Multiple Trait Bayesian Analysis of Partitioned Genetic Trends Accounting for Uncertainty in Genetic Parameters. An Example With the Pirenaica and Rubia Gallega Beef Cattle Breeds

Abstract

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.

FIGURE 1

Posterior mean (line) and highest posterior distribution with 95% probability (shadow) of the whole population genetic trends for Birth Weight (BW), Weight at 210 days (W210), Cold Carcass Weight (CCW), Carcass Conformation (CON), and Carcass Fatness (FAT) in Pirenaica (left) and Rubia Gallega (right) breeds. [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

Posterior mean (line) and highest posterior distribution with 95% probability (shadow) of the partitioned genetic trends in six different clusters (non‐reproductive—NR‐, Dams of dams and non‐reproductive –DFNR‐, Dam of Sires‐DS‐, Sires with a progeny lower than 20‐S20‐, Sires with a progeny between 20 and 49‐S50‐, and Sires with progeny higher than 50‐S+‐) for Birth Weight (BW), Weight at 210 days (W210), Cold Carcass Weight (CCW), Carcass Conformation (CONF), and Fatness (FAT) in Pirenaica (left) and Rubia Gallega breeds (right). [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Total genetic progress (WP, in red), associated with sires with progeny higher than 50 (S+, in green), and linked with the other clusters (Others, in blue) for Carcass Conformation and from 1997 to 2007 and from 2007 to 2017. [Colour figure can be viewed at wileyonlinelibrary.com]

FIGURE 4

Posterior mean, highest posterior densities (HPDs) at 95% and posterior probability over zero for the whole population (WP, in red), dams of sires (DS, in green), and sires with a progeny higher of 50 (S+, in blue) yearly genetic response for Carcass Conformation in the Pirenaica population. [Colour figure can be viewed at wileyonlinelibrary.com]