Analysis of the Genetic Parameters for Dairy Linear Appraisal and Zoometric Traits: A Tool to Enhance the Applicability of Murciano-Granadina Goats Major Areas Evaluation System

Animals (Basel). 2023 Mar 21;13(6):1114. doi: 10.3390/ani13061114.

Abstract

Selection for zoometrics defines individuals' productive longevity, endurance, enhanced productive abilities and consequently, their long-term profitability. When zoometric analysis is aimed at large highly selected populations or in those at different levels of selection, linear appraisal systems (LAS) provide a timely response. This study estimates genetic and phenotypic parameters for zoometric/LAS traits in Murciano-Granadina goats, estimating genetic and phenotypic correlations among all traits, and determining whether major area selection would be appropriate or if adaptability strategies may need to be followed. Heritability estimates for the zoometric/LAS traits were low to high, ranging from 0.09 to 0.43, and the accuracy of estimation has improved after decades, rendering standard errors negligible. Scale inversion of specific traits may need to be performed before major areas selection strategies are implemented. Genetic and phenotypic correlations suggests that negative selection against thicker bones and higher rear insertion heights indirectly results in the optimization of selection practices in the rest of the traits, especially those in the structure, capacity and mammary system major areas. The integration and implementation of the strategies proposed within the Murciano-Granadina breeding program maximizes selection opportunities and the sustainable international competitiveness of the Murciano-Granadina goat in the dairy goat breed panorama.

Keywords: adaptability; conservation; does; genetic and phenotypic correlations; heritability; local breed; variance components.

Grants and funding

The present research was carried during the covering period of a predoctoral contract (FPU Fellowship) funded by the Spanish Ministry of Science and Innovation and was developed under the context a Ramón y Cajal Post-Doctoral fellowship financially supported by MCIN/AEI/10.13039/501100011033 and European Union Europea “NextGenerationEU”/PRTR” (Recovery, Transformation and Resilience Plan—Funded by the European Union—NextGenerationEU).