Phenotypic causal networks between boar taint compounds measured in biopsies and carcasses
Margareth Evangelista Botelho
A B * , Marcos Soares Lopes C D , Pramod K. Mathur C , Egbert F. Knol C , Daniele B. D. Marques A , Paulo Sávio Lopes A , Fabyano Fonseca e Silva A , Simone Eliza Facioni Guimarães A and Renata Veroneze A
A Departament of Animal Science from Universidade Federal de Viçosa, Vicosa 36570-999, Brazil.
B Present address: Empresa de Pesquisa Agropecuária de Minas Gerais, Instituto Tecnológico de Agropecuária de Pitangui, Pitangui 35650-000, Brazil.
C Topigs Norsvin Research Center, Beuningen 6641 SZ, Netherlands.
D Topigs Norsvin, Curitiba, PR 80420-210, Brazil.
Animal Production Science 63(3) 291-300 https://doi.org/10.1071/AN21277
Submitted: 20 May 2021 Accepted: 26 September 2022 Published: 28 October 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Boar taint compounds (androstenone, skatole and indole) can be measured in pig carcasses, after slaughter or, alternatively, in biopsies of subcutaneous adipose tissue from selected living pigs. Measuring these compounds via biopsy enables data collection in selected animals and high standardisation regarding tissue-collection procedures for phenotyping. Because different analytical methods can be used to measure boar taint compounds, it is important to better understand the relationship between boar taint compounds measured in biopsies and in carcasses.
Aims: This research aimed to identify the causal relationship and causal effects among boar taint compounds (androstenone, skatole and indole) measured in pig adipose tissue from carcasses and biopsies.
Methods: The concentrations of androstenone (AC), skatole (SC) and indole (IC) measured in adipose tissue from pig carcasses and the concentrations of androstenone (AB), skatole (SB) and indole (IB) measured in biopsies were used to fit a multi-trait Structural Equation Model (SEM) considering causal network graphs obtained via inductive causation algorithm with or without a priori information. Models were compared using the deviance information criterion (DIC).
Key results: The best DIC was obtained in a model with a causal structure built using a priori information; however, this model was considered inappropriate, because it returned several null genetic correlations among traits described as positively correlated. The best structure returned using only inductive causation algorithm was IB → SC ← AB ← AC ← SB: SC → IC, which was obtained with an 80–70% high-probability distribution interval. This model returned positive genetic correlations and improved goodness-of-fit compared with the multi-trait model in all cases. Several causal relationships among boar taint compounds in carcasses and biopsies were identified.
Conclusion: Boar taint compounds measured in biopsies have direct effects on boar taint compounds measured in carcasses.
Implications: Knowledge concerning the causal structure of boar taint compounds may be used in breeding programs, helping in the formulation of selection indexes and improving the ability for prediction and selection of this complex trait (boar taint).
Keywords: androstenone, biopsy, boar taint, carcass, causal structure, IC algorithm, indole, skatole.
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