Biology subjects

Runcie, D. E.

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

25 records · Page 2Linked to original sources

bioRxiv2021

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G+T, G+M and G+T+M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

genetics↗

bioRxiv2021

Demonstration of local adaptation of maize landraces by reciprocal transplantation

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, as well as reported highland-adaptive traits such as anthocyanin pigmentation and macrohair density. 67k DArTseq markers were generated from field specimens to allow comparison 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. While population genetic structure largely recapitulates drift during post-domestication dispersal, landrace phenotypes reflect adaptations to native elevation. Quantitative trait QST was greater than neutral FST for many traits, signaling divergent 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 our Mexican sites. Highland populations expressed generally higher anthocyanin pigmentation than lowland populations, and more so in the highland site than in the lowland site. Macrohair density was largely non-plastic, and Mexican landraces and highland landraces were generally more pilose. Analysis of{delta} 13C indicated that lowland populations may have lower WUE. Each population demonstrated garden-specific correlations between highland trait expression and fitness, with stronger positive correlations in the highland site. 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.

evolutionary biology↗

bioRxiv2020

Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

genetics↗

bioRxiv2020

Innovation, conservation and repurposing of gene function in plant root cell type development

Plant species have evolved myriads of solutions to adapt to dynamic environments, including complex cell type development and regulation. To understand this diversity, we profiled tomato root cell type translatomes and chromatin accessibility. Using xylem differentiation in tomato, relative to Arabidopsis, examples of functional innovation, repurposing and conservation of transcription factors are described. Repurposing and innovation of genes are further observed within an exodermis regulatory network and illustrate its function. Translatome analyses of rice, tomato and Arabidopsis tissues suggest that root meristems are more conserved, and that the functions of constitutively expressed genes are more conserved than those of cell type/tissue-enriched genes. These observations suggest that higher-order properties of cell type and pan-cell type regulation are conserved between plants and animals. One Sentence SummaryPan-species cell type translatome and chromatin accessibility data reveal novelty, conservation and repurposing of gene function.

genomics↗

bioRxiv2020

Implications of genetic heterogeneity for plant translocation during ecological restoration

Ecological restoration often requires translocating plant material from distant sites. Yet published guidelines for seed transfer are available for very few species. Accurately predicting how plants will perform when transferred requires multi-year and multi-environment field trials and comprehensive follow-up work. In this study, we analyzed the genetic structure of an important shrub used in ecological restorations in the Southern Rocky Mountains called alder-leaf mountain mahogany (Cercocarpus montanus). We sequenced DNA from 1440 plants in 48 populations across a broad geographic range. We found that genetic heterogeneity among populations reflected the complex climate and topography across which the species is distributed. We identified several temperature and precipitation variables that were useful predictors of genetic differentiation and can be used to generate seed transfer recommendations. These results will be valuable for defining management and restoration practices for mountain mahogany and other widespread montane plant species.

ecology↗

bioRxiv2019

Gene regulatory effects of a large chromosomal inversion in highland maize

Chromosomal inversions play an important role in local adaptation. Inversions can capture multiple locally adaptive functional variants in a linked block by repressing recombination. However, this recombination suppression makes it difficult to identify the genetic mechanisms that underlie an inversions role in adaption. In this study, we explore how large-scale transcriptomic data can be used to dissect the functional importance of a 13 Mb inversion locus (Inv4m) found almost exclusively in highland populations of maize (Zea mays ssp. mays). Inv4m introgressed into highland maize from the wild relative Zea mays ssp. mexicana, also present in the highlands of Mexico, and is thought to be important for the adaptation of these populations to cultivation in highland environments. First, using a large publicly available association mapping panel, we confirmed that Inv4m is associated with locally adaptive agronomic phenotypes, but only in highland fields. Second, we created two families segregating for standard and inverted haplotypess of Inv4m in a isogenic B73 background, and measured gene expression variation association with Inv4m across 9 tissues in two experimental conditions. With these data, we quantified both the global transcriptomic effects of the highland Inv4m haplotype, and the local cis-regulatory variation present within the locus. We found diverse physiological effects of Inv4m, and speculate that the genetic basis of its effects on adaptive traits is distributed across many separate functional variants. Author SummaryChromosomal inversions are an important type of genomic structural variant. However, mapping causal alleles within their boundaries is difficult because inversions suppress recombination between homologous chromosomes. This means that inversions, regardless of their size, are inherited as a unit. We leveraged the high-dimensional phenotype of gene expression as a tool to study the genetics of a large chromosomal inversion found in highland maize populations in Mexico - Inv4m. We grew plants carrying multiple versions of Inv4m in a common genetic background, and quantified the transcriptional reprogramming induced by alternative alleles at the locus. Inv4m has been shown in previous studies to have a large effect on flowering, but we show that the functional variation within Inv4m affects many developmental and physiological processes. Author ContributionsT. Crow, R. Rellan-Alvarez, R. Sawers and D. Runcie conceived and designed the experiment. M. Aguilar-Rangel, J. Rodr[g]uez, R. Rellan-Alvarez and R. Sawers generated the segregating families. T. Crow, J. Ta, S. Nojoomi, M. Aguilar-Rangel, J. Rodr[g]uez D. Gates, D. Runcie performed the experiment. T. Crow, D. Gates, D. Runcie analyzed the data. T. Crow, D. Runcie wrote the original manuscript, and R. Rellan-Alvarez and R. Sawers provided review and editing.

genomics↗

bioRxiv2018

Fast and general-purpose linear mixed models for genome-wide genetics

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.\n\nAuthor summaryThe goal of quantitative genetics is to characterize the relationship between genetic variation and variation in quantitative traits such as height, productivity, or disease susceptibility. A statistical method known as the linear mixed effect model has been critical to the development of quantitative genetics. First applied to animal breeding, this model now forms the basis of a wide-range of modern genomic analyses including genome-wide associations, polygenic modeling, and genomic prediction. The same model is also widely used in ecology, evolutionary genetics, social sciences, and many other fields. Mixed models are frequently multi-faceted, which is necessary for accurately modeling data that is generated from complex experimental designs. However, most genomic applications use only the simplest form of linear mixed methods because the computational demands for model fitting can be too great. We develop a flexible approach for fitting linear mixed models to genome scale data that greatly reduces their computational burden and provides flexibility for users to choose the best statistical paradigm for their data analysis. We demonstrate improved accuracy for genetic association tests, increased power to discover causal genetic variants, and the ability to provide accurate summaries of model uncertainty using both simulated and real data examples.

genomics↗