@article{lima_aviles_alpers_mcfarland_kaeppler_ertl_romay_gage_holland_beissinger_et al._2023, title={2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project}, volume={24}, ISSN={["2730-6844"]}, url={https://doi.org/10.1186/s12863-023-01129-2}, DOI={10.1186/s12863-023-01129-2}, abstractNote={Abstract}, number={1}, journal={BMC GENOMIC DATA}, author={Lima, Dayane Cristina and Aviles, Alejandro Castro and Alpers, Ryan Timothy and McFarland, Bridget A. and Kaeppler, Shawn and Ertl, David and Romay, Maria Cinta and Gage, Joseph L. and Holland, James and Beissinger, Timothy and et al.}, year={2023}, month={May} }
 @article{lima_aviles_alpers_perkins_schoemaker_costa_michel_kaeppler_ertl_romay_et al._2023, title={2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative}, volume={16}, ISSN={["1756-0500"]}, DOI={10.1186/s13104-023-06430-y}, abstractNote={Abstract}, number={1}, journal={BMC RESEARCH NOTES}, author={Lima, Dayane Cristina and Aviles, Alejandro Castro and Alpers, Ryan Timothy and Perkins, Alden and Schoemaker, Dylan L. and Costa, Martin and Michel, Kathryn J. and Kaeppler, Shawn and Ertl, David and Romay, Maria Cinta and et al.}, year={2023}, month={Sep} }
 @article{lima_washburn_varela_chen_gage_romay_holland_ertl_lopez-cruz_aguate_et al._2023, title={Genomes to Fields 2022 Maize genotype by Environment Prediction Competition}, volume={16}, ISSN={["1756-0500"]}, url={https://doi.org/10.1186/s13104-023-06421-z}, DOI={10.1186/s13104-023-06421-z}, abstractNote={Abstract}, number={1}, journal={BMC RESEARCH NOTES}, author={Lima, Dayane Cristina and Washburn, Jacob D. and Varela, Jose Ignacio and Chen, Qiuyue and Gage, Joseph L. and Romay, Maria Cinta and Holland, James and Ertl, David and Lopez-Cruz, Marco and Aguate, Fernando M. and et al.}, year={2023}, month={Jul} }
 @article{kick_wallace_schnable_kolkman_alaca_beissinger_edwards_ertl_flint-garcia_gage_et al._2023, title={Yield prediction through integration of genetic, environment, and management data through deep learning}, volume={13}, ISSN={["2160-1836"]}, DOI={10.1093/g3journal/jkad006}, abstractNote={Abstract}, number={4}, journal={G3-GENES GENOMES GENETICS}, author={Kick, Daniel R. and Wallace, Jason G. and Schnable, James C. and Kolkman, Judith M. and Alaca, Baris and Beissinger, Timothy M. and Edwards, Jode and Ertl, David and Flint-Garcia, Sherry and Gage, Joseph L. and et al.}, year={2023}, month={Apr} }
 @article{gage_mali_mcloughlin_khaipho-burch_monier_bailey-serres_vierstra_buckler_2022, title={Variation in upstream open reading frames contributes to allelic diversity in maize protein abundance}, volume={119}, ISSN={["1091-6490"]}, DOI={10.1073/pnas.2112516119}, abstractNote={Significance}, number={14}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, author={Gage, Joseph L. and Mali, Sujina and McLoughlin, Fionn and Khaipho-Burch, Merritt and Monier, Brandon and Bailey-Serres, Julia and Vierstra, Richard D. and Buckler, Edward S.}, year={2022}, month={Apr} }
 @article{yield prediction through integration of genetic, environment, and management data through deep learning_2022, volume={7}, url={http://dx.doi.org/10.1101/2022.07.29.502051}, DOI={10.1101/2022.07.29.502051}, abstractNote={Abstract}, journal={[]}, publisher={Cold Spring Harbor Laboratory}, year={2022}, month={Jul} }
 @article{feldmann_gage_turner-hissong_ubbens_2021, title={Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants}, volume={4}, url={http://dx.doi.org/10.1002/ppj2.20023}, DOI={10.1002/ppj2.20023}, abstractNote={Abstract}, number={1}, journal={The Plant Phenome Journal}, publisher={Wiley}, author={Feldmann, Mitchell J. and Gage, Joseph L. and Turner-Hissong, Sarah D. and Ubbens, Jordan R.}, year={2021}, month={Jan} }
 @article{franco_gage_bradbury_johnson_miller_buckler_romay_2020, title={A Maize Practical Haplotype Graph Leverages Diverse NAM Assemblies}, volume={8}, url={http://dx.doi.org/10.1101/2020.08.31.268425}, DOI={10.1101/2020.08.31.268425}, abstractNote={Abstract}, journal={[]}, publisher={Cold Spring Harbor Laboratory}, author={Franco, Jose A. Valdes and Gage, Joseph L. and Bradbury, Peter J. and Johnson, Lynn C. and Miller, Zachary R. and Buckler, Edward S. and Romay, M. Cinta}, year={2020}, month={Aug} }
