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Wu H, Yi Q, Ma W, Yan L, Guan S, Wang L, Yang G, Tan X, Ji P, Liu G. Genome-wide analysis for the melatonin trait associated genes and SNPs in dairy goat ( Capra hircus) as the molecular breeding markers. Front Genet 2023; 14:1118367. [PMID: 37021000 PMCID: PMC10067595 DOI: 10.3389/fgene.2023.1118367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/28/2023] [Indexed: 04/07/2023] Open
Abstract
Previous studies have reported that the endogenous melatonin level is positively associated with the quality and yield of milk of cows. In the current study, a total of 34,921 SNPs involving 1,177 genes were identified in dairy goats by using the whole genome resequencing bulked segregant analysis (BSA) analysis. These SNPs have been used to match the melatonin levels of the dairy goats. Among them, 3 SNPs has been identified to significantly correlate with melatonin levels. These 3 SNPs include CC genotype 147316, GG genotype 147379 and CC genotype 1389193 which all locate in the exon regions of ASMT and MT2 genes. Dairy goats with these SNPs have approximately 5-fold-higher melatonin levels in milk and serum than the average melatonin level detected in the current goat population. If the melatonin level impacts the milk production in goats as in cows, the results strongly suggest that these 3 SNPs can serve as the molecular markers to select the goats having the improved milk quality and yield. This is a goal of our future study.
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Affiliation(s)
- Hao Wu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
- Sanya Institute of China Agricultural University, Sanya, China
- Hainan Yazhou Bay Seed Laboratory, Sanya, China
| | - Qi Yi
- Sanya Institute of China Agricultural University, Sanya, China
| | - Wenkui Ma
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Laiqing Yan
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shengyu Guan
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Likai Wang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Guang Yang
- Sanya Institute of China Agricultural University, Sanya, China
| | - Xinxing Tan
- Inner Mongolia Grassland Hongbao Food Co., Ltd., Bayannaoer, China
| | - Pengyun Ji
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Guoshi Liu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agricultural, Beijing Key Laboratory for Animal Genetic Improvement, College of Animal Science and Technology, China Agricultural University, Beijing, China
- Sanya Institute of China Agricultural University, Sanya, China
- *Correspondence: Guoshi Liu,
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Yao S, Liu Y, Liu X, Liu G. Effects of SNPs in AANAT and ASMT Genes on Milk and Peripheral Blood Melatonin Concentrations in Holstein Cows ( Bos taurus). Genes (Basel) 2022; 13:genes13071196. [PMID: 35885979 PMCID: PMC9322776 DOI: 10.3390/genes13071196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 02/01/2023] Open
Abstract
Aralkylamine N-acetyltransferase (AANAT) and acetylserotonin O-methyltransferase (ASMT), the two rate-limiting enzymes for melatonin synthesis, regulate melatonin production in mammals. Through analysis of the milk melatonin level and dairy herd improvement (DHI) index, it was found that the melatonin concentration in milk was significantly negatively correlated with the 305 day milk yield (305M) and peak milk yield (PeakM) (p < 0.05), while it was significantly positively correlated with the serum melatonin concentration (p < 0.05). The full-length of AANAT and ASMT were sequenced and genotyped in 122 cows. Three SNPs in AANAT and four SNPs in ASMT were significantly related to MT levels in the milk and serum (p < 0.05). The SNPs in AANAT were temporarily denoted as N-SNP1 (g.55290169 T>C), N-SNP2 (g.55289357 T>C), and N-SNP3 (g.55289409 C>T). The SNPs in ASMT were temporarily denoted as M-SNP1 (g.158407305 G>A), M-SNP2 (g.158407477 A>G), M-SNP3 (g.158407874 G>A), and M-SNP4 (g.158415342 T>C). The M-SNP1, M-SNP2, and M-SNP3 conformed to the Hardy−Weinberg equilibrium (p > 0.05), while other SNPs deviated from the Hardy−Weinberg equilibrium (p < 0.05). The potential association of MT production and each SNP was statistically analyzed using the method of linkage disequilibrium (LD). The results showed that N-SNP2 and N-SNP3 had some degree of LD (D′ = 0.27), but M-SNP1 and M-SNP2 had a strong LD (D′ = 0.98). Thus, the DHI index could serve as a prediction of the milk MT level. The SNPs in AANAT and ASMT could be used as potential molecular markers for screening cows to produce high melatonin milk.
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