Haplotype-based genome-wide association studies for carcass and growth traits in chicken

Abstract

There have been several genome-wide association study ( GWAS ) reported for carcass, growth, and meat traits in chickens. Most of these studies have been based on single SNPs GWAS. In contrast, haplotype-based GWAS reports have been limited. In the present study, 2 Northeast Agricultural University broiler lines divergently selected for abdominal fat content ( NEAUHLF ) and genotyped with the chicken 60K SNP chip were used to perform a haplotype-based GWAS. The lean and fat chicken lines were selected for abdominal fat content for 11 yr. Abdominal fat weight was significantly different between the 2 lines; however, there was no difference for body weight between the lean and fat lines. A total of 132 haplotype windows were significantly associated with abdominal fat weight. These significantly associated haplotype windows were primarily located on chromosomes 2, 4, 8, 10, and 26. Seven candidate genes, including SHH , LMBR1 , FGF7 , IL16 , PLIN1 , IGF1R , and SLC16A1 , were located within these associated regions. These genes may play important roles in the control of abdominal fat content. Two regions on chromosomes 3 and 10 were significantly associated with testis weight. These 2 regions were previously detected by the single SNP GWAS using this same resource population. TCF21 on chromosome 3 was identified as a potentially important candidate gene for testis growth and development based on gene expression analysis and the reported function of this gene. TCF12 , which was previously detected in our SNP by SNP interaction analysis, was located in a region on chromosome 10 that was significantly associated with testis weight. Six candidate genes, including TNFRSF1B , PLOD1 , NPPC , MTHFR , EPHB2 , and SLC35A3 , on chromosome 21 may play important roles in bone development based on the known function of these genes. In addition, several regions were significantly associated with other carcass and growth traits, but no candidate genes were identified. The results of the present study may be helpful in understanding the genetic mechanisms of carcass and growth traits in chickens.

Genome-wide detection of CNVs and their association with performance traits in broilers

“…The significant CNV segments associated with body weight gain (GGA3: 64169030-64171297) overlapped with previously mapped QTLs for body weight at 49 days of age (QTL #30854, [33]), comb weight (QTL #127114, [34]), residual feed intake (QTL #64556, [35]), and testis weight (QTL #213559, [36]). The significant CNV segment associated with BW35, BW41 and BW42 (GGA3: 97801202-97809208) also overlapped with two QTLs described above (QTL #30854 and #127114).…”

Identification of candidate genes associated with slaughter traits in F2 chicken population using genome‐wide association study

“…2018; Zhang et al . 2020a,b). However, all of the genes mapped by these results can be regarded as candidate genes affecting the phenotype of this trait.…”

“…2011), carcass and growth traits (Zhang et al . 2020a), bone size and mass (Guo et al . 2020) and meat quality, are associated with segments on GGA4.…”

Genome-Wide Association Study of Growth Performance and Immune Response to Newcastle Disease Virus of Indigenous Chicken in Rwanda

“…The use of GWAS in chickens has been to ascertain major loci related to economic traits (Liu et al, 2019;Saelao et al, 2019;Zhang et al, 2020). Most economic traits in animals display quantitative variation, which is regulated by many QTLs with comparatively small effects and altered by the environment (Tsudzuki et al, 2007).…”

“…Growth performance traits are among the furthermost important economic traits in a poultry venture (Jin et al, 2015). Significant improvements in the study of growth performance traits in chicken have been realized, and many related genes and QTLs have been reported (Zhang et al, 2020). Over 1,500 QTLs, covering the majority of the chicken genome has been linked with growth performance traits (Hu et al, 2010;Sharmaa et al, 2015;Noorai et al, 2019).…”