Integration of dominance effects into genomic models for enhancing the understanding of heterosis in dairy cattle
DOI:
https://doi.org/10.33448/rsd-v15i2.50700Keywords:
Dairy cattle, Fatty acid, GWAS, Holstein, Non-additive effect, Simulation, SOX5 gene.Abstract
Molecular heterozygosity and heterosis are gaining importance in the evaluation of various species. However, the exploration of estimated heterosis based on the genome in purebred populations remains limited. We aimed at investigating the use of heterozygosity as a potential indicator of genomic heterosis in purebred populations. Using a simulated dataset for milk production and quality traits, we considered three scenarios (h² = 0.10, 0.30, and 0.50). We adapted the Genome-Wide Association Study (GWAS) to capture non-additive variations for milk yield (MY), fat percentage (FP), protein percentage (PP), casein percentage (CP), and polyunsaturated fatty acid percentage (PUFA). Heterozygosity ranged from 35.4% to 35.6% in the simulated scenario, with genomic heterosis ranging from 3.2% to 17.0%. Regression coefficients emphasized the significance of heterozygosity for genomic heterosis, varying from 4.8 to 6.07. In real data, most identified genomic regions showed consistency between additive and non-additive models. An increase of 1.96 kg/day in MY was associated with a one-unit increase in heterozygosity, along with a 0.0059% increase in PUFA. Eight genes, including the SOX5 gene associated with PUFAs, have been identified in the literature as important for human health. Selection based on heterozygosity is proposed to favor genomic heterosis, and considering dominance effects in GWAS contributes to marker and QTL identification with potential heterozygous advantages. Our study significantly contributes to understanding how heterosis can be utilized for animal selection in pure breeds using genomic information. Our findings suggest predictive approaches tailored to include genetic dominance effects in genetic evaluation and genome-wide association studies.
References
Abdollahi-Arpanahi, R., Gianola, D., & Peñagaricano, F. (2020). Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes. Genetics Selection Evolution, 52, 1-15. https://doi.org/10.1186/s12711-020-00531-z
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. https://doi.org/10.3168/jds.2009-2730
Aguilar, I. (2014). Short introduction to BLUPF90 family programs. http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=slides_2014_lva.pdf
Aguilar, I., Legarra, A., Cardoso, F., Masuda, Y., Lourenco, D., & Misztal, I. (2019). Frequentist p-values for large-scale-single step Genomic-wide association, with an application to birth weight in American Angus cattle. Genetics Selection Evolution, 51, 1-8. https://doi.org/10.1186/s12711-019-0469-3
Alillo, H., Pryce, J. E., González-Recio, O., Cocks, B. G., Goddard, M. E., & Hayes, B. J. (2017). Including nonadditive genetic effects in mating programs to maximize dairy farm profitability. Journal of Dairy Science, 100, 1203-1222. https://doi.org/10.3168/jds.2016-11261
Amadeu, R. R., Cellon, C., Lara, L., Resende, M., Oliveira, I., Ferrao, L., Muñoz, P., & Garcia, A. (2021). AGHmatrix: Relationship Matrices for Diploid and Autopolyploid Species. https://github.com/prmunoz/AGHmatrix
Bagheri, M., Miraie-Ashtiani, R., Moradi-Shahrbabak, M., Nejati-Javaremi, A., Pakdel, A., von Borstel, U. U., Pimentel, E. C. G., & Konig, S. (2013). Selective genotyping and logistic regression analyses to identify favorable SNP-genotypes for clinical mastitis and production traits in Holstein dairy cattle. Livestock Science, 151, 140-151. https://doi.org/10.1016/j.livsci.2012.11.018
