López-Carbonell David, Legarra Andrés, Altarriba Juan, Hervás-Rivero Carlos, Sánchez-Díaz Manuel, Varona Luis
Genética Cuantitativa y Mejora Animal, Universidad de Zaragoza, Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain.
CDCB, Bowie, Maryland, USA.
J Anim Breed Genet. 2025 Jul;142(4):454-462. doi: 10.1111/jbg.12918. Epub 2024 Dec 19.
Genetic trends are a valuable tool for analysing the efficiency of breeding programs. They are calculated by averaging the predicted breeding values for all individuals born within a specific time period. Moreover, partitioned genetic trends allow dissecting the contributions of several selection paths to overall genetic progress. These trends are based on the linear relationship between breeding values and the Mendelian sampling terms of ancestors, enabling genetic trends to be split into contributions from different categories of individuals. However, (1) the use of predicted breeding values in calculating partitioned genetic trends depends on the variance components used and (2) a multiple trait analysis allows accounting for selection on correlated traits. These points are often not considered. To overcome these limitations, we present a software called "TM_TRENDS." This software performs a Bayesian analysis of partitioned genetic trends in a multiple trait model, accounting for uncertainty in the variance components. To illustrate the capabilities of this tool, we analysed the partitioned genetic trends for five traits (Birth Weight, Weight at 210 days, Cold Carcass Weight, Carcass Conformation, and Fatness Conformation) in two Spanish beef cattle breeds, Pirenaica and Rubia Gallega. The global genetic trends showed an increase in Carcass Conformation and a decrease in Birth Weight, Weight at 210 days, Cold Carcass Weight, and Fatness Conformation. These trends were partitioned into six categories: non-reproductive individuals, dams of females and non-reproductive individuals, dams of sires, sires with fewer than 20 progeny, sires between 20 and 50 progeny, and sires with more than 50 progeny. The results showed that the main source of genetic progress comes from sires with more than 50 progenies, followed by dams of males. Additionally, two additional features of the Bayesian analysis are presented: the calculation of the posterior probability of the global and partitioned genetic response between two time points, and the calculation of the posterior probability of positive (or negative) genetic progress.
遗传趋势是分析育种计划效率的一项重要工具。它通过对特定时间段内出生的所有个体的预测育种值求平均来计算。此外，划分后的遗传趋势有助于剖析多条选择路径对总体遗传进展的贡献。这些趋势基于育种值与祖先孟德尔抽样项之间的线性关系，使得遗传趋势能够被分解为不同类别个体的贡献。然而，（1）在计算划分后的遗传趋势时使用预测育种值取决于所使用的方差成分，并且（2）多性状分析能够考虑对相关性状的选择。这些要点常常未被考虑到。为克服这些限制，我们推出了一款名为“TM_TRENDS”的软件。该软件在多性状模型中对划分后的遗传趋势进行贝叶斯分析，同时考虑方差成分中的不确定性。为说明此工具的功能，我们分析了西班牙两个肉牛品种皮雷纳卡（Pirenaica）和鲁维亚加列戈（Rubia Gallega）的五个性状（出生体重、210日龄体重、冷胴体重、胴体形态和脂肪形态）的划分后遗传趋势。总体遗传趋势显示胴体形态有所增加，而出生体重、210日龄体重、冷胴体重和脂肪形态有所下降。这些趋势被划分为六个类别：非繁殖个体、母畜（雌性和非繁殖个体）、父畜的母畜、后代少于20头的父畜、后代在20至50头之间的父畜以及后代多于50头的父畜。结果表明，遗传进展的主要来源是后代多于50头的父畜，其次是公畜的母畜。此外，还介绍了贝叶斯分析的另外两个特点：两个时间点之间总体和划分后遗传响应的后验概率计算，以及正向（或负向）遗传进展的后验概率计算。
