Biology subjects

Kaeppler, S. M.

Publications and source records attributed to Kaeppler, S. M..

10 recordsLinked to original sources

bioRxiv2026

A breeder in the sky: scoring flowering with fewer flights

Flowering time is a trait of broad interest and importance to both plant breeders and plant biologists. Unlike many other traits, flowering time cannot be scored accurately from a single observation; its measurement requires repeated observations of the same experiments over time. Both classification- and object-detection-based approaches have demonstrated the potential to estimate flowering time from Unmanned aerial vehicle (UAV) imagery rather than by manual observation, but they still require data collection at many time points. Here, we train and deploy a regression-based framework for scoring flowering time. Using a dataset of more than 200,000 UAV images and associated flowering-time records collected across 27 environments, we demonstrate that this approach enables the prediction of flowering time from as few as one observation per field experiment and successfully generalizes to field experiments in environments not represented in the training dataset. These results indicate that regression-based approaches for predicting flowering time from sparse UAV observations have the potential to substantially reduce the data-collection burden in field experiments

plant biology↗

bioRxiv2026

An open field phenomics resource for multimodal maize yield prediction across divergent environments

Temporal drone phenotyping captures crop development, but irregular flight schedules complicate comparisons across environments. We release curated imagery from 356 flights across 19 Genomes to Fields environments containing 1,180 maize (Zea mays L.) hybrids. To evaluate its utility, we integrated functional principal components of vegetation index and weather trajectories with genomic information. Combinined genomic and phenomic kernels improved yield prediction, reaching correlations up to r = 0.501 for held-out hybrids in environments represented in training and 0.408 when environments were also withheld. Accumulated growing degree days offered no consistent predictive advantage over days after planting, and weather contributed modest, task-dependent gains. A transformer neural process learned directly from irregular observations, serving as a novel application of neural process models in agriculture. Mapping vegetation index functional principal components identified recurrent quantitative trait loci on chromosomes 3 and 7. This resource and its reproducible analyses guide the use of temporal spectral data for crop prediction and genetic discovery.

plant biology↗

bioRxiv2025

Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery

Genomic and phenomic analyses suggest additional heritable phenomic features can improve modeling of important end traits like senescence or yield. Field phenotyping generally uses trait values averaged across individual experimental units (plants or numerous plants within plots), ignoring the full distributional pattern of collected measures. Images of plants or plots, as captured by drones (unoccupied aerial vehicles / UAVs / drones), can be viewed as individual distribution functions that capture biological information. This study introduces and validates distributional data analysis in two crops and experiment types - cotton (Gossypium hirsutum L.) single plant vegetation index (VI) analysis and maize (Zea mays L.) plot-level yield predictions. In both crops, the concept of within-day variance decomposition was demonstrated. In cotton, genotypes exerted significant influences on temporal quantile functions of VIs. Maize yield prediction using distributional data with elastic-net regression indicated improvements in yield prediction between 12.7%-21.6% with quantiles outside the conventionally used median responsible for added predictive power. A novel data visualization method for per-pixel heritability allowed distributional features to be explainable and interpretable. These results have implications for future plant phenomic studies, indicating that distributional data analysis applied across temporal imagery captures novel, heritable, and interpretable biological signal that is lost when working with conventional measures of central tendency such as mean or median summary values of experimental units. SignificanceRepeated aerial imaging of agricultural experiments produces image data sets that capture plant development in high spatial and temporal resolutions. Frequently, images are summarized by measures of central tendency, such as mean or median values. Here, functional data distributional methods were applied to cotton (Gossypium hirsutum L.) and maize (Zea mays L.) image data, capturing more information than standard approaches. Cotton genotypes significantly impacted distributional spectral data while in maize, distributional data enabled more accurate predictions of grain yield versus models trained with median data alone. Distributional data were more explainable by genetics, with novel data visualization techniques able to shine light on specific parts of plant imagery with high and low genetic variance.

plant biology↗

bioRxiv2025

Divergence of Bowman-Birk Protease Inhibitor Family into seed-specific and environmentally responsive subfamilies in Legume and Soybean: Implication for Legume Seed Composition Improvement.

