Single-Step Methodology for Genomic Evaluation in Turkeys (Meleagris gallopavo)

Emhimad E A Abdalla; Flavio S Schenkel; Hakimeh Emamgholi Begli; Owen W Willems; Pieter van As; Ryley Vanderhout; Benjamin J Wood; Christine F Baes

doi:10.3389/fgene.2019.01248

Single-Step Methodology for Genomic Evaluation in Turkeys (Meleagris gallopavo)

Front Genet. 2019 Dec 20:10:1248. doi: 10.3389/fgene.2019.01248. eCollection 2019.

Authors

Emhimad E A Abdalla¹, Flavio S Schenkel¹, Hakimeh Emamgholi Begli¹, Owen W Willems², Pieter van As³, Ryley Vanderhout¹, Benjamin J Wood^{1

2

4}, Christine F Baes^{1

5}

Affiliations

¹ Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, Canada.
² School of Veterinary Science, University of Queensland, Gatton, QLD, Australia.
³ Hendrix Genetics Research Technology & Service B.V., Boxmeer, Netherlands.
⁴ Hybrid Turkeys, Kitchener, ON, Canada.
⁵ Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland.

Abstract

Genomic information can contribute significantly to the increase in accuracy of genetic predictions compared to using pedigree relationships alone. The main objective of this study was to compare the prediction ability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic BLUP (ssGBLUP) models. Turkey records of feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking ability were provided by Hybrid Turkeys, Kitchener, Canada. This data was analyzed using pedigree-based and single-step genomic models. The genomic relationship matrix was calculated either using observed allele frequencies, all allele frequencies equal to 0.5 or with a different scaling. To avoid potential problems with inversion, three different weighting factors were applied to combine the genomic and pedigree matrices. Across the studied traits, ssGBLUP had higher heritability estimates and significantly outperformed PBLUP in terms of accuracy. Walking ability was genetically negatively correlated to body weight and breast meat yield; however, it was not correlated to feed conversion ratio (FCR) or residual feed intake (RFI). Body weight showed a moderate positive genetic correlation to feed conversion ratio, residual feed intake and breast meat yield. Feed conversion ratio was strongly correlated to residual feed intake (0.68 ± 0.06). There was almost no genetic correlation between breast meat yield and feed efficiency traits. Larger differences in accuracy between PBLUP and ssGBLUP were observed for traits with lower heritability. Results of the three weighting factors showed only slight differences and an increase in accuracy of prediction compared to PBLUP. Slightly different levels of bias were observed across the models, but were higher among the traits; BMY was the only trait that had a regression coefficient higher than 1 (1.38 to 1.41). We show that incorporating genomic information increases the prediction accuracy for preselection of young candidate turkeys for the five traits investigated. Single-step genomic prediction showed substantially higher accuracy estimates than the pedigree-based model, and only slight differences in bias were observed across the alternate models.

Keywords: accuracy; bias; genetic correlation; genomic selection; pedigree best linear unbiased prediction; single-step blending.