Birchler, J. A., Yao, H., Chudalayandi, S., Vaiman, D., & Veitia, R. A. (2010). Heterosis. The Plant Cell, 22(7), 2105-2112. https://doi.org/10.1105/tpc.110.076133
Cao, Z., Gu, Y., Yang, Q., He, Y., Fetouh, M. I., Warner, R. M., & Deng, Z. (2019). Genome-wide identification of quantitative trait loci for important plant and flower traits in petunia using a high-density linkage map and an interspecific recombinant inbred population derived from Petunia integrifolia and P. axillaris. Horticulture Research, 6, 1-15. https://doi.org/10.1038/s41438-018-0091-5
Chein, F. (2019). Introdução aos modelos de regressão linear. Brasilia. ENAP. ISBN: 978-85-256-0115-5. https://bibliotecadigital.enap.gov.br/bitstream/1/4788/1/Livro_Regress%C3%A3o%20Linear.pdf
Christensen, O. F., & Lund, M. (2010). Genomic prediction when some animals are not genotyped. Genetics Selection Evolution, 42, 1-8. https://doi.org/10.1186/1297-9686-42-2
Clasen, J. B., Norberg, E., Madsen, P., Pederson, J., & Kargo, M. (2017). Estimation of genetic parameters and heterosis for longevity in crossbred Danish dairy cattle. Journal of Dairy Science, 100, 6337-6342. https://doi.org/10.3168/jds.2017-12627
Deng, H. W., & Fu, Y. X. (1998). Conditions for positive and negative correlations between fitness and heterozygosity in equilibrium populations. Genetics, 148, 1333-1340. https://doi.org/10.1093/genetics/148.3.1333
Fragomeni, B. O., Misztal, I., Lourenco, D. L., Aguilar, I., Okimoto, R., & Muir, W. M. (2014). Changes in variance explained by top SNP windows over generations for three traits in broiler chicken. Frontiers in Genetics, 1, 1-7. https://doi.org/10.3389/fgene.2014.00332
Gauthier, S. F., Pouliot, S. F., & Maoubois, J. L. (2006). Growth factors from bovine milk and colostrum: composition, extraction and biological activities. Le Lait, 86, 99-125. https://doi.org/10.1051/lait:2005048
Gutiérrez-Gil, B., Arranz, J. J., Pong-Wong, R., Garcia-Gámez, E., Kijas, J., & Wiener, P. (2014). Application of selection mapping to identify genomic associated with dairy production in sheep. PLoS ONE, 9, 1-13. https://doi.org/10.1371/journal.pone.0094623
Habier, D., Fernando, R. L., Kizilkaya, K., & Garrick, D. J. (2011). Extension for the Bayesian alphabet for genomic selection. BMC Bioinformatics, 12, 1-12. https://doi.org/10.1186/1471-2105-12-186
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008). Data and theory point to mainly additive genetic variance for complex traits. PLoS ONE, 4, 1-10. https://doi.org/10.1371/journal.pgen.1000008
ICAR – International Committee for Animal Recording. (2017). Section 2: Cattle milk recording. https://www.icar.org/Guidelines/02-Overview-Cattle-Milk-Recording.pdf
Iung, L. H. S., Petrini, J., Ramírez-Díaz, J., Salvian, M., Rovadoscki, G. A., Pilonetto, F., Dauria, B. D., Machado, P. F., Coutinho, L. L., Wiggans, G. R., & Mourão, G. B. (2019). Genome-wide association study for milk production traits in a Brazilian Holstein population. Journal of Dairy Science, 102, 5305-5314. https://doi.org/10.3168/jds.2018-14811
Kargo, M., Clasen, J. B., Nielsen, H. M., Byskov, K., & Norberg, E. (2021). Short communication: Heterosis and breed effects for milk production and udder health traits in crosses between Danish Holstein, Danish Red, and Danish Jersey. Journal of Dairy Science, 104, 678-682. https://doi.org/10.3168/jds.2019-17866
Kelleher, M. M., Berry, D. R., Keraney, J. F., McParland, S., Buckley, F., & Purifield, D. C. (2017). Inference of population structure of purebred dairy and beef cattle using high-density genotype data. Animal, 11, 15-23. https://doi.org/10.1017/S1751731116001099
Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92, 4656-4663.