Bowman-Birk inhibitors (BBI) are an ancient class of serine protease inhibitors originating prior to the emergence of the angiosperms. While BBIs have been preserved in the legume (Fabaceae) and cereal (Poaceae) families, they have been lost in many other divergent lineages. However, their underlying molecular evolution and regulation of BBI remain largely uncharacterized. Our study shows that BBIs in legumes and cereals are encoded by two large and divergent gene families. BBI genes in legumes have further diversified into two subfamilies with distinct gene expression patterns. Genes in one legume BBI subfamily are specifically expressed in seeds while BBI genes in the other legume subfamily and cereal do not have significant expressions in any examined tissues including seed, root, leaf and flower. The soybean BBI gene family shows evidence of expansion via whole genome, segmental and tandem duplication. Protein sequence and structural analysis predicts that functional domains for double-headed inhibitory loops and binding abilities to trypsin and chymotrypsin are largely preserved within the soybean BBI family. The seed-specific subfamily genes are specifically expressed at maturation stages and not at embryogenesis stages. The other, non-seed BBI subfamily genes are highly responsive to a distinct spectrum of signals related to abiotic and biotic stresses. Their specific expression under non-essential biological processes for plant growth and development suggests that, although BBIs have been retained in both cereals and legumes, likely due to their role in enhancing plant fitness under natural selection pressures, they are not involved in core developmental processes. This may explain why BBIs were lost in many divergent plant lineages and support their well-established roles in plant adaptation to environmental stress. Having knocked out the seed-specific BBIs through a CRISPR/Cas9 approach, we have successfully generated soybeans which exhibited 69.4 - 73.7% reduction of trypsin inhibitor activity and 76.4 - 79.4% reduced chymotrypsin inhibitor activity. The edited soybean did not show significant changes in key agronomic traits, supporting that the functions of BBIs are not essential. While BBIs in soybean seeds may have a desirable function in natural selection, they are antinutrients from an applied perspective for their use in feed and food. It provides an opportunity to reduce BBIs in seeds for quality improvement. Our findings provide insights into molecular evolution, regulation, and function of BBI in plants, and successfully demonstrate engineering BBI in seeds to result in production of food and feed of higher nutritional value with minimal impacts on the agronomic performance of the plant.

plant biology↗

bioRxiv2023

A practical method to improve the efficiency of pollination in maize breeding and genetics research

Seed increase through manual pollination is a critical part of maize breeding and genetics research to advance generations in breeding programs, to create desired research crosses, and produce hybrid seed for trials. Pollination in the field and in controlled environments relies on the availability of high-quality pollen at the time that recipient silks are receptive. Generally, pollinations are made by capturing pollen from the tassel in a paper pollinating bag placed on the tassels one day prior to pollination and newly released pollen is then transferred to silks on the target plant. In the field, maize pollen is only viable for one to four hours following dehiscence and the rate of desiccation is influenced by environmental conditions. We have developed a method which increases the lifespan of pollen and allows pollen from a single tassel to be used to pollinate many ears by mixing fresh pollen with a dilutant that can be stored for multiple days. We identified characteristics of the size of suitable substrates and selected a PEEK based substrate for regular utilization. We evaluated pollen viability and empirically demonstrated the capability to store pollen up to nine days when pollen is mixed with a PEEK substrate and stored at 6{degrees}C. The pollen storage method was used to make successful pollinations across 24 maize inbred lines tested and was generally equivalent to the standard manual pollination process. This method has the potential to increase the efficiency of breeding operations and may be useful in an array of genetic studies. Core Ideas* Manual pollinations in breeding and genetics research requires pollen available when recipient silks are viable. * The method collects and stores maize pollen for at least five days and facilitates efficient pollination. * Pollen is mixed with polyetheretherketone and uses field-collected pollen and simple storage conditions. * The method can increase the number of pollinations per tassel and generates a reasonable number of viable seeds.

genetics↗

bioRxiv2022

Genetic mapping of flowering time and plant height in a maize Stiff Stalk MAGIC population

The Stiff Stalk heterotic pool is a foundation of US maize seed parent germplasm and has been heavily utilized by both public and private maize breeders since its inception in the 1930s. Flowering time and plant height are critical characteristics for both inbred parents and their test crossed hybrid progeny. To study these traits, a six parent multiparent advanced generation intercross (MAGIC) population was developed including maize inbred lines B73, B84, PHB47 (B37 type), LH145 (B14 type), PHJ40 (novel early Stiff Stalk), and NKH8431 (B73/B14 type). A set of 779 doubled haploid lines were evaluated for flowering time and plant height in two field replicates in 2016 and 2017, and a subset of 689 and 561 doubled haploid lines were crossed to two testers, respectively, and evaluated as hybrids in two locations in 2018 and 2019 using an incomplete block design. Markers were derived from a Practical Haplotype Graph built from the founder whole genome assemblies and genotype-by-sequencing and exome capture-based sequencing of the population. Genetic mapping utilizing an update to R/qtl2 revealed differing profiles of significant loci for both traits between 636 of the DH lines and two sets of 571 and 472 derived hybrids. Genomic prediction was used to test the feasibility of predicting hybrid phenotypes based on the per se data. Predictive abilities were highest on direct models trained using the data they would predict (0.55 to 0.63), and indirect models trained using per se data to predict hybrid traits had slightly lower predictive abilities (0.49 to 0.55). Overall, this finding is consistent with the overlapping and non-overlapping significant QTL found within the per se and hybrid populations and suggests that selections for phenology traits can be made effectively on doubled haploid lines before hybrid data is available. Core IdeasA multi-parent advanced generation intercross (MAGIC) mapping population was developed from six founder Stiff Stalk maize inbreds with commercial relevance. Genetic mapping utilizing an update to R/qtl2 was demonstrated for flowering and plant height traits. Genetic mapping using maize inbred and hybrid information was compared and provided insight into trait expression in inbreds relative to heterotic testcross hybrids.

