Biology subjects

Runcie, D. E.

Publications and source records attributed to Runcie, D. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Cell-specific Na+ accumulation is linked to symplastic transport in tomato leaves

Soil salinization is a growing global threat that limits crop productivity. To cope with sodium (Na) stress, plants have evolved tolerance mechanisms, including excluding Na from shoot tissues and tolerating elevated Na within shoots through tissue- and cellular-level mechanisms. Most current knowledge of Na accumulation comes from organ- or whole-plant measurements that lack the spatial resolution needed to resolve cellular tolerance mechanisms. Here, we used histological approaches to map leaf Na distribution in tomato (Solanum) species with contrasting salt-tolerance strategies. In the Na-excluding domesticated tomato (cv. M82), Na was largely confined to the bundle sheath, whereas Na-including wild relatives accumulated Na throughout the blade mesophyll. Consistent with these cell population-specific Na patterns, M82, but not S. pennellii, exhibited reduced symplastic transport and plasmodesmal permeability under salt stress. A genetic screen combined with transcriptome profiling implicated Plasmodesmata-Located Protein 1 (PDLP1), a regulator of callose-mediated plasmodesmal closure, in establishing symplastic domains in M82 that restrict Na movement into the mesophyll. Moreover, PDLP1 expression negatively correlated with mesophyll Na+ levels across wild and domesticated tomatoes. Collectively, these results link cellular Na enrichment patterns to symplastic connectivity and suggest that PDLP1-mediated regulation of plasmodesmata contributes to leaf-level salt-tolerance strategies. HighlightsO_LICell type-specific Na accumulation differs between domesticated tomato (Solanum lycopersicum cv. M82) and its wild relative S. pennellii. C_LIO_LIAdditional salt-tolerant wild tomato relatives exhibit leaf Na enrichment patterns similar to S. pennellii. C_LIO_LISalt stress reduces symplastic transport and plasmodesmal permeability in M82 leaves but not in S. pennellii. C_LIO_LIAn introgression line (IL6-4) between the two tomato species, which carries S. pennellii Plasmodesmata-Located Protein 1 (SpPDLP1), shows S. pennellii-like Na enrichment patterns. C_LIO_LIPDLP1 expression shows a negative correlation with mesophyll Na+ levels across tomato species. C_LI

plant biology↗

bioRxiv2026

Maize genetic diversity is largely unstructured by human ethnolinguistic diversity in its center of origin

Population structure and environmental features often capture major axes of genetic variation in many species. Yet the impacts of human activity often remain unquantified. For domesticated species that rely on human activity for survival and dispersal, human movements and cultural differences may play key roles patterning genetic diversity. Maize is a staple crop of enormous cultural importance to indigenous peoples of the Americas, but cannot survive or disperse without farmers. Using publicly available genotyping and passport data from almost 2,000 traditional maize varieties, more than 500 whole genome sequences of humans from Mexico, and indigenous linguistic maps, we quantify anthropogenic effects on maize genetic diversity in the Americas. Maize shows very little overall structure, highlighting the effectiveness of indigenous farmers in moving and mixing maize populations. While principal components of maize diversity show meaningful correlations to human genetic diversity, our linear modeling suggests little additional impact of human population structure beyond shared geography. Though differences in maize diversity are often patterned by language locally, we find only weak genome-wide effects at larger spatial scales. Despite the relatively weak global signal of anthropogenic effects, linguistic GWAS, outlier FST analyses, and selection scans identified loci associated with specific languages. Leveraging landscape-level sequencing data, we highlight how anthropogenic factors have shaped patterns of maize genetic diversity across Mesoamerica.

evolutionary biology↗

bioRxiv2026

The MexMAGIC population reveals the genetic architecture of clinal trait variation in Mexican native maize

