ArticleBMC genomics2026
Identification of candidate genes and metabolites associated with lactation performance in Kazakh mares using blood multi-omics and machine learning.
Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe Kazakh mare, an indigenous breed of Xinjiang, exhibits strong adaptability to arid and cold conditions while maintaining relatively stable milk production under low-input extensive farming systems. However, its genetic improvement has been constrained by traditional management practices.
resultsIn this study, we monitored milk yield and milk composition over a 105-day lactation period and recorded 15 phenotypic traits. Milk yield was significantly correlated with body length, teat diameter, and teat length. The high-yield (HY) versus low-yield (LY) and high-fat (HF) versus low-fat (LF) comparisons identified 286 and 627 differentially expressed genes (DEGs), respectively. Several candidate genes were identified, including PMP22, FAM83A, HSD17B3, AGPAT4, SLC50A1, and ERBB3, which were associated with pathways including PI3K-Akt signaling, MAPK signaling, and triglyceride metabolism. High-yield mares showed metabolic differences characterized by enrichment of pathways related to the tricarboxylic acid (TCA) cycle, suggesting altered energy and intermediary metabolism involving carbohydrates, lipids, and amino acids. Metabolites associated with these differences included glycerone, α-D-glucose, D-galactose, glycerol, L-histidine, and anserine. In addition, machine learning analysis identified GLDC as a candidate gene potentially associated with milk fat percentage, possibly through its association with histidine. However, this relationship requires further validation.
conclusionUsing peripheral blood samples, this study integrated differential expression analysis, mixed linear models, and machine learning approaches to identify candidate genes and metabolites associated with lactation performance in Kazakh mares. The results provide preliminary insights into molecular and metabolic features associated with lactation traits in this breed. These findings may serve as a reference for future molecular breeding and nutritional studies.
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