Single-step genomic evaluation for growth traits in a Mexican Braunvieh cattle population

Valerio-Hernández, Jonathan Emanuel; Ruíz-Flores, Agustín; Nilforooshan, Mohammad Ali; Pérez-Rodríguez, Paulino

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Anim Biosci > Accepted Articles

Article

https://doi.org/10.5713/ab.22.0158 [Accepted] Published online February 28, 2023.

Single-step genomic evaluation for growth traits in a Mexican Braunvieh cattle population

Jonathan Emanuel Valerio-Hernández¹

, Agustín Ruíz-Flores¹

, Mohammad Ali Nilforooshan³

, Paulino Pérez-Rodríguez^2,*

¹Departamento de Zootecnia, Universidad Autónoma Chapingo, Km 38.5 Carretera México-Texcoco. Chapingo, Estado de México. CP 56227, Mexico
²Programa de Estadística, Colegio de Postgraduados. Montecillo, Estado de México. CP 56264, Mexico
³Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand

Correspondence: Paulino Pérez-Rodríguez, Tel: +52-595-952-0200, Fax: +52-595-952-0200, Email: perpdgo@colpos.mx

Received: 19 April 2022 • Revised: 2 August 2022 • Accepted: 27 February 2023

Abstract

Objective
The objective was to compare (pedigree-based) BLUP, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population.
Methods
Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed.
Results
The random contemporary group (CG) effect explained about 50, 45, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP.
Conclusion
The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

Keywords: BLUP; Braunvieh; Genomic Selection; GBLUP; ssGBLUP