Campus Units

Animal Science

Document Type

Article

Publication Version

Published Version

Publication Date

6-2020

Journal or Book Title

Poultry Science

Volume

99

Issue

6

First Page

2833

Last Page

2840

DOI

10.1016/j.psj.2020.01.019

Abstract

Several genomic methods were applied for predicting shell quality traits recorded at 4 different hen ages in a White Leghorn line. The accuracies of genomic prediction of single-step GBLUP and single-trait Bayes B were compared with predictions of breeding values based on pedigree-BLUP under single-trait or multitrait models. Breaking strength (BS) and dynamic stiffness (Kdyn) measurements were collected on 18,524 birds from 3 consecutive generations, of which 4,164 animals also had genotypes from an Affymetrix 50K panel containing 49,591 SNPs after quality control edits. All traits had low to moderate heritability, ranging from 0.17 for BS to 0.34 for Kdyn. The highest accuracies of prediction were obtained for the multitrait single-step model. The use of marker information resulted in higher prediction accuracies than pedigree-based models for almost all traits. A genome-wide association study based on a Bayes B model was conducted to detect regions explaining the largest proportion of genetic variance. Across all 8 shell quality traits analyzed, 7 regions each explaining over 2% of genetic variance and 54 regions each explaining over 1% of genetic variance were identified. The windows explaining a large proportion of genetic variance overlapped with several potential candidate genes with biological functions linked to shell formation. A multitrait repeatability model using a single-step method is recommended for genomic evaluation of shell quality in layer chickens.

Comments

This article is published as Wolc, A., Wioleta Drobik-Czwarno, Tomasz Jankowski, J. Arango, P. Settar, J. E. Fulton, R. L. Fernando, D. J. Garrick, and J. C. M. Dekkers. "Accuracy of genomic prediction of shell quality in a White Leghorn line." Poultry Science 99, no. 6 (2020): 2833-2840. doi: 10.1016/j.psj.2020.01.019.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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