Principal Component Analysis of Internal Egg Quality and some Performance Traits of Azarbayjan Native Chickens

One of the main problems of multiple-trait genetic evaluation in poultry breeding is high computing costs. Principal components analysis (PCA) is a method for reducing the number of traits in correlated trait analysis. The aim of the present study was to determine the most effective principal components (PCs) of internal egg quality and some performance traits of Azarbayjan native chickens. Records were measured from 1500 native hens, including body weight at sexual maturity (BWSM), age at first egg production (ASM), egg volume (EV), yolk percent (YP), yolk width (YWI), yolk weight (YW), yolk Index (YI), yolk dry matter (YDM), yolk depth (YD), yolk pH (YPH), yolk coefficient (YC), the ratio of yolk weight to albumen weight (RYA), albumen percent (AP), Hough Unit (HU), albumen pH (APH), albumen height (AH), albumen dry matter (ADM), albumen weight (AW). Descriptive statistics, phenotypic Pearson coefficients and principal components analysis were evaluated using SAS software. The results showed that the range of Pearson correlation coefficients for phenotypic traits varied from zero to 0.98. The correlation coefficients more than 0.6 between HU and AH, YP and RYA, YW and YC, YP and YWI, YP and YW, YP and YDM, YP and YC¡ YWI and YW, YWI and YDM, YWI and RYA, YW and RYA, YI and YD, YDM and YC, YDM and RYA, YC and RYA, AP and AW, and ADM and AW were evaluated. The PCA analysis showed that the eight first principal components explained about 90.21% of the total variation of internal egg quality traits. Using PCA in internal egg quality, BWSM and ASM showed that YW, YDM, YC, YP, RYA, YWI, AP, AH, AW and HU have great impact on the total phenotypic variance. So, this method can be used for internal egg quality and performance trait analysis of native hens and can reduce computing costs and time of genetic evaluation of multiple traits.