 @article{mcfarland_alkhalifah_bohn_bubert_buckler_ciampitti_edwards_ertl_gage_falcon_et al._2020, title={Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets}, volume={13}, url={http://dx.doi.org/10.1186/s13104-020-4922-8}, DOI={10.1186/s13104-020-4922-8}, abstractNote={Abstract}, number={1}, journal={BMC Research Notes}, publisher={Springer Science and Business Media LLC}, author={McFarland, Bridget A. and AlKhalifah, Naser and Bohn, Martin and Bubert, Jessica and Buckler, Edward S. and Ciampitti, Ignacio and Edwards, Jode and Ertl, David and Gage, Joseph L. and Falcon, Celeste M. and et al.}, year={2020}, month={Dec} }
 @article{gage_monier_giri_buckler_2020, title={Ten Years of the Maize Nested Association Mapping Population: Impact, Limitations, and Future Directions}, volume={32}, url={http://dx.doi.org/10.1105/tpc.19.00951}, DOI={10.1105/tpc.19.00951}, abstractNote={Abstract}, number={7}, journal={The Plant Cell}, publisher={Oxford University Press (OUP)}, author={Gage, Joseph L. and Monier, Brandon and Giri, Anju and Buckler, Edward S.}, year={2020}, month={Jul}, pages={2083–2093} }
 @article{genome-wide association analysis of stalk biomass and anatomical traits in maize._2019, url={http://europepmc.org/articles/PMC6357476}, DOI={10.1186/s12870-019-1653-x}, abstractNote={Maize stover is an important source of crop residues and a promising sustainable energy source in the United States. Stalk is the main component of stover, representing about half of stover dry weight. Characterization of genetic determinants of stalk traits provide a foundation to optimize maize stover as a biofuel feedstock. We investigated maize natural genetic variation in genome-wide association studies (GWAS) to detect candidate genes associated with traits related to stalk biomass (stalk diameter and plant height) and stalk anatomy (rind thickness, vascular bundle density and area). Using a panel of 942 diverse inbred lines, 899,784 RNA-Seq derived single nucleotide polymorphism (SNP) markers were identified. Stalk traits were measured on 800 members of the panel in replicated field trials across years. GWAS revealed 16 candidate genes associated with four stalk traits. Most of the detected candidate genes were involved in fundamental cellular functions, such as regulation of gene expression and cell cycle progression. Two of the regulatory genes (Zmm22 and an ortholog of Fpa) that were associated with plant height were previously shown to be involved in regulating the vegetative to floral transition. The association of Zmm22 with plant height was confirmed using a transgenic approach. Transgenic lines with increased expression of Zmm22 showed a significant decrease in plant height as well as tassel branch number, indicating a pleiotropic effect of Zmm22. Substantial heritable variation was observed in the association panel for stalk traits, indicating a large potential for improving useful stalk traits in breeding programs. Genome-wide association analyses detected several candidate genes associated with multiple traits, suggesting common regulatory elements underlie various stalk traits. Results of this study provide insights into the genetic control of maize stalk anatomy and biomass.}, journal={BMC plant biology}, year={2019}, month={Jan} }
 @article{gage_richards_lepak_kaczmar_soman_chowdhary_gore_buckler_2019, title={In‐Field Whole‐Plant Maize Architecture Characterized by Subcanopy Rovers and Latent Space Phenotyping}, volume={2}, url={http://dx.doi.org/10.2135/tppj2019.07.0011}, DOI={10.2135/tppj2019.07.0011}, abstractNote={
Core Ideas
Subcanopy rovers enabled 3D characterization of thousands of hybrid maize plots.
Machine learning produces heritable latent traits that describe plant architecture.
Rover‐based phenotyping is far more efficient than manual phenotyping.
Latent phenotypes from rovers are ready for application to plant biology and breeding.
