Biology subjects

Tross, M. C.

Publications and source records attributed to Tross, M. C..

8 recordsLinked to original sources

bioRxiv2024

3D Reconstruction Enables High-Throughput Phenotyping and Quantitative Genetic Analysis of Phyllotaxy

Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180{degrees} angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.

plant biology↗

bioRxiv2024

Off-the-shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum

Seed color is a complex phenotype linked to both the impact of grains on human health and consumer acceptance of new crop varieties. Today seed color is often quantified via either qualitative human assessment or biochemical assays for specific colored metabolites. Imaging-based approaches have the potential to be more quantitative than human scoring while lower cost than biochemical assays. We assessed the feasibility of employing image analysis tools trained on rice (Oryza sativa) or wheat (Triticum aestivum) seeds to quantify seed color in sorghum (Sorghum bicolor ) using a dataset of > 1,500 images. Quantitative measurements of seed color from images were substantially more consistent across biological replicates than human assessment. Genome-wide association studies conducted using color phenotypes for 682 sorghum genotypes identified more signals near known seed color genes in sorghum with stronger support than manually scored seed color for the same experiment. Previously unreported genomic intervals linked to variation in seed color in our study co-localized with a gene encoding an enzyme in the biosynthetic pathway leading to anthocyanins, tannins, and phlobaphenes - colored metabolites in sorghum seeds - and with the sorghum ortholog of a transcription factor shown to regulate several enzymes in the same pathway in rice. The cross-species transferability of image analysis tools, without the retraining, may aid efforts to develop higher value and health-promoting crop varieties in sorghum and other specialty and orphan grain crops.

plant biology↗

bioRxiv2024

Models trained to predict differential expression across plant organs identify distal and proximal regulatory regions

AO_SCPLOWBSTRACTC_SCPLOWA large proportion of standing phenotypic variation is explained by genetic variation in noncoding regulatory regions. However, tools for the automated identification and characterization of noncoding regulatory sequences in genomes have lagged far behind those employed to annotate and predict the functions of protein coding sequences. We developed a modified transformer model and trained it to predict relative patterns of expression across a diverse set of tissues given a large sequence window for each gene of interest in the maize (Zea mays) genome. Nucleotides in the input DNA sequence with high saliency in gene expression pattern prediction overlapped with regions identified via comparative genomic or chromatin-based approaches as potential regulatory sequences. High saliency regions identified in a second species, sorghum (Sorghum bicolor), without species-specific training were also associated with potential regulatory sequences in noncoding regions upstream and downstream of each gene of interest. The potential impact of a scaleable and transferable approach to identifying regulatory sequences using saliency calculated from large context window models spans multiple applications. Specific use cases could include genome annotation, interpretation of natural genetic variation, and targeted editing in noncoding regions to alter patterns of levels of gene expression.

plant biology↗

bioRxiv2023

Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel

Hyperspectral reflectance data can be collected from large plant populations in a high-throughput manner in both controlled and field environments. The efficacy of using hyperspectral leaf reflectance as a proxy for traits that typically require significant labor and time to collect has been evaluated in a number of studies. Commonly, estimating plant traits using hyperspectral reflectance involves collecting substantial amounts of ground truth data from plant populations, which may not be feasible for many researchers. In this study, we explore the potential of data-driven approaches to analyze hyperspectral reflectance data with little to no ground truth phenotypic measurements. Evaluations were performed using data on the reflectance of 2,151 individual wavelengths of light from the leaves of maize plants harvested from 1,658 field plots of a replicated trial including representatives of 752 maize genotypes from the Wisconsin Diversity Panel. We reduced the dimensionality of this dataset using an autoencoder neural network and principal component analyses, producing 10 latent variables and principal components, respectively. A subset of these principal components and latent variables demonstrated significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by genetic factors. Moreover, correlations were observed between variables derived from the autoencoder network and principal components with molecular traits. Notably, the most relevant latent variable (LV8) showed a much stronger correlation with chlorophyll content (R2 = 0.59) compared to the most correlated principal component (PC2; R2 = 0.31). Furthermore, one latent variable exhibited modestly better performance than a partial least squares regression model in estimating leaf chlorophyll content (PLSR; R2 = 0.58, LV8; R2 = 0.59). A number of genetic markers in the maize genome were significantly correlated with variation in different latent variables in genome wide association studies. In a number of cases, significant signals in genome wide association studies were adjacent to genes with plausible links to traits expected to influence leaf hyperspectral reflectance patterns.

plant biology↗

bioRxiv2023

Imitating the 'breeder's eye': predicting grain yield from measurements of non-yield traits

Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency and rate of genetic improvement in future breeding cycles. However, field trials conducted in one environment provide limited insight into how well crop varieties will perform in other environments. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties indicates that plant breeders will need to make decisions about new crop varieties without knowing the future environments those crop varieties will encounter in farmers fields. Therefore, significant improvements in cross-environment prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the twenty-first century. To address this challenge, we conducted linked yield trials of 752 public maize genotypes in two distinct environments: Lincoln, Nebraska, and East Lansing, Michigan. Our findings confirmed that genomic predictions of yield can outperform direct yield measurements used to train the genomic prediction model in predicting yield in a second environment. Additionally, we developed and trained another trait-based yield prediction model, which we refer to as the Silicon Breeders Eye (SBE). Our results demonstrate that SBE prediction has comparable predictive power to genomic prediction models. SBE prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations of individuals, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

genetics↗

bioRxiv2023

Grain Utilization by the Gut Microbiome as a Human Health Phenotype to Identify Multiple Effect Loci in Genome-Wide Association Studies of Sorghum bicolor

A growing epidemic of complex lifestyle diseases such as obesity and metabolic diseases are explained in part by dysbiosis of the human gut microbiome. The gut microbiome, comprising trillions of microorganisms, contributes to functions ranging from digestion to the immune system. Diet plays a critical role in determining the species composition and functionality of the gut microbiome. Substantial functional metabolic diversity exists within the cultivated grain crops which directly or indirectly provide more than half of all calories consumed by humans around the globe, however much of this diversity is poorly characterized and the effects of such diversity on the human gut microbiome is not well studied. We employed a quantitative genetics approach to identify genetic variants in sorghum that alter the composition and function of human gut microbes. Using an automated high-throughput phenotyping method based on in vitro microbiome fermentation of grain from a diverse population of Sorghum bicolor cultivars, we demonstrate sorghum genetics can explain effects of grain variation on fermentation patterns of bacterial taxa across multiple human microbiomes. In a genome-wide analysis using a sorghum association panel, we identified fifteen multiple-effect loci (MEL) where different alleles in the sorghum genome produced changes in seed that affect the abundance of multiple bacterial taxa across two human microbiomes in automated in vitro fermentations. In a number of cases parallel genome-wide association studies conducted for biochemical and agronomic traits identified seed traits potentially causal for the link between sorghum genetics and human microbiome outcomes. This work demonstrates that genetic factors affecting sorghum seed can drive significant effects on human gut microbes, particularly bacterial taxa considered beneficial. Understanding these relationships will enable targeted crop breeding strategies to improve human health through gut microbiome modulation.

genomics↗

bioRxiv2022

Association Mapping Across a Multitude of Traits Collected in Diverse Environments Identifies Pleiotropic Loci in Maize.

Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data - 18M markers - from two partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least seven US states and scored for 162 distinct trait datasets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be three genes based a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g. above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype by environment interaction.

plant biology↗

bioRxiv2021

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

plant biology↗