Genetic parameters, genetic trends, and principal component analysis for productive and reproductive traits of Guzera beef cattle

Abstract

The objectives of this study were to estimate genetic parameters and genetic trends and perform principal component analysis for the following traits: body weight adjusted at 210 days of age (W210), one year (W365), and yearling or 450 days of age (W450); weight gain from birth to weaning or 210 days of age (WGBW), from weaning to 365 days of age (WGW365), and from one year to yearling (WGY); and scrotal circumference adjusted at 365 (SC365) and 450 (SC450) days of age of Guzera beef cattle. Variance components were estimated under a two-trait animal model using the restricted maximum likelihood method. The fixed effects included in the model were birth season, contemporary group, and covariable age of dam at calving as linear and quadratic effects. Heritability estimates ranged from 0.10 (WGY) to 0.41 (W450 and SC450). The studied traits could be used as selection criteria because of their sufficient additive genetic variability to respond to the selection process. However, lower selection efficiency is expected for WGBW and WGY. Genetic trends studied were significant for all traits, except WGY. The W365 and W450 traits belonged to the "Genetic Merit Total" index and responded to selection with favorable genetic gain per year. The traits that were not included in the selection index showed favorable positive genetic correlations attributed to the indirect selection processes. The principal component analysis identified two groups of traits. The first group related to body weight and weight gain and the second related to the scrotal circumference, indicating that selection could be applied to one group of traits within each observed group. The genetic gains were attributed in the same direction for the other traits belonging to the groups. Thus, we recommend selecting animals for W365 based on first group, and SC365 based on second.

Estimación de parámetros genéticos para caracteres de crecimiento en bovinos criollos Romosinuano

“…Los valores genéticos directos para el peso al nacimiento, al destete y a los 16 meses de edad (VG-PN, VG-PD y VG-P16m, respectivamente) fueron estimados por medio del mejor predictor lineal insesgado (BLUP) implementado en software WOMBAT (15). Luego haciendo uso de dichos valores genéticos, se llevó a cabo un análisis de componentes de principales mediante los paquetes FactoMineR (17) y factoextra (18) implementados en el software R. El objetivo de este análisis consistió en resumir la información contenida en los valores genéticos de cada carácter analizado en un conjunto más pequeño de nuevas dimensiones compuestas o variables que se pudieran visualizar gráficamente, llamadas componentes principales, y explorar como los cambios genéticos en un carácter podrían afectar a los otros, e indicar cuál de ellos podría usarse como criterio de selección (8,9,11,13). Para ello, los valores genéticos directos fueron estandarizados (a media cero y desvío estándar igual a uno) utilizando la distribución normal estándar (z=(x-ẋ)/S, siendo x igual a los valores genéticos directos originales, ẋ el valor promedio de dichos valores genéticos y S que corresponde al desvío estándar respectivo).…”

“…Esta discrepancia pudo ser debido a diferencias genéticas entre poblaciones, a los modelos estadísticos utilizados y al sistema de manejo (efecto ambiental); son iguales en PN y mayores en PD y P16 meses a los obtenidos por Ossa et al (4) quienes reportan valores de 0.17±0.03, 0.09±0.02 y 0.06±0.03 respectivamente. Al respecto, Cardellino y Rovira (20) reportan que la heredabilidad es una valor relativo y no absoluto, en el sentido de que es propio de la población que sirvió para su estimación, no obstante, aluden que dicho valor se puede extrapolar a otras poblaciones con estructura genética similar y expuestas a un medio ambiente común.De acuerdo con Tramonte et al(11), los caracteres medidos hasta el destete pueden estar influenciados por efectos maternos. En este estudio se pudo evidenciar que existen también caracteres que al ser medidos después de este evento pueden estar sujetos a dicho efecto, en este caso el peso a los 16 meses de edad.…”

Multivariate analysis of body morphometric traits in conjunction with performance of reproduction and milk traits in crossbred progeny of Murrah × Jafarabadi buffalo (Bubalus bubalis) in North-Eastern Brazil

“…PCA has been widely used in animal production in order to understand the relationship between several traits that are of economic interest, and to understand the contribution of each trait in each PC [13][14][15][16][17][18][19][20][22][23][24][25][26][27]57,58]. In this study, we verified that the first six PCs explained 82.14% of the total variance, and the importance of each body measurement in the composition of these six PCs were determined by the correlations between the original variables and PCs, such as their weighting coefficients ( Table 2).…”

“…Therefore, the selection of animals while considering the association between body morphometric traits and productive/reproductive traits can be effective for obtaining suitable biotypes for each production system. Research has shown the association between biotypes, and productive and reproductive traits [3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19]. However, there are limited studies examining functional biotype and their relationship with productive/reproductive in the buffalo.…”

“…This technique aims to study several traits simultaneously, which can be difficult to measure or hard for selection programs to understand, especially when there is a negative genetic correlation between them [14]. Multivariate techniques, such as principal component analysis (PCA) and canonical correlation analysis (CCA), have been widely used in animal production study [7,[12][13][14][15][16][17][19][20][21][22][23][24][25][26][27][28][29]. PCA has been used to clarify the structural relationship between different traits and to eliminate redundant traits, thus reducing the sample size of original set of variables [30,31].…”