Related Experiment Video
Updated: Jan 18, 2026

Quantification of Orofacial Phenotypes in Xenopus
Published on: November 6, 2014
Explaining Global Turkey Biometric Diversity Through Principal Component Analysis
José Ignacio Salgado Pardo1, Antonio González Ariza2, Laura Carranco Medina1
1Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14071 Córdoba, Spain.
None:
The morphological diversity of the domestic turkey is still an open question in poultry research. For this reason, a meta-analysis with 97 reports from 28 morphometric characterization studies covering 15 different turkey genotypes was carried out in the present study. Biometric measurements and indices collected from the articles were used as independent variables in three principal component analyses. The highest variance explaining power was achieved in the analysis including only biometric indices, with more than 70% in the first two principal components for both sexes. The 'leg length', 'body mass', 'shape', and 'tarsus' indices were those with higher explanatory power, the latter two particularly so in females. In addition, 'head' was such a high variance explaining body region, especially in males, while for females, the 'leg' showed high variability between breeds. The spatial representation of observations drew an interesting grouping pattern, proposing an 'African' and 'Mediterranean' trunk of turkeys based just on biometric traits. The correlation matrix showed positive and negative associations between the variables, especially stronger in females. Breast circumference was negatively correlated with weight and size traits, suggesting that turkey landraces differ in body conformation and environmental requirements. Despite data limitations, particularly in terms of available breed reports and measures taken, consistent results were obtained. The results of the present work could be common guidelines for the phenotypic characterization of turkey breeds worldwide.
Related Concept Videos
Variability: Analysis
The range is a simple measure of variability, indicating the difference between the highest and...
Causes of Similarity-Dissimilarity Effect
One-Way ANOVA
Genetic Variation
Genes exist in different versions called alleles,...
Two-Way ANOVA
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
Genetics of Speciation

