adult plant or end‐of‐season traits. Uncovering genetic, environmental and developmental patterns behind the observed phenotypic variation under natural field conditions is needed. Using a sorghum (Sorghum bicolor) genetic population evaluated for plant height in seven natural field conditions, we investigated the major pattern that differentiated these environments. We then examined the physiological relevance of the identified environmental index by investigating the developmental trajectory of the population with multistage height measurements in four additional environments and conducting crop growth modelling. We found that diurnal temperature range (DTR) during the rapid growth period of sorghum development was an effective environmental index. Three genetic loci (Dw1,Dw3andqHT7.1) were consistently detected for individual environments, reaction‐norm parameters across environments and growth‐curve parameters through the season. Their genetic effects changed dynamically along the environmental gradient and the developmental stage. A conceptual model with three‐dimensional reaction norms was proposed to showcase the interconnecting components: genotype, environment and development. Beyond genomic and environmental analyses, further integration of development and physiology at the whole‐plant and molecular levels into complex trait dissection would enhance our understanding of mechanisms underlying phenotypic variation.