The accuracy of breeding values for body size latent trait in pigs under different prediction models

Document Type : Original Research Article (Regular Paper)


1 Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

2 Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53706, USA

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


The present study was performed to quantify a latent variable for body size (BS) from the five linear body measurements, including body length (BL), body height (BH), chest width (CW), chest girth (CG), and tube girth (TG). The study population consisted of N= 5573 Yorkshire pigs, 592 individuals out of them were genotyped using a PorcineSNP80 BeadChip. The body size latent variable was determined using Confirmatory Factor Analysis (CFA). Then, the accuracy of breeding values was obtained using pedigree-based best linear unbiased prediction (PBLUP), genomic best linear unbiased prediction (GBLUP), and single-step genomic best linear unbiased prediction (ssGBLUP) models. The overall fit indices, including standardized root mean square residual (SRMR), root mean square error of approximation (RMSEA), Tucker-Lewis Index (TLI), and comparative fit index (CFI) were obtained for the BS as 0.03, 0.09, 0.93, and 0.96, respectively which imply the adequacy of the considered model for BS construct. The performance of models was measured in a 5-fold cross-validation with 10 repeats to get a more accurate measure of the model's performance. The accuracy of models was compared via the correlation between predicted breeding values (PBV) and estimated breeding values (EBV) metric which was 0.37, 0.30, and 0.28 for PBLUP, ssGBLUP, and GBLUP, respectively. Furthermore, the goodness of fit is measured by the mean square of error (MSE) and Pearson's correlations r(y, ) between observed and predicted phenotypes. The lowest MSE and the highest Pearson's correlations were obtained under PBLUP while the highest MSE and the lowest Pearson's correlations were obtained under GBLUP. The obtained results showed the GBLUP method generally provided lower prediction accuracies than PBLUP and ssGBLUP methods, and also ssGBLUP generated lower prediction accuracy than traditional PBLUP. The performance of ssGBLUP and GBLUP was lower than expected mainly due to the small number of genotyped animals.


Main Subjects

Aguilar, I., Misztal, I., Johnson, D.L., Legarra, A., Tsuruta, S., Lawlor, T.J., 2010. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743-752.
Bentler, P. M., 1990. Comparative fit indexes in structural models. Psychological Bulletin 107, 238-246.
Browning, B. L., Browning, S. R., 2009. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. The American Journal of Human Genetics 84, 210-223.
Choi, T., Lim, D., Park, B, Sharma, A., Kim, J.J.,  Kim, S., Lee, S.H., 2017. Accuracy of genomic breeding value prediction for intramuscular fat using different genomic relationship matrices in Hanwoo (Korean cattle). Asian-Australasian Journal of Animal Sciences 30, 907-911.
Christensen, O.F., Lund, M.S., 2010. Genomic prediction when some animals are not genotyped. Genetics Selection Evolution 42, 2.
Christensen, O.F., Madsen, P., Nielsen, B., Ostersen, T., Su, G., 2012. Single-step methods for genomic evaluation in pigs. Animal 6, 1565-1571.
Dekkers, J.C.M., Mathur, P.K., Knol, E.F., 2011. Genetic improvement of the pig. In: Rothschild, M.F., Ruvinsky, A. (Eds.), The Genetics of the Pig, CAB International, UK, pp. 390-425.
Forni, S., Aguilar, I., Misztal, I., 2011. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genetics Selection Evolution 43:1.
Gao, H.D., Christensen, O. F., Madsen, P., Nielsen, U. S., Zhang, Y., Lund, M. S., Guosheng, S., 2012. Comparison on genomic predictions using three GBLUP methods and two single-step blending methods in the Nordic Holstein population. Genetics Selection Evolution 44:8.
Goddard, M.E., Hayes, B.J., 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Review Genetics 10, 381-391.
Koivula, M., Stranden, I., Poso, J., Aamand, G.P., Mantysaari, E.A., 2015. Single-step genomic evaluation using multitrait random regression model and test-day data. Journal of Dairy Science 98, 2775-2784.
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, 532.
Liu, H., Song, H., Jiang, Y., Jiang, Y., Zhang, F., Liu, Y., Shi, Y., Ding, X., Wang, C., 2021. A single-step genome wide association study on body size traits using imputation-based whole-genome sequence data in Yorkshire pigs. Frontiers in Genetics 12:629049.
Legarra, A., Aguilar, I., Misztal, I., 2009. A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656-4663.
Lopes, J.S., Rorato, P.R.N., Mello, F.C.B., Freitas, M.S.D., Prestes, A.M., Garcia, D.A., Oliveira, M.M.D., 2019. Strategies to control inbreeding in a pig breeding program: a simulation study. Ciencia Rural, 49.
Lourenco, D.A.L., Misztal, I., Tsuruta, S., Aguilar, I., Ezra, E., Ron, M., Shirak, A., Weller, J.I., 2014. Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of Dairy Science 97, 1742–1752.
Meuwissen, T.H.E., Hayes, B.J., Goddard, M.E., 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.
Meyer, K., 2013. WOMBAT- A Programme for Mixed Model Analyses by Restricted Maximum Likelihood. User Notes, Animal Genetics and Breeding Unit, Armidale, Australia.
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, p.e00304.
Niu, P., Kim, S., Choi, B., Kim, T., Kim, J., Kim, K., 2013. Porcine insulinlike growth factor 1 (IGF1) gene polymorphisms are associated with body size variation. Genes Genomics 35, 523-528.
Ohnishi, C., Satoh, M., 2018. Estimation of genetic parameters for performance and body measurement traits in Duroc pigs selected for average daily gain, loin muscle area, and backfat thickness. Livestock Science 214, 161-166.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., De Bakker, P.I., Daly, M.J., Sham, P.C., 2007. Plink: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559-575.
R Development Core Team, 2021. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Rahmatalla,  S.A., Arends,  D.,  Reissmann, M., Wimmers, K., Reyer, H., Brockmann, G.A., 2018. Genome-wide association study of body morphological traits in Sudanese goats. Animal Genetics 49, 478-482.
Rosseel, Y., 2012. lavaan: an R package for structural equation modeling. Journal of Statistical Software 48, 1-36.
Sargolzaei, M., Iwaisaki, H., Colleau, J.J., 2006. CFC: A tool for monitoring genetic diversity, Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil.
Silva, H.T., Paiva, J.T., Botelho, M.E., Carrara, E.R., Lopes, P.S., Silva, F.F., Veroneze, R., Ferraz, J.B.S., 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.
Song, H., Zhang, J., Zhang, Q., Ding, X., 2019. Using different single-step strategies to improve the efficiency of genomic prediction on body measurement traits in pig. Frontiers in Genetics 9, 730.
Steiger, J. H., 1990. Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research 25, 173-180.
VanRaden, P.M., 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414-4423.