Automated Author Profile

CW Yang

Current S-Index

1.3

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.7

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

13.5%

Average FAIR Score per dataset

Total Citations

2

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

THE SINGLE NUCLEOTIDE POLYMORPHISMS OF MYOSTATIN GENE AND THEIR ASSOCIATIONS WITH GROWTH AND CARCASS TRAITS IN DAHENG BROILER

ABSTRACT Myostatin (MSTN) is a negative regulator of skeletal muscle growth. In order to investigate whether there is a correlation between MSTN polymorphisms and chicken production performance, in this study, single nucleotide polymorphisms (SNPs) in MSTN gene were examined across 180 Daheng broilers by direct sequencing of PCR product, and the correlations between the genotype and body weight at the age of 1-10 weeks and carcass traits at the age of 73 day were analyzed. Five SNPs (rs313622770, rs313744840, rs316247861, rs314431084, rs317126751) of MSTN gene were identified across Daheng broiler samples, and four haplotypes were reconstructed based on the five SNPs. Results of association analysis showed that four (rs313622770, rs313744840, rs316247861 and rs317126751) of these SNPs had significant association with some growth traits (p<0.05), but there were no significant effect on carcass traits and the four SNPs were strong linkage. For rs314431084, there was no significant correlation between different genotypes and growth or carcass traits. The AA genotype of rs313622770, GG genotype of rs313744840, CC genotype of rs316247861, TT genotype of rs317126751 were good for chicken growth. Diplotypes were significantly associated with chest muscle and leg muscle weight (p<0.05). Overall, these results provide evidence that polymorphisms in MSTN gene are associated with growth traits in chicken. The SNPs in MSTN gene could be utilized as potential markers for marker-assisted selection (MAS) during chicken breeding.

Authors

  • XX Zhang ;
  • JS Ran ;
  • T Lian ;
  • ZQ Li ;
  • CW Yang ;
  • XS Jiang ;
  • HR Du ;
  • ZF Cui ;
  • YP Liu
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.11350643January 2019

THE SINGLE NUCLEOTIDE POLYMORPHISMS OF MYOSTATIN GENE AND THEIR ASSOCIATIONS WITH GROWTH AND CARCASS TRAITS IN DAHENG BROILER

ABSTRACT Myostatin (MSTN) is a negative regulator of skeletal muscle growth. In order to investigate whether there is a correlation between MSTN polymorphisms and chicken production performance, in this study, single nucleotide polymorphisms (SNPs) in MSTN gene were examined across 180 Daheng broilers by direct sequencing of PCR product, and the correlations between the genotype and body weight at the age of 1-10 weeks and carcass traits at the age of 73 day were analyzed. Five SNPs (rs313622770, rs313744840, rs316247861, rs314431084, rs317126751) of MSTN gene were identified across Daheng broiler samples, and four haplotypes were reconstructed based on the five SNPs. Results of association analysis showed that four (rs313622770, rs313744840, rs316247861 and rs317126751) of these SNPs had significant association with some growth traits (p<0.05), but there were no significant effect on carcass traits and the four SNPs were strong linkage. For rs314431084, there was no significant correlation between different genotypes and growth or carcass traits. The AA genotype of rs313622770, GG genotype of rs313744840, CC genotype of rs316247861, TT genotype of rs317126751 were good for chicken growth. Diplotypes were significantly associated with chest muscle and leg muscle weight (p<0.05). Overall, these results provide evidence that polymorphisms in MSTN gene are associated with growth traits in chicken. The SNPs in MSTN gene could be utilized as potential markers for marker-assisted selection (MAS) during chicken breeding.

Authors

  • XX Zhang ;
  • JS Ran ;
  • T Lian ;
  • ZQ Li ;
  • CW Yang ;
  • XS Jiang ;
  • HR Du ;
  • ZF Cui ;
  • YP Liu
1 Citation0 Mentions13% FAIR0.7 Dataset Index
10.6084/m9.figshare.11350643.v1January 2019