Defining the genetic basis of local adaptation is fundamental to evolutionary biology and crop improvement. Theory predicts that when selective pressures track differences in the environment, a cline will be established. Such clines might be exploited to uncover adaptive variation by association of alleles with environmental stressors. However, monotonic phenotypic change over a cline is not necessarily mirrored by adaptive genetic variants. Furthermore, population structure can complicate the interpretation of genotype-environment association. To test the assumptions of genotype-environment association in a crop species, we developed a multi-parent advanced generation inter-cross (MAGIC) population using eight Mexican native maize varieties sourced from distinct agroecological zones. We mapped two clinal traits (tassel branching and flowering time) differing in genetic architecture. Variation in tassel branch number was dominated by a single QTL with allele effects that aligned well with a negative elevational cline. In contrast, we mapped 11 flowering time QTL with allele effects that were not consistently correlated with any one source environmental factor and distinct loci donated by highland and lowland early maturing varieties. Our observations support the theoretical result that genotype-environment association will be strongest under simple genetic architecture, although identification of adaptive alleles may still be confounded by population structure. Plain Language: Nine thousand years of careful selection and cultivation by indigenous farmers has generated a rich diversity of native Mexican maize (corn) varieties, grown from sea level to high mountains, and from jungle to semidesert. By crossing native varieties adapted to different locations, we can uncover important genetic variants conferring tolerance to environmental stressors.

genetics↗

bioRxiv2026

The use of cross-validation has overestimated the value of genomic selection in plant breeding

Genomic Selection (GS) is widely considered to be a transformative approach for plant breeding, and has been a subject of well over a thousand papers since its proposal 25 years ago. The reduced costs of marker genotyping and genome sequencing, the proliferation of powerful statistical methods, and innovative breeding schemes that leverage GS have promised a revolution in the speed, efficiency, and precision of plant breeding. However, clear evidence of dramatically improved breeding outcomes using GS is difficult to find in the literature. I argue here that the most commonly presented evidence of GS success--high estimated accuracies of Genomic Prediction (GP) models as evaluated by crossvalidation--may be giving a highly misleading impression about the value of GS, at least in moderate-sized breeding programs. Estimating GP accuracy by cross-validation is only appropriate when GS is used to increase selection intensity, one of four key control parameters of the breeders equation and usually the least cost-effective way to increase genetic gain. If GS is instead used to increase the accuracy of selection among a fixed set of candidates or used to speed up breeding cycles, cross-validation-based estimates can be dramatically inaccurate, in ways that differ among breeding populations and traits. Instead, I show that analytical expressions and computational simulations are more informative about the likelihood of success of GS than cross-validation, and can be more effectively employed to evaluate GS program design.

genetics↗

bioRxiv2025

The origins and adaptive consequences of polyploidy in a dominant prairie grass

Polyploidy is ubiquitous across North American prairies, which provide essential ecosystem services and rich soil for agriculture. Yet the mechanism driving polyploid abundance is unclear. Multiple hypotheses have been proposed including polyploid abundance is proportional to the opportunity for whole genome duplication (WGD), and WGD alters phenotypes that may increase fitness. We tested these two hypotheses together in the mixed-ploidy species Andropogon gerardi, a dominant grass species in endangered North American tallgrass prairies. Leveraging a novel, phased allopolyploid reference genome, we found the A. gerardi hexaploid arose after the C4 grassland expansion in the early Pleistocene, when glacial cycles likely increased secondary contact between the diploid progenitors. We sequenced A. gerardi from 25 popula-tions and examined cytotype performance and morphology in a controlled environment to investigate the consequences of the contemporary mixed-ploidy populations. We found the 9x A. gerardi cytotype is a neopolyploid and a result of recurrent WGD events. Further, we demonstrate the 9x neopolyploids have greater growth and a decreased stomatal pore index, which is adaptive in xeric climates where the 9x cy-totype is most common. Together, our results support both hypotheses for polyploid abundance in North America: WGD is a product of opportunity and can have immediate fitness consequences. Although the changes to fitness may provide an advantage to 9x A. gerardi, the establishment of 9x may lower overall population fitness due to the lower reproductive viability of 9x individuals. Significance statementPolyploid species are abundant in North American prairies and make up many of the dominant species in the ecosystem. This prominence could be a result of whole genome duplication conferring an advantage that increases the frequency of polyploids or could simply indicate that the opportunity for whole genome duplication is higher in this ecosystem, or both. Through examining three polyploidization events in A. gerardi, the dominant species in endangered tallgrass North American prairies, we found whole genome duplication is both surprisingly common and confers traits that are beneficial in some environments.

