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The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs.

TitleThe impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs.
Publication TypeJournal Article
Year of Publication2020
AuthorsAliakbari, A, Delpuech, E, Labrune, Y, Riquet, J, Gilbert, H
JournalGenet Sel Evol
Volume52
Issue1
Pagination57
Date Published2020 Oct 07
ISSN1297-9686
Abstract

BACKGROUND: Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits.

RESULTS: Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses.

CONCLUSIONS: Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

DOI10.1186/s12711-020-00576-0
Alternate JournalGenet Sel Evol
PubMed ID33028194
PubMed Central IDPMC7539441
Grant ListANR-16-CE20-0003 / / Agence Nationale de la Recherche /