Genetic parameter estimates of growth curve and reproduction traits in Japanese quail

被引:45
|
作者
Narinc, Dogan [1 ]
Karaman, Emre [1 ]
Aksoy, Tulin [1 ]
Firat, Mehmet Ziya [1 ]
机构
[1] Akdeniz Univ, Fac Agr, Dept Anim Sci, TR-07100 Antalya, Turkey
关键词
genetic parameter; growth curve; reproduction trait; Gibbs sampling; COTURNIX-COTURNIX-JAPONICA; TERM DIVERGENT SELECTION; ANIMAL-MODEL ANALYSIS; BODY-WEIGHT; EGG-PRODUCTION; SEXUAL-MATURITY; LINES; SHAPE; AGE; HERITABILITIES;
D O I
10.3382/ps.2013-03508
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
The goal of selection studies in broilers is to obtain genetically superior chicks in terms of major economic traits, which are mainly growth rate, meat yield, and feed conversion ratio. Multiple selection schedules for growth and reproduction are used in selection programs within commercial broiler dam lines. Modern genetic improvement methods have not been applied in experimental quail lines. The current research was conducted to estimate heritabilities and genetic correlations for growth and reproduction traits in a Japanese quail flock. The Gompertz equation was used to determine growth curve parameters. The Gibbs sampling under a multi-trait animal model was applied to estimate the heritabilities and genetic correlations for these traits. A total of 948 quail were used with complete pedigree information to estimate the genetic parameters. Heritability estimates of BW, absolute and relative growth rates at 5 wk of age (AGR and RGR), beta(0) and beta(2) parameters, and age at point of inflection (IPT) of Gompertz growth curve, total egg number (EN) from the day of first lay to 24 wk of age were moderate to high, with values ranging from 0.25 to 0.40. A low heritability (0.07) for fertility (FR) and a strong genetic correlation (0.83) between FR and EN were estimated in our study. Body weight exhibited negative genetic correlation with EN, FR, RGR, and IPT. This genetic antagonism among the mentioned traits may be overcome using modern poultry breeding methods such as selection using multi-trait best linear unbiased prediction and crossbreeding.
引用
收藏
页码:24 / 30
页数:7
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