plant biology↗

bioRxiv2025

Why Accuracy Metrics Fall Short in Comparing Phenomic and Genomic Prediction Models

Phenomic selection is a new paradigm in plant breeding that uses high-throughput phenotyping technologies and machine learning models to predict traits of new individuals and make selections. This can allow breeders to evaluate more plants in higher throughput more accurately, resulting in faster rates of gain and reduced labor costs. However, phenomic prediction models are frequently benchmarked against genomic prediction models using cross-validation to demonstrate their usefulness to breeders. We argue that this is inappropriate for two reasons: 1) differences in the accuracy statistic measured by cross-validation do not reliably indicate differences in the accuracy parameter of the breeders equation, which we show analytically and through re-analysis of data from three representative phenomic prediction studies, and 2) phenomic and genomic selection tools influence other parameters of the breeders equation, so comparing accuracy, even if done properly, is insufficient to advocate for one approach over the other. We conclude that phenomic selection may be useful, but comparisons of accuracy between genomic prediction and phenomic prediction models are not.

genetics↗

bioRxiv2024

Convergent evolution of epigenome recruited DNA repair across the Tree of Life

Mutations fuel evolution while also causing diseases like cancer. Epigenome-targeted DNA repair can help organisms protect important genomic regions from mutation. However, the adaptive value, mechanistic diversity, and evolution of epigenome-targeted DNA repair systems across the tree of life remain unresolved. Here, we investigated the evolution of histone reader domains fused to the DNA repair protein MSH6 (MutS Homolog 6) across over 4,000 eukaryotes. We uncovered a paradigmatic example of convergent evolution: MSH6 has independently acquired distinct histone reader domains; PWWP (metazoa) and Tudor (plants), previously shown to target histone modifications in active genes in humans (H3K36me3) and Arabidopsis (H3K4me1). Conservation in MSH6 histone reader domains shows signatures of natural selection, particularly for amino acids that bind specific histone modifications. Species that have gained or retained MSH6 histone readers tend to have larger genome sizes, especially marked by significantly more introns in genic regions. These patterns support previous theoretical predictions about the co-evolution of genome architectures and mutation rate heterogeneity. The evolution of epigenome-targeted DNA repair has implications for genome evolution, health, and the mutational origins of genetic diversity across the tree of life. Short SummaryFusions between histone reader domains and the mismatch repair protein MSH6 have evolved multiple times across Eukaryotes and show evidence of selection, providing mechanistic and theoretical insight into the forces shaping genomic mutation rate heterogeneity.

evolutionary biology↗

bioRxiv2024

The utility of environmental data from traditionalvarieties for climate-adaptive maize breeding

Maintaining crop yields in the face of climate change is a major challenge facing plant breeding today. Considerable genetic variation exists in ex-situ collections of traditional crop varieties, but identifying adaptive loci and testing their agronomic performance in large populations in field trials is costly. Here, we study the utility of climate and genomic data for identifying promising traditional varieties to incorporate into maize breeding programs. To do so, we use phenotypic data from more than 4,000 traditional maize varieties grown in 13 trial environments. First, we used genotype data to predict environmental characteristics of germplasm collections to identify varieties that may be locally adapted to target environments. Second, we used environmental GWAS (envGWAS) to identify genetic loci associated with historical divergence along climatic gradients, such as the putative heat shock protein hsftf9 and the large-scale adaptive inversion Inv4m. Finally, we compared the value of environmental data and envGWAS-prioritized loci to genomic data for prioritizing traditional varieties. We find that maize yield traits are best predicted by genomic data, and that envGWAS-identified variants provide little direct predictive information over patterns of population structure. We also find that adding environment-of-origin variables does not improve yield component prediction over kinship or population structure alone, but could be a useful selection proxy in the absence of sequencing data. While our results suggest little utility of environmental data for selecting traditional varieties to incorporate in breeding programs, environmental GWAS is nonetheless a potentially powerful approach to identify individual novel loci for maize improvement, especially when coupled with high density genotyping.

evolutionary biology↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

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 winners 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, classical 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.

genetics↗

bioRxiv2024

Why is usefulness rarely useful?

