Biomedical subjects
Publications and source records attributed to Edward S Buckler.
Dr. Laura Zahn asked the authors, Dr. Jingjing Zhai and Dr. Edward (Ed) S. Buckler, to tell us about their research relating to their Cell Genomics paper, "PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants."
Despite the plethora of knowledge about the benefits of meiotic recombination and numerous theoretical studies examining how recombination rates evolve, there is a general lack of empirical support and consensus across species. To fill this knowledge gap, we characterized the evolution of recombination landscape in maize during its domestication from teosinte and related the observed changes to established theoretical frameworks. Through examining recombination in experimental populations of maize and teosinte and the population genomics approach of identifying historical recombination events using ancestral recombination graph inference to generate saturated maize and teosinte recombination maps, we found that during domestication, maize experienced a 12% increase in its genome-wide recombination rate. Furthermore, maize evolved higher recombination rates on the long arms of chromosomes in regions closer to centromeres, where recombination is generally very low. The repatterning of crossover events came from changes in global crossover positioning rather than alterations in cis-acting chromatin factors. Consequently, we found evidence of selection acting on trans-acting recombination modifiers affecting crossover interference and controlling the interference-dependent class I crossover pathway. We show that CO repatterning was likely beneficial for maize fitness, as significant recombination rate increases were predominantly in gene-rich regions, which harbor domestication-related variation. This work suggests genomic and mechanistic processes leading to the evolution of meiotic recombination landscape in response to directional selection pressure and provides evidence for the evolutionary advantage of recombination.
Historical plant breeding, which optimizes phenotypes through selective crossing guided by phenotypic evaluation and molecular markers, is limited by evolutionary constraints that hinder rapid crop improvement. A new paradigm, precision breeding, circumvents these limitations by targeting genetic variants through functional molecular knowledge. To generate this knowledge at scale, sequence-based deep learning leverages high-quality genome sequence data to predict variant effects at base-pair resolution. When linked to agronomically important traits, these predictions enable breeders to prioritize variants for precision selection or editing. Although it is still in the early stages of development, we foresee three key applications for this approach: introgressing genes from distant breeding pools, purging deleterious mutations and designing new plant ideotypes. Looking ahead, refined computational models will facilitate targeted editing and the systematic redesign of complex physiological processes to address emerging breeding goals under shifting environmental conditions.
Nitrogen (N) fertilization in crop production significantly impacts ecosystems, often disrupting natural plant-microbe-soil interactions and causing environmental pollution. This study tested the hypothesis that diverse species adapting independently to various environments might exhibit a wide range of rhizosphere nutrient management strategies, and some of them may be conducive to an efficient N economy for crops. We analyzed the N cycle in the rhizospheres of 36 Andropogoneae grass species related to maize and sorghum and observed significant phylogenetic variation among their impacts on N availability and losses. All three annual species examined, including sorghum and maize, function as N 'Conservationists', reducing soil nitrification potential and conserving NH4 +. In contrast, seven of the assayed perennial species enhance nitrification and leaching ('Leachers'). Four other species exhibit similar nitrification stimulation effects but limited NO3 - losses ('Nitrate Keepers'). We complemented the controlled phenotypic evaluation with an evolutionary-ecological analysis of the same species. We identified several soil characteristics associated with the phylogenetic variation in rhizosphere N dynamics across grasses and highlighted the crucial roles of a few transporter genes in soil N management and utilization. In addition to the ecological and genetic insights, these findings offer valuable guidelines for future maize breeding efforts to enhance agricultural N efficiency and sustainability.
Assembled genomes and their associated annotations have transformed our study of gene function. However, each new annotated assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses the conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million transcript models in Zea mays ssp. mays (maize), reelGene classified 28% as incorrectly annotated or non-functional. We find that reelGene classifies 92.2% of genes in the maize proteome and 99.2% of genes within the maize classical gene list as functional. reelGene also provides a way to further investigate genome biology- for instance, reelGene indicates that 10.3% of dispensable genes in B73 are functional, and within retained duplicate genes, reelGene identifies a 30% bias toward the retention of the M1 subgenome when one copy is functional and the other is non-functional. As an annotation-evaluating tool, reelGene is directly applicable to species of the Andropogoneae tribe, including other important crops like sorghum and miscanthus. As a community resource, reelGene has been integrated onto MaizeGDB both as a browser track and as an individual Shiny App, allowing researchers to evaluate gene model accuracy and further investigate genome biology.
