Maddahi Narges, Sadeghi Mostafa, Sarghale Ali Jalil, Saatchi Mahdi, Davar Siar Mohammad Kazem, Kholghi Muna
Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
Department of Animal Science and Aquaculture, Dalhousie University, Truro, B2N 5E3, Canada.
Sci Rep. 2025 Sep 1;15(1):32168. doi: 10.1038/s41598-025-09103-x.
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.
全基因组关联研究（GWAS）是识别与各种表型相关的基因组区域的最强大工具之一。确定影响伊朗荷斯坦奶牛产奶性状的基因对于理解这些性状背后的遗传机制以及提高未来的产奶量至关重要。因此，本研究采用单步GWAS，旨在识别伊朗荷斯坦牛群体中与产奶量（MY）、乳脂率（FP）、乳蛋白率（PP）和体细胞计数（SCC）性状相关的基因组区域、基因和通路。在本研究中，使用30K（来自牛群1的150头动物）和50K（来自牛群2的60头动物）SNP芯片对210头动物进行基因分型。然后使用1000头公牛基因组计划参考面板将基因型推算到全基因组序列水平，从而得到6,583,595个用于GWAS分析的高可信度推算SNP。基于全基因组范围内10条染色体（2、5、7、17、19、21、24、26和28）上P值<1×10⁻⁸的显著性阈值，与产奶性状相关的基因组区域包括184个显著的SNP标记（产奶量、乳脂、乳蛋白和体细胞计数，分别有86、18、22和58个显著的SNP标记）。对于FP、PP、MY和SCS性状，分别在显著SNP附近鉴定出5、6、9和7个候选基因。具有重要生物学作用的关键基因包括ATE1、FGFR2、ALDH1A3、CHSY1、GABRG3、FBXO36、PID1、TRIP12、CD52、WDTC1、MATN1、CIDEA、LYZ、CPM、FBXO42、MAML3、SGMS2、HADH、CYP2U1、SCLT1和THRSP。因此，ATE1、FGFR2和LYZ基因不仅是乳房健康和牛奶质量的关键标记，也是基因组选择和旨在提高奶牛群抗病能力的治疗应用的有希望的候选基因。我们的研究发现了与产奶性状相关的新SNP，这对未来的家畜育种计划可能具有重要价值。