genomics↗

bioRxiv2020

Shared genetic architecture underlying root metaxylem phenotypes under drought stress in cereals

Root metaxylem are phenotypically diverse structures whose function is related to their anatomy, particularly under drought stress. Much research has dissected the genetic machinery underlying metaxylem phenotypes in dicots, but monocots are relatively unexplored. In maize (Zea mays), a robust pipeline integrated a GWAS of root metaxylem phenes under well-watered and water stress conditions with a gene co-expression network to identify candidate genes most likely to impact metaxylem phenotypes. We identified several promising candidate genes in 14 gene co-expression modules inferred to be functionally relevant to xylem development. We also identified five gene candidates that co-localized in multiple root metaxylem phenes in both well-watered and water stress conditions. Using a rice GWAS conducted in parallel, we detected overlapping genetic architecture influencing root metaxylem phenotypes by identifying eight pairs of syntenic candidate genes significantly associated with metaxylem phenes. There is evidence that the genes of these syntenic pairs may be involved in biosynthetic processes related to the cell wall, hormone signaling, oxidative stress responses, and drought responses. Our study demonstrates a powerful new strategy for identifying promising gene candidates and suggests several gene candidates that may enhance our understanding of vascular development and responses to drought in cereals. One sentence summaryCross-species genome-wide association studies and a gene coexpression network identified genes associated with root metaxylem phenotypes in maize under water stress and non-stress and rice.

plant biology↗

bioRxiv2019

Characterizing allele-by-environment interactions using maize introgression lines

Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to allele-by-environment interactions. To study potential patterns of allele-by-environment interactions, fifteen near-isogenic lines (NILs) with >90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess allele-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the allele-by-environment interaction varies across traits. Some introgressed segments were more prone to genotype-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of allele-by-environment interaction is useful in considerations of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.\n\nKey MessageSignificant allele-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome.

plant biology↗

bioRxiv2019

Variation and inheritance of small RNAs in maize inbreds and F1 hybrids

Small RNAs (sRNAs) regulate gene expression, play important roles in epigenetic pathways, and have been hypothesised to contribute to hybrid vigor in plants. Prior investigations have provided valuable insights into associations between sRNAs and heterosis, often using a single hybrid genotype or tissue. However, our understanding of the role of sRNAs and their potential value to plant breeding are limited by an incomplete picture of sRNA variation between diverse genotypes and development stages. Here, we provide a deep exploration of sRNA variation and inheritance among a panel of 108 maize samples spanning five tissues from eight inbred parents and 12 hybrid genotypes, covering a spectrum of heterotic groups, genetic variation, and levels of heterosis for various traits. We document substantial developmental and genotypic influences on sRNA expression, with varying patterns for 21-nt, 22-nt and 24-nt sRNAs. We provide a detailed view of the distribution of sRNAs in the maize genome, revealing a complex make-up that also shows developmental plasticity, particularly for 22-nt sRNAs. sRNAs exhibited substantially more variation between inbreds as compared to observed variation for gene expression. In hybrids, we identify locus-specific examples of non-additive inheritance, mostly characterised as partial or complete dominance, but rarely outside the parental range. However, the global abundance of 21-nt, 22-nt and 24-nt sRNAs varies very little between inbreds and hybrids, suggesting that hybridization affects sRNA expression principally at specific loci rather than on a global scale. This study provides a valuable resource for understanding the potential role of sRNAs in hybrid vigor.\n\nOne-sentence summaryCharacterizing the roles of development and genotype in driving expression variation of different small RNA populations in maize inbreds and their F1 hybrids.

genomics↗

bioRxiv2017

Genotype-by-Environment Interactions Affecting Heterosis in Maize

The environment can influence heterosis, the phenomena in which the offspring of two inbred parents exhibits phenotypic performance beyond the inbred parents for specific traits. In this study we measured 25 traits in a set of 47 maize hybrids and their inbred parents grown in 16 different environments, and each had varying levels of average productivity. By quantifying 25 vegetative and reproductive traits across the life cycle we were able to analyze interactions between the environment and multiple distinct instances of heterosis. The magnitude and rank among hybrids of better-parent heterosis (BPH) varied for the different traits and environments. Across the traits, a higher within plot variance was observed for inbred lines compared to hybrids. However, for most traits, variance across environments was not significantly different for inbred lines compared to hybrids. Further, for many traits the correlations of BPH to hybrid performance and BPH to better parent performance were of comparable magnitude. These results indicate that inbreds and hybrids are showing similar trends in environmental response and are both contribute to genotype-by-environment interactions for heterosis. This study highlights that degree of heterosis is not an inherent trait of a specific hybrid, but varies depending on the trait measured and the environment where that trait is measured. Studies that attempt to correlate molecular processes with heterosis are hindered by the fact that heterosis is not a consistent attribute of a specific hybrid.

genetics↗