Integration of association and computational methods reveals functional variants of LEPR gene for abdominal fat content in chickens

Abstract

Leptin receptor (LEPR) plays a vital role in obesity in humans and animals. The objective of this study is to assess LEPR functional variants for chicken adipose deposition by integration of association and in-silico analysis using a unique chicken population, the Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF). Five online bioinformatics tools were used to predict the functionality of the single nucleotide polymorphisms (SNPs) in coding region. Further, the possible structure–function relationship of high confidence SNPs was determined by bioinformatics analyses, including the conservation and stability analysis based on amino acid residues, prediction of protein ligand-binding sites, and the superposition of protein tertiary structure. Meanwhile, we analyzed the association between abdominal fat traits and 20 polymorphisms of chicken LEPR gene. The integrated results showed that rs731962924 (N867I) and rs13684622 (C1002R) could lead to striking changes in the structure and function of proteins, of which rs13684622 (C1002R) was significantly associated with abdominal fat weight (AFW, P=0.0413) and abdominal fat percentage (AFP, P=0.0260) in chickens. Therefore, we are of the opinion that rs13684622 (C1002R) may be an essential functional SNP affecting chicken abdominal fat deposition, and potentially applied to improvement of broiler abdominal fat in molecular marker-assisted selection (MAS) program. Additionally, the coupling of association with computer electronic predictive analysis provides a new avenue to identify important molecular markers for breeders.

Keywords

• chicken
• LEPR
• nsSNPs
• bioinformatics tools
• abdominal fat content
• association analysis