Searching for causal relationships among latent variables concerning performance, carcass, and meat quality traits in broilers

Silva, Hugo Teixeira; Paiva, José Teodoro de; Botelho, Margareth Evangelista; Carrara, Eula Regina; Lopes, Paulo Sávio; Silva, Fabyano Fonseca e; Veroneze, Renata; Ferraz, José Bento Sterman; Eler, Joanir Pereira; Mattos, E. C.; Gaya, Leila de Genova

doi:10.1111/jbg.12653

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…The application of SEM animal breeding dates to the landmark paper by Gianola and Sorensen (2004), who extended the quantitative genetic theory to situations with linear recursiveness between phenotypes and established their parametric equivalence to MTM. Once the theory was put forward, several authors used it to study causal relationships between phenotypes, evaluating the impact of local interventions in livestock species, such as dairy goats (de los Campos et al, 2006), dairy cattle (Wu et al, 2007), pigs (Peñagaricano et al, 2015), broilers (Texeira et al, 2021) and rabbits (Mora, Velasco‐Galilea, et al, 2022). The use of SEM in animal breeding has been recently reviewed by Varona and González‐Recio (2023).…”

Section: Introductionmentioning

confidence: 99%

Conditioning on the causal network prevents indirect response to selection

Bonamy,

Fernández,

Giovambattista

et al. 2023

J Animal Breeding Genetics

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Multiple trait animal models (MTM) allow to estimate the breeding values (BV) of several traits simultaneously while accounting for genetic and environmental correlations among them. However, relationships among traits may not be reciprocal but rather causal in nature. In these cases, and given a causal network, structural equations models (SEM) arise as a more appropriate methodology. Although MTM and SEM have been shown to be parametrically equivalent, the estimated breeding value (EBV) obtained from either one or the other should be interpreted differently. In this study, we investigated the impact of using these estimates on the response to selection for a causal network comprising five different traits through a stochastic simulation experiment. Three different selection targets were assayed, involving traits located upstream, midstream and downstream this causal network. We first considered the case in which traits were causally related but not genetically correlated. The current results support our hypothesis that MTM will absorb causal relationships as genetic correlations and, consequently, change the response to selection achieved as compared with SEM. We found no differences on the response to selection when the target trait was located at the top of the causal network, but noticeable differences were detected on upstream traits when selection pressure was placed on midstream or downstream traits. We also assayed a scenario in which causal effects and genetic correlations act simultaneously and found that selection based on BVs estimated using SEM diminished the indirect response in traits upstream the causal network.

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Section: Introductionmentioning

confidence: 99%

Conditioning on the causal network prevents indirect response to selection

Bonamy,

Fernández,

Giovambattista

et al. 2023

J Animal Breeding Genetics

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Deciphering the genetic basis of behavioral traits in dogs: Observed-trait GWAS and latent-trait GWAS analysis reveal key genes and variants

Mahmoodi,

Ayatollahi Mehrgardi,

Momen

et al. 2024

The Veterinary Journal

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Genome-wide association study identifies significant SNP and related genes associated with body size in Yorkshire pigs using latent variable modelling

Sanjari Banestani,

Esmailizadeh,

Momen

et al. 2023

J. Agric. Sci.

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This study aimed to quantify a latent variable for body size (BS) in pigs by using five linear body measurements including body length (BL), body height (BH), chest width (CW), chest girth (CG) and tube girth (TG), and also to identify the most associated single nucleotide polymorphisms (SNP) and related genes with BS by using the genomic best linear unbiased prediction (GBLUP) based genome-wide association study (GWAS) or GBLUP-GWAS methodology. To perform a GWAS on the BS latent trait, we used a mixed linear model and identified a total of 53 significant SNPs. Additionally, we found that nine genes, including Rho GTPase activating protein 12 (ARHGAP12), transmembrane protein 108 (TMEM108), T-cell lymphoma invasion and metastasis inducing factor 1 (TIAM1), ras homologue gene family member B (RHOB), POU class 4 homeobox 1 (POU4F1), follistatin-related protein 4 (FSTL4), cellular communication network factor 2 (CCN2), beaded filament structural protein 2 (BFSP2) and attractin-like protein 1 (ATRNL1) were associated with the BS trait in pigs. These genes are involved in several biological processes, including the regulation of anatomical structure, morphogenesis, the regulation of cell size and growth. The results suggest that the identified SNP and related genes may play important roles in regulating the growth and development of pigs. The results imply that these genes could be promising candidates for further exploration of the underlying mechanisms of body size variation. Furthermore, the findings have significant practical implications for enhancing the efficiency and profitability of pig farming through genetic selection.

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Searching for causal relationships among latent variables concerning performance, carcass, and meat quality traits in broilers

Cited by 3 publications

References 40 publications

Conditioning on the causal network prevents indirect response to selection

Conditioning on the causal network prevents indirect response to selection

Deciphering the genetic basis of behavioral traits in dogs: Observed-trait GWAS and latent-trait GWAS analysis reveal key genes and variants

Genome-wide association study identifies significant SNP and related genes associated with body size in Yorkshire pigs using latent variable modelling

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