Publications by authors named "Edward S Buckler"

159 Publications

Somatic variations led to the selection of acidic and acidless orange cultivars.

Nat Plants 2021 Jul 17;7(7):954-965. Epub 2021 Jun 17.

Key Laboratory of Horticultural Plant Biology (Ministry of Education), Huazhong Agricultural University, Wuhan, P. R. China.

Somatic variations are a major source of genetic diversification in asexual plants, and underpin clonal evolution and the breeding of asexual crops. Sweet orange is a model species for studying somatic variation because it reproduces asexually through apomixis and is propagated asexually through grafting. To dissect the genomic basis of somatic variation, we de novo assembled a reference genome of sweet orange with an average of three gaps per chromosome and a N50 contig of 24.2 Mb, as well as six diploid genomes of somatic mutants of sweet oranges. We then sequenced 114 somatic mutants with an average genome coverage of 41×. Categorization of the somatic variations yielded insights into the single-nucleotide somatic mutations, structural variations and transposable element (TE) transpositions. We detected 877 TE insertions, and found TE insertions in the transporter or its regulatory genes associated with variation in fruit acidity. Comparative genomic analysis of sweet oranges from three diversity centres supported a dispersal from South China to the Mediterranean region and to the Americas. This study provides a global view on the somatic variations, the diversification and dispersal history of sweet orange and a set of candidate genes that will be useful for improving fruit taste and flavour.
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http://dx.doi.org/10.1038/s41477-021-00941-xDOI Listing
July 2021

Synthetic promoter designs enabled by a comprehensive analysis of plant core promoters.

Nat Plants 2021 Jun 3;7(6):842-855. Epub 2021 Jun 3.

Department of Genome Sciences, University of Washington, Seattle, WA, USA.

Targeted engineering of plant gene expression holds great promise for ensuring food security and for producing biopharmaceuticals in plants. However, this engineering requires thorough knowledge of cis-regulatory elements to precisely control either endogenous or introduced genes. To generate this knowledge, we used a massively parallel reporter assay to measure the activity of nearly complete sets of promoters from Arabidopsis, maize and sorghum. We demonstrate that core promoter elements-notably the TATA box-as well as promoter GC content and promoter-proximal transcription factor binding sites influence promoter strength. By performing the experiments in two assay systems, leaves of the dicot tobacco and protoplasts of the monocot maize, we detect species-specific differences in the contributions of GC content and transcription factors to promoter strength. Using these observations, we built computational models to predict promoter strength in both assay systems, allowing us to design highly active promoters comparable in activity to the viral 35S minimal promoter. Our results establish a promising experimental approach to optimize native promoter elements and generate synthetic ones with desirable features.
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http://dx.doi.org/10.1038/s41477-021-00932-yDOI Listing
June 2021

Conserved noncoding sequences provide insights into regulatory sequence and loss of gene expression in maize.

Genome Res 2021 May 27. Epub 2021 May 27.

Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, USA.

Thousands of species will be sequenced in the next few years; however, understanding how their genomes work, without an unlimited budget, requires both molecular and novel evolutionary approaches. We developed a sensitive sequence alignment pipeline to identify conserved noncoding sequences (CNSs) in the Andropogoneae tribe (multiple crop species descended from a common ancestor ∼18 million years ago). The Andropogoneae share similar physiology while being tremendously genomically diverse, harboring a broad range of ploidy levels, structural variation, and transposons. These contribute to the potential of Andropogoneae as a powerful system for studying CNSs and are factors we leverage to understand the function of maize CNSs. We found that 86% of CNSs were comprised of annotated features, including introns, UTRs, putative -regulatory elements, chromatin loop anchors, noncoding RNA (ncRNA) genes, and several transposable element superfamilies. CNSs were enriched in active regions of DNA replication in the early S phase of the mitotic cell cycle and showed different DNA methylation ratios compared to the genome-wide background. More than half of putative -regulatory sequences (identified via other methods) overlapped with CNSs detected in this study. Variants in CNSs were associated with gene expression levels, and CNS absence contributed to loss of gene expression. Furthermore, the evolution of CNSs was associated with the functional diversification of duplicated genes in the context of maize subgenomes. Our results provide a quantitative understanding of the molecular processes governing the evolution of CNSs in maize.
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http://dx.doi.org/10.1101/gr.266528.120DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8256870PMC
May 2021

Underground Heterosis for Melons Yield.

