Biology subjects

Schlautman, B.

Publications and source records attributed to Schlautman, B..

6 recordsLinked to original sources

bioRxiv2026

Leveraging whole-genome re-sequencing for diversity, population structure, and a public mid-density genotyping enrichment panel in crimson clover (Trifolium incarnatum L.) for breeding purposes

AO_SCPLOWBSTRACTC_SCPLOWCrimson clover (Trifolium incarnatum L.) is an obligately outcrossing, cool-season annual legume valued for forage and cover cropping, yet genomic resources to support systematic improvement are limited. We performed the first and most comprehensive whole-genome resequencing (WGR) of global crimson clover germplasm to (i) characterize diversity and population structure and (ii) develop a public mid-density enrichment capture panel for breeding applications. A core set of 45 accessions sequenced at [~]50X generated 5.84 million variants, while 149 additional accessions sequenced at [~]2.54X yielded 17.05 million variants. After stringent filtering, we retained 542,790 high-confidence SNPs from the high-coverage dataset and [~]2.4 million from the low-pass cohort. Population analyses (PCA, ADMIXTURE) revealed compact clustering of cultivars, broader dispersion of wild and uncertain-status accessions, and low overall differentiation (FST = 0.0105) with excess heterozygosity (FIS = -0.0592), consistent with obligate outcrossing. Guided by these resources, we designed a 28,913-SNP TWIST hybrid-capture panel enriched for genic regions and evenly distributed across seven chromosomes. This panel is being deployed within Auburn Universitys crimson clover breeding program to support population improvement and cultivar development. The resulting genomic resources provide a reproducible, mid-density genotyping platform for trait discovery, predictive breeding, and diversity monitoring. Together, these advances bring crimson clover genomic resources on par with other legumes such as soybean (Glycine max (L.) Merr.) and alfalfa (Medicago sativa), establishing a robust foundation for genomics-assisted improvement of this key cover and forage crop in U.S. sustainable agriculture. CORE IDEASO_LIWhole-genome re-sequencing of 194 crimson clover accessions revealed >21 M variants. C_LIO_LIHigh-confidence SNP catalogs from 50X and 2X data enable cost-effective genotyping. C_LIO_LIGenetic diversity is weakly structured, with cultivars clustering narrowly by origin. C_LIO_LIA 28,913 SNP enrichment panel delivers uniform genome coverage and >75% genic content. C_LIO_LIThese genomic tools accelerate GWAS, genomic selection, and breeding innovation. C_LI

genomics↗

bioRxiv2025

Leveraging High-dimensional Seed Colorspace in Phenomic Selection Models for Herbaceous Perennial Crops

Herbaceous perennial crops, which only recently entered the domestication pipeline, offer the potential of ecological benefits in agricultural systems but remain underdeveloped, partly due to slow breeding cycles. Early-stage phenomic selection, the process of using high-dimensional secondary traits to predict target traits, may accelerate improvement. We evaluated whether seed traits derived from image scans could be used to predict germination traits in three perennial crop candidate species: sainfoin (Onobrychis viciifolia), intermediate wheatgrass (Thinopyrum intermedium), and silflower (Silphium integrifolium). Over 20,000 seeds were scanned, and PlantCV was used to extract 692 hue, saturation, and value (HSV) color features along with five morphological traits. We demonstrated that seed color, morphology, and germination traits were all influenced by maternal family, but patterns of correlation between germination traits and color and morphology traits are complex. To enable prediction from high-dimensional seed traits, we constructed relationship matrices from combinations of HSV and morphology features for use in phenomic selection models to predict germination proportion and timing. Model performance varied by trait, species, and cohort, and in many cases, predictions were significantly better than chance. HSV features, when combined with morphology, often yielded the most accurate predictions, with performance reaching a maximum of r = 0.33. These results demonstrate the potential of low-cost, image-based phenomic data to inform early-stage selection and support the development of sustainable perennial crops. Plain language summaryMany of the worlds most important crops (e.g., wheat, maize, soy) live for less than one year, leaving soil bare for many months and subject to erosion. Longer-lived (perennial) alternatives to these crops have the potential to support a more sustainable agricultural system by improving soil health and reducing the need for chemical inputs. However, perennial herbaceous plants were not domesticated by early farmers, and now lag behind annual crops in development. They take longer to breed and often lack the genetic resources needed for modern breeding approaches. In this study, we tested whether simple seed images derived from color scanners could be used to predict key traits like germination proportion and timing in emerging perennial crops. We found that seed color and shape varied by maternal family and that color, especially when combined with seed size and shape, could help predict germination performance. These predictions were significantly better than chance. This work shows that low-cost, image-based seed data can support early-stage selection in breeding programs, especially for undeveloped perennial crops, helping save time, reduce cost, and accelerate progress toward more sustainable agriculture.

plant biology↗

bioRxiv2025

RegenPGC View: Semantic Segmentation of Perennial Groundcover Cropping Systems to Restore American Croplands

Modern, conventional row crop agricultural production relies on clean tillage of croplands and bare soil during the dormant season. While this paradigm of crop production has undoubtedly led to great increases in grain yields and efficiency, it has also resulted in significant soil erosion, groundwater contamination, degradation of local ecology, and hypoxic deadzones in US watersheds. Cover cropping with perennial plant species has been proposed as a way to mitigate these negative effects of crop production while having a minimum impact on crop yields. Measuring establishment of these perennial groundcovers (PGC) in research trials is subjective, tedious, and time-consuming when calculated with traditional methods whereas image based analyses are objective, efficient, and reproducible. For this project we have developed a deep learning approach using state of the art CNN architectures to estimate PGC establishment in research plots using a variety of open-source and internal image datasets. Our novel approach uses region of interest (ROI) markers in the field, to bound the predictions which improves upon other methods. We deployed the models on AWS Sagemaker serverless endpoints, and built a lightweight Django web application to host the images and inference services. Researchers will be able to acquire plot images with smartphone cameras and get fast, reliable data from their research trials using this "Local Sensing" data collection approach. We envision that this framework can be used by other researchers and growers as PGC adoption spreads throughout the Midwestern crop production areas.

