Dataset Information

Application of Bayesian causal inference and structural equation model to animal breeding.

ABSTRACT: Optimized breeding goals and management practices for the improvement of target traits requires knowledge regarding any potential functional relationships between them. Fitting a structural equation model (SEM) allows for inferences about the magnitude of causal effects between traits to be made. In recent years, an adaptation of SEM was proposed in the context of quantitative genetics and mixed models. Several studies have since applied the SEM in the context of animal breeding. However, fitting the SEM requires choosing a causal structure with prior biological or temporal knowledge. The inductive causation (IC) algorithm can be used to recover an underlying causal structure from observed associations between traits. The results of the papers, which are introduced in this review, showed that using the IC algorithm to infer a causal structure is a helpful tool for detecting a causal structure without proper prior knowledge or with uncertain relationships between traits. The reports also presented that fitting the SEM could infer the effects of interventions, which are not given by correlations. Hence, information from the SEM provides more insights into and suggestions on breeding strategy than that from a multiple-trait model, which is the conventional model used for multitrait analysis.

SUBMITTER: Inoue K

PROVIDER: S-EPMC7187322 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Application of Bayesian causal inference and structural equation model to animal breeding.

Inoue Keiichi K

Animal science journal = Nihon chikusan Gakkaiho 20200101 1

Optimized breeding goals and management practices for the improvement of target traits requires knowledge regarding any potential functional relationships between them. Fitting a structural equation model (SEM) allows for inferences about the magnitude of causal effects between traits to be made. In recent years, an adaptation of SEM was proposed in the context of quantitative genetics and mixed models. Several studies have since applied the SEM in the context of animal breeding. However, fittin ...[more]

PMID: 32219948

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Project description:BackgroundVarious forms of life stressors have been implicated as causes of antenatal depression. However, there is a lack of understanding of which forms of stress lead to antenatal depression and through what mechanisms. Modeling stress processes within a theoretical model framework can enhance an understanding of the mechanisms underlying relationships between stressors and stress outcomes. This study used the stress process model framework to explore the causal mechanisms underlying antenatal depression in Gondar, Ethiopia.MethodsQuestionnaires, using an Online Data collection Kit (ODK) tool were administered face-to-face in 916 pregnant women in their second and third trimesters. Pregnant women were included from six randomly selected urban districts in Gondar, Ethiopia during June and August 2018. The Edinburgh Postnatal Depression Scale (EPDS) was used to screen for antenatal depression. A Structural Equation Model (SEM) was employed to explore the direct, indirect, and total effect of stressors and mediators of antenatal depression.ResultSixty-three participants (6.9%) reported symptoms of depression. Of these, 16 (4.7%) and 47 (8.1%) were in their second and third trimesters, respectively. The SEM demonstrated several direct effects on antenatal depression scores including unplanned pregnancy (standardized β = 0.15), having a history of common mental health disorder (standardized β = 0.18) and fear of giving birth to the current pregnancy (standardized β = 0.29), all of which were associated with a higher depression score. Adequate food access for the last 3 months (standardized β = - 0.11) was associated with decreased depression score. Social support (β = - 0.21), marital agreement (β = - 0.28), and partner support (β = -.18) appeared to partially mediate the link between the identified stressors and the risk of antenatal depression.ConclusionBoth direct and indirect effects contributed to higher antenatal depression score in Ethiopian women. The three psychosocial resources namely marital agreement, social and partner support, mediated reduced antenatal depression scores. Early screening of antenatal depression and enhancing the three psychosocial resources would help to improve maternal resilience.

Dataset Information

Application of Bayesian causal inference and structural equation model to animal breeding.

Publications

Application of Bayesian causal inference and structural equation model to animal breeding.

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