Marques, D. B. D., Bastiaansen, J. W. M., Broekhuijse, M. L. W. J., Lopes, M. S., Knol, E. F., Harlizius, B., Guimarães, S. E. F., Silva, F. F., & Lopes, P. S. (2018). Weighted single-step GWAS and gene network analysis reveal new candidate genes for semen traits in pigs. Genetics Selection Evolution, 50, 1-14. https://doi.org/10.1186/s12711-018-0412-z
Masuda, Y. (2021). Introduction to BLUPF90 suit programs Standard edition. University of Georgia. http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=tutorial_blupf90.pdf
Misztal, I., Legarra, A., & Aguilar, I. (2009). Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. Journal of Dairy Science, 92, 4648-4655. https://doi.org/10.3168/jds.2009-2064
Misztal, I., Tsuruta, S., Lourenco, D., & Masuda, Y. (2018). Manual for BLUPF90 family of programs. University of Georgia, Athens, USA. http://nce.ads.uga.edu/wiki/doku.php?id=start
Nicolini, P., Amorín, R., Han, Y., & Peñagarciano, F. (2018). Whole-genome scan reveals significant non-additive effects for sire conception rate in Holstein cattle. BMC Genetics, 19, 1-18. https://doi.org/10.1186/s12863-018-0600-4
Petrini, J., Iung, L. H. S., Rodriguez, M. A. P., Salvian, M., Pértille, F., Rovadoscki, G. A., Cassoli, L. D., Coutinho, L. L., Machado, P. F., Wiggans, G. R., & Mourão, G. B. (2016). Genetic parameters for milk fatty acids, milk yield and quality traits of a Holstein cattle population reared under tropical conditions. Animal Breeding and Genetics, 133, 384-395. https://doi.org/10.1111/jbg.12205
Pereira, A. S. et al. (2018). Metodologia da pesquisa científica. [free ebook]. Santa Maria: Editora da UFSM.
Perez Lopez, C. (2022). Técnicas de análise preditiva de dados. Modelos de regressão linear múltipla. Editora Scientific Books. ISBN-13: 978-1446702123
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
Risemberg, R. I. C. et al. (2026). A importância da metodologia científica no desenvolvimento de artigos científicos. E-Acadêmica Journal, 7(1), e0171675. https://eacademica.org/eacademica/article/view/675
Rodriguez, M. A. P., Petrini, J., Ferreira, E. M., Mourão, L. R. M. B., Salvian, M., Cassoli, L. D., Pires, A. V., Machado, P. F., & Mourão, G. B. (2014). Concordance analysis between estimation methods of milk fatty acids content. Food Chemistry, 156, 170–175. https://doi.org/10.1016/j.foodchem.2014.01.092
Rovadoscki, G. A., Pertile, S. F. N., Alvarenga, A. B., Cesar, A. S. M., Pértille, F., Petrini, J., Franzo, V., Soares, W. V. B., Morota, G., Spangler, M. L., Pinto, L. F. B., Carvalho, G. G. P., Lanna, D. P. D., Coutinho, L. L., & Mourão, G. B. (2018). Estimates of Genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep. BMC Genomics, 19, 1-14. https://doi.org/10.1186/s12864-018-4777-8
Saborío-Montero, A., Vargas-Leitón, B., Romero-Zúñiga, J. J., & Camacho-Sandoval, J. (2018). Additive genetic and heterosis effects for milk fever in a population of Jersey, Holstein x Jersey, and Holstein cattle under grazing conditions. Journal of Dairy Science, 101, 9128-9134. https://doi.org/10.3168/jds.2017-142234
Silva, F. L., Pinto, M. S., Carvalho, A. F., & Perrone, I. T. (2016). Fator de crescimento transformador beta (TGF-β). Revista do Instituto de Laticínios Cândido Tostes, 70, 226-238. https://doi.org/10.14295/2238-6416.v70i4.384
Shitsuka, R. et al. (2014). Matemática fundamental para tecnologia. (2nd Ed.). Editora Érica.
