Genomic Prediction in Local Breeds: The Rendena Cattle as a Case Study.

Beniamino Tuliozi,Roberto Mantovani,Nadia Guzzo,Cristina Sartori,Enrico Mancin

doi:10.3390/ani11061815

Beniamino Tuliozi, Roberto Mantovani + Show 3 more

Open Access

https://doi.org/10.3390/ani11061815

Copy DOI

Abstract

Simple SummaryAlthough genomic selection is being used in many livestock species, it has not yet been considered in local breeds due to the lower population size and the potential less effective impact on the genetic evaluation of these breeds. The current research aims to investigate how genomic data can impact the accuracy of genetic predictions for beef traits in Rendena, a small local cattle breed of the North-East of Italy selected for a dual purpose. Classical animal models using only phenotypic information were compared with two models that integrated genomic data with pedigree information. The genomic models presented better accuracy in estimated breeding values of the animals than the ‘classical’ animal model, especially the ‘simpler’ one assuming homogeneous variances of single nucleotide polymorphisms. Our results show that the inclusion of genomic information can be successfully applied to breeding selection scenarios even in small local cattle breeds such as Rendena.The maintenance of local cattle breeds is key to selecting for efficient food production, landscape protection, and conservation of biodiversity and local cultural heritage. Rendena is an indigenous cattle breed from the alpine North-East of Italy, selected for dual purpose, but with lesser emphasis given to beef traits. In this situation, increasing accuracy for beef traits could prevent detrimental effects due to the antagonism with milk production. Our study assessed the impact of genomic information on estimated breeding values (EBVs) in Rendena performance-tested bulls. Traits considered were average daily gain, in vivo EUROP score, and in vivo estimate of dressing percentage. The final dataset contained 1691 individuals with phenotypes and 8372 animals in pedigree, 1743 of which were genotyped. Using the cross-validation method, three models were compared: (i) Pedigree-BLUP (PBLUP); (ii) single-step GBLUP (ssGBLUP), and (iii) weighted single-step GBLUP (WssGBLUP). Models including genomic information presented higher accuracy, especially WssGBLUP. However, the model with the best overall properties was the ssGBLUP, showing higher accuracy than PBLUP and optimal values of bias and dispersion parameters. Our study demonstrated that integrating phenotypes for beef traits with genomic data can be helpful to estimate EBVs, even in a small local breed.

Highlights

Rendena is a dual-purpose cattle breed indigenous to the North-East of Italy
We investigated if the inclusion of genomic data in the estimate of breeding values for three key beef traits measured during performance tests in Rendena might
EUROP was the trait with lowest heritability, 0.304, while average daily gain (ADG) and dressing percentage (DP) showed an h2 of 0.335 and 0.392, respectively

Summary

Introduction

Rendena is a dual-purpose cattle breed indigenous to the North-East of Italy. This breed is included within the “European Federation of Cattle Breeds of the Alpine System”(FERBA), an organization whose main purpose consists in the preservation and promotion of local cattle breeds of the alpine system (http://www.ferba.info, accessed on 20 April2021). Rendena is a dual-purpose cattle breed indigenous to the North-East of Italy. As is the case with many indigenous breeds, a greater genetic diversity than specialized and cosmopolitan breeds is expected for the Rendena [1]. This remarkable biodiversity is of great ecological importance and can be a beneficial factor for the survival of the local population. Traditional breeds such as Rendena provide additional benefits to the local human population such as economic advantages, ecosystem services, and cultural benefits, such as preservation of cultural heritage and tradition of a specific area [1]. Beef attitude plays a less important role than milk in the selection index, an increase in the accuracy of the selection for this feature over time could prevent its detriment due to the antagonism with milk production [3]

Objectives

Methods

Results

Discussion

Conclusion