J Exp Bot 2021 May 16. Epub 2021 May 16.

Plant Science Institute, Agricultural Research Organization, Newe Ya'ar Research Center, Ramat Yishay, Israel.

Heterosis, the superiority of hybrids over their parents, is a major genetic force associated with plant fitness and crop yield enhancement. Understanding and predicting heterosis is crucial for evolutionary biology, as well as for plant and animal breeding. We investigated root-mediated yield heterosis in melons (Cucumis melo) by characterizing common variety grafted onto 190 hybrid rootstocks resulting from crossing 20 diverse inbreds in a diallel-mating scheme. Hybrid rootstocks improved yield by more than 40% compared to their parents and the best hybrid outperformed the reference commercial variety by 65% under both optimal and minimal irrigation treatments. To characterize the genetics of the underground heterosis we conducted whole-genome re-sequencing of the 20 founder lines, and showed that parental genetic distance was no predictor for the level of heterosis. Through inference of the 190 hybrids genotypes from their parental genomes, followed by genome-wide association analysis, we mapped multiple root-mediated yield QTLs. The yield enhancement of the four best-performing hybrid rootstocks was validated in multiple experiments with four different scion varieties. While root biology is receiving increased attention, most of the research is conducted using plants not amenable to grafting and, as a result, it is difficult to separate root and shoot effects. Here, we use the rich genetic and genomic resources of Cucumis melo, where grafting is a common practice, to dissect a unique phenomenon of root-mediated yield heterosis, by directly evaluating in the field the contribution of the roots to fruit yield. Our grafting approach is complementary to the common roots genetics research path that focuses mainly on variation in root system architecture rather than the ultimate root-mediated whole-plant performance, and is a step towards discovery of candidate genes involved in root function and yield enhancement.
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http://dx.doi.org/10.1093/jxb/erab219DOI Listing
May 2021

Joint analysis of days to flowering reveals independent temperate adaptations in maize.

Heredity (Edinb) 2021 Jun 22;126(6):929-941. Epub 2021 Apr 22.

USDA-ARS, Ithaca, NY, USA.

Domesticates are an excellent model for understanding biological consequences of rapid climate change. Maize (Zea mays ssp. mays) was domesticated from a tropical grass yet is widespread across temperate regions today. We investigate the biological basis of temperate adaptation in diverse structured nested association mapping (NAM) populations from China, Europe (Dent and Flint) and the United States as well as in the Ames inbred diversity panel, using days to flowering as a proxy. Using cross-population prediction, where high prediction accuracy derives from overall genomic relatedness, shared genetic architecture, and sufficient diversity in the training population, we identify patterns in predictive ability across the five populations. To identify the source of temperate adapted alleles in these populations, we predict top associated genome-wide association study (GWAS) identified loci in a Random Forest Classifier using independent temperate-tropical North American populations based on lines selected from Hapmap3 as predictors. We find that North American populations are well predicted (AUC equals 0.89 and 0.85 for Ames and USNAM, respectively), European populations somewhat well predicted (AUC equals 0.59 and 0.67 for the Dent and Flint panels, respectively) and that the Chinese population is not predicted well at all (AUC is 0.47), suggesting an independent adaptation process for early flowering in China. Multiple adaptations for the complex trait days to flowering in maize provide hope for similar natural systems under climate change.
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http://dx.doi.org/10.1038/s41437-021-00422-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178344PMC
June 2021

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.

Front Genet 2020 8;11:592769. Epub 2021 Mar 8.

Department of Plant and Soil Sciences, University of Delaware, Newark, DE, United States.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.
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http://dx.doi.org/10.3389/fgene.2020.592769DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7982677PMC
March 2021

Eleven biosynthetic genes explain the majority of natural variation in carotenoid levels in maize grain.

Plant Cell 2021 May;33(4):882-900

Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824.

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize (Zea mays) is a food staple. Extensive natural variation exists for carotenoids in maize grain. Here, to understand its genetic basis, we conducted a joint linkage and genome-wide association study of the US maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percentage of phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide an in-depth genome-level understanding of the genetic and molecular control of carotenoids in plants. In addition, these findings provide a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.
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http://dx.doi.org/10.1093/plcell/koab032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8226291PMC
May 2021

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

G3 (Bethesda) 2021 02;11(2)

Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.
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http://dx.doi.org/10.1093/g3journal/jkaa050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8022981PMC
February 2021

Comparative evolutionary genetics of deleterious load in sorghum and maize.