plant biology↗

bioRxiv2019

Building a botanical foundation for perennial agriculture: Global inventory of wild, perennial herbaceous Fabaceae species

O_LIConcerns about soil health and stability are focusing attention on crops that deliver both agricultural products and ecological services. Deep rooted, perennial plants that build soil organic matter, support diverse below-ground microbial communities, and produce edible seeds are key components underpinning ecological intensification; however few perennial, herbaceous crops have been domesticated for food.\nC_LIO_LITo facilitate development of edible, perennial, herbaceous crops, including perennial grains, we constructed an online resource of wild, perennial, herbaceous species - the Perennial Agriculture Project Global Inventory (PAPGI; http://www.tropicos.org/Project/PAPGI). The first component of this project focuses on wild, perennial, herbaceous Fabaceae species. We extracted taxonomic names and descriptors from the International Legume Database and Information Service. Names were added to PAPGI, a special project within the botanical database TROPICOS, where they link to specimen records and ethnobotanical and toxicological data. PAPGI includes 6,644 perennial, herbaceous Fabaceae species. We built a searchable database of more than 60 agriculturally important traits. Here we highlight food and forage uses for 314 legume species, and toxicological data for 278 species.\nC_LIO_LIThe novel contribution of PAPGI is its focus on wild, perennial herbaceous species that generally have not entered the domestication process but that hold promise for development as perennial food crops. By extracting botanical information relevant for agriculture we provide a dynamic resource for breeders and plant scientists working to advance ecological intensification of agriculture, and for conservation managers working to preserve wild species of potential agricultural importance.\nC_LI\n\nSocietal Impact StatementAgroecosystems are constantly evolving to meet the needs of a growing population in a sustainable manner. Perennial, herbaceous crops deliver both agricultural products and ecological services. Until recently, edible, perennial, herbaceous crops, including perennial grains, were absent from agriculture. Perennial, herbaceous crops can be developed through wide hybridization between annual crops and perennial relatives or by de novo domestication of wild species. The diversity of wild, perennial, herbaceous legume species documented by the PAPGI increases resources available to breeders of perennial, herbaceous legumes, and raises awareness about previously untapped wild plant diversity in future crop development.

evolutionary biology↗

bioRxiv2016

SOFIA: an R package for enhancing genetic visualization with Circos

Visualization of data from any stage of genetic and genomic research is one of the most useful approaches for detecting potential errors, ensuring accuracy and reproducibility, and presentation of the resulting data. Currently software such as Circos, ClicO FS, and RCircos, among others, provide tools for plotting a variety of genetic data types in a concise manner for data exploration and presentation. However, each of the programs have one or more disadvantages that limit their usability in data exploration or construction of publication quality figures, such as inflexibility in formatting and configuration, reduced image quality, lack of potential for automation, or requirements of high-level computational expertise. Therefore, we developed the R package SOFIA, which leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. The advantages of integrating both R and Circos into SOFIA are numerous. R is a very powerful, mid-level programming language widely used among the genetic and genomic research community, while Circos has proven to be a novel software for arranging genomic data to create aesthetical publication quality circular figures. Producing Circos figures in R with SOFIA is simple, requires minimal coding experience, even for complex figures that incorporate high-dimensional genetic information, and allows simultaneous analysis and visual exploration of genomic and genetic data in a single programming environment.

genetics↗

bioRxiv2016

Construction of a high-density American cranberry (Vaccinium macrocarpon Ait.) composite map using genotyping-by-sequencing for multi-pedigree linkage mapping

The American cranberry (Vaccinium macrocarpon Ait.) is a recently domesticated, economically important, fruit crop with limited molecular resources. New genetic resources could accelerate genetic gain in cranberry through characterization of its genomic structure and by enabling molecular-assisted breeding strategies. To increase the availability of cranberry genomic resources, genotyping-by-sequencing (GBS) was used to discover and genotype thousands of single nucleotide polymorphisms (SNPs) within three inter-related cranberry full-sib populations. Additional simple sequence repeat (SSR) loci were added to the SNP datasets and used to construct bin maps for the parents of the populations, which were then merged to create the first high-density cranberry composite map containing 6073 markers (5437 SNPs and 636 SSRs) on 12 linkage groups (LGs) spanning 1124 cM. Interestingly, higher rates of recombination were observed in maternal than paternal gametes. The large number of markers in common (mean of 57.3) and the high degree of observed collinearity (mean Pair-wise Spearman Rank Correlations > 0.99) between the LGs of the parental maps demonstrates the utility of GBS in cranberry for identifying polymorphic SNP loci that are transferable between pedigrees and populations in future trait-association studies. Furthermore, the high-density of markers anchored within the component maps allowed identification of segregation distortion regions, placement of centromeres on each of the 12 LGs, and anchoring of genomic scaffolds. Collectively, the results represent an important contribution to the current understanding of cranberry genomic structure and to the availability of molecular tools for future genetic research and breeding efforts in cranberry.

genomics↗