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Open Access Highly Accessed Research article

Genome-enabled prediction of quantitative traits in chickens using genomic annotation

Gota Morota1*, Rostam Abdollahi-Arpanahi2, Andreas Kranis34 and Daniel Gianola156

Author Affiliations

1 Department of Animal Sciences, University of Wisconsin-Madison, Wisconsin, USA

2 Department of Animal Science, University College of Agriculture and Natural Resources, Karaj, Iran

3 Aviagen, Midlothian, UK

4 The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, UK

5 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Wisconsin, USA

6 Department of Dairy Science, University of Wisconsin-Madison, Wisconsin, USA

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BMC Genomics 2014, 15:109  doi:10.1186/1471-2164-15-109

Published: 7 February 2014

Abstract

Background

Genome-wide association studies have been deemed successful for identifying statistically associated genetic variants of large effects on complex traits. Past studies have found enrichment of trait-associated SNPs in functionally annotated regions, while depletion was reported for intergenic regions (IGR). However, no systematic examination of connections between genomic regions and predictive ability of complex phenotypes has been carried out.

Results

In this study, we partitioned SNPs based on their annotation to characterize genomic regions that deliver low and high predictive power for three broiler traits in chickens using a whole-genome approach. Additive genomic relationship kernels were constructed for each of the genic regions considered, and a kernel-based Bayesian ridge regression was employed as prediction machine. We found that the predictive performance for ultrasound area of breast meat from using genic regions marked by SNPs was consistently better than that from SNPs in IGR, while IGR tagged by SNPs were better than the genic regions for body weight and hen house egg production. We also noted that predictive ability delivered by the whole battery of markers was close to the best prediction achieved by one of the genomic regions.

Conclusions

Whole-genome regression methods use all available quality filtered SNPs into a model, contrary to accommodating only validated SNPs from exonic or coding regions. Our results suggest that, while differences among genomic regions in terms of predictive ability were observed, the whole-genome approach remains as a promising tool if interest is on prediction of complex traits.

Keywords:
Whole-genome prediction; Annotation; SNP; Chicken