Mate selection plays an important role in breeding programs. The usefulness criterion was proposed as a criterion for mate selection, combining information on both the mean and standard deviation of the potential offspring, particularly in clonally propagated outbred species where large family sizes are possible. Predicting mean values of offspring of a cross is generally easier than predicting the standard deviation, especially in outbred species where the linkage of alleles is often unknown and phasing is required. In this study, we developed a method for estimating phasing accuracy from unphased genotype data on possible parental lines and evaluated whether the accuracy was sufficient to predict family standard deviations of possible crosses using a set of simulations spanning a wide range of genetic architectures and genotypes from a real strawberry breeding population. We find that despite highly accurate computational phasing, predicting family standard deviations and using predicted values of the usefulness criterion per possible cross confers little benefit relative to simply selecting parents based on predicted family means. Therefore even in this species, which is clonally propagated, outbred, and produces large families, we find the usefulness criterion unlikely to be useful.

genetics↗

bioRxiv2024

MegaLMM improves genomic predictions in new environments using environmental covariates

Multi-environment trials (METs) are crucial for identifying varieties that perform well across a target population of environments (TPE). However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed. We recently developed MegaLMM, a statistical model that can leverage hundreds of trials to significantly improve genetic value prediction accuracy within METs. Here, we extend MegaLMM to enable genomic prediction in new environments by learning regressions of latent factor loadings on Environmental Covariates (ECs) across trials. We evaluated the extended MegaLMM using the maize Genome-To-Fields dataset, consisting of 4402 varieties cultivated in 195 trials with 87.1% of phenotypic values missing, and demonstrated its high accuracy in genomic prediction under various breeding scenarios. Furthermore, we showcased MegaLMMs superiority over univariate GBLUP in predicting trait performance of experimental genotypes in new environments. Finally, we explored the use of higher-dimensional quantitative ECs and discussed when and how detailed environmental data can be leveraged for genomic prediction from METs. We propose that MegaLMM can be applied to plant breeding of diverse crops and different fields of genetics where large-scale linear mixed models are utilized.

genetics↗

bioRxiv2023

Phenotypic variation in maize can be largely explained by genetic variation at transcription factor binding sites

Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over two-hundred thousand genetic variants (termed binding-QTL) linked to cis-element occupancy. Three lines of evidence support the functional significance of binding-QTL: i) they coincide with numerous known causative loci that regulate traits, including VGT1, Trehalase1, and the MITE transposon near ZmNAC111 under drought; ii) their footprint bias is mirrored between inbred parents and by ChIP-seq; iii) partitioning genetic variation across genomic regions demonstrates that binding-QTL capture the majority of heritable trait variation across [~]70% of 143 phenotypes. Our study provides a promising approach to make previously hidden cis-variation more accessible for genetic studies and multi-target engineering of complex traits.

genomics↗

bioRxiv2022

Allele-specific expression reveals multiple paths to highland adaptation in maize

Maize is a staple food of smallholder farmers living in highland regions up to 4,000 meters above sea level worldwide. Mexican and South American highlands are two major highland maize growing regions, and population genetic data suggests the maizes adaptation to these regions occurred largely independently, providing a case study for parallel evolution. To better understand the mechanistic basis of highland adaptation, we crossed maize landraces from 108 highland and lowland sites of Mexico and South America with the inbred line B73 to produce F1 hybrids and grew them in both highland and lowland sites in Mexico. We identified thousands of genes with divergent expression between highland and lowland populations. Hundreds of these genes show patterns of convergent evolution between Mexico and South America. To dissect the genetic architecture of the divergent gene expression, we developed a novel allele-specific expression analysis pipeline to detect genes with divergent functional cis-regulatory variation between highland and lowland populations. We identified hundreds of genes with divergent cis-regulation between highland and lowland landrace alleles, with 20 in common between regions, further suggesting convergence in the genes underlying highland adaptation. Further analyses suggest multiple mechanisms contribute to this convergence. Our findings reveal a complex genetic architecture of cis-regulatory alleles underlying adaptation to highlands in maize. Although the vast majority of evolutionary changes associated with highland adaptation were region-specific, our findings highlight an important role for convergence at the gene expression and gene regulation levels as well.