INTRODUCTION: Cassava (Manihot esculenta) is an annual root crop which provides the major source of calories for over half a billion people around the world. Since its domestication ~10,000 years ago, cassava has been largely clonally propagated through stem cuttings. Minimal sexual recombination has led to an accumulation of deleterious mutations made evident by heavy inbreeding depression. METHODS: To locate and characterize these deleterious mutations, and to measure selection pressure across the cassava genome, we aligned 52 related Euphorbiaceae and other related species representing millions of years of evolution. With single base-pair resolution of genetic conservation, we used protein structure models, amino acid impact, and evolutionary conservation across the Euphorbiaceae to estimate evolutionary constraint. With known deleterious mutations, we aimed to improve genomic evaluations of plant performance through genomic prediction. We first tested this hypothesis through simulation utilizing multi-kernel GBLUP to predict simulated phenotypes across separate populations of cassava. RESULTS: Simulations showed a sizable increase of prediction accuracy when incorporating functional variants in the model when the trait was determined by<100 quantitative trait loci (QTL). Utilizing deleterious mutations and functional weights informed through evolutionary conservation, we saw improvements in genomic prediction accuracy that were dependent on trait and prediction. CONCLUSION: We showed the potential for using evolutionary information to track functional variation across the genome, in order to improve whole genome trait prediction. We anticipate that continued work to improve genotype accuracy and deleterious mutation assessment will lead to improved genomic assessments of cassava clones.
Epistatic interactions among quantitative trait loci (QTL) contribute substantially to the variation in complex traits. The main objectives of this study were to (i) compare three- vs. four-step genome scans to identify three-way epistatic interactions among QTL belonging to a metabolic pathway, (ii) investigate by computer simulations the power and proportion of false positives (PFP) for detecting three-way interactions among QTL in recombinant inbred line (RIL) populations derived from a nested mating design, and (iii) compare these estimates to those obtained for detecting three-way interactions among QTL in RIL populations derived from diallel and different partial diallel mating designs. The single-nucleotide polymorphism haplotype data of B73 and 25 diverse maize inbreds were used to simulate the production of various RIL populations. Compared to the three-step genome scan, the power to detect three-way interactions was higher with the four-step genome scan. Higher power to detect three-way interactions was observed for RILs derived from optimally allocated distance-based designs than from nested designs or diallel designs. The power and PFP to detect three-way interactions using a nested design with 5000 RILs were for both the 4-QTL and the 12-QTL scenario of a magnitude that seems promising for their identification.
Association mapping, a high-resolution method for mapping quantitative trait loci based on linkage disequilibrium, holds great promise for the dissection of complex genetic traits. The recent assembly and characterization of maize association mapping panels, development of improved statistical methods, and successful association of candidate genes have begun to realize the power of candidate-gene association mapping. Although the complexity of the maize genome poses several significant challenges to the application of association mapping, the ongoing genome sequencing project will ultimately allow for a thorough genome-wide examination of nucleotide polymorphism-trait association.
Over the past 10,000 years, man has used the rich genetic diversity of the maize genome as the raw material for domestication and subsequent crop improvement. Recent research efforts have made tremendous strides toward characterizing this diversity: structural diversity appears to be largely mediated by helitron transposable elements, patterns of diversity are yielding insights into the number and type of genes involved in maize domestication and improvement, and functional diversity experiments are leading to allele mining for future crop improvement. The development of genome sequence and germplasm resources are likely to further accelerate this progress.
Rice, maize, sorghum, wheat, barley and the other major crop grasses from the family Poaceae (Gramineae) are mankind's most important source of calories and contribute tens of billions of dollars annually to the world economy (FAO 1999, http://www.fao.org; USDA 1997, http://www.usda.gov). Continued improvement of Poaceae crops is necessary in order to continue to feed an ever-growing world population. However, of the major crop grasses, only rice (Oryza sativa), with a compact genome of approximately 400 Mbp, has been sequenced and annotated. The Gramene database (http://www.gramene.org) takes advantage of the known genetic colinearity (synteny) between rice and the major crop plant genomes to provide maize, sorghum, millet, wheat, oat and barley researchers with the benefits of an annotated genome years before their own species are sequenced. Gramene is a one stop portal for finding curated literature, genetic and genomic datasets related to maps, markers, genes, genomes and quantitative trait loci. The addition of several new tools to Gramene has greatly facilitated the potential for comparative analysis among the grasses and contributes to our understanding of the anatomy, development, environmental responses and the factors influencing agronomic performance of cereal crops. Since the last publication on Gramene database by D. H. Ware, P. Jaiswal, J. Ni, I. V. Yap, X. Pan, K. Y. Clark, L. Teytelman, S. C. Schmidt, W. Zhao, K. Chang et al. [(2002), Plant Physiol., 130, 1606-1613], the database has undergone extensive changes that are described in this publication.
As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.
The external appearance of flowering plants is determined to a large extent by the forms of flower-bearing branch systems, known as inflorescences, and their position in the overall structure of the plant. Branches and branching patterns are produced by tissues called shoot apical meristems. Thus, inflorescence architecture reflects meristem number, arrangement and activity, and the duration of meristem activity correlates with branch length. The inflorescences of maize, unlike those of related grasses such as rice and sorghum, predominantly lack long branches, giving rise to the tassel and familiar corncob. Here we report the isolation of the maize ramosa1 gene and show that it controls inflorescence architecture. Through its expression in a boundary domain near the nascent meristem base, ramosa1 imposes short branch identity as branch meristems are initiated. A second gene, ramosa2, acts through ramosa1 by regulating ramosa1 gene expression levels. ramosa1 encodes a transcription factor that appears to be absent in rice, is heterochronically expressed in sorghum, and may have played an important role in maize domestication and grass evolution.
