Bayesian networks and structural equation models reveal genetic causal relationships between productivity, defense, and climate-adaptability traits in interior lodgepole pine

• Autores: Cappa, Eduardo Pablo; Klutsch, Jennifer G.; Benowicz, Andy; Munilla, Sebastián; Mansfield, Shawn D.; Erbilgin, Nadir; Thomas, Barb R.; El-Kassaby, Yousry A.
• Año de publicación: 2025
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: BAYESIAN NETWORKS; STRUCTURAL EQUATION MODEL; GENETIC CAUSAL EFFECTS; MULTIPLE-TRAIT ANALYSIS
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

This study investigates the integration of Bayesian networks (BN) and structural equation models (SEM) to explore genomic relationships among nine traits related to productivity, defense, and climate-adaptability in an interior lodgepole pine breeding program. Data from 392 open-pollinated trees, genotyped with 25,099 SNP markers, were analyzed. The traditional multi-trait model (MTM) served as a benchmark for comparing SEM in estimating genetic (co)variance components, genetic correlations, breeding value (BV) predictions, and predictive ability, using both pedigree- (ABLUP) and genomic-based (GBLUP) individual-tree mixed models. The Hill-Climbing algorithm identified 12 significant causal structures (λ) among traits. Strong positive causal effects included tree height (HT) on wood density (WD) (λHT→WD = 0.413) and on stable carbon isotope ratio (C13) (λHT→C13 = 0.565), and limonene (LIMO) on carbon assimilation rate (CAR) (λLIMO→CAR = 0.368). The most influential causal relationship was HT → C13, followed by resistance to western gall rust (WGR) → CAR, CAR → LIMO, and WGR → C13. SEM incorporated these relationships, capturing both direct and indirect effects. Compared with MTM, SEM yielded lower residual variances, higher additive variances, and higher heritability estimates for all traits. The λ values from SEM correlated strongly with genetic correlations (0.932), with similarly high correlations between models (0.929), though SEM produced lower posterior mean correlations. BV correlations between models were high (ABLUP > 0.82, GBLUP > 0.84), but some reranking occurred among the top 39-trees (ABLUP > 0.71, GBLUP > 0.42). ABLUP and GBLUP-SEM models outperformed MTM in predictive ability, with mean gains of 6.62% and 6.03%, mainly for conditioned traits. BN-SEM enhances understanding of trait networks, improving genomic evaluations and breeding strategies in forest trees.
Fil: Cappa, Eduardo Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; Argentina
Fil: Klutsch, Jennifer G.. University of Alberta; Canadá. Northern Forestry Centre; Canadá
Fil: Benowicz, Andy. Alberta Forestry and Parks; Canadá
Fil: Munilla, Sebastián. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Unidad Ejecutora de Investigaciones en Producción Animal. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Unidad Ejecutora de Investigaciones en Producción Animal; Argentina
Fil: Mansfield, Shawn D.. University of British Columbia; Canadá
Fil: Erbilgin, Nadir. University of Alberta; Canadá
Fil: Thomas, Barb R.. University of Alberta; Canadá
Fil: El-Kassaby, Yousry A.. University of British Columbia; Canadá