TY - JOUR
T1 - Inferring phenotypic causal networks for tick infestation, Babesia bovis infection, and weight gain in Hereford and Braford cattle using structural equation models
AU - Cavani, Ligia
AU - Lopes, Fernando Brito
AU - Giglioti, Rodrigo
AU - Bresolin, Tiago
AU - Campos, Gabriel Soares
AU - Okino, Cintia Hiromi
AU - Gulias-Gomes, Claudia Cristina
AU - Caetano, Alexandre Rodrigues
AU - Oliveira, Márcia Cristina de Sena
AU - Cardoso, Fernando Flores
AU - Rosa, Guilherme Jordão de Magalhães
AU - de Oliveira, Henrique Nunes
N1 - Funding Information:
This work was supported by São Paulo Research Foundation ( FAPESP ) (grant number 2017/08940–3, grant number 2015/13024–0, and grant number 2016/07216–7).
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/8
Y1 - 2020/8
N2 - Tick infestation and associated diseases (i.e., babesiosis) constitute major drawbacks for improvement of beef cattle productivity in the tropics, mainly when purebred and crossbred taurine animals are used. Host-parasite-pathogen interactions form complex biological systems that are poorly understood and which significantly affect production and quality traits in ways yet to be dissected and described. This research was carried out to investigate potential causal relationships, through the use of structural equation modeling (SEM), among tick counts (TC), Babesia bovis infection level (IB) and the gains in weight: from birth to adjusted weaning age (WG), and from weaning to yearling (YG). Statistical analyses were conducted in three steps: 1) Partition of (co)variances into genetic and residual components using Bayesian multiple-trait modeling (MTM); of 2) Search for plausible causal structures by applying the inductive causation (IC) algorithm to the residual (co)variances obtained in the first step; and 3) Final analysis using SEM, which was based on the causal network learned from the IC algorithm. The most plausible results comprised three direct links between traits: WG→YG, TC→WG, and WG→IB with structural coefficients posterior means equal to -0.3026, 6.3620, and 0.0004, respectively. The final inferred directed acyclic graph (DAG) suggests that interventions on TC would directly affect WG, which would then affected YG; moreover, WG could also present a small positive effect on IB.
AB - Tick infestation and associated diseases (i.e., babesiosis) constitute major drawbacks for improvement of beef cattle productivity in the tropics, mainly when purebred and crossbred taurine animals are used. Host-parasite-pathogen interactions form complex biological systems that are poorly understood and which significantly affect production and quality traits in ways yet to be dissected and described. This research was carried out to investigate potential causal relationships, through the use of structural equation modeling (SEM), among tick counts (TC), Babesia bovis infection level (IB) and the gains in weight: from birth to adjusted weaning age (WG), and from weaning to yearling (YG). Statistical analyses were conducted in three steps: 1) Partition of (co)variances into genetic and residual components using Bayesian multiple-trait modeling (MTM); of 2) Search for plausible causal structures by applying the inductive causation (IC) algorithm to the residual (co)variances obtained in the first step; and 3) Final analysis using SEM, which was based on the causal network learned from the IC algorithm. The most plausible results comprised three direct links between traits: WG→YG, TC→WG, and WG→IB with structural coefficients posterior means equal to -0.3026, 6.3620, and 0.0004, respectively. The final inferred directed acyclic graph (DAG) suggests that interventions on TC would directly affect WG, which would then affected YG; moreover, WG could also present a small positive effect on IB.
KW - Babesiosis
KW - Causality
KW - Genetic parameter
KW - Inductive causation
KW - Tick resistance
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U2 - 10.1016/j.livsci.2020.104032
DO - 10.1016/j.livsci.2020.104032
M3 - Article
AN - SCOPUS:85084295915
SN - 1871-1413
VL - 238
JO - Livestock Science
JF - Livestock Science
M1 - 104032
ER -