Hostname: page-component-76d6cb85b7-xh428 Total loading time: 0 Render date: 2026-07-17T18:33:23.127Z Has data issue: false hasContentIssue false
  • English
  • Français

Single-step Bayesian regression methods for genomic evaluation of milk yield of Murrah buffaloes

Published online by Cambridge University Press:  08 August 2025

Thalita Bianca Paiva ,
José Macedo ,
Jaliston Júlio Alves ,
Daniel Santos ,
Rusbel Raul Aspilcueta-Borquis ,
Humberto Tonhati  and
Francisco Araujo Neto Open the ORCID record for Francisco Araujo Neto [Opens in a new window]
Thalita Bianca Paiva
Affiliation:
Instituto Federal de Ciência e Tecnologia Goiano - IFGoiano, Rio Verde, GO, Brazil
José Macedo
Affiliation:
Instituto Federal de Ciência e Tecnologia Goiano - IFGoiano, Rio Verde, GO, Brazil
Jaliston Júlio Alves
Affiliation:
Instituto Federal de Ciência e Tecnologia Goiano - IFGoiano, Rio Verde, GO, Brazil
Daniel Santos
Affiliation:
Faculdade de Ciências Agrárias e Veterinárias de Jaboticabal -UNESP, Jaboticabal, São Paulo, Brazil
Rusbel Raul Aspilcueta-Borquis
Affiliation:
Universidade Tecnológica Federal do Paraná – UFTPR, Dois Vizinhos, PR, Brazil
Humberto Tonhati
Affiliation:
Faculdade de Ciências Agrárias e Veterinárias de Jaboticabal -UNESP, Jaboticabal, São Paulo, Brazil
Francisco Araujo Neto*
Affiliation:
Instituto Federal de Ciência e Tecnologia Goiano - IFGoiano, Rio Verde, GO, Brazil
*
Corresponding author: Francisco Araujo Neto; Email: francisco.neto@ifgoiano.edu.br
Get access Rights & Permissions [Opens in a new window]

Abstract

In this Research Communication we describe the application of single-step Bayesian regression (ssBR) models to predict milk yield of Murrah buffaloes. Milk production records of 2,026 cows in their first lactation were used. Using 270-day cumulative milk yield records as phenotype, genomic breeding values were predicted and their accuracies and dispersions were calculated by five methods: BayesA (ssBA), BayesB (ssBB), BayesC (ssBC); Bayesian Lasso (ssBL); and Bayesian ridge regression (ssBRR). For models based on mixture distributions (ssBB and ssBC), the proportions of markers having effect (π) were assumed as fixed, with respective values of 99% or 90%, or as unknown, where two approaches to estimate π were applied (ssBayesBπ and ssBayesCπ). The accuracy values found ranged from 0.550 (ssBBπ) to 0.584 (ssBCπ) and, the dispersion estimates ranged from 0.867 (ssBA) to 0.958 (ssBRR). The results indicated that Bayesian Lasso was the most suitable model for genetic evaluation of milk yield by buffaloes, considering accuracy and dispersion as criteria.

