Two-Stage Machine Learning-Based GWAS for Wool Traits in Central Anatolian Merino Sheep

    November 2025 in “ Agriculture ”
    Yunus Arzık, Mehmet Kızılaslan, Sedat Behrem … Mehmet Ulaş Çınar
    Studysummary This study applied a machine learning-based genomic analysis to identify genetic markers associated with wool traits in Central Anatolian Merino sheep, successfully highlighting loci relevant to fiber diameter, staple length, and greasy fleece yield, which could inform breeding programs to enhance wool quality and yield.
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    Research cited in this study 6

    1. Genetics of Wool and Cashmere Fibre: Progress, Challenges, and Future Research Animals · 2024
    2. Genomic Characterization of Quality Wool Traits in Spanish Merino Sheep Genes · 2024
    3. Whole-Genome Resequencing Reveals Selection Signal Related to Sheep Wool Fineness Animals · 2023
    4. An Integrated Transcriptome Atlas of Embryonic Hair Follicle Progenitors, Their Niche, and the Developing Skin Developmental cell · 2015
    5. The Hair Follicle as a Dynamic Mini-Organ Current Biology · 2009
    6. Control of Human Hair Growth by Neurotrophins: Brain-Derived Neurotrophic Factor Inhibits Hair Shaft Elongation, Induces Catagen, and Stimulates Follicular Transforming Growth Factor Beta 2 Expression Journal of Investigative Dermatology · 2005