Improvement of genomic prediction accuracy for residual feed intake by prioritizing genetic markers identified by genome-wide association and gene expression

Title
Improvement of genomic prediction accuracy for residual feed intake by prioritizing genetic markers identified by genome-wide association and gene expression
Publication Date
2020
Author(s)
de las Heras-Saldana, Sara
( author )
OrcID: https://orcid.org/0000-0002-8665-6160
Email: sdelash2@une.edu.au
UNE Id une-id:sdelash2
Moghaddar, Nasir
( author )
OrcID: https://orcid.org/0000-0002-3600-7752
Email: nmoghad4@une.edu.au
UNE Id une-id:nmoghad4
Clark, Samuel A
( author )
OrcID: https://orcid.org/0000-0001-8605-1738
Email: sclark37@une.edu.au
UNE Id une-id:sclark37
Van Der Werf, Julius H J
( author )
OrcID: https://orcid.org/0000-0003-2512-1696
Email: jvanderw@une.edu.au
UNE Id une-id:jvanderw
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
ICMS Australasia
Place of publication
Brisbane, Australia
UNE publication id
une:1959.11/51527
Abstract

Genomic selection strategies applied to complex traits like residual feed intake (RFI) could improve the selection for feed efficiency in livestock. In the last decade, more information about the undelying genetic architecture for traits like RFI have been obtained through genomic wide association studies (GWAS) and transcriptomic studies. The aim of this study was to test whether combining information from GWAS and gene expression significantly associated (GSA) results could improve the accuracy of genomic prediction. We evaluated the gain in accuracy of prediction of RFI in 2190 Angus steers using medium-density and high-density SNP panels (770k) and by adding pre-selected SNPs (top SNPs) most significant in GWAS and close GSA from a gene expression. Two cross-validation designs were compared, one where the same dataset was used for training and GWAS discovery (4CV) and one where the discovery was separated from the training set (4x4CV). There was no improvement in prediction when using 770k compared with the medium density SNP panel. The 4x4CV design increase in accuracy by 1.2 and 2.7 percent point when top-SNPs (-log10(P)=3.5) were used, compared to using only 50k or 770k, respectively. The 4CV design showed lower accuracy when using top SNPs and the predictions were much more biased. The use of top SNPs in combination with selected SNPs located inside GSA reduced the bias in prediction compared with using only top SNPs in 4x4CV and slightly increased the accuracy of prediction. Genomic prediction accuracy can be improved when using selected SNPs from GWAS and GSA.

Link
Citation
ICQG 6, Abstracts 2020, p. 126-126
Start page
126
End page
126

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