Biology subjects

Schnable, J. C.

Publications and source records attributed to Schnable, J. C..

At least 37 records · Page 2Linked to original sources

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

Genotype-specific nonphotochemical quenching responses to nitrogen deficit are linked to chlorophyll a to b ratios

Non-photochemical quenching (NPQ) protects plants from photodamage caused by excess light energy. The mechanism of NPQ appears to be conserved across flowering plants. However, substantial variation in NPQ has been reported within different genotypes of the same species grown under the environmental conditions. Individual maize genotypes from a diversity panel exhibited a range of responses to low nitrogen with some genotypes exhibiting increased NPQ between control and low nitrogen conditions and others exhibiting no change. These patterns were consistent for the same genotypes across multiple field seasons. NPQ increases under low nitrogen were correlated with shifts in the ratio of chlorophyll a to chlorophyll b consistent with a decrease in reaction centers. Both photosynthetic capacity and dry biomass accumulation decreased more in maize genotypes which were unable to maintain constant NPQ levels between control and low N conditions. Collectively these results suggest that the ability to maintain sufficient numbers of reaction centers under low nitrogen conditions and avoid dissipating a greater proportion of absorbed light energy via the NPQ pathway may play a key role in increasing carbon fixation and productivity in nitrogen-limited environments. HighlightsO_LISubstantial variation in NPQ kinetics exists in maize on both low and control N. C_LIO_LIIn early and late-vegetative stages a similar portion of genotypes increased, no-change or decreased NPQmax in response to low N while in the post-flowering stage substantially more genotypes decreased NPQmax. C_LIO_LIIn low nitrogen conditions, the NPQmax strongly correlates with shifts in Chl a/Chl b ratios. C_LI

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

Rhythmic lipid and gene expression responses to chilling in panicoid grasses

Chilling stress threatens plant growth and development, particularly affecting membrane fluidity and cellular integrity. Understanding plant membrane responses to chilling stress is important for unraveling the molecular mechanisms of stress tolerance. Whereas core transcriptional responses to chilling stress and stress tolerance are conserved across species, the associated changes in membrane lipids appear to be less conserved, as which lipids are affected by chilling stress varies by species. Here, we investigated changes in gene expression and membrane lipids in response to chilling stress during one diurnal cycle in sorghum (Sorghum bicolor), Urochloa (browntop signal grass, Urochloa fusca) (lipids only), and foxtail millet (Setaria italica), leveraging their evolutionary relatedness and differing levels of chilling-stress tolerance. We show that most chilling-induced lipid changes are conserved across the three species, while we observed distinct, time-specific responses in chilling-tolerant foxtail millet, indicating the presence of a finely orchestrated adaptive mechanism. We detected diurnal rhythmicity in lipid responses to chilling stress in the three grasses, which were also present in Arabidopsis (Arabidopsis thaliana), suggesting the conservation of rhythmic patterns across species and highlighting the importance of accounting for diurnal effects. When integrating lipid datasets with gene expression profiles, we identified potential candidate genes that showed corresponding transcriptional changes in response to chilling stress, providing insights into the differences in regulatory mechanisms between chilling-sensitive sorghum and chilling-tolerant foxtail millet. Significance StatementPlants respond to low-temperature stress in myriad ways. While core transcriptional changes are conserved across species, specific adaptive strategies do exist. However, membrane lipid responses during chilling do not appear to be conserved. Here, we collected samples from control and chilling stress-treated seedlings [PSC4] to assess gene expression and membrane lipids in three panicoid grasses to show that lipid metabolic changes follow a daily rhythm. Lipid changes in chilling-tolerant foxtail millet occurred at specific time points, partly explaining the difficulty in finding conserved chilling-induced lipid changes in previous reports. We identified specific orthologs in sorghum and foxtail millet that showed a correlation between gene expression and lipid metabolic changes; these orthologs may be used as potential target genes for developing chilling-tolerant sorghum varieties.

