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<p>One of the most powerful tools for identifying genomic regions associated with various phenotypes is GWAS. Identifying genes influencing milk production traits in Iranian Holstein dairy cows is crucial to understanding the genetic mechanisms underlying these traits and improving future milk production. Therefore, using a single-step GWAS, this study aimed to identify genomic regions, genes, and pathways associated with milk yield (MY), milk fat percentage (FP), milk protein percentage (PP), and somatic cell count (SCC) traits in the Iranian Holstein cattle population. In this study, 210 animals were genotyped using 30K (150 animals from Herd 1) and 50K (60 animals from Herd 2) SNP arrays. Genotypes were then imputed to whole-genome sequence level using the 1000 Bull Genomes Project reference panel, resulting in 6,583,595 high-confidence imputed SNPs forGWAS analysis. Genomic regions associated with milk production traits included 184 significant SNP markers (milk yield, milk fat, milk protein, and somatic cell count, with 86, 18, 22, and 58 significant SNP markers, respectively) based on a significance threshold of P value &lt;&#xa0;1 × 10⁻⁸ across 10 chromosomes (2, 5, 7, 17, 19, 21, 24, 26, and 28). For the traits FP, PP, MY, and SCS, 5, 6, 9, and 7 candidate genes were identified near the significant SNPs, respectively. Key genes with important biological roles included <i>ATE1</i>, <i>FGFR2</i>, <i>ALDH1A3</i>, <i>CHSY1</i>, <i>GABRG3</i>, <i>FBXO36</i>, <i>PID1</i>, <i>TRIP12</i>, <i>CD52</i>, <i>WDTC1</i>, <i>MATN1</i>, <i>CIDEA</i>, <i>LYZ</i>, <i>CPM, FBXO42</i>, <i>MAML3, SGMS2</i>, <i>HADH</i>, <i>CYP2U1, SCLT1</i> and <i>THRSP</i>. Therefore, the <i>ATE1</i>, <i>FGFR2</i>, and <i>LYZ</i> genes is not only a key marker for udder health and milk quality but also a promising candidate for genomic selection and therapeutic applications aimed at improving disease resistance in dairy herds. Our research led to the discovery of novel SNPs linked to milk production traits, which could be valuable for future livestock breeding programs.</p>

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Identification of candidate gene networks affecting the number of somatic cells count and milk production in Iranian Holstein cows using Genome-wide association study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Narges Maddahi,
  • Mostafa Sadeghi,
  • Ali Jalil Sarghale,
  • Mahdi Saatchi,
  • Mohammad Kazem Davar Siar,
  • Muna Kholghi

摘要

One of the most powerful tools for identifying genomic regions associated with various phenotypes is GWAS. Identifying genes influencing milk production traits in Iranian Holstein dairy cows is crucial to understanding the genetic mechanisms underlying these traits and improving future milk production. Therefore, using a single-step GWAS, this study aimed to identify genomic regions, genes, and pathways associated with milk yield (MY), milk fat percentage (FP), milk protein percentage (PP), and somatic cell count (SCC) traits in the Iranian Holstein cattle population. In this study, 210 animals were genotyped using 30K (150 animals from Herd 1) and 50K (60 animals from Herd 2) SNP arrays. Genotypes were then imputed to whole-genome sequence level using the 1000 Bull Genomes Project reference panel, resulting in 6,583,595 high-confidence imputed SNPs forGWAS analysis. Genomic regions associated with milk production traits included 184 significant SNP markers (milk yield, milk fat, milk protein, and somatic cell count, with 86, 18, 22, and 58 significant SNP markers, respectively) based on a significance threshold of P value < 1 × 10⁻⁸ across 10 chromosomes (2, 5, 7, 17, 19, 21, 24, 26, and 28). For the traits FP, PP, MY, and SCS, 5, 6, 9, and 7 candidate genes were identified near the significant SNPs, respectively. Key genes with important biological roles included ATE1, FGFR2, ALDH1A3, CHSY1, GABRG3, FBXO36, PID1, TRIP12, CD52, WDTC1, MATN1, CIDEA, LYZ, CPM, FBXO42, MAML3, SGMS2, HADH, CYP2U1, SCLT1 and THRSP. Therefore, the ATE1, FGFR2, and LYZ genes is not only a key marker for udder health and milk quality but also a promising candidate for genomic selection and therapeutic applications aimed at improving disease resistance in dairy herds. Our research led to the discovery of novel SNPs linked to milk production traits, which could be valuable for future livestock breeding programs.