Multivariate analysis as a tool for phenotypic characterization of an endangered breed

Abstract

Goats are important from a socioeconomic perspective for the poor in arid regions, worldwide. Nevertheless, more than half of the local breeds in the world are threatened and have not been fully characterized. The Canindé is one of the main local breeds of northeastern Brazil and, like most, their effective numbers have fallen over the years and needs to be characterized. Many tools are available for assessing the phenotypic profile of a breed and multivariate techniques are important when considering all variables simultaneously. The present study utilized multivariate techniques for phenotypic characterization of the Canindé goat breed from 11 morphometric variables (HL = head length; FW = face width; HW = head width; BL = body length; CG = chest girth; WH = wither height; SRH = sacral region height; CW = croup width; CL = croup length, SP = shin perimeter and ES = ear size) and morphological variables of qualitative character (presence and absence of earrings, horns, beard, abnormal teat number and hair length) collected from herds from different states (populations) in the northeast of Brazil. Multivariate analysis allowed the differentiation and characterization of the evaluated individuals, HL, FW, WH, SRH and BL variables were the most important to define the phenotypic profile of the studied populations. Examined whether spatial organization of individuals was assessed in each state of the Brazil demonstrated considerable diversity of phenotypes within breeds from the different states. These data could be used successfully in a conservation breeding programme.

Assessment of genetic diversity in goat genetic groups of Kerala, (India) using morphobiometric markers

“…Jimcy et al (2011) reported that discriminant analysis based on morphometric measurements revealed that the most discriminative variables were head width and body length, followed by shin circumference and rump length. According to Arandas et al (2016), discriminant analysis revealed that qualitative traits like hair, beard, earring, politetia, horn and morphometric characteristics like head length, face width, body length, wither height and sacral region height were the most important for breed characterization of endangered Canindé goat breed of northeastern Brazil. P h e n ot yp i c differentiation was noticed not only for different breeds like Malabari and AB, but also for CB and Malabari populations from different districts of Kerala suggesting geographical isolation as a reason for differentiation.…”

Head and tail morphology of Pelibuey, Katahdin and Blackbelly rams in Colima, México

“…Asimismo, se calculó la capacidad de estas funciones canónicas para asignar a cada carnero a su raza como el porcentaje de la asignación correcta de cada raza. Antes de esta valoración, con la lambda de Wilks se probó si la función discriminante era significativa (Yakubu e Ibrahim, 2011; Asamoah-Boaheng y Sam, 2016; Gomes et al, 2016).…”

“…La caracterización fenotípica es una técnica ampliamente utilizada que constituye la base para diferenciar grupos de animales y/o razas a través de sus características distintivas (Gomes et al, 2016) y provee información esencial para la planeación y el manejo de los recursos genéticos animales (FAO, 2012).…”

Linear discriminant analysis of structure within African eggplant ‘Shum’

“…After clustering, it was imperative to carryout MANOVA to eliminate data redundancy, which could have occurred as a result of introducing 'cluster' as a predictor variable for the response of different traits measured. After MANOVA, linear discriminant analysis (LDA) considers 'cluster' as a dependent (response) variable as predicted by measured traits (Arandas et al, 2017).…”

“…The LDA has been applied in medicine, animal (Arandas et al, 2017) and plant research. In plants, LDA has been applied in various studies such as taxonomic and germplasm characterisation (Herklotz et al, 2017), phenotypic changes evaluation in plant species over time (Alberti et al, 2017) and crop diseases detection on remote sensing generated data (Bajwa et al, 2017).…”