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BMC genomics2026; 27(1); 791; doi: 10.1186/s12864-026-13131-w

Identification of candidate genes and metabolites associated with lactation performance in Kazakh mares using blood multi-omics and machine learning.

Abstract: The 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. Results: In 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. Conclusions: Using 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.
© 2026. The Author(s).
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Publication Date: 2026-07-06 PubMed ID: 42410339PubMed Central: PMC13617780DOI: 10.1186/s12864-026-13131-wGoogle Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This research aimed to identify genes and metabolites linked to milk production traits in Kazakh mares, a hardy horse breed adapted to harsh environments.
  • Using blood samples and multi-omics data combined with machine learning, the study discovered candidate molecular markers and pathways influencing lactation performance.

Background and Significance

  • Breed characteristics: Kazakh mares are native to Xinjiang and known for their resilience to arid and cold climates, as well as their ability to maintain milk production even in low-input farming.
  • Challenges: Traditional management practices limit genetic improvement efforts for milk yield and quality in this breed.
  • Purpose: This study aims to uncover genetic and metabolic factors underlying milk yield and composition to aid future breeding and nutritional intervention strategies.

Study Design and Data Collection

  • Lactation monitoring: Milk yield and composition were measured over a 105-day lactation period.
  • Phenotypic traits: A total of 15 traits were recorded, including body length, teat diameter, and teat length.
  • Correlations: Milk yield showed significant positive correlations with body length and teat dimensions, indicating these traits relate to production capacity.

Genomic and Transcriptomic Analysis

  • Group comparisons: Mares were grouped into high-yield (HY) vs. low-yield (LY) and high-fat (HF) vs. low-fat (LF) milk producers.
  • Differential gene expression: Identified 286 DEGs between HY and LY groups and 627 DEGs between HF and LF groups.
  • Candidate genes: Key genes associated with lactation traits included:
    • PMP22 (Peripheral myelin protein 22)
    • FAM83A (Family with sequence similarity 83 member A)
    • HSD17B3 (Hydroxysteroid 17-beta dehydrogenase 3)
    • AGPAT4 (1-Acylglycerol-3-phosphate O-acyltransferase 4)
    • SLC50A1 (Sugar transporter SWEET1)
    • ERBB3 (Erb-B2 receptor tyrosine kinase 3)
  • Pathway enrichment: These genes were involved in biological pathways such as:
    • PI3K-Akt signaling pathway (regulates cell growth and metabolism)
    • MAPK signaling pathway (involved in cell proliferation and differentiation)
    • Triglyceride metabolism (important for milk fat synthesis)

Metabolomic Findings

  • Energy metabolism: High-yield mares exhibited altered metabolic profiles indicating enhanced activity in energy-generating pathways.
  • TCA cycle: Enrichment in tricarboxylic acid cycle intermediates suggests shifts in energy and intermediary metabolism.
  • Specific metabolites associated with lactation traits:
    • Glycerone
    • α-D-glucose
    • D-galactose
    • Glycerol
    • L-histidine (an amino acid)
    • Anserine (a dipeptide related to antioxidant functions)

Machine Learning Insights

  • Analytic approach: Machine learning models were applied to the multi-omics data to identify potential gene-metabolite relationships influencing milk traits.
  • Key candidate gene: GLDC (Glycine decarboxylase) was highlighted as potentially linked to milk fat percentage.
  • Putative mechanism: The association between GLDC and milk fat may be mediated by its relationship with histidine metabolism, though this requires further experimental validation.

Conclusions and Implications

  • The integrative multi-omics approach successfully identified genes and metabolites correlated with variation in milk yield and fat content in Kazakh mares.
  • Results provide molecular insights into lactation performance traits and highlight candidate pathways that could be targets for genetic selection or nutritional optimization.
  • This foundational work supports future molecular breeding programs aimed at improving milk production in indigenous horse breeds adapted to challenging environments.
  • Using peripheral blood as a sampling source demonstrates a non-invasive method to study lactation biology in large animals.

Cite This Article

APA
Meng C, Luo P, Ren W, Xie X, Zeng Y, Wang J, Yao X, Meng J. (2026). Identification of candidate genes and metabolites associated with lactation performance in Kazakh mares using blood multi-omics and machine learning. BMC Genomics, 27(1), 791. https://doi.org/10.1186/s12864-026-13131-w

Publication

BMC genomics
ISSN: 1471-2164
NlmUniqueID: 100965258
Country: England
Language: English
Volume: 27
Issue: 1
PII: 791

Researcher Affiliations

Meng, Chen
  • College of Animal Science, Equine Industry Research Institute, Xinjiang Agricultural University, Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China.
Luo, Penghui
  • Xinjiang Uyghur Autonomous Region Animal Husbandry Station, Urumqi, Xinjiang, 830052, China.
Ren, Wanlu
  • College of Animal Science, Equine Industry Research Institute, Xinjiang Agricultural University, Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi, Xinjiang, 830052, China.
Xie, Xiaoyu
  • Xinjiang Uyghur Autonomous Region Animal Husbandry Station, Urumqi, Xinjiang, 830052, China.
Zeng, Yaqi
  • College of Animal Science, Equine Industry Research Institute, Xinjiang Agricultural University, Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi, Xinjiang, 830052, China.
Wang, Jianwen
  • College of Animal Science, Equine Industry Research Institute, Xinjiang Agricultural University, Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi, Xinjiang, 830052, China.
Yao, Xinkui
  • College of Animal Science, Equine Industry Research Institute, Xinjiang Agricultural University, Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi, Xinjiang, 830052, China.
Meng, Jun
  • College of Animal Science, Equine Industry Research Institute, Xinjiang Agricultural University, Xinjiang Agricultural University, Urumqi, Xinjiang, 830052, China. junm86@xjau.edu.cn.
  • Xinjiang Key Laboratory of Equine Breeding and Exercise Physiology, Urumqi, Xinjiang, 830052, China. junm86@xjau.edu.cn.

MeSH Terms

  • Animals
  • Lactation / genetics
  • Female
  • Horses / genetics
  • Horses / blood
  • Horses / metabolism
  • Horses / physiology
  • Machine Learning
  • Multiomics
  • Phenotype
  • Milk / metabolism
  • Milk / chemistry
  • Gene Expression Profiling
  • Metabolome
  • Genomics

Grant Funding

  • XJ2026G118 / the Study on the Effect of Mare Milk-Derived Peptides on High-Fat Diet-Induced Metabolic Disorders and Its Regulatory Mechanism
  • XJARS-11 / Xinjiang Uygur Autonomous Region Dairy Industry Technology System Project
  • 2024B02013-2 / Construction of equine milk probiotic library and R&D and demonstration of functional equine milk products
  • 2022A02013-1 / the Major Science and Technology Project of the Xinjiang Uygur Autonomous Region

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: The experimental protocol was approved by the Institutional Animal Care and Use Committee of Xinjiang Agricultural University (Urumqi, China) under approval number 2023020. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

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