Biology subjects

Mural, R. V.

Publications and source records attributed to Mural, R. V..

18 recordsLinked to original sources

bioRxiv2026

Phenotypic plasticity, stalk geometry, and noncoding variation underpin stalk lodging resistance in maize

Stalk lodging causes severe yield losses in maize (Zea mays L.) worldwide, worsening food and feed security. Stalk lodging resistance is influenced by multiple traits at various levels of biological organization, collectively referred to as intermediate traits, but their identities, genetic bases, and interrelationships remain poorly resolved. Here, evaluation of multiple geometric and structural intermediate traits in a maize diversity panel across four environments showed that macroenvironmental variation is the predominant driver of phenotype plasticity and that plasticity varies with internode position along the stalk, consistent with height-dependent mechanosensing. Major and minor diameters, moment of inertia, and rind penetration resistance, were genetically tractable and showed strong genetic correlations with stalk flexural stiffness. Multivariate analyses revealed two distinct but complementary mechanistic pathways, represented by cross-sectional geometry and rind architecture, that contribute to stalk mechanical performance. Association analyses using whole-genome resequencing data identified 705 SNPs associated with intermediate traits, fewer than 20% of which overlapped genic regions, indicating that most associated variation resides outside annotated genes. Interestingly, about 22% of SNPs were shared between at least two traits, indicating substantial shared genetic control among intermediate traits. Candidate gene analyses highlighted novel promising candidate loci associated with intermediate traits while recovering genes previously implicated in stalk lodging resistance. The predominance of noncoding associations further suggests that regulatory variation may contribute substantially to natural variation in intermediate traits underlying stalk lodging resistance.

genetics↗

bioRxiv2025

Influence of plant genotype on nodule microbiome composition and function in seasonal yellow pea varieties

Background and AimsLegume root nodules host symbiotic rhizobia essential for nitrogen fixation but also harbor diverse non-rhizobial taxa that remain poorly characterized. Yellow pea (Pisum sativum) cultivars adapted to distinct seasonal growth (spring and winter) offer an opportunity to explore whether host genotype influences nodule microbiome composition and function. This study investigates the taxonomic and functional profiles of nodule-associated microbial communities in seasonal yellow pea varieties. MethodsA field experiment with 6 field pea cultivars (spring and winter types) was conducted in South Dakota. Surface-sterilized root nodules were subjected to full-length 16S rRNA gene sequencing using Oxford Nanopore technology. Reads were quality filtered, organellar sequences removed, and taxonomic classification performed with the EMU pipeline. Microbial diversity, community structure, and core taxa were analyzed using R, with predicted functions inferred by FAPROTAX. ResultsThe nodule microbiome was dominated by Rhizobium, accounting for up to 98% of classified reads. After excluding Rhizobium, non-rhizobial diversity revealed a conserved core microbiome shared across cultivars, including cyanobacteria with potential phototrophic and diazotrophic traits. Minor seasonal differences were observed, with winter cultivars exhibiting higher evenness and specific associations. ConclusionYellow pea nodules harbor a stable, cyanobacteria-enriched core microbiome, largely consistent across seasonal cultivars. Season-specific microbial patterns suggest potential host-genotype influences, warranting further validation.

plant biology↗

bioRxiv2024

Bridging Genomic Gaps: The Pan-Grass Syntenic Gene Set in Grass Crop Evolution

Understanding orthologous and homeologous relationships among genes can inform our understanding of how evolution and artificial selection have shaped the genomes of related species to adapt to different niches and produce different phenotypes. High-confidence information on orthologous relationships can also increase the impact of the functional characterization in one species by ensuring this information is available to researchers studying orthologs in other species. The grasses are ecologically and economically critical plants, and many genes are conserved in syntenic blocks between species. Here we leverage that conserved gene order to create and describe the Pan-Grass Syntenic Gene Set (PGSGS), a curated database of orthologous and homeologous relationships among the genes of 17 grass species, including both major and orphan crops as well as wild grasses. As a demonstration of the utility of the PGSGS, the dynamics of chromosome structure and gene content changes after polypoidy in several grass lineages are analyzed. This dataset serves as a seminal foundation for improving our understanding of genome evolution subsequent to polyploidy, as well as encouraging and enabling integration of functional genetic and functional genomic information across the grasses, the original promise of the "grasses as a single genetic system" model. SIGNIFICANCE STATEMENTThe Pan-Grass Syntenic Gene Set (PGSGS) leverages our understanding of the genomic background of major grass species, including rice, maize, sorghum, wheat, barley, millet, and sugarcane. It represents a milestone for deciphering the genetic foundations of essential traits such as yield, disease resistance, and environmental adaptability - key factors contributing to agriculture and food security. Scrutinizing pan-syntenic genes unveils the intricate interplay of diverse gene acquisition and loss mechanisms, potentially speeding molecular breeding knowledge transference. The PGSGS emerges as a valuable resource for gene annotation, with implications for improving grass crops.