The teosintes, the closest wild relatives of maize, are important resources for the study of maize genetics and evolution and for plant breeding. We genotyped 237 individual teosinte plants for 93 microsatellites. Phylogenetic relationships among species and subspecific taxa were largely consistent with prior analyses for other types of molecular markers. Plants of all species formed monophyletic clades, although relationships among species were not fully resolved. Phylogenetic analysis indicated that the Mexican annual teosintes divide into two clusters that largely correspond to the previously defined subspecies, Z. mays ssp. parviglumis and ssp. mexicana, although there are a few samples that represent either evolutionary intermediates or hybrids between these two subspecies. The Mexican annual teosintes show genetic substructuring along geographic lines. Hybridization or introgression between some teosintes and maize occurs at a low level and appears most common with Z. mays ssp. mexicana. Phylogeographic and phylogenetic analyses of the Mexican annual teosintes indicated that ssp. parviglumis diversified in the eastern part of its distribution and spread from east to west and that ssp. mexicana diversified in the Central Plateau of Mexico and spread along multiple paths to the north and east. We defined core sets of collections of Z. mays ssp. mexicana and ssp. parviglumis that attempt to capture the maximum number of microsatellite alleles for given sample sizes.
Crop improvement and the dissection of complex genetic traits require germplasm diversity. Although this necessary phenotypic variability exists in diverse maize, most research is conducted using a small subset of inbred lines. An association population of 302 lines is now available--a valuable research tool that captures a large proportion of the alleles in cultivated maize. Provided that appropriate statistical models correcting for population structure are included, this tool can be used in association analyses to provide high-resolution evaluation of multiple alleles. This study describes the population structure of the 302 lines, and investigates the relationship between population structure and various measures of phenotypic and breeding value. On average, our estimates of population structure account for 9.3% of phenotypic variation, roughly equivalent to a major quantitative trait locus (QTL), with a high of 35%. Inclusion of population structure in association models is critical to meaningful analyses. This new association population has the potential to identify QTL with small effects, which will aid in dissecting complex traits and in planning future projects to exploit the rich diversity present in maize.
Cereal starch production forms the basis of subsistence for much of the world's human and domesticated animal populations. Starch concentration and composition in the maize (Zea mays ssp mays) kernel are complex traits controlled by many genes. In this study, an association approach was used to evaluate six maize candidate genes involved in kernel starch biosynthesis: amylose extender1 (ae1), brittle endosperm2 (bt2), shrunken1 (sh1), sh2, sugary1, and waxy1. Major kernel composition traits, such as protein, oil, and starch concentration, were assessed as well as important starch composition quality traits, including pasting properties and amylose levels. Overall, bt2, sh1, and sh2 showed significant associations for kernel composition traits, whereas ae1 and sh2 showed significant associations for starch pasting properties. ae1 and sh1 both associated with amylose levels. Additionally, haplotype analysis of sh2 suggested this gene is involved in starch viscosity properties and amylose content. Despite starch concentration being only moderately heritable for this particular panel of diverse maize inbreds, high resolution was achieved when evaluating these starch candidate genes, and diverse alleles for breeding and further molecular analysis were identified.
UNLABELLED: The goal of this project is to simplify access to genomic diversity and phenotype data, thereby encouraging reuse of this data. The Genomic Diversity and Phenotype Connection (GDPC) accomplishes this by retrieving data from one or more data sources and by allowing researchers to analyze integrated data in a standard format. GDPC is written in JAVA and provides (1) data sources available as web services that transfer XML formatted data via the SOAP protocol; (2) a JAVA API for programmatic access to data sources; and (3) a front-end application that allows users to manage data sources, retrieve data based on filters, sort/group data based on property values and save/open the data as XML files. AVAILABILITY: The source code, compiled code, documentation and GDPC Browser are freely available at: www.maizegenetics.net/gdpc/index.html the current release of GDPC is version 1.0, with updated releases planned for the future. Comments are welcome.
Future advances in plant genomics will make it possible to scan a genome for polymorphisms associated with qualitative and quantitative traits. Before this potential can be realized, we must understand the nature of linkage disequilibrium (LD) within a genome. LD, the nonrandom association of alleles at different loci, plays an integral role in association mapping, and determines the resolution of an association study. Recently, association mapping has been exploited to dissect quantitative trait loci (QTL). With the exception of maize and Arabidopsis, little research has been conducted on LD in plants. The mating system of the species (selfing versus outcrossing), and phenomena such as population structure and recombination hot spots, can strongly influence patterns of LD. The basic patterns of LD in plants will be better understood as more species are analyzed.
Genomics has developed a wide range of tools to identify genes that play roles in specific pathways. However, relating individual genes and alleles to agronomic traits is still quite challenging. We describe how association analysis can be used to relate natural variation at candidate genes with agronomic phenotypes. Association approaches in plants can provide very high resolution and can evaluate a wide range of alleles rapidly. We discuss issues related to experimental design, germplasm sample, molecular assay, population structure, and statistical analysis necessary for association analysis in plants.