Nat Plants 2021 01 15;7(1):17-24. Epub 2021 Jan 15.

Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.

Sorghum and maize share a close evolutionary history that can be explored through comparative genomics. To perform a large-scale comparison of the genomic variation between these two species, we analysed ~13 million variants identified from whole-genome resequencing of 499 sorghum lines together with 25 million variants previously identified in 1,218 maize lines. Deleterious mutations in both species were prevalent in pericentromeric regions, enriched in non-syntenic genes and present at low allele frequencies. A comparison of deleterious burden between sorghum and maize revealed that sorghum, in contrast to maize, departed from the domestication-cost hypothesis that predicts a higher deleterious burden among domesticates compared with wild lines. Additionally, sorghum and maize population genetic summary statistics were used to predict a gene deleterious index with an accuracy greater than 0.5. This research represents a key step towards understanding the evolutionary dynamics of deleterious variants in sorghum and provides a comparative genomics framework to start prioritizing these variants for removal through genome editing and breeding.
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http://dx.doi.org/10.1038/s41477-020-00834-5DOI Listing
January 2021

Building a tRNA thermometer to estimate microbial adaptation to temperature.

Nucleic Acids Res 2020 12;48(21):12004-12015

Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.

Because ambient temperature affects biochemical reactions, organisms living in extreme temperature conditions adapt protein composition and structure to maintain biochemical functions. While it is not feasible to experimentally determine optimal growth temperature (OGT) for every known microbial species, organisms adapted to different temperatures have measurable differences in DNA, RNA and protein composition that allow OGT prediction from genome sequence alone. In this study, we built a 'tRNA thermometer' model using tRNA sequence to predict OGT. We used sequences from 100 archaea and 683 bacteria species as input to train two Convolutional Neural Network models. The first pairs individual tRNA sequences from different species to predict which comes from a more thermophilic organism, with accuracy ranging from 0.538 to 0.992. The second uses the complete set of tRNAs in a species to predict optimal growth temperature, achieving a maximum ${r^2}$ of 0.86; comparable with other prediction accuracies in the literature despite a significant reduction in the quantity of input data. This model improves on previous OGT prediction models by providing a model with minimum input data requirements, removing laborious feature extraction and data preprocessing steps and widening the scope of valid downstream analyses.
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http://dx.doi.org/10.1093/nar/gkaa1030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708079PMC
December 2020

Genetic elucidation of interconnected antibiotic pathways mediating maize innate immunity.

Nat Plants 2020 11 26;6(11):1375-1388. Epub 2020 Oct 26.

Section of Cell and Developmental Biology, University of California at San Diego, La Jolla, CA, USA.

Specialized metabolites constitute key layers of immunity that underlie disease resistance in crops; however, challenges in resolving pathways limit our understanding of the functions and applications of these metabolites. In maize (Zea mays), the inducible accumulation of acidic terpenoids is increasingly considered to be a defence mechanism that contributes to disease resistance. Here, to understand maize antibiotic biosynthesis, we integrated association mapping, pan-genome multi-omic correlations, enzyme structure-function studies and targeted mutagenesis. We define ten genes in three zealexin (Zx) gene clusters that encode four sesquiterpene synthases and six cytochrome P450 proteins that collectively drive the production of diverse antibiotic cocktails. Quadruple mutants in which the ability to produce zealexins (ZXs) is blocked exhibit a broad-spectrum loss of disease resistance. Genetic redundancies ensuring pathway resiliency to single null mutations are combined with enzyme substrate promiscuity, creating a biosynthetic hourglass pathway that uses diverse substrates and in vivo combinatorial chemistry to yield complex antibiotic blends. The elucidated genetic basis of biochemical phenotypes that underlie disease resistance demonstrates a predominant maize defence pathway and informs innovative strategies for transferring chemical immunity between crops.
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http://dx.doi.org/10.1038/s41477-020-00787-9DOI Listing
November 2020

Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors.

Nat Commun 2020 10 9;11(1):5089. Epub 2020 Oct 9.

State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China.

