Biology subjects

Brooks, S. A.

Publications and source records attributed to Brooks, S. A..

6 recordsLinked to original sources

bioRxiv2025

United States stakeholder insights on genetic testing for equine health and breeding

The S1094 United States Department of Agriculture (USDA) Multistate Research Project is a community of scientists working together to utilize equine genetics and genomics to improve the health and well-being of horses while also providing educational and genetic testing resources to those in the equine industry. To assess the knowledge and priorities of stakeholders regarding genetic testing, we conducted an online survey targeting horse owners and enthusiasts in the United States. Data were collected from March to October 2024 with 412 respondents from 44 states completing the survey. Most of the survey participants were horse owners, and the two most common age ranges for respondents were 31-50 (33.6%) and 51-70 (33.3%) years old. The two most common use categories for horses reported by respondents were sport and stock horses. The survey revealed that a majority of respondents were confident in their knowledge of genetics and that they relied primarily on breed and discipline organizations, as well as universities for their genetic information. Secondarily they relied upon social media and their veterinarians. Respondents expressed significant interest in genetic testing for diversity and performance traits. Across all horse use categories, arthritis, colic and intestinal disease, laminitis, metabolic disorders, and tendon and ligament issues had the greatest level of concern - all of which are complex genetic traits and will require collaborative efforts to discern levels of genetic involvement. Results from the survey underscore the need for continued outreach and education of horse owners, as well as further development of genetic testing tools to support informed decision-making among stakeholders in the equine industry. Lay SummaryGenetic testing helps horse owners, breeders, and breed registries and associations verify parentage, evaluate health, and improve performance. The S1094 USDA Multistate Research Project has a mission of improving genomics and genetics tools, as well as providing educational resources for those in the equine industry. To better understand the knowledge and priorities of stakeholders in the equine industry, we conducted an online survey netting responses from 412 individuals in the United States. Respondents were primarily horse owners who self-identified as confident in their knowledge of horse genetics. Furthermore, those responding indicated that their primary resources for information about genetics were breed and discipline association websites and universities. They also expressed interest in genetic testing for diversity and performance traits. Across all horse use categories, arthritis, colic and intestinal disease, laminitis, metabolic disorders, and tendon and ligament issues had the greatest level of concern. Given the complex nature of these conditions, collaborative efforts will be required to discern levels of genetic involvement. Respondents felt that genetic testing and education efforts have significant impacts on the equine industry because they aid owners in making breeding decisions and in the management of horse health and well-being. Teaser TextThis nationwide survey of U.S. horse owners discerned strong stakeholder interest in equine genetic testing, determined sources for knowledge acquisition, and highlighted key priorities for the development of genetic tools and educational materials.

genetics↗

bioRxiv2025

refineDLC: an advanced post-processing pipeline for DeepLabCut outputs

DeepLabCut has notably transformed behavioral and locomotor research by enabling precise, markerless pose estimation through advanced deep learning methods. Despite broad adoption of this approach across species and behaviors, current usage frequently overlooks the challenges of quantitative kinematic analyses due to the complexity and noise within raw model outputs. Researchers are often challenged by the computational expertise required to clean, refine, and interpret the generated data effectively. Here, we introduce refineDLC, a comprehensive post-processing pipeline explicitly designed to streamline the conversion of noisy DeepLabCut outputs into robust, analytically reliable kinematic data. Our user-friendly pipeline incorporates critical data cleaning steps--including inversion of y-coordinates for intuitive spatial interpretation, removal of zero-value frames, and exclusion of irrelevant body part labels. Additionally, it integrates robust, dual-stage filtering methods based on likelihood scores and positional changes, significantly enhancing the accuracy and consistency of data. Finally, our approach offers multiple interpolation strategies, effectively managing missing values to maintain data continuity and integrity. We evaluated the performance and versatility of our pipeline across two distinct datasets: controlled locomotion in cattle and field-recorded, highly variable trotting horse videos. Our results demonstrated substantial improvements in the quality and interpretability of processed outputs, transforming initially noisy and inconsistent data into physiologically meaningful kinematic patterns. RefineDLC successfully reduced data variability and eliminated false-positive labeling errors, offering reliable and ready-to-analyze datasets irrespective of recording conditions and animal species. By simplifying the transformation from raw DeepLabCut outputs to meaningful kinematic insights, refineDLC significantly enhances accessibility and usability, enabling a broader range of researchers--especially those with limited programming expertise--to perform precise quantitative analyses. Looking forward, adaptive filtering algorithms and real-time quality assessment features are potential enhancements that could further optimize performance, expand pipeline applicability, and automate analysis. Thus, RefineDLC not only addresses the current limitations in markerless tracking technologies but also sets the stage for future advancements in precision phenotyping, behavioral ecology, animal science, and conservation biology.

animal behavior and cognition↗

bioRxiv2023

Technical Note: DeepLabCut-Display: open-source desktop application for visualizing and analyzing two-dimensional gait parameter data in livestock

