Modeling a latent variable for body size using morphometric traits in cattle

Articles in Press

Document Type : Research Article (Regular Paper)

Authors

1 Department of Animal Science, Faculty of Agriculture, University of Jiroft, P.O. Box 364, Jiroft, Iran

2 Animal Science Research Department, Yazd Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO), Yazd, Iran

Abstract

This study aimed to quantify a latent variable for body size (BS) in cattle by using six morphometric traits, including body height at withers (HW), body length (BL), hip width (HpW), chest depth (CD), shoulder width (SW), and chest width (CW). The statistical measures for goodness of fit, including comparative fit index (CFI), Tucker-Lewis Index (TLI), and standardized root mean square residual (SRMR), were 0.94, 0.91, and 0.05, respectively, and appropriately indicate the adequacy of the confirmatory factor model proposed for the latent variable of BS. The standardized factor loadings of HW, BL, HpW, CD, SW, and CW for describing BS were 0.83, 0.76, 0.82, 0.89, 0.80, and 0.40, respectively, and statistically significant (P<0.01), implying that the observed variables were appropriate indicators of the corresponding BS latent trait. All correlations among morphometric traits were positive and statistically significant (P<0.01), ranging from 0.26 (CW-SW) to 0.77 (HW-CD). The correlations between the BS latent trait and the considered morphometric traits were also positive and statistically significant (P<0.01); ranged from 0.42 (CW-BS) to 0.93 (CD-BS). It was concluded that the proposed confirmatory factor analysis model showed adequate fit indices for constructing the BS latent trait, implying that the proposed framework adequately captures the underlying relationships among the observed variables. Overall, the study provided a robust framework for applying BS in contexts such as phenotypic evaluations, where a latent construct can capture the complexity of morphometric traits more effectively than individual traits alone.

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References

Anas, M., Zhao, B., Yu, H., Dahlen, C.R., Swanson, K.C., Ringwall, K.A., Hulsman Hanna, L.L., 2025. Multi-trait phenotypic modeling through factor analysis and Bayesian network learning to develop latent reproductive, body conformational, and carcass-associated traits in admixed beef heifers. Frontiers in Genetics 16, 1551967.
Bentler, P.M., 1990. Comparative fit indexes in structural models. Psychological Bulletin 107, 238-246.
Bousbia, A., Boudalia, S., Gueroui, Y., Hadded, K., Bouzaoui, A., Kiboub, D., Symeon, G., 2021. Use of multivariate analysis as a tool in the morphological characterization of the main indigenous bovine ecotypes in northeastern Algeria. PLoS One 16(7), e0255153.
Gianola, D., Sorensen, D., 2004. Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes. Genetics 167, 1407-1424.
Kominakis, A., Hager-Theodorides, A.L., Zoidis, E., Saridaki, A., Antonakos, G., Tsiamis, G., 2017. Combined GWAS and guilt-by-association-based prioritization analysis identifies functional candidate genes for body size in sheep. Genetics Selection Evolution 49, 41.
Leal-Gutierrez, J.D., Rezende, F.M., Elzo, M.A., Johnson, D., Penagaricano, F., Mateescu, R.G., 2018. Structural equation modeling and whole-genome scans uncover chromosome regions and enriched pathways for carcass and meat quality in beef. Frontiers in Genetics 9, 1-13.
Lynch, M., Walsh, B., 1998. Genetics and Analysis of Quantitative Traits. Oxford University Press, Sinauer Associates, Inc., Sunderland, MA.
Momen, M., Bhatta, M., Hussain, W., Yu, H., Morota, G., 2021. Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. Plant Direct 5, e00304.
Penagaricano, F., Valente, B. D., Steibel, J. P., Bates, R. O., Ernst, C. W., Khatib, H., Rosa, G. J. M., 2015. Searching for causal networks involving latent variables in complex traits: Application to growth, carcass, and meat quality traits in pigs. Journal of Animal Science 93, 4617-4623.
R Development Core Team, 2025. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Rosseel, Y., 2012. lavaan: An R package for structural equation modeling. Journal of Statistical Software 48, 1-36.
Sanjari Banestani, E., Esmailizadeh, A., Momen, M., Ayatollahi Mehrgardi, A., Mokhtari, M., 2023. Genome-wide association study identifies significant SNP and related genes associated with body size in Yorkshire pigs using latent variable modelling. Journal of Agricultural Science 161, 599-605.
SAS. 2010. SAS User’s Guide, Version 9.4. Statistical Analysis System. SAS Institute Inc. Cary, North Carolina, USA.
Schumacker, E.R., Lomax, G.R., 1996. A Beginner’s Guide to Structural Equation Modeling. Erlbaum, Mahwah, NJ.
Silva, H.T., Paiva, J.T., Botelho, M.E., Carrara, E.R., Lopes, P.S., Silva, F.F., Veroneze, R., Sterman Ferraz, J.B., Eler, J.P., Mattos, E.C., Gaya, L.G., 2021. Searching for causal relationships among latent variables concerning performance, carcass, and meat quality traits in broilers. Journal of Animal Breeding and Genetics 139, 181-192.
Wright, S., 1921. Correlation and causation. Journal of Agricultural Research 20, 557-585.
Xu, L., Luo, H., Zhang, X., Lu, H., Zhang, M., Ge, J., Zhang, T., Yan, M., Tan, X., Huang, X., et al., 2022. Factor analysis of genetic parameters for body conformation traits in dual-purpose Simmental cattle. Animals 12, 2433.
Yakubu, A., Hingir, A.V., Abdullah, A-R., 2018. Multivariate analysis of sexual dimorphism in the morphometric traits of Muturu cattle in north central Nigeria. Nigerian Journal of Genetics 32, 8-15.
Yougbare, B., Soudre, A., Ouedraogo, D., Zoma, B.L., Tapsoba, A.S.R., Sanou, M., et al., 2021. Genome-wide association study of trypanosome prevalence and morphometric traits in purebred and crossbred Baoule cattle of Burkina Faso. PLoS One 16(8), e0255089.
Yu, H., Campbell, M.T., Zhang, Q., Walia, H., Morota, G., 2019. Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. Genes, Genomes Genetics 9, 1975-1986.
Journal of Livestock Science and Technologies

Articles in Press, Accepted Manuscript
Available Online from 25 February 2026

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