Collecting useful, interpretable, and biologically relevant phenotypes in a resource‐efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable subcanopy rovers are an efficient and scalable way to generate sensor‐based datasets of in‐field crop plants. Rovers equipped with lidar can produce three‐dimensional reconstructions of entire hybrid maize (Zea mays L.) fields. In this study, we collected 2103 lidar scans of hybrid maize field plots and extracted phenotypic data from them by latent space phenotyping. We performed latent space phenotyping by two methods, principal component analysis and a convolutional autoencoder, to extract meaningful, quantitative latent space phenotypes (LSPs) describing whole‐plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating that they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating that the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.}, number={1}, journal={The Plant Phenome Journal}, publisher={Wiley}, author={Gage, Joseph L. and Richards, Elliot and Lepak, Nicholas and Kaczmar, Nicholas and Soman, Chinmay and Chowdhary, Girish and Gore, Michael A. and Buckler, Edward S.}, year={2019}, month={Jan}, pages={1–11} }
 @misc{lidar point clouds of hybrid maize_2019, DOI={10.25739/zxp6-g188}, journal={CyVerse Data Commons}, year={2019} }
 @article{multiple maize reference genomes impact the identification of variants by genome-wide association study in a diverse inbred panel_2019, url={http://dx.doi.org/10.3835/plantgenome2018.09.0069}, DOI={10.3835/plantgenome2018.09.0069}, abstractNote={Use of a single reference genome for genome‐wide association studies (GWAS) limits the gene space represented to that of a single accession. This limitation can complicate identification and characterization of genes located within presence–absence variations (PAVs). In this study, we present the draft de novo genome assembly of ‘PHJ89’, an ‘Oh43’‐type inbred line of maize (Zea mays L.). From three separate reference genome assemblies (‘B73’, ‘PH207’, and PHJ89) that represent the predominant germplasm groups of maize, we generated three separate whole‐seedling gene expression profiles and single nucleotide polymorphism (SNP) matrices from a panel of 942 diverse inbred lines. We identified 34,447 (B73), 39,672 (PH207), and 37,436 (PHJ89) transcripts that are not present in the respective reference genome assemblies. Genome‐wide association studies were conducted in the 942 inbred panel with both the SNP and expression data values to map Sugarcane mosaic virus (SCMV) resistance. Highlighting the impact of alternative reference genomes in gene discovery, the GWAS results for SCMV resistance with expression values as a surrogate measure of PAV resulted in robust detection of the physical location of a known resistance gene when the B73 reference that contains the gene was used, but not the PH207 reference. This study provides the valuable resource of the Oh43‐type PHJ89 genome assembly as well as SNP and expression data for 942 individuals generated from three different reference genomes.}, journal={The Plant Genome}, year={2019} }
 @article{residual heterozygosity and epistatic interactions underlie the complex genetic architecture of yield in diploid potato._2019, url={https://doi.org/10.1534/genetics.119.302036}, DOI={10.1534/genetics.119.302036}, abstractNote={Abstract}, journal={Genetics}, year={2019}, month={Mar} }
 @article{gage_de_clayton_2018, title={Comparing Genome-Wide Association Study Results from Different Measurements of an Underlying Phenotype.}, volume={9}, url={http://europepmc.org/abstract/med/30262522}, DOI={10.1534/g3.118.200700}, abstractNote={Abstract}, journal={G3 (Bethesda, Md.)}, author={Gage, JL and de, Leon N and Clayton, MK}, year={2018}, month={Sep} }
 @article{gage_white_edwards_kaeppler_de_2018, title={Selection Signatures Underlying Dramatic Male Inflorescence Transformation During Modern Hybrid Maize Breeding.}, volume={9}, url={http://europepmc.org/abstract/med/30257936}, DOI={10.1534/genetics.118.301487}, abstractNote={Abstract}, journal={Genetics}, author={Gage, JL and White and Edwards, JW and Kaeppler, S and de, Leon N}, year={2018}, month={Sep} }
 @article{gage_miller_spalding_kaeppler_leon_2017, title={TIPS: a system for automated image-based phenotyping of maize tassels}, volume={13}, url={http://dx.doi.org/10.1186/s13007-017-0172-8}, DOI={10.1186/s13007-017-0172-8}, abstractNote={The maize male inflorescence (tassel) produces pollen necessary for reproduction and commercial grain production of maize. The size of the tassel has been linked to factors affecting grain yield, so understanding the genetic control of tassel architecture is an important goal. Tassels are fragile and deform easily after removal from the plant, necessitating rapid measurement of any shape characteristics that cannot be retained during storage. Some morphological characteristics of tassels such as curvature and compactness are difficult to quantify using traditional methods, but can be quantified