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Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner’s strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams’ methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

 

In Mediterranean climates, the timing of seasonal rains determines germination, flowering phenology and fitness. As climate change alters seasonal precipitation patterns, it is important to ask how these changes will affect the phenology and fitness of plant populations. We addressed this question experimentally with the annual plant speciesArabidopsis thaliana.

In a first experiment, we manipulated the date of rainfall onset and recorded germination phenology on sand and soil substrates. In a second experiment, we manipulated germination date, growing season length and mid‐season drought to measure their effects on flowering time and fitness. Within each experiment, we manipulated seed dormancy and flowering time using multilocus near‐isogenic lines segregating strong and weak alleles of the seed dormancy geneDOG1and the flowering time geneFRI. We synthesized germination phenology data from the first experiment with fitness functions from the second experiment to project population fitness under different seasonal rainfall scenarios.

Germination phenology tracked rainfall onset but was slower and more variable on sand than on soil. Many seeds dispersed on sand in spring and summer delayed germination until the cooler temperatures of autumn. The high‐dormancyDOG1allele also prevented immediate germination in spring and summer. Germination timing strongly affected plant fitness. Fecundity was highest in the October germination cohort and declined in spring germinants. The late floweringFRIallele had lower fecundity, especially in early fall and spring cohorts. Projections of population fitness revealed that: (1) Later onset of autumn rains will negatively affect population fitness. (2) Slow, variable germination on sand buffers populations against fitness impacts of variable spring and summer rainfall. (3) Seasonal selection favours high dormancy and early flowering genotypes in a Mediterranean climate with hot dry summers. The high‐dormancyDOG1allele delayed germination of spring‐dispersed fresh seeds until more favourable early fall conditions, resulting in higher projected population fitness.

These findings suggest that Mediterranean annual plant populations are vulnerable to changes in seasonal precipitation, especially in California where rainfall onset is already occurring later. The fitness advantage of highly dormant, early flowering genotypes helps explain the prevalence of this strategy in Mediterranean populations.

Read the freePlain Language Summaryfor this article on the Journal blog.

 

The seasonal timing of seed germination determines a plant’s realized environmental niche, and is important for adaptation to climate. The timing of seasonal germination depends on patterns of seed dormancy release or induction by cold and interacts with flowering-time variation to construct different seasonal life histories. To characterize the genetic basis and climatic associations of natural variation in seed chilling responses and associated life-history syndromes, we selected 559 fully sequenced accessions of the model annual species Arabidopsis thaliana from across a wide climate range and scored each for seed germination across a range of 13 cold stratification treatments, as well as the timing of flowering and senescence. Germination strategies varied continuously along 2 major axes: 1) Overall germination fraction and 2) induction vs. release of dormancy by cold. Natural variation in seed responses to chilling was correlated with flowering time and senescence to create a range of seasonal life-history syndromes. Genome-wide association identified several loci associated with natural variation in seed chilling responses, including a known functional polymorphism in the self-binding domain of the candidate gene DOG1. A phylogeny of DOG1 haplotypes revealed ancient divergence of these functional variants associated with periods of Pleistocene climate change, and Gradient Forest analysis showed that allele turnover of candidate SNPs was significantly associated with climate gradients. These results provide evidence that A. thaliana ’s germination niche and correlated life-history syndromes are shaped by past climate cycles, as well as local adaptation to contemporary climate. 

Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments. 

Populations are locally adapted when they exhibit higher fitness than foreign populations in their native habitat. Maize landrace adaptations to highland and lowland conditions are of interest to researchers and breeders. To determine the prevalence and strength of local adaptation in maize landraces, we performed a reciprocal transplant experiment across an elevational gradient in Mexico. We grew 120 landraces, grouped into four populations (Mexican Highland, Mexican Lowland, South American Highland, South American Lowland), in Mexican highland and lowland common gardens and collected phenotypes relevant to fitness and known highland‐adaptive traits such as anthocyanin pigmentation and macrohair density. 67k DArTseq markers were generated from field specimens to allow comparisons between phenotypic patterns and population genetic structure. We found phenotypic patterns consistent with local adaptation, though these patterns differ between the Mexican and South American populations. Quantitative trait differentiation (Q_ST) was greater than neutral allele frequency differentiation (F_ST) for many traits, signaling directional selection between pairs of populations. All populations exhibited higher fitness metric values when grown at their native elevation, and Mexican landraces had higher fitness than South American landraces when grown in these Mexican sites. As environmental distance between landraces’ native collection sites and common garden sites increased, fitness values dropped, suggesting landraces are adapted to environmental conditions at their natal sites. Correlations between fitness and anthocyanin pigmentation and macrohair traits were stronger in the highland site than the lowland site, supporting their status as highland‐adaptive. These results give substance to the long‐held presumption of local adaptation of New World maize landraces to elevation and other environmental variables across North and South America.

 

Understanding the impact of elevated CO₂(eCO₂) in global agriculture is important given climate change projections. Breeding climate‐resilient crops depends on genetic variation within naturally varying populations. The effect of genetic variation in response to eCO₂is poorly understood, especially in crop species. We describe the different ways in whichSolanum lycopersicumand its wild relativeS. pennelliirespond to eCO₂, from cell anatomy, to the transcriptome, and metabolome. We further validate the importance of translational regulation as a potential mechanism for plants to adaptively respond to rising levels of atmospheric CO₂.