Authors
Narges Maddahi, Mostafa Sadeghi, Seyed Reza Miraee Ashtiani, Muna Kholghi, Ali Jalil Sarghale
Published in
BMC genomics. Volume 26. Issue 1. Pages 656. Jul 11, 2025. Epub Jul 11, 2025.
Abstract
The reproduction process in domestic animals is one of the most important challenges of animal husbandry. Fertility is an important trait that contributes to herd profitability and can be improved by genomic information. One of the best ways to investigate the association between single nucleotide polymorphisms (SNPs) and phenotypic performance is the genome-wide association study (GWAS). The aim of our study was to identify the genomic regions affecting reproductive traits, interval between first and last insemination (IFL), days open (DO), days from calving to first service (DFS), number of services per conception (NSPC), age at first calving (AFC) and age at first insemination (AFI) using SNP chip data in Iranian Holstein cows.
GWAS analysis for all reproductive traits based on the significant-association threshold P < 1 × 10-8, led to the identification of 55 single nucleotide polymorphisms (SNPs) for IFL (n = 3), DFS (n = 0), DO (n = 5), NSPC (n = 5), AFI (n = 33), and AFC (n = 9) traits. Based on the results of gene ontology analysis, 54 different candidate genes for reproductive traits were identified in this study. For IFL, NSPCC, DO, AFC, and AFI traits 4, 8, 11, 10, and 21 candidate genes were identified in the vicinity of significant SNPs, respectively. Key genes with biologically important positions for heifers (ATG7, PTPN5, STAC, GAD2, PLXDC2, KARS1, PRIM2, and ZNF597) and cows (LPL, SERP2, BIRC6, CENPU, PIK3C3, and MYLK3) can be mentioned.
Our results identified 55 marker-trait associations (MTAs) and 54 different candidate genes associated with reproductive traits. As a result, the SNPs and candidate genes discovered in this study can be used in genomic experiments to improve the reproductive performance of Iranian Holstein dairy cows and provide new information about the genetic architecture of these traits.
PMID:
40646471
Bibliographic data and abstract were imported from PubMed on 13 Jul 2025.
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