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↗

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↗

bioRxiv2023

Transcriptome brings variations of gene expression, alternative splicing, and structural variations into gene-scale trait dissection in soybean

Genome-wide association study (GWAS) identifies trait-associated loci, but due in part to slow decay of linkage disequilibrium (LD), identifying the causal genes can be a bottleneck. Transcriptome-wide association study (TWAS) addresses this by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci (eQTLs) with GWAS results. Here, we used self-pollinated soybean as a model to evaluate the application of TWAS in the genetic dissection of traits in plant species that exhibit slow LD decay. The first RNA-Seq analysis of a soybean diversity panel was conducted, which identified the genetic regulation of 29,286 genes. Different TWAS solutions were less affected by LD and robust with source of expression that identified known genes related to traits from different development stages and tissues. A novel gene named pod color L2 was identified via TWAS and functionally validated by genome editing. Our introduction of the new exon proportion feature significantly improves the capture of expression variations resulting from structural variations and alternative splicing. As a result, the genes identified by our TWAS approach exhibited a diverse range of causal variations, including SNP, insertion/deletion, gene fusion, copy number variation, and alternative splicing. Using our TWAS approach, we identified genes associated with flowering time, including both previously known candidates and novel genes that had not been linked to this trait before, providing complementary insights with GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.

genetics↗

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

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the models sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield - those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

plant biology↗

bioRxiv2022

Machine learning reveals conserved chromatin patterns determining meiotic recombination sites in plants

Distribution of meiotic recombination events in plants has been associated with local chromatin and DNA characteristics, chromosome landmark proximity, and other features1-7. However, relative importance of these characteristics is unclear and it is unknown if they are sufficient to unambiguously determine recombination landscape8. Here, we analyzed over 40 DNA sequence, chromatin, and chromosome location features of maize and Arabidopsis recombination sites using machine learning9,10. We discovered that a combination of just three features, CG methylation, CHG methylation, and nucleosome occupancy, enabled identification of exact crossover site with 90% accuracy. These results imply redundancy of most recombination site characteristics. Recombination takes place in a small fraction of the genome with chromatin features distinct from those of genome at large. Surprisingly, crossover sites show elevated heterochromatin histone marks despite low DNA methylation. Crossover site features show broad evolutionary conservation, which will enable creating genetic maps in species where conventional mapping is unfeasible.

plant biology↗

bioRxiv2022

Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel

Access to biologically available nitrogen is a key constraint on plant growth in both natural and agricultural settings. Variation in tolerance to nitrogen deficit stress and productivity in nitrogen limited conditions exists both within and between plant species. Here we quantified variation in the metabolic, physiological, and morphological responses of a sorghum association panel assembled to represent global genetic diversity to long term, moderate, nitrogen deficit stress and the relationship of these responses to grain yield under both conditions. Grain yield exhibits substantial genotype by environment interaction while many other morphological and physiological traits exhibited consistent responses to nitrogen stress across the population. Large scale nontargeted metabolic profiling for a subset of lines in both conditions identified a range of metabolic responses to long term nitrogen deficit stress as well as several metabolites associated with variation in the degree of yield plasticity specific sorghum genotypes exhibited in response to nitrogen deficit stress.

plant biology↗

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 historically balanced locus under recent directional selection in responding to changed nitrogen conditions during modern maize breeding

Understanding the patterns of selection during plant evolution and recent crop improvement processes is the central topic in plant breeding and genetics. As an essential macronutrient for plant growth and development, nitrogen (N) is a key factor in affecting plant adaptation and crop improvement. The widespread adoption of less expensive industrial N fixation has dramatically reshaped plant morphology by favoring compact maize plants to tolerant crowding stress. The associated genetic changes, however, have not been systematically studied. Here, we investigated maize inbred lines developed before and after the 1960s -- the time point when inorganic N fertilizer started to be widely used for maize production. We identified a strong selective sweep exhibiting pronounced genomic differentiation between Old-Era (pre-1960s) and New-Era (post-1960s) inbred lines. Further study revealed population genetics statistics in the sweep exhibited patterns consistent with historical balancing selection. This balanced genomic interval is associated with a number of morphological, physiological, and metabolite traits related to vegetative N responses. A cluster of three glutamate receptor-like (GLR) genes is located within the region targeted by selection. Functional characterizations suggested differences in transcriptional activity of the GLR genes between the haplotypes carried by Old-Era and New-Era inbred lines likely play an essential role in mediating distinct N responses. The identification of both targets of selection and changes in the regulation of N responsive genes between maize lines developed in different eras sheds light on the N sensing and regulation pathways and paves the way to developing N resilient crops.

genomics↗

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

Maize root-associated microbes likely under adaptive selection by the host to enhance phenotypic performance

The root-associated microbiome (rhizobiome) plays a non-negligible role in determining plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We show that the abundance of many root-associated microbes within a functional core microbial community of 150 abundant and consistently reproducible microbial groups is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in low N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association studies conducted using rhizobiome phenotypes identified n = 467 microbe-associated plant loci (MAPLs) in the maize genome linked to variation in the abundance of n = 115 microbial groups in the maize rhizosphere. In 62/115 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. This study provides insights into harnessing the full potential of root-associated microbial symbionts in maize production.

plant biology↗