Genome-wide association study of growth curve parameters reveals novel genomic regions and candidate genes associated with metatarsal bone traits in chickens.

The growth and development of chicken bones have an enormous impact on the health and production performance of chickens. However, the development pattern and genetic regulation of the chicken skeleton are poorly understood. This study aimed to evaluate metatarsal bone growth and development patterns in chickens via non-linear models, and to identify the genetic determinants of metatarsal bone traits using a genome-wide association study (GWAS) based on growth curve parameters. Data on metatarsal length (MeL) and metatarsal circumference (MeC) were obtained from 471 F2 chickens (generated by crossing broiler sires, derived from a line selected for high abdominal fat, with Baier layer dams) at 4, 6, 8, 10, and 12 weeks of age. Four non-linear models (Gompertz, Logistic, von Bertalanffy, and Brody) were used to fit the MeL and MeC growth curves. Subsequently, the estimated growth curve parameters of the mature MeL or MeC (A), time-scale parameter (b), and maturity rate (K) from the non-linear models were utilized as substitutes for the original bone data in GWAS. The Logistic and Brody models displayed the best goodness-of-fit for MeL and MeC, respectively. Single-trait and multi-trait GWASs based on the growth curve parameters of the Logistic and Brody models revealed 4 618 significant single nucleotide polymorphisms (SNPs), annotated to 332 genes, associated with metatarsal bone traits. The majority of these significant SNPs were located on Gallus gallus chromosome (GGA) 1 (167.433-176.318 Mb), GGA2 (96.791-103.543 Mb), GGA4 (65.003-83.104 Mb) and GGA6 (64.685-95.285 Mb). Notably, we identified 12 novel GWAS loci associated with chicken metatarsal bone traits, encompassing 35 candidate genes. In summary, the combination of single-trait and multi-trait GWASs based on growth curve parameters uncovered numerous genomic regions and candidate genes associated with chicken bone traits. The findings benefit an in-depth understanding of the genetic architecture underlying metatarsal growth and development in chickens.

Estimation of genetic parameters for cumulative egg numbers in a broiler dam line by using a random regression model.

The random regression model (RRM) methodology was applied to the estimation of genetic parameters for cumulative egg numbers and monthly egg production in a broiler dam line. The data were extracted from records of a commercial dam line in 2001 to 2003. A total of 99,193 records from 6,475 hens and 9,111 pedigreed animals were used in the current study. The variance components were estimated using Gibbs sampling procedure. According to the Bayesian information criterion and Bayes factor, an RRM with Legendre polynomial of 2 orders for hatching groups and additive genetic effects and of 4 orders for permanent environmental effects was chosen as the optimal model for cumulative egg numbers in the broiler dam line. The heritability estimates of the cumulative egg numbers between wk 1 and 40 of production ranged from 0.16 to 0.54, whereas heritability estimates from wk 12 to 20 of production were moderate. The ratios of permanent environmental variance to phenotypic variance were large, indicating that the RRM could produce better estimates of additive genetic effects. The genetic and phenotypic correlations between cumulative egg numbers at different production weeks estimated with the optimal RRM were generally higher when the overlapping weeks were greater. In addition, genetic parameters for monthly egg production could also be obtained by the optimal RRM, and the heritability estimates ranged from 0.03 to 0.18. It was suggested that early selection based on cumulative egg numbers in the first 19 wk of production could effectively improve annual egg production in the broiler dam line.