Biology subjects

Runcie, D.

Publications and source records attributed to Runcie, D..

9 recordsLinked to original sources

bioRxiv2026

The teosinte mexicana chromosomal inversion Inv4m modulates maize flowering time, plant height, and growth regulation gene networks

Chromosomal inversions facilitate local adaptation by maintaining co-adapted allele complexes as a single inherited unit, but the genes driving their phenotypic effects are rarely identified. Inv4m is an inversion from the highland teosinte Zea mays ssp. mexicana that is nearly fixed at 2500 masl in Mexican traditional varieties but absent in temperate maize. While association studies link Inv4m to flowering time, its key adaptive trait, the mechanisms connecting it to this phenotype remain unknown. Here, we generate near isogenic lines by introgressing the highland Michoacan 21 (Mi21) Inv4m haplotype into temperate B73 through eight backcross generations. We assemble the Inv4m-Mi21 NIL genome and, aligning to three Zea reference genomes, provide the first precise delimitation of Inv4m, a 15.2 Mb region with breakpoints overlapping knob repeat arrays. In field trials across Pennsylvania and North Carolina, Inv4m consistently accelerated flowering, whereas its plant height effect reversed sign between regions, a genotype by environment interaction consistent with environment-dependent fitness. Using RNA-seq, we identify 465 differentially expressed genes, the strongest from a cluster of JUMONJI histone demethylases (JMJ). Comparing five Zea genomes, the B73 reference carries a lineage-specific tandem expansion of five JMJ paralogs while highland genotypes carrying Inv4m retain a single ancestral copy, a difference that accounts for most of the clusters differential expression. We then show Inv4m disrupts growth related coexpression modules, with the JMJ cluster losing connectivity, and traced a trans regulatory network linking it to cell proliferation genes including pcna2 and sec6. In summary, we identify candidate genes and networks underlying Inv4ms effects and propose that part of this inversions adaptive value may reside in a dosage-sensitive regulator whose action propagates through a downstream network of genes that are involved in growth and flowering and are important for highland adaptation. Recombination suppression may thus protect a co-adapted regulatory architecture rather than independent alleles alone. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC="FIGDIR/small/732082v1_ufig1.gif" ALT="Figure 1"> View larger version (106K): org.highwire.dtl.DTLVardef@85400org.highwire.dtl.DTLVardef@c2f820org.highwire.dtl.DTLVardef@301c0aorg.highwire.dtl.DTLVardef@80e715_HPS_FORMAT_FIGEXP M_FIG C_FIG

Plant Biology↗

bioRxiv2026

Uncoupling the Effects of Highland Maize Chromosomal Inversion Inv4m from Leaf Phosphorus Deficiency Responses

Local adaptation of a species involves the selection of adaptive alleles that confer a fitness advantage in their local environment. Inversions prevent recombination between the standard and inverted heterozygous hybrids. Inversions can play a crucial role in local adaptation by locking together a set of co-adapted alleles, acting as supergenes. Inv4m is a 13 Mb inversion in maize prevalent in highland maize and highland wild relatives from Mexico. Maize from the highlands of the Trans-Mexican volcanic belt has been shown to be well-adapted to volcanic, acidic soils with low phosphorus availability. Inv4m carries several genes involved in P acquisition and utilization. We therefore tested the hypothesis that Inv4m contributes to maize adaptation to these environments through enhanced phosphorus acquisition or utilization. Alternatively, Inv4m possible adaptive value may operate through constitutive developmental effects independent of nutrient stress responses. To test this hypothesis, we introgressed a highland maize variety from the highlands of Michoacan, Mexico, carrying Inv4m into the temperate line B73 and developed Near-Introgression Lines (NILs) carrying Inv4m. We then grew NILs carrying the inversion and controls without it in soils with different phosphorus levels and evaluated the fitness effects of the inversion, as well as changes in gene expression using RNA-Seq. Our results show that P starvation elicits highly conserved transcriptomic, lipidomic, and ionomic responses, independently of the Inv4m inversion genotype. Therefore, phosphorus deficiency does not seem to be driving the adaptive value of Inv4m. Additionally, we observed a phosphorus modulated transcriptional gradient from the collar leaf downward, characterized by a decrease in the expression of photosynthesis genes and an increase in the expression of senescence-associated genes, corresponding to the positional onset and initial stages of sequential leaf senescence. Although the magnitude of the phosphorus response increased with leaf age, we did not observe significant interactions with Inv4m. Our multi-omics analysis of the maize phosphorus starvation response identified and characterized two coordinately regulated molecular programs, light harvesting shutdown and accelerated senescence, whose deployment depends on leaf developmental stage, with older leaves below the collar integrating nutrient limitation into the natural progression toward senescence.

plant biology↗

bioRxiv2024

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions

The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plants response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in routine field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, with and without pedo-climatic data, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a hybrid panel of 244 maize Dent lines crossed to a Flint tester. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV1), and in the second one, partially observed hybrids are predicted (CV2). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. This highlights their efficiency in capturing regulation processes in relation to the growth conditions. For the field traits, we observed that only the additive components of omics were useful and only slightly improved predictive abilities for predicting new hybrids (CV1, model MegaGAO) and for predicting partially observed hybrids (CV2, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest for predicting productivity, if the omics costs were to drop significantly. Our study confirms the potential of omics to predict additive and non-additive genetic effects, resulting in a potentially high increase in predictive abilities compared to standard genomic prediction models. Key MessageTranscriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits.

