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The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.
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http://dx.doi.org/10.3390/ijms25179271 | DOI Listing |
Theor Appl Genet
September 2025
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
The German Federal Ex Situ Genebank for Agricultural and Horticultural Crops (IPK) harbours over 3000 pea plant genetic resources (PGRs), backed up by corresponding information across 16 key agronomic and economical traits. The unbalanced structure and inconsistent format of this historical data has precluded effective leverage of genebank accessions, despite the opportunities contained in its genetic diversity. Therefore, a three-step statistical approach founded in linear mixed models was implemented to enable a rigorous and targeted data curation.
View Article and Find Full Text PDFAllergol Immunopathol (Madr)
September 2025
Department of Pediatrics, İstinye University, İstanbul, Turkey.
Objectives: Food allergy (FA) is a growing public health concern, imposing significant psychosocial burdens on families and necessitating strict allergen avoidance. The unpredictability of severe reactions is associated with increased anxiety, dietary restrictions, and reduced quality of life.
Methods: We conducted a cross-sectional study including 77 mothers of children (0-12 years) with FA and 71 mothers of healthy children.
Plant Dis
September 2025
South Dakota State University, 2380 Research Parkway, 113B Seed Tech, Brookings, Brookings, South Dakota, United States, 57007;
Bacterial leaf streak (BLS), caused by pv. (), has recently emerged as a significant threat to wheat production in the Northern Great Plains region of the US. Deploying resistant cultivars is an economical and practical method of controlling BLS.
View Article and Find Full Text PDFMicrobiol Spectr
September 2025
Anhui Provincial Center for Disease Control and Prevention, Hefei, China.
The 2022/2023 season witnessed a rapid resurgence of H1N1pdm09 in Anhui Province, China, surpassing previous years, prompting an examination of hemagglutinin (HA) gene mutations and cross-immunity in this study. Anhui Province's surveillance data established the detection threshold for H1N1pdm09 using the Moving Epidemic Method. Joinpoint regression compared weekly percent change (WPC) rates.
View Article and Find Full Text PDFFront Genet
August 2025
Qingdao Agricultural University, Qingdao, China.
Introduction: Identifying genetic markers associated with economically important traits in dairy goats helps enhance breeding efficiency, thereby increasing industry value. However, the potential genetic structure of key economic traits in dairy goats is still largely unknown.
Methods: This study used three genome-wide association study (GWAS) models (GLM, MLM, FarmCPU) to analyze dairy goat milk production traits (milk yield, fat percentage, protein percentage, lactose percentage, ash percentage, total dry matter, and somatic cell count).