by image-based phenotyping tools. These constraints necessitate the development of an efficient method for capturing natural-state tassel morphology and complementary automated analytical methods that can quickly and reproducibly quantify traits of interest such as height, spread, and branch number. This paper presents the Tassel Image-based Phenotyping System (TIPS), which provides a platform for imaging tassels in the field immediately following removal from the plant. TIPS consists of custom methods that can quantify morphological traits from profile images of freshly harvested tassels acquired with a standard digital camera in a field-deployable light shelter. Correlations between manually measured traits (tassel weight, tassel length, spike length, and branch number) and image-based measurements ranged from 0.66 to 0.89. Additional tassel characteristics quantified by image analysis included some that cannot be quantified manually, such as curvature, compactness, fractal dimension, skeleton length, and perimeter. TIPS was used to measure tassel phenotypes of 3530 individual tassels from 749 diverse inbred lines that represent the diversity of tassel morphology found in modern breeding and academic research programs. Repeatability ranged from 0.85 to 0.92 for manually measured phenotypes, from 0.77 to 0.83 for the same traits measured by image-based methods, and from 0.49 to 0.81 for traits that can only be measured by image analysis. TIPS allows morphological features of maize tassels to be quantified automatically, with minimal disturbance, at a scale that supports population-level studies. TIPS is expected to accelerate the discovery of associations between genetic loci and tassel morphology characteristics, and can be applied to maize breeding programs to increase productivity with lower resource commitment.}, number={1}, journal={Plant Methods}, author={Gage, Joseph L. and Miller, Nathan D. and Spalding, Edgar P. and Kaeppler, Shawn M. and Leon, Natalia}, year={2017}, pages={21} }
 @article{gage_jarquin_romay_lorenz_buckler_kaeppler_alkhalifah_bohn_campbell_edwards_et al._2017, title={The effect of artificial selection on phenotypic plasticity in maize}, volume={8}, ISSN={["2041-1723"]}, url={https://doi.org/10.1038/s41467-017-01450-2}, DOI={10.1038/s41467-017-01450-2}, abstractNote={Abstract}, number={1}, journal={NATURE COMMUNICATIONS}, publisher={Springer Nature}, author={Gage, Joseph L. and Jarquin, Diego and Romay, Cinta and Lorenz, Aaron and Buckler, Edward S. and Kaeppler, Shawn and Alkhalifah, Naser and Bohn, Martin and Campbell, Darwin A. and Edwards, Jode and et al.}, year={2017}, month={Nov} }
 @article{spindel_wright_chen_cobb_gage_harrington_lorieux_ahmadi_mccouch_2013, title={Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations.}, volume={8}, url={http://www.ncbi.nlm.nih.gov/pubmed/23918062}, DOI={10.1007/s00122-013-2166-x}, abstractNote={Genotyping by sequencing (GBS) is the latest application of next-generation sequencing protocols for the purposes of discovering and genotyping SNPs in a variety of crop species and populations. Unlike other high-density genotyping technologies which have mainly been applied to general interest "reference" genomes, the low cost of GBS makes it an attractive means of saturating mapping and breeding populations with a high density of SNP markers. One barrier to the widespread use of GBS has been the difficulty of the bioinformatics analysis as the approach is accompanied by a high number of erroneous SNP calls which are not easily diagnosed or corrected. In this study, we use a 384-plex GBS protocol to add 30,984 markers to an indica (IR64) × japonica (Azucena) mapping population consisting of 176 recombinant inbred lines of rice (Oryza sativa) and we release our imputation and error correction pipeline to address initial GBS data sparsity and error, and streamline the process of adding SNPs to RIL populations. Using the final imputed and corrected dataset of 30,984 markers, we were able to map recombination hot and cold spots and regions of segregation distortion across the genome with a high degree of accuracy, thus identifying regions of the genome containing putative sterility loci. We mapped QTL for leaf width and aluminum tolerance, and were able to identify additional QTL for both phenotypes when using the full set of 30,984 SNPs that were not identified using a subset of only 1,464 SNPs, including a previously unreported QTL for aluminum tolerance located directly within a recombination hotspot on chromosome 1. These results suggest that adding a high density of SNP markers to a mapping or breeding population through GBS has a great value for numerous applications in rice breeding and genetics research.}, journal={TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik}, author={Spindel, Jennifer and Wright, Mark and Chen, Charles and Cobb, Joshua and Gage, Joseph and Harrington, Sandra and Lorieux, Mathias and Ahmadi, Nourollah and McCouch, Susan}, year={2013}, month={Aug} }