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Research article - Peer-reviewed, 2022

Genomic prediction with whole-genome sequence data in intensely selected pig lines

Ros-Freixedes, Roger; Johnsson, Martin; Whalen, Andrew; Chen, Ching-Yi; Valente, Bruno D.; Herring, William O.; Gorjanc, Gregor; Hickey, John M.;

Abstract

Background Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage. Methods We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests. Results The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected. Conclusions Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

Published in

Genetics Selection Evolution

2022, volume: 54, article number: 65
Publisher: BMC

Authors' information

Ros-Freixedes, Roger
University of Edinburgh
Ros-Freixedes, Roger
Universitat de Lleida
University of Edinburgh
Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics
Whalen, Andrew
University of Edinburgh
Chen, Ching-Yi
Pig Improvement Company
Valente, Bruno
Pig Improvement Company
Herring, William
Pig Improvement Company
Gorjanc, Gregor
UK Research and Innovation (UKRI)
Gorjanc, Gregor
University of Edinburgh
Hickey, John M.
University of Edinburgh

UKÄ Subject classification

Animal and Dairy Science

Publication Identifiers

DOI: https://doi.org/10.1186/s12711-022-00756-0

URI (permanent link to this page)

https://res.slu.se/id/publ/119181