### INTRODUCTION

*i.e.*, α, asymptotic final body weight; β, inflection point at which 50% of the asymptotic body weight is realized; γ, constant scale that is proportional to the overall growth rate) in KNC.

### MATERIALS AND METHODS

### Animal care

### Animals

_{0}and F

_{1}generations of KNC resource pedigree. The KNC nuclear pedigree consisted of 83 F

_{0}founders (15 males and 68 females) and 585 F

_{1}progeny (282 males, 303 females). Within-line mating was practiced in which a total of three cockerels were mated with 4 to 5 hens to produce F

_{1}birds. The F

_{1}progeny from two batches were categorized into the five lines: Red brown (R) (n = 135), White (W) (n = 122), Yellow brown (Y) (n = 130), Gray brown (G) (n = 110), Black (L) (n = 88) based on their plumage color. This resource population be made of 68 full-sib families with 3 to 20 birds (average 10.6). With regard to half-sib families, the population was consisted of 15 half-sib families ranging from 28 to 59 birds (average 44.5). Dams were not used for hatching and brooding. All animals were nurtured under standard breeding and management procedures implemented by the National Institute of Animal Science (NIAS) of Korea. The same environmental and feeding regime was provided throughout the experimental period of 20 weeks.

### Growth data collection

### Growth curve data analysis

^{2}value was computed by using the SAS NLIN procedure. In addition, convergence properties (

*i.e.*, number of chickens that the model did not converge) and convergence weight (

*i.e.*, number of chickens for which the converged weight at 20 weeks of age was 5 times heavier than their actual weight) were used as criteria for the fit of growth curves. These two criteria were obtained using Minitab’s programming language [14]. Equations for the three growth curve models are given in Table 1. For each individual KNC, three parameters (

*i.e.*α, asymptotic live body weight, β, inflection point at which 50% of the asymptotic weight is realized; and γ, a constant scale that is proportional to the overall growth rate) from the best-fit equation were extracted using Minitab’s programming language.

### Genetic parameter estimation

**Y**is a vector corresponds to the phenotypic values for weight gain traits and growth curve parameter traits;

**b**is the vector of fixed effects including batch, lines, sex, and;

**a**is a vector of random additive genetic effects, assumed to be

**A**is considered as additive genetic relationship matrix obtained from the nuclear F

_{1}pedigree and

**e**is a vector of random residual effects assumed to be

**I**is the identity matrix and

**X**and

**Z**are incidence matrices related to fixed and random effects, respectively.

**y**

**and**

_{1}**y**

**are vectors of measured phenotypes for the two traits under consideration;**

_{2}**b**

**and**

_{1}**b**

**are vectors of fixed effects (**

_{2}*i.e.*, sex, batch, and line) for the traits;

**a**

**and**

_{1}**a**

**are vectors of the random additive genetic effects for the traits;**

_{2}**X**

**and**

_{1}**X**

**are the incidence matrices relating measures of the traits to fixed effects;**

_{2}**Z**

**and**

_{1}**Z**

**are the incidence matrices relating phenotypic observations with random additive genetic effects; and**

_{2}**e**

**and**

_{1}**e**

**are vectors of random residuals. The expectation and variance of the bivariate model were:**

_{2}**A**: the additive genetic relationship matrix;

**I**is the identity matrix.

**a**and

**e**were assumed to be normally distributed with zero mean and (co)variances, as stated above. Phenotypic correlation coefficients were computed using the Minitab program [14].

### RESULTS

### Growth curve determination and descriptive statistics

*i.e.*, Gompertz, von Bertalanffy, and logistic models) to fit the growth curve of the KNC population from hatching to 20 weeks of age (Table 2). In terms of R

^{2}values, the three models performed similarly in fitting the growth curve of KNCs. However, the logistic curves showed the best performance both in the convergence properties and the convergence weight (Table 2). Thus, the logistic model chose to conduct the subsequent analyses. The descriptive statistics of the growth curve parameter values (

*i.e.*, the asymptotic live body weight α [grams], the scaling parameter β [wk], and the intrinsic growth rate γ [wk]) estimated from the logistic growth curve function are summarized in Table 3. The growth curves of KNC individuals were plotted from actual longitudinal body weight data from hatching to 20 weeks of age (Supplementary Figure S1).

### Heritability estimates of weight gain and growth curve parameters

### Genetic and phenotypic correlations

### DISCUSSION

*i.e.*, α, β, and γ) as a single trait to estimate genetic parameters in the moderate sized KNC population. In terms of the convergence property and weight, the logistic model described the growth pattern of KNC better than did the other two models (Table 1). Thus, the logistic growth curve function was selected to estimate growth curve parameters for further analyses.

*e.g.*body weight at 6 weeks of age, body weight at 8 weeks of age) in KNC [13]. Compared to the previous study, we analyzed weight gain data given two-week of period (

*e.g.*, GR6–8) in this study. As a result, we could clearly differentiate high or low heritability (Table 4). High heritabilities were observed from early stage of growth, which are likely to reflect genetic variability. On the contrary, low heritabilities were observed from late stage of growth, which are likely to reflect genetic invariability. For the genetic phenotypic correlation coefficients, analysis of weight gain data allowed us to detect both positive and negative signs which could not be seen the previous study [13]. The growth curve parameter was heritable with a low to moderate estimate, and low genetic and phenotypic correlation with weight gain, and consequently the alteration of the growth curve of KNC by selection.