The transcription regulatory network inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex network. In this study, we use large-scale ChIP-seq to reconstruct it in the maize leaf, and train machine-learning models to predict TF binding and co-localization. The resulting network covers 77% of the expressed genes, and shows a scale-free topology and functional modularity like a real-world network. TF binding sequence preferences are conserved within family, while co-binding could be key for their binding specificity. Cross-species comparison shows that core network nodes at the top of the transmission of information being more conserved than those at the bottom. This study reveals the complex and redundant nature of the plant transcription regulatory network, and sheds light on its architecture, organizing principle and evolutionary trajectory.
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http://dx.doi.org/10.1038/s41467-020-18832-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547689PMC
October 2020

Identification of miRNA-eQTLs in maize mature leaf by GWAS.

BMC Genomics 2020 Oct 6;21(1):689. Epub 2020 Oct 6.

Advanced Plant Biotechnology Center, National Chung Hsing University, No 145 Xingda Rd, South Dist, Taichung, 402, Taiwan.

Background: MiRNAs play essential roles in plant development and response to biotic and abiotic stresses through interaction with their target genes. The expression level of miRNAs shows great variations among different plant accessions, developmental stages, and tissues. Little is known about the content within the plant genome contributing to the variations in plants. This study aims to identify miRNA expression-related quantitative trait loci (miR-QTLs) in the maize genome.

Results: The miRNA expression level from next generation sequencing (NGS) small RNA libraries derived from mature leaf samples of the maize panel (200 maize lines) was estimated as phenotypes, and maize Hapmap v3.2.1 was chosen as the genotype for the genome-wide association study (GWAS). A total of four significant miR-eQTLs were identified contributing to miR156k-5p, miR159a-3p, miR390a-5p and miR396e-5p, and all of them are trans-eQTLs. In addition, a strong positive coexpression of miRNA was found among five miRNA families. Investigation of the effects of these miRNAs on the expression levels and target genes provided evidence that miRNAs control the expression of their targets by suppression and enhancement.

Conclusions: These identified significant miR-eQTLs contribute to the diversity of miRNA expression in the maize penal at the developmental stages of mature leaves in maize, and the positive and negative regulation between miRNA and its target genes has also been uncovered.
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http://dx.doi.org/10.1186/s12864-020-07073-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541240PMC
October 2020

Natural variation for carotenoids in fresh kernels is controlled by uncommon variants in sweet corn.

Plant Genome 2020 03 24;13(1):e20008. Epub 2020 Apr 24.

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.

Sweet corn (Zea mays L.) is highly consumed in the United States, but does not make major contributions to the daily intake of carotenoids (provitamin A carotenoids, lutein and zeaxanthin) that would help in the prevention of health complications. A genome-wide association study of seven kernel carotenoids and twelve derivative traits was conducted in a sweet corn inbred line association panel ranging from light to dark yellow in endosperm color to elucidate the genetic basis of carotenoid levels in fresh kernels. In agreement with earlier studies of maize kernels at maturity, we detected an association of β-carotene hydroxylase (crtRB1) with β-carotene concentration and lycopene epsilon cyclase (lcyE) with the ratio of flux between the α- and β-carotene branches in the carotenoid biosynthetic pathway. Additionally, we found that 5% or less of the evaluated inbred lines possessing the shrunken2 (sh2) endosperm mutation had the most favorable lycE allele or crtRB1 haplotype for elevating β-branch carotenoids (β-carotene and zeaxanthin) or β-carotene, respectively. Genomic prediction models with genome-wide markers obtained moderately high predictive abilities for the carotenoid traits, especially lutein, and outperformed models with less markers that targeted candidate genes implicated in the synthesis, retention, and/or genetic control of kernel carotenoids. Taken together, our results constitute an important step toward increasing carotenoids in fresh sweet corn kernels.
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http://dx.doi.org/10.1002/tpg2.20008DOI Listing
March 2020

A sorghum practical haplotype graph facilitates genome-wide imputation and cost-effective genomic prediction.

Plant Genome 2020 03 25;13(1):e20009. Epub 2020 Mar 25.

Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA.

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome-wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage-only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57-.73 and are similar to prediction accuracies obtained with genotyping-by-sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low-coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost-effective option for genomic selection.
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http://dx.doi.org/10.1002/tpg2.20009DOI Listing
March 2020

The genetic architecture of the maize progenitor, teosinte, and how it was altered during maize domestication.

PLoS Genet 2020 05 14;16(5):e1008791. Epub 2020 May 14.

Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

The genetics of domestication has been extensively studied ever since the rediscovery of Mendel's law of inheritance and much has been learned about the genetic control of trait differences between crops and their ancestors. Here, we ask how domestication has altered genetic architecture by comparing the genetic architecture of 18 domestication traits in maize and its ancestor teosinte using matched populations. We observed a strongly reduced number of QTL for domestication traits in maize relative to teosinte, which is consistent with the previously reported depletion of additive variance by selection during domestication. We also observed more dominance in maize than teosinte, likely a consequence of selective removal of additive variants. We observed that large effect QTL have low minor allele frequency (MAF) in both maize and teosinte. Regions of the genome that are strongly differentiated between teosinte and maize (high FST) explain less quantitative variation in maize than teosinte, suggesting that, in these regions, allelic variants were brought to (or near) fixation during domestication. We also observed that genomic regions of high recombination explain a disproportionately large proportion of heritable variance both before and after domestication. Finally, we observed that about 75% of the additive variance in both teosinte and maize is "missing" in the sense that it cannot be ascribed to detectable QTL and only 25% of variance maps to specific QTL. This latter result suggests that morphological evolution during domestication is largely attributable to very large numbers of QTL of very small effect.
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http://dx.doi.org/10.1371/journal.pgen.1008791DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266358PMC
May 2020

Ten Years of the Maize Nested Association Mapping Population: Impact, Limitations, and Future Directions.

Plant Cell 2020 07 12;32(7):2083-2093. Epub 2020 May 12.

U.S. Department of Agriculture-Agricultural Research Service, Ithaca, New York 14853

It has been just over a decade since the release of the maize () Nested Association Mapping (NAM) population. The NAM population has been and continues to be an invaluable resource for the maize genetics community and has yielded insights into the genetic architecture of complex traits. The parental lines have become some of the most well-characterized maize germplasm, and their de novo assemblies were recently made publicly available. As we enter an exciting new stage in maize genomics, this retrospective will summarize the design and intentions behind the NAM population; its application, the discoveries it has enabled, and its influence in other systems; and use the past decade of hindsight to consider whether and how it will remain useful in a new age of genomics.
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http://dx.doi.org/10.1105/tpc.19.00951DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7346555PMC
July 2020

Dominance Effects and Functional Enrichments Improve Prediction of Agronomic Traits in Hybrid Maize.

Genetics 2020 05 9;215(1):215-230. Epub 2020 Mar 9.

Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853

Single-cross hybrids have been critical to the improvement of maize ( L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated three agronomic traits: days to silking (DTS), plant height (PH), and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.
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http://dx.doi.org/10.1534/genetics.120.303025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7198274PMC
May 2020

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

BMC Res Notes 2020 Feb 12;13(1):71. Epub 2020 Feb 12.

Texas A&M University, College Station, TX, 77843, USA.

Objectives: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017.

Data Description: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.
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http://dx.doi.org/10.1186/s13104-020-4922-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017475PMC
February 2020

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum.

G3 (Bethesda) 2020 02 6;10(2):769-781. Epub 2020 Feb 6.

Plant Breeding and Genetics Section, School of Integrative Plant Science,

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum ( (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.
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http://dx.doi.org/10.1534/g3.119.400759DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7003104PMC
February 2020

Widespread long-range cis-regulatory elements in the maize genome.

Nat Plants 2019 12 18;5(12):1237-1249. Epub 2019 Nov 18.

Department of Plant Biology, University of Georgia, Athens, GA, USA.

Genetic mapping studies on crops suggest that agronomic traits can be controlled by gene-distal intergenic loci. Despite the biological importance and the potential agronomic utility of these loci, they remain virtually uncharacterized in all crop species to date. Here, we provide genetic, epigenomic and functional molecular evidence to support the widespread existence of gene-distal (hereafter, distal) loci that act as long-range transcriptional cis-regulatory elements (CREs) in the maize genome. Such loci are enriched for euchromatic features that suggest their regulatory functions. Chromatin loops link together putative CREs with genes and recapitulate genetic interactions. Putative CREs also display elevated transcriptional enhancer activities, as measured by self-transcribing active regulatory region sequencing. These results provide functional support for the widespread existence of CREs that act over large genomic distances to control gene expression.
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http://dx.doi.org/10.1038/s41477-019-0547-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6904520PMC
December 2019

Multiple genes recruited from hormone pathways partition maize diterpenoid defences.