evolutionary biology↗

bioRxiv2022

MegaBayesianAlphabet: Mega-scale Bayesian Regression methods for genome-wide prediction and association studies with thousands of traits

Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated data are challenging. We developed MegaBayesianAlphabet to simultaneously analyze genetic variants underlying thousands of traits using the flexible priors of the Bayesian Alphabet family. As a demonstration, we implemented the BayesC prior in the R package MegaLMM and applied it to both simulated and real data sets. Our analyses show that the resulting model MegaBayesC can effectively use high-dimensional phenotypic data to improve the accuracy of genetic value prediction, the reliability of marker discovery, and the accuracy of marker effect size estimation in genome-wide analyses.

genetics↗

bioRxiv2022

Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high dimensional Bayesian Sparse Factor Genetic modelling to 3,385 gene expression traits from Drosophila melanogaster and from D. serrata to explore how genetic variance is distributed across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme (>3 IQR from the median) values. This observation, in the two independently sampled species, suggests that the House of Cards (HoC) model might apply not only to individual expression traits, but also to emergent co-expression phenotypes. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were outliers for multivariate factors but not for any individual traits. We observed other consistent differences between heritable multivariate factors with outlier lines versus those factors that conformed to a Gaussian distribution of genetic effects, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

genetics↗

bioRxiv2022

3' RNA sequencing does not increase power or reduce costs for gene expression analysis

BackgroundSequencing RNA transcripts for gene expression profiling is a popular and important technique with broad utility in biological sciences. We set out to comprehensively compare the two most popular methods for generating sequencing libraries for differential gene expression analysis: 3-end sequencing, which generates libraries from the 3 end of an RNA transcript; and traditional RNA sequencing, which generates libraries from whole RNA transcripts. We include three species in our experiment to test whether our findings replicate across genomes and genome assemblies. ResultsWe found similar levels of precision and power to detect differentially expressed genes between the two methods. Notably, whole transcript RNA-seq performed better in the non-traditional model species included in our study. ConclusionOverall, we recommended whole transcript RNA sequencing for the added benefits of alternative splicing detection, and gene-body variant detection.

genomics↗

bioRxiv2021

Analysis of genotype by environment interactions in a maize mapping population

Genotype by environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype by environment interaction in a maize multi-parent advanced generation intercross population grown across five environments. We found that genotype by environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. In order to understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype by environment variance. We also performed a genome-wide association study to identify markers associated with genotype by environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype by environment interactions in this population.

plant biology↗

bioRxiv2021

Modeling Allelic Diversity of Multi-parent Mapping Populations Affects Detection of Quantitative Trait Loci

The search for quantitative trait loci (QTL) that explain complex traits such as yield and flowering time has been ongoing in all crops. Methods such as bi-parental QTL mapping and genome-wide association studies (GWAS) each have their own advantages and limitations. Multi-parent advanced generation intercross (MAGIC) populations contain more recombination events and genetic diversity than bi-parental mapping populations and reduce the confounding effect of population structure that is an issue in association mapping populations. Here we discuss the results of using a MAGIC population of doubled haploid (DH) maize lines created from 16 diverse founders to perform QTL mapping. We compare three models that assume bi-allelic, founder, and ancestral haplotype allelic states for QTL. The three methods have different power to detect QTL for a variety of agronomic traits. Although the founder approach finds the most QTL, there are also QTL unique to each method, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time QTL, qDTA8, which contains vgt1, suggests a potential epistatic interaction and highlights the strengths and weaknesses of each method. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and show the limitations of binary SNP data for identifying multi-allelic QTL.9

plant biology↗