AbstractGait assessments are a key part of determining the wellbeing of livestock. Techniques for gait assessment have traditionally involved human-eye inspections or reflective markers, but markerless computer vision methods have been developed in recent years. Despite many computer vision tools providing high-quality pose estimations in an efficient manner, they lack post-processing functionality. A review of model performance and calculation of gait parameters is a necessary step to fully harness the capability of this new technology. Thus, this study developed DeepLabCut-Display, an open-source desktop software application. DeepLabCut-Display allows a user to upload the video and coordinate data associated with the output of DeepLabCut, a prominent pose-estimation software tool. A user can review the video and coordinate data in parallel, filter points by a likelihood threshold, and automatically calculate gait parameters. Specific video frames, filtered data, and gait parameters can be exported from the application for further usage. The source code is publicly hosted on GitHub alongside installation and usage instructions. DeepLabCut-Display, the product of interdisciplinary and collaborative design between software developers and animal scientists, will alleviate a critical bottleneck in processing of data for locomotor analysis in livestock. Summary StatementO_LIDeepLabCut-Display is a utility to dynamically visualize raw marker coordinates, and to automatically produce gait parameters for locomotion analysis of horses and other livestock. C_LI Lay SummaryArtificial intelligence systems that can predict and track the positions of objects are now being applied in many fields, including animal science. Veterinarians and animal scientists use these systems to create pose estimations, a digital label of anatomical landmarks overlaid on a video of an animal in motion. They are used to quantify the subjects motion and detect anomalies that may be indicative of disease or injury. Pose estimation systems are efficient and accurate, but they lack features like data visualization and post-processing analysis that are necessary to make determinations about the animals motion. This study developed DeepLabCut-Display, a software application that can visualize the data from a pose estimation system and provides a set of tools for further analysis. After a user is done with analysis, they can save the results to their computer. The application was made by a collaboration between software developers and animal scientists, highlighting how interdisciplinary teams are effective at producing useful software.

animal behavior and cognition↗

bioRxiv2019

Whole Genome Detection of Sequence and Structural Polymorphism in Six Diverse Horses

The domesticated horse has played a unique role in human history, serving not just as a source of animal protein, but also as a catalyst for long-distance migration and military conquest. As a result, the horse developed unique physiological adaptations to meet the demands of both their climatic environment and their relationship with man. Completed in 2009, the first domesticated horse reference genome assembly (EquCab 2.0) produced most of the publicly available genetic variations annotations in this species. Yet, there are around 400 geographically and physiologically diverse breeds of horse. To enrich the current collection of genetic variants in the horse, we sequenced whole genomes from six horses of six different breeds: an American Miniature, a Percheron, an Arabian, a Mangalarga Marchador, a Native Mongolian Chakouyi, and a Tennessee Walking Horse. Aside from extreme contrasts in body size, these breeds originate from diverse global locations and each possess unique adaptive physiology. A total of 1.3 billion reads were generated for the six horses with coverage between 15x to 24x per horse. After applying rigorous filtration, we identified and functionally annotated 8,128,658 Single Nucleotide Polymorphisms (SNPs), and 830,370 Insertions/Deletions (INDELs), as well as novel Copy Number Variations (CNVs) and Structural Variations (SVs). Our results revealed putatively functional variants including genes associated with size variation like ANKRD1 and HMGA1 in the very large Percheron and the ZFAT gene in the American Miniature horse. We detected a copy number gain in the Latherin gene that may be the result of evolutionary selection for thermoregulation by sweating, an important component of athleticism and heat tolerance. The newly discovered variants were formatted into user-friendly browser tracks and will provide a foundational database for future studies of the genetic underpinnings of diverse phenotypes within the horse. Author SummaryThe domesticated horse played a unique role in human history, serving not just as a source of dietary animal protein, but also as a catalyst for long-distance migration and military conquest. As a result, the horse developed unique physiological adaptations to meet the demands of both their climatic environment and their relationship with man. Although the completion of the horse reference genome yielded the discovery of many genetic variants, the remarkable diversity across breeds of horse calls for additional effort in quantification of the breadth of genetic polymorphism within this unique species. Here, we present genome re-sequencing and variant detection analysis for six horses belonging to geographically and physiologically diverse breeds. We identified and annotated not just single nucleotide polymorphisms (SNPs), but also large insertions and deletions (INDELs), copy number variations (CNVs) and structural variations (SVs). Our results illustrate novel sources of polymorphism and highlight potentially impactful variations for phenotypes of body size and conformation. We also detected a copy number gain in the Latherin gene that could be the result of an evolutionary selection for thermoregulation through sweating. Our newly discovered variants were formatted into easy-to-use tracks that can be easily accessed by researchers around the globe.

genomics↗

bioRxiv2018

EquCab3, an Updated Reference Genome for the Domestic Horse

EquCab2, a high-quality reference genome for the domestic horse, was released in 2007. Since then, it has served as the foundation for nearly all genomic work done in equids. Recent advances in genomic sequencing technology and computational assembly methods have allowed scientists to improve reference assemblies of large animal and plant genomes in terms of contiguity and composition. In 2014, the equine genomics research community began a project to improve the reference sequence for the horse, building upon the solid foundation of EquCab2 and incorporating new short-read data, long-read data, and proximity ligation data. The result, EquCab3, is presented here. The count of non-N bases in the incorporated chromosomes is improved from 2.33Gb in EquCab2 to 2.41Gb from EquCab3. Contiguity has also been improved nearly 40-fold with a contig N50 of 4.5Mb and scaffold contiguity enhanced to where all but one of the 32 chromosomes is comprised of a single scaffold.

genomics↗

bioRxiv2017

Development of a high-density, 2M SNP genotyping array and 670k SNP imputation array for the domestic horse

BackgroundTo date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array.\n\nResultsUsing whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of [~]5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor (Affymetrix, now Thermo Fisher Scientific) a set of [~]2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of [~]670 thousand SNPs (MNEc670k), was designed for genotype imputation.\n\nConclusionsHere, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.

genomics↗