genomics↗

bioRxiv2024

Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions

Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcripts impacts on plant metabolism has employed data from controlled environments. Here we generate and employ data on variation in the abundance of twenty-six metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies) and transcripts (through both transcriptome-wide association studies and an explainable AI approach based on the random forest) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation.

genomics↗

bioRxiv2024

Off-the-shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum

Seed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today seed color is often quantified via either qualitative human assessment or biochemical assays for specific colored metabolites. Imaging-based approaches have the potential to be more quantitative than human scoring while lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor ) using a dataset of > 1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome-wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co-localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes - colored metabolites in sorghum seeds - and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross-species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health-promoting crop varieties in sorghum and other specialty and orphan grain crops.

plant biology↗

bioRxiv2023

Imitating the 'breeder's eye': predicting grain yield from measurements of non-yield traits

Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency and rate of genetic improvement in future breeding cycles. However, field trials conducted in one environment provide limited insight into how well crop varieties will perform in other environments. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties indicates that plant breeders will need to make decisions about new crop varieties without knowing the future environments those crop varieties will encounter in farmers fields. Therefore, significant improvements in cross-environment prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the twenty-first century. To address this challenge, we conducted linked yield trials of 752 public maize genotypes in two distinct environments: Lincoln, Nebraska, and East Lansing, Michigan. Our findings confirmed that genomic predictions of yield can outperform direct yield measurements used to train the genomic prediction model in predicting yield in a second environment. Additionally, we developed and trained another trait-based yield prediction model, which we refer to as the Silicon Breeders Eye (SBE). Our results demonstrate that SBE prediction has comparable predictive power to genomic prediction models. SBE prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations of individuals, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

genetics↗

bioRxiv2023

Population level gene expression can repeatedly link genes to functions in maize

Transcriptome-Wide Association Studies (TWAS) can provide single gene resolution for candidate genes in plants, complementing Genome-Wide Association Studies (GWAS) but efforts in plants have been met with, at best, mixed success. We generated expression data from 693 maize genotypes, measured in a common field experiment, sampled over a two-hour period to minimize diurnal and environmental effects, using full-length RNA-seq to maximize the accurate estimation of transcript abundance. TWAS could identify roughly ten times as many genes likely to play a role in flowering time regulation as GWAS conducted data from the same experiment. TWAS using mature leaf tissue identified known true positive flowering time genes known to act in the shoot apical meristem, and trait data from new environments enabled the identification of additional flowering time genes without the need for new expression data. eQTL analysis of TWAS-tagged genes identified at least one additional known maize flowering time gene through trans-eQTL interactions. Collectively these results suggest the gene expression resource described here can link genes to functions across different plant phenotypes expressed in a range of tissues and scored in different experiments.

plant biology↗

bioRxiv2023

Comprehensive analysis of the Aquaporin genes in Eucalyptus grandis suggests potential targets for drought stress tolerance

