Document Type : Research article
Authors
1
Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt.
2
Genetics and Genetic Engineering Department, Faculty of Veterinary Medicine, Zagazig University, Zagazig, Egypt.
Abstract
This study aims to explore the relationships between categorical variables using Correspondence Analysis (CA), a statistical technique designed to visualize and interpret associations in contingency tables. By applying CA, we seek to uncover underlying patterns in the structure of count data. The variables under study were structural chromosomal aberrations and some diseases related to infertility in Egyptian buffalo. Structural chromosomal aberrations were divided into 6 categories (gap, break, deletion, fragment, ring chromosome and centromeric attenuation). Groups of animal diseases were 9 categories (control, repeat breeder, anestrum, retained placenta, free-martin, vaginal prolapse, uterine prolapse, uterine torsion and habitual abortion). The Chi-square test of independence revealed a statistically significant association, indicating a relationship between chromosomal aberrations and infertility groups. CA further supported this association, with a total inertia of 0.178, suggesting that approximately 17.8% of the variation in the data is explained by the relationship between these two variables. Dimensions 1 and 2 captured most of the data structure, explaining 53.5% and 27.2% of the variance, respectively. Uterine torsion and abortion were highly contributed to explaining data variance in dimension 1. For dimension-2, uterine prolapse, uterine torsion, and control were also highly contributed to explaining variance. For the second variable, centromeric attenuation was highly significant for dimension-1 and fragment and centromeric attenuation were highly contributed to variance explaining than other structural chromosomal aberrations. Animal breeders can use CA techniques in their farms to facilitate understanding the pattern of their data and graphical representation of large data set.
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