Nat Plants 2019 10 16;5(10):1043-1056. Epub 2019 Sep 16.

Section of Cell and Developmental Biology, University of California San Diego, La Jolla, CA, USA.

Duplication and divergence of primary pathway genes underlie the evolution of plant specialized metabolism; however, mechanisms partitioning parallel hormone and defence pathways are often speculative. For example, the primary pathway intermediate ent-kaurene is essential for gibberellin biosynthesis and is also a proposed precursor for maize antibiotics. By integrating transcriptional coregulation patterns, genome-wide association studies, combinatorial enzyme assays, proteomics and targeted mutant analyses, we show that maize kauralexin biosynthesis proceeds via the positional isomer ent-isokaurene formed by a diterpene synthase pair recruited from gibberellin metabolism. The oxygenation and subsequent desaturation of ent-isokaurene by three promiscuous cytochrome P450s and a new steroid 5α reductase indirectly yields predominant ent-kaurene-associated antibiotics required for Fusarium stalk rot resistance. The divergence and differential expression of pathway branches derived from multiple duplicated hormone-metabolic genes minimizes dysregulation of primary metabolism via the circuitous biosynthesis of ent-kaurene-related antibiotics without the production of growth hormone precursors during defence.
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http://dx.doi.org/10.1038/s41477-019-0509-6DOI Listing
October 2019

Transcriptome-Wide Association Supplements Genome-Wide Association in .

G3 (Bethesda) 2019 09 4;9(9):3023-3033. Epub 2019 Sep 4.

Institute for Genomic Diversity

Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.
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http://dx.doi.org/10.1534/g3.119.400549DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6723120PMC
September 2019

The multi-allelic APRR2 gene is associated with fruit pigment accumulation in melon and watermelon.

J Exp Bot 2019 08;70(15):3781-3794

Plant Science Institute, Agricultural Research Organization, Newe Ya'ar Research Center, Ramat Yishay, Israel.

Color and pigment contents are important aspects of fruit quality and consumer acceptance of cucurbit crops. Here, we describe the independent mapping and cloning of a common causative APRR2 gene regulating pigment accumulation in melon and watermelon. We initially show that the APRR2 transcription factor is causative for the qualitative difference between dark and light green rind in both crops. Further analyses establish the link between sequence or expression level variations in the CmAPRR2 gene and pigment content in the rind and flesh of mature melon fruits. A genome-wide association study (GWAS) of young fruit rind color in a panel composed of 177 diverse melon accessions did not result in any significant association, leading to an earlier assumption that multiple genes are involved in shaping the overall phenotypic variation in this trait. Through resequencing of 25 representative accessions and allelism tests between light rind accessions, we show that multiple independent single nucleotide polymorphisms in the CmAPRR2 gene are causative of the light rind phenotype. The multi-haplotypic nature of this gene explains the lack of detection power obtained through genotyping by sequencing-based GWAS and confirms the pivotal role of this gene in shaping fruit color variation in melon. This study demonstrates the power of combining bi- and multi-allelic designs with deep sequencing, to resolve lack of power due to high haplotypic diversity and low allele frequencies. Due to its central role and broad effect on pigment accumulation in fruits, the APRR2 gene is an attractive target for carotenoid bio-fortification of cucurbit crops.
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http://dx.doi.org/10.1093/jxb/erz182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685648PMC
August 2019

Genome-Wide Association and Genomic Prediction Models of Tocochromanols in Fresh Sweet Corn Kernels.

Plant Genome 2019 03;12(1)

Sweet corn ( L.), a highly consumed fresh vegetable in the United States, varies for tocochromanol (tocopherol and tocotrienol) levels but makes only a limited contribution to daily intake of vitamin E and antioxidants. We performed a genome-wide association study of six tocochromanol compounds and 14 derivative traits across a sweet corn inbred line association panel to identify genes associated with natural variation for tocochromanols and vitamin E in fresh kernels. Concordant with prior studies in mature maize kernels, an association was detected between γ-tocopherol methyltransferase (vte4) and α-tocopherol content, along with () and () for tocotrienol variation. Additionally, two kernel starch synthesis genes, () and (), were associated with tocotrienols, with the strongest evidence for in combination with fixed, strong and alleles, accounting for the greater amount of tocotrienols in and lines. In prediction models with genome-wide markers, predictive abilities were higher for tocotrienols than tocopherols, and these models were superior to those that used marker sets targeting a priori genes involved in the biosynthesis and/or genetic control of tocochromanols. Through this quantitative genetic analysis, we have established a key step for increasing tocochromanols in fresh kernels of sweet corn for human health and nutrition.
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http://dx.doi.org/10.3835/plantgenome2018.06.0038DOI Listing
March 2019

Metabolome-Scale Genome-Wide Association Studies Reveal Chemical Diversity and Genetic Control of Maize Specialized Metabolites.