This study delves into the comprehensive analysis of AQP genes in Eucalyptus grandis, providing insights into their genomic abundance, diversification, expression patterns across tissues, and responses to drought stress. We identified 53 AQP genes in the Eucalyptus grandis genome, categorized into four subfamilies: AQP-NIP, AQP-SIP, AQP-PIP, and AQP-TIP. This abundance of AQP genes is a reflection of gene duplications, both tandem and whole-genome, which have shaped their expansion. The chromosomal distribution of these genes reveals their widespread presence across the genome, with some subfamilies exhibiting more tandem duplications, suggesting distinct roles and evolutionary pressures. Sequence analysis uncovered characteristic motifs specific to different AQP subfamilies, demonstrating the diversification of protein and targeting. The expression profiles of AQP genes in various tissues in both Arabidopsis thaliana and Eucalyptus grandis showcased variations, with root tissues showing higher expression levels. Notably, AQP-PIP genes consistently exhibited robust expression across tissues, highlighting their importance in maintaining water regulation within plants. Furthermore, the study investigated the response of AQP genes to drought stress and rehydration, revealing differential expression patterns. EgAQP-NIP and EgAQP-TIP genes were up-regulated during drought stress, emphasizing their role in osmotic equilibrium and water transport. Conversely, EgAQP-PIP genes showed down-regulation during drought stress but were up-regulated upon rehydration, indicating their involvement in water movement across cell membranes. Overall, this research contributes to our understanding of AQP genes in Eucalyptus grandis, shedding light on their genomic evolution, expression patterns, and responses to environmental challenges, particularly drought stress. This information can be valuable for future studies aimed at enhancing the drought resilience of woody perennial plants like Eucalyptus grandis.

plant biology↗

bioRxiv2023

THI1 Gene Evolutionary Trends: A Comprehensive Plant-Focused Assessment via Data Mining and Large-Scale Analysis

Molecular evolution analysis typically involves identifying selection pressure and reconstructing evolutionary trends. This process usually necessitates access to specific data related to a target gene or gene family within a particular group of organisms. While recent advancements in high-throughput sequencing techniques have resulted in the rapid accumulation of extensive genomics and transcriptomics data and the creation of new databases in public repositories, extracting valuable insights from such vast datasets remains a significant challenge for researchers. Here, we elucidated the evolutionary history of THI1, a gene responsible for encoding thiamine thiazole synthase. The thiazole ring is a precursor for vitamin B1 and crucial cofactor in primary metabolic pathways. We conducted a comprehensive search for THI1 information within public repositories with careful curation to achieve this. Our searches reveal an evolutionary trend of 702 THI1 homologs of Archaea and Eukarya, with a detailed focus on plants. The green lineage of these organisms preserved the THI4 protein domain throughout its diversification by incorporating the N-terminus and targeting chloroplasts. Furthermore, evolutionary pressures and lifestyle appear to be associated with retention of TPP-riboswitch sites and consequent dual post-transcriptional regulation of the de novo biosynthesis pathway in basal groups. Multicopy retention of THI1 is not a typical plant pattern, even successive rounds of genome duplications. Additionally, we identified the diversification of cis-regulatory sites in plants with the conservation of biological processes associated with the initial stages of seed development and preservation of the transcriptional pattern during the diurnal cycle. Our data mining of 484 transcriptome datasets supports this finding and brings a new look at public repositories and evolutionary trends to THI1.

plant biology↗

bioRxiv2023

Grain Utilization by the Gut Microbiome as a Human Health Phenotype to Identify Multiple Effect Loci in Genome-Wide Association Studies of Sorghum bicolor

A growing epidemic of complex lifestyle diseases such as obesity and metabolic diseases are explained in part by dysbiosis of the human gut microbiome. The gut microbiome, comprising trillions of microorganisms, contributes to functions ranging from digestion to the immune system. Diet plays a critical role in determining the species composition and functionality of the gut microbiome. Substantial functional metabolic diversity exists within the cultivated grain crops which directly or indirectly provide more than half of all calories consumed by humans around the globe, however much of this diversity is poorly characterized and the effects of such diversity on the human gut microbiome is not well studied. We employed a quantitative genetics approach to identify genetic variants in sorghum that alter the composition and function of human gut microbes. Using an automated high-throughput phenotyping method based on in vitro microbiome fermentation of grain from a diverse population of Sorghum bicolor cultivars, we demonstrate sorghum genetics can explain effects of grain variation on fermentation patterns of bacterial taxa across multiple human microbiomes. In a genome-wide analysis using a sorghum association panel, we identified fifteen multiple-effect loci (MEL) where different alleles in the sorghum genome produced changes in seed that affect the abundance of multiple bacterial taxa across two human microbiomes in automated in vitro fermentations. In a number of cases parallel genome-wide association studies conducted for biochemical and agronomic traits identified seed traits potentially causal for the link between sorghum genetics and human microbiome outcomes. This work demonstrates that genetic factors affecting sorghum seed can drive significant effects on human gut microbes, particularly bacterial taxa considered beneficial. Understanding these relationships will enable targeted crop breeding strategies to improve human health through gut microbiome modulation.