Keywords

Bubalus bubalisgenomic evaluationSNP markerprediction ability

Information

Type
Research Article
Information
Journal of Dairy Research , Volume 92 , Issue 1 , February 2025 , pp. 66 - 68
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Hannah Dairy Research Foundation.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Aguilar, I, Misztal, I, Johnson, DL, Legarra, A, Tsuruta, S and Lawlor, TJ (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.2009-2730CrossRefGoogle Scholar
Aspilcueta-Borquis, RR, Araujo Neto, FR, Santos, DJ, Hurtado-Lugo, NA and Silva, JA, Tonhati, H (2015) Multiple-trait genomic evaluation for milk yield and milk quality traits using genomic and phenotypic data in buffalo in Brazil. Genetics and Molecular Research 14, 18009–18017. https://doi.org/10.4238/2015.December.22.27CrossRefGoogle ScholarPubMed
Cesarani, A, Biffani, S, Garcia, A, Lourenco, D, Bertolini, G, Neglia, G, Misztal, I and Macciotta, NPP (2021) Genomic investigation of milk production in Italian buffalo. Italian Journal of Animal Science 20, 539–547. https://doi.org/10.1080/1828051X.2021.1902404CrossRefGoogle Scholar
Fernando, RL, Dekkers, JC and Garrick, DJ (2014) A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genetics Selection Evolution 46, 50. https://doi.org/10.1186/1297-9686-46-50CrossRefGoogle ScholarPubMed
Geweke, J (1992) Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bernado, JM, Berger, JO, Dawid, AP and Smith, AFM (eds), Bayesian Statistics 4. Oxford: Clarendon Press.Google Scholar
Heidelberger, P and Welch, PD (1983) Simulation run length control in the presence of an initial transient. Opinions in Research 31, 1109–1144. https://doi.org/10.1287/opre.31.6.1109CrossRefGoogle Scholar
Herrera, JRV, Flores, EB, Duijvesteijn, N, Moghaddar, N and van der Werf, JH (2021) Accuracy of genomic prediction for milk production traits in Philippine Dairy Buffaloes. Frontiers in Genetics 12, 682576. https://doi.org/10.3389/fgene.2021.682576CrossRefGoogle ScholarPubMed
Kumar, M, Vohra, V, Rawan, P and Lathwal, SS (2023) Genetic analysis of milk and milk composition traits in Murrah buffaloes using Bayesian inference. Animal Biotechnology 34, 3280–3286. https://doi.org/10.1080/10495398.2022.2130797CrossRefGoogle ScholarPubMed
Lee, J, Cheng, H, Garrick, D, Golden, B, Dekkers, J, Park, K, Lee, D and Fernando, R (2017) Comparison of alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo beef cattle. Genetics Selection and Evolution 49, 2. https://doi.org/10.1186/s12711-016-0279-9CrossRefGoogle ScholarPubMed
Lee, S, Dang, C, Choy, Y, Do, C, Cho, K, Kim, J, Kim, Y and Lee, J (2019) Comparison of genome-wide association and genomic prediction methods for milk production traits in Korean Holstein cattle. Asian-Australasian Journal of Animal Science 32, 913–921. https://doi.org/10.5713/ajas.18.0847CrossRefGoogle Scholar
Plummer, M, Best, N, Cowles, K and Vines, K (2006) CODA: Convergence Diagnosis and Output Analysis for MCMC. R News, 6, 7–11.Google Scholar
Santos, JCG, Araujo Neto, FR, Fernandez, GS, Santos, DJA,Cunha, FP, Aspilcueta-Borquis, RR and Tonhati, H (2024) Comparison of single-step methods for genomic prediction of age at first calving in dairy buffaloes. The Journal of Agricultural Science, 1–7. Published online 2024. https://doi.org/10.1017/S002185962400036Google Scholar
Yin, L, Zhang, H, Li, X, Zhao, S and Liu, X (2022) Hibayes: An R package to fit individual-level, summary-level and single-step bayesian regression models for genomic prediction and genome-wide association studies. bioRxiv. https://doi.org/10.1101/2022.02.12.480230CrossRefGoogle Scholar
Zhou, L, Mrode, R, Zhang, S, Zhang, Q, Li, B and Liu, JF (2018) Factors affecting GEBV accuracy with single-step Bayesian models. Heredity 120, 100–109. https://doi.org/10.1038/s41437-017-0010-9CrossRefGoogle ScholarPubMed
Watanabe, RN, Nascimento, GB, Bernardes, PA, Costa, RM, Lôbo, RB, Baldi, F and Munari, DP (2021) Inclusion of genomic information in estimation of genetic parameters for body weights and visual scores in Nelore cattle. Revista Brasileira de Zootecnia 50, e20200077. https://doi.org/10.37496/rbz5020200077CrossRefGoogle Scholar
Supplementary material: File

Paiva et al. supplementary material

Paiva et al. supplementary material
Download Paiva et al. supplementary material(File)
File 128.4 KB