Principal Component Analysis of Different Economic Traits in Layer Chicken

Principal component analysis is a multivariate technique that transforms a number of possibly correlated variables into smaller number of uncorrelated variables leads to dimension reduction. Various economic traits of layer chicken are used for selection of parent birds which need to adjust for selection strategies to augment the genetic improvement.

The data was collected from 2020-2021 which includes weekly body weight (g) from 0 day to 20^th week and 40^th week, Body weight (g) at first egg production, age at sexual maturity (days), weight of first egg (g), egg numbers at 40^th week, egg weight at 40^th week (g), egg numbers at 52^nd week, egg weight at 52^nd week (g). The least squares mean was estimated considering three different genetic groups of layer chicken (N=450, 150 each group). The main focus of this study was to identify the principal components for economic traits in layer chicken. Further varimax rotation method was applied for the transformation of components to approximate simple structure.

The genetic group Desi cross 1 performed better than Desi cross 2 followed by Punjab Red layer chicken. A total of three principal components were obtained which explained a total variance of 75.524%. Principal component 1 had high loads on body weight 10^th week to 20^th week (BW 10-BW 20) and BWSM and had a variance of 38.892%. Similarly, PC2 and PC3 explained variance of 27.072% and 9.560% respectively and had high loads on 1 week body weight to 9-week body weight (BW1-BW9) and age at sexual maturity, 40-week egg production, 52-week egg production respectively. From this study it was included that PCA can be used for selecting the economic traits for breeding purpose of layer chicken.