genomics↗

bioRxiv2023

Comparative GWAS identifies a role for Mendel green pea gene in the nonphotochemical quenching kinetics of sorghum, maize, and arabidopsis

Photosynthetic organisms must cope with rapid fluctuations in light intensity. Nonphotochemical quenching (NPQ) enables the dissipation of excess light energy as heat under high light conditions, whereas its relaxation under low light maximizes photosynthetic productivity. We quantified variation in NPQ kinetics across a large sorghum (Sorghum bicolor) association panel in four environments, uncovering significant genetic control for NPQ. A genome-wide association study (GWAS) identified 20 unique regions in the sorghum genome associated with NPQ. We detected strong signals from the sorghum ortholog of Arabidopsis thaliana SUPPRESSOR OF VARIEGATION3 (SVR3) involved in plastid-nucleus signaling and tolerance to cold. By integrating GWAS results for NPQ across maize (Zea mays) and sorghum association panels, we identified a second gene, NON-YELLOWING 1 (NYE1), originally identified by Gregor Mendel in pea (Pisum sativum) and involved in the degradation of photosynthetic pigments in light-harvesting complexes, along with OUTER ENVELOPE PROTEIN 37 (OEP37), that encodes a transporter in chloroplast envelope. Analysis of nye1 insertion alleles in A. thaliana confirmed the effect of this gene on NPQ kinetics across monocots and eudicots. We extended our comparative genomics GWAS framework across the entire maize and sorghum genomes, identifying four additional loci involved in NPQ kinetics. These results provide a baseline for engineering crops with improved NPQ kinetics and increasing the accuracy and speed of candidate gene identification for GWAS in species with high linkage disequilibrium.

plant biology↗

bioRxiv2022

Measurement of expression from a limited number of genes is sufficient to predict flowering time in maize

Changing patterns of weather and climate are limiting breeders ability to conduct trials in the same environments in which their released varieties will be grown 7-10 years later. Flowering time plays a crucial role in determining regional adaptation, and mismatch between flowering time and environment can substantially impair yield. Different approaches based on genetic markers or gene expression can be used to predict flowering time before conducting large scale field evaluation and phenotyping. The more accurate prediction of a trait using genetic markers could be hindered due to all the intermediate steps (i.e. transcription, translation, epigenetic modification, and epistasis among others) connecting the trait and their genetic basics. The use of some intermediate steps as predictors could improve the accuracy of the model. Here, we are using two public gene expression (RNA-Seq) data-sets from 14-day-old-maize-seedling roots and whole-seedling tissue at v1 stage (10 day after planting) for which flowering data (days to anthesis and days to silking expressed in growing degree days) and genetic markers were also available to test the predictability of flowering time. In total, 20 different combinations between phenotypic and gene expression data-sets were evaluated. To explore prediction accuracy a random forest model was trained with the expression values of 44,303 gene models hosted in the current B73 maize reference version 5 and then the feature importance was scored based on the decrease in root mean squared error. Later several random forest models with different subsets of the most important features (genes) were trained, and this process was repeated ten times. Results from these analyses show a curve in the prediction accuracy, with an increase in the prediction accuracy as the top most important genes were added. The maximum accuracy was attained when 500 genes for whole-seedling and 100 genes for root gene expression data were used in the analysis, and thereafter adding more genes lead to a decrease in the prediction accuracy. The highest prediction accuracy using the top-most important genes was higher than that of using randomly selected whole-genome 400,000 SNPs. Finally, we described the genes controlling flowering time by looking at the most important genes in the Random forest model with the expression data from all genes. We further found MADS-transcription factor 69 (Mads69) using whole-seedling gene expression and the MADS-transcription factor 67 (Mads67) using root gene expression data, both genes previously described with effect on flowering time. Here, we aim to demonstrate the potential of selecting and using the expression of most informative genes to predict a complex trait, also to demonstrate the robustness and limitations of this analysis by using phenotypic data-sets from different environments.