Sorensen, M. K., Norberg, E., Pedersen, J., & Christensen, L. G. (2008). Invited Review: Crossbreeding in Dairy Cattle: A Danish Perspective. Journal of Dairy Science, 91, 4116-4128. https://doi.org/10.3168/jds.2008-1273
Tambasco-Talhari, D., Alencar, M. M., Paz, C. C. P., Cruz, G. M., Rodrigues, A. A., Packer, I. U., Coutinho, L. L., & Regitano, L. C. A. (2005). Molecular marker heterozygosities and genetic distances as correlates of production traits in F1 bovine crosses. Genetics and Molecular Biology, 28, 218-224. https://doi.org/10.1590/S1415-47572005000200007
Tiezzi, F., Parker-Gaddis, K. L., Cole, J. B., Clay, J. S., & Maltecca, C. (2015). A genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure. PLoS ONE, 10, 1-15. https://doi.org/10.1371/journal.pone.0114919
Tsairidou, S., Allen, A. R., Pong-Wong, R., McBride, S. H., Wright, D. M., Matika, O., Pooley, C. M., McDowell, S. W. J., Glass, E. J., Skuce, R. A., Bishop, S. C., & Woolliams, J. A. (2018). An analysis of effects of heterozygosity in dairy cattle for bovine tuberculosis resistance. Animal Genetics, 49, 103-109. https://doi.org/10.1111/age.12637
VanRaden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91, 4414-4423. https://doi.org/10.3168/jds.2007-0980
VanRaden, P. M. (2015). findhap.f90 – Find haplotypes and impute genotypes using multiple chip sets and sequence data. http://aipl.arsusda.gov/software/findhap
Veitia, R. A. (2010). A generalized model of gene dosage and dominant negative effects in macromolecular complexes. FASEB Journal, 24, 994–1002. https://doi.org/10.1096/fj.09-146969
Vidotti, M. S., Lyra, D. H., Morosini, J. S., Granato, I. S. C., Quecine, M. C., Azevedo, J. L., & Fritsche-Neto, R. (2019). Additive and heterozygous (dis)advantages GWAS models reveal candidate genes involved in the genotypic variation of maize hybrids to Azospirillum brasilense. PLoS ONE, 14, 1-21. https://doi.org/10.1371/journal.pone.0222788
Vieira, S. (2021). Introdução à bioestatística. Editora GEN/Guanabara Koogan.
Vitezica, Z., Varona, L., & Legarra, A. (2013). On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics, 195, 1223-1230. https://doi.org/10.1534/genetics.113.155176
Wang, H., Misztal, I., Aguilar, I., & Legarra, A. (2012). Genome-wide association mapping including phenotypes from relatives without genotypes. Genetics Research, 94, 73-83. https://doi.org/10.1017/S0016672312000274
Wang, H., Misztal, I., Aguilar, I., Legarra, A., Fernando, R. L., Vitezica, Z., Okimoto, R., Wing, T., Hawken, R., & Muir, W. M. (2014). Genome-wide association mapping including phenotypes from relatives without genotypes in single-step (ssGWAS) for 6-week body weight in broiler chickens. Frontiers in Genetics, 5, 1-10. https://doi.org/10.3389/fgene.2014.00134
Xiao, J., Li, J., Yuan, L., & Tanksley, S. D. (1995). Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics, 140, 745-754. https://doi.org/10.1093/genetics/140.2.745
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Copyright (c) 2026 Fabrício Pilonetto, Giovanni Coelho Ladeira, Juliana Petrini, Mayara Salvian, Izally Carvalho Gervásio, Flavia de Oliveira Scarpino van Cleef, Darlene dos Santos Daltro, Aline Zampar, Paulo Fernando Machado, Luiz Lehmann Coutinho, Gerson Barreto Mourão
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