Plant Cell 2019 05 28;31(5):937-955. Epub 2019 Mar 28.

Boyce Thompson Institute, Ithaca, New York 14853

Cultivated maize () has retained much of the genetic diversity of its wild ancestors. Here, we performed nontargeted liquid chromatography-mass spectrometry metabolomics to analyze the metabolomes of the 282 maize inbred lines in the Goodman Diversity Panel. This analysis identified a bimodal distribution of foliar metabolites. Although 15% of the detected mass features were present in >90% of the inbred lines, the majority were found in <50% of the samples. Whereas leaf bases and tips were differentiated by flavonoid abundance, maize varieties (stiff-stalk, nonstiff-stalk, tropical, sweet maize, and popcorn) showed differential accumulation of benzoxazinoid metabolites. Genome-wide association studies (GWAS), performed for 3,991 mass features from the leaf tips and leaf bases, showed that 90% have multiple significantly associated loci scattered across the genome. Several quantitative trait locus hotspots in the maize genome regulate the abundance of multiple, often structurally related mass features. The utility of maize metabolite GWAS was demonstrated by confirming known benzoxazinoid biosynthesis genes, as well as by mapping isomeric variation in the accumulation of phenylpropanoid hydroxycitric acid esters to a single linkage block in a citrate synthase-like gene. Similar to gene expression databases, this metabolomic GWAS data set constitutes an important public resource for linking maize metabolites with biosynthetic and regulatory genes.
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http://dx.doi.org/10.1105/tpc.18.00772DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6533025PMC
May 2019

A k-mer grammar analysis to uncover maize regulatory architecture.

BMC Plant Biol 2019 Mar 15;19(1):103. Epub 2019 Mar 15.

Institute for Genomic Diversity, Cornell University, 175 Biotechnology Building, Ithaca, 14853, NY, USA.

Background: Only a small percentage of the genome sequence is involved in regulation of gene expression, but to biochemically identify this portion is expensive and laborious. In species like maize, with diverse intergenic regions and lots of repetitive elements, this is an especially challenging problem that limits the use of the data from one line to the other. While regulatory regions are rare, they do have characteristic chromatin contexts and sequence organization (the grammar) with which they can be identified.

Results: We developed a computational framework to exploit this sequence arrangement. The models learn to classify regulatory regions based on sequence features - k-mers. To do this, we borrowed two approaches from the field of natural language processing: (1) "bag-of-words" which is commonly used for differentially weighting key words in tasks like sentiment analyses, and (2) a vector-space model using word2vec (vector-k-mers), that captures semantic and linguistic relationships between words. We built "bag-of-k-mers" and "vector-k-mers" models that distinguish between regulatory and non-regulatory regions with an average accuracy above 90%. Our "bag-of-k-mers" achieved higher overall accuracy, while the "vector-k-mers" models were more useful in highlighting key groups of sequences within the regulatory regions.

Conclusions: These models now provide powerful tools to annotate regulatory regions in other maize lines beyond the reference, at low cost and with high accuracy.
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http://dx.doi.org/10.1186/s12870-019-1693-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6419808PMC
March 2019

The genetic architecture of teosinte catalyzed and constrained maize domestication.

Proc Natl Acad Sci U S A 2019 03 6;116(12):5643-5652. Epub 2019 Mar 6.

Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706;

The process of evolution under domestication has been studied using phylogenetics, population genetics-genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance-covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory.
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http://dx.doi.org/10.1073/pnas.1820997116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6431195PMC
March 2019

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

Proc Natl Acad Sci U S A 2019 03 6;116(12):5542-5549. Epub 2019 Mar 6.

Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, 100081 Beijing, China

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: () gene-family-guided splitting and () ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3' UTR is more important for fine-tuning mRNA abundance levels while the 5' UTR is more important for large-scale changes.
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http://dx.doi.org/10.1073/pnas.1814551116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6431157PMC
March 2019
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