genomics↗

bioRxiv2022

A Common Resequencing-Based Genetic Marker Dataset for Global Maize Diversity

Maize (Zea mays ssp. mays) populations exhibit vast amounts of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies, and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here we employ resequencing data from 1,276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of [~]366 million segregating variants and [~]46 million high confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering time genes using previously published trait datasets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

plant biology↗

bioRxiv2022

Dissecting the genetic architecture of sunflower head diameter using genome-wide association study

Sunflower (Helianthus annuus L.) plays an essential role in meeting the edible oil demand worldwide. Sunflower seed yield can be decomposed into several yield component traits, one of which is the head diameter. In 2019, 2020, and 2022, we evaluated the head diameter phenotypic variation on a set of diverse sunflower accessions (N=342) in replicated field trials. By combining three years of field data, the broad sense heritability (H2) of the head diameter trait was estimated to be 0.88. Then, a subset of N=274 accessions was genotyped by using the tunable genotyping-by-sequencing (tGBS) method, resulting in 226,779 high-quality SNPs. With these SNPs and the head diameter phenotype, the genome-wide association study (GWAS) was conducted using two statistical approaches: 1) the mixed linear model (MLM) and 2) the fixed and random model circulating probability unification (farmCPU). The MLM and farmCPU GWAS approaches identified 106 and 8 significant SNPs that were placed close to 53 and 21 genes, respectively. Two significant peaks were identified in MLM, with a strong signal on chromosome 10 and a less strong signal on chromosome 16. The farmCPU method detected the same signals on chromosomes 10 and 16 and several additional significant signals on other chromosomes. The head diameter associated genetic loci and the underlying candidate genes can be leveraged for further functional validation and serve as a basis for sunflower oil yield improvement.

genomics↗

bioRxiv2022

Association Mapping Across a Multitude of Traits Collected in Diverse Environments Identifies Pleiotropic Loci in Maize.

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data - 18M markers - from two partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least seven US states and scored for 162 distinct trait datasets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be three genes based a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g. above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype by environment interaction.

plant biology↗

bioRxiv2022

A role for heritable transcriptomic variation in maize adaptation to temperate environments

Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments. We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression. While genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7,614 genes. Rare allele burden within genomic intervals with trans-eQTLs correlated with extremes of expression in target genes as previously reported for cis-eQTLs. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in phytohormone pathways, cell wall biosynthesis, lipid metabolism and stress response. Several genes involved in cell proliferation, flower development, DNA replication and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

bioinformatics↗

bioRxiv2021

Quantitative resistance loci to southern rust mapped in a temperate maize diversity panel

Southern rust is a severe foliar disease of maize resulting from infection with the obligate biotrophic fungus, Puccinia polysora. The disease reduces photosynthetic productivity which reduces yields with the greatest yield losses (up to 50%) associated with earlier onset infections. Puccinia polysora urediniospores overwinter only in tropical and subtropical regions but cause outbreaks when environmental conditions favor initial infection. Increased temperatures and humidity during the growing season, combined with an increased frequency of moderate winters are likely to increase the frequency of severe southern rust outbreaks in the US corn belt. In summer 2020, a severe outbreak of Southern Rust was observed in eastern Nebraska (NE), USA. Disease incidence severity showed significant variation among maize genotypes. A replicated maize association panel planted in Lincoln, NE was scored for disease severity. Genome wide association studies identified four loci associated with significant quantitative variation in disease severity which were associated with candidate genes with plausible links to quantitative disease resistance and a transcriptome wide association study conducted identified additional associated genes. Together these results indicate substantial diversity in resistance to southern rust exists among current temperate adapted maize germplasm, including several candidate loci which may explain observed variation in resistance to southern rust.

plant biology↗

bioRxiv2020

Meta-Analysis Identifies Pleiotropic Loci Controlling Phenotypic Trade-offs in Sorghum

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate both pleiotropy and genotype by environment interactions. Here we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome wide association studies conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35-43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

plant biology↗