genetics↗

bioRxiv2023

Two teosintes made modern maize

Despite its global importance as a crop with broad economic, dietary, and cultural importance, the origins of maize and its closest wild relatives remained the topic of vigorous debate for nearly a century. Molecular analyses ultimately concluded that maize was domesticated once from a common ancestor with its closest extant relative, the lowland wild grass Zea mays ssp. parviglumis. But neither the current genetic model nor earlier models based on archaeological data account for the totality of available data, and recent work has highlighted the potential contribution of a second wild relative, the highland Zea mays ssp. mexicana. Here we present a detailed population genetic analysis of the contributions of both wild taxa to modern maize diversity using the largest sample of traditional maize varieties sequenced to date. We show that all modern maize can trace its origin to an ancient admixture event between domesticated ancient maize and Zea mays ssp. mexicana in the highlands of Mexico ca 5300 cal BP, some 4,000 years after domestication began. We show that variation in admixture is a key component of modern maize genetic and phenotypic diversity, both at the level of individual loci and as a factor driving a substantial component of additive genetic variation across a number of agronomic traits. Our results clarify the long-debated origin of modern maize, highlight the potential contributions of crop wild relatives to agronomic improvement, and raise new questions about the anthropogenic mechanisms underlying multiple waves of dispersal throughout the Americas. One-Sentence SummaryOur results clarify the long-debated origin of modern maize and highlight the contributions of crop wild relatives to the agronomic improvement of modern varieties.

evolutionary biology↗

bioRxiv2021

Teosinte introgression modulates phosphatidylcholine levels and induces early maize flowering time

Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp.parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability, and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern US, Canada and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time.

plant biology↗

bioRxiv2020

Social Network Analysis of the Genealogy of Strawberry: Retracing the Wild Roots of Heirloom and Modern Cultivars

The widely recounted story of the origin of cultivated strawberry (Fragaria x ananassa) oversimplifies the complex interspecific hybrid ancestry of the highly admixed populations from which heirloom and modern cultivars have emerged. To develop deeper insights into the three century long domestication history of strawberry, we reconstructed the genealogy as deeply as possible--pedigree records were assembled for 8,851 individuals, including 2,656 cultivars developed since 1775. The parents of individuals with unverified or missing pedigree records were accurately identified by applying exclusion analysis to array-genotyped single nucleotide polymorphisms. We identified 187 wild octoploid and 1,171 F. x ananassa founders in the genealogy, from the earliest hybrids to modern cultivars. The pedigree networks for cultivated strawberry are exceedingly complex labyrinths of ancestral interconnections formed by diverse hybrid ancestry, directional selection, migration, admixture, bottlenecks, overlapping generations, and recurrent hybridization with common ancestors that have unequally contributed allelic diversity to heirloom and modern cultivars. Fifteen to 333 ancestors were predicted to have transmitted 90% of the alleles found in country-, region-, and continent-specific populations. Using parent-offspring edges in the global pedigree network, we found that selection cycle lengths over the last 200 years of breeding have been extraordinarily long (16.0-16.9 years/generation) but decreased to a present-day range of 6.0-10.0 years/generation. Our analyses uncovered conspicuous differences in the ancestry and structure of North American and European populations and shed light on forces that have shaped phenotypic diversity in F. x ananassa.

plant biology↗

bioRxiv2019

Single-gene resolution of locally adaptive genetic variation in Mexican maize

Threats to crop production due to climate change are one of the greatest challenges facing plant breeders today. While considerable adaptive variation exists in traditional landraces, natural populations of crop wild relatives, and ex situ germplasm collections, separating adaptive alleles from linked deleterious variants that impact agronomic traits is challenging and has limited the utility of these diverse germplasm resources. Modern genome editing techniques such as CRISPR offer a potential solution by targeting specific alleles for transfer to new backgrounds, but such methods require a higher degree of precision than traditional mapping approaches can achieve. Here we present a high-resolution genome-wide association analysis to identify loci exhibiting adaptive patterns in a large panel of more than 4500 traditional maize landraces representing the breadth of genetic diversity of maize in Mexico. We evaluate associations between genotype and plant performance in 13 common gardens across a range of environments, identifying hundreds of candidate genes underlying genotype by environment interaction. We further identify genetic associations with environment across Mexico and show that such loci are associated with variation in yield and flowering time in our field trials and predict performance in independent drought trials. Our results indicate that the variation necessary to adapt crops to changing climate exists in traditional landraces that have been subject to ongoing environmental adaptation and can be identified by both phenotypic and environmental association.

evolutionary biology↗

bioRxiv2019

Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa using a G-matrix

BackgroundAngiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Those morphological and chemical traits can form highly correlated sets of traits. It is not always clear which of these are used by pollinators as primary targets of selection and which would be indirectly selected by being linked to those primary targets. Quantitative genetics tools for predicting multiple traits response to selection have been developed since long and have advanced our understanding of evolution of genetically correlated traits in various biological systems. We use these tools to predict the evolutionary trajectories of floral traits and understand the selection pressures acting on them. ResultsWe used data from an artificial and a pollinator (bumblebee, hoverfly) selection experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the responses of most floral traits including volatiles were predicted in the right direction in artificial- and bumblebee-selection experiment, revealing direct and indirect targets of bumblebee selection. Genetic covariance had a mix of constraining and facilitating effects on evolutionary responses. We further revealed how G-matrices evolved in the selection processes. ConclusionsOverall, our integrative study shows that floral signals, and especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also needs to be considered for a better understanding of floral trait evolution.

evolutionary biology↗

bioRxiv2019

Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods

Incorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful.

genetics↗