Can multi-subpopulation reference sets improve the genomic predictive ability for pigs?1

Fangmann, A.; Bergfelder-Drüing, S.; Tholen, Ernst; Simianer, Henner; Erbe, Malena

doi:10.2527/jas.2015-9508

Cited by 6 publications

(8 citation statements)

References 22 publications

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“…They concluded that genetic connectedness between these Large White subpopulations was too low. The size of their reference and validation sets was also small (Fangmann et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Genomic data reveals large similarities among Canadian and French maternal pig lines

Boré¹,

Brito

Jafarikia

et al. 2018

Can. J. Anim. Sci.

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Combining reference populations from different countries and breeds could be an affordable way to enlarge the size of the reference populations for genomic prediction of breeding values. Therefore, the main objectives of this study were to assess the genetic diversity within and between two Canadian and French pig breeds (Landrace and Yorkshire) and the genomic relatedness among populations to evaluate the feasibility of an across-country reference population for pig genomic selection. A total of 14 756 pigs were genotyped on two single nucleotide polymorphism (SNP) chip panels (∼65K SNPs). A principal component analysis clearly discriminated Landrace and Yorkshire breeds, and also, but to a lesser extent, the Canadian and French purebred pigs of each breed. Linkage disequilibrium (LD) between adjacent SNPs was similar within Yorkshire populations. However, levels of LD were slightly different for Landrace populations. The consistency of gametic phase was very high between Yorkshire populations (0.96 at 0.05 Mb) and high for Landrace (0.88 at 0.05 Mb). Based on consistency of gametic phase, Canadian and French pig maternal lines are genetically close to each other. These results are promising, as they indicate that the accuracy of estimated genomic breeding values may increase by combining reference populations from the two countries.

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“…They concluded that genetic connectedness between these Large White subpopulations was too low. The size of their reference and validation sets was also small (Fangmann et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Genomic data reveals large similarities among Canadian and French maternal pig lines

Boré¹,

Brito

Jafarikia

et al. 2018

Can. J. Anim. Sci.

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“…Given the presence of (ancestral) relationships between animals and the greater consistency of LD between genetically-related lines within a breed than between breeds that have been separated for decades, using a multi-line reference population may be more beneficial than using a multi-breed reference population [ 16 ]. However, the changes in allele frequency since separation of the lines may still represent a challenge for using a multi-line reference population [ 21 ]. To the best of our knowledge, the use of a multi-line genomic evaluation strategy in small, related lines using real data has not been studied, in spite of the existence of numerous related lines worldwide.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…However, combining different pig populations may be challenging even if the lines belong to the same breeding company because the divergence may have happened a long time ago and breeding objectives are different across lines. For these reasons, only a few studies have investigated combining multiple lines, populations, or breeds for genomic predictions in pigs (Fangmann et al, 2015;Aliakbari et al, 2020). The PCA result showed a clear separation among the three lines (representing three different breeds) in our study.…”

Section: Multi-line Genomic Evaluation With Upg and Mfmentioning

confidence: 53%

“…The possible reasons for different Γ values in purebred lines between the current study and the study conducted by Xiang et al (2017) could be the use of terminal breeds and maternal breeds and different SNP data and lines from different companies. Fangmann et al (2015) investigated using multi-subpopulation reference sets to improve the predictive ability of genomic predictions in pigs; however, almost no benefit was reported, even though all the subpopulations diverged from German Large White pigs. Predictive abilities were reduced when distantly related subpopulations were added to the training data.…”

Section: Multi-line Genomic Evaluation With Upg and Mfmentioning

confidence: 99%

Multi-line ssGBLUP evaluation using preselected markers from whole-genome sequence data in pigs

et al. 2023

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Genomic evaluations in pigs could benefit from using multi-line data along with whole-genome sequencing (WGS) if the data are large enough to represent the variability across populations. The objective of this study was to investigate strategies to combine large-scale data from different terminal pig lines in a multi-line genomic evaluation (MLE) through single-step GBLUP (ssGBLUP) models while including variants preselected from whole-genome sequence (WGS) data. We investigated single-line and multi-line evaluations for five traits recorded in three terminal lines. The number of sequenced animals in each line ranged from 731 to 1,865, with 60k to 104k imputed to WGS. Unknown parent groups (UPG) and metafounders (MF) were explored to account for genetic differences among the lines and improve the compatibility between pedigree and genomic relationships in the MLE. Sequence variants were preselected based on multi-line genome-wide association studies (GWAS) or linkage disequilibrium (LD) pruning. These preselected variant sets were used for ssGBLUP predictions without and with weights from BayesR, and the performances were compared to that of a commercial porcine single-nucleotide polymorphisms (SNP) chip. Using UPG and MF in MLE showed small to no gain in prediction accuracy (up to 0.02), depending on the lines and traits, compared to the single-line genomic evaluation (SLE). Likewise, adding selected variants from the GWAS to the commercial SNP chip resulted in a maximum increase of 0.02 in the prediction accuracy, only for average daily feed intake in the most numerous lines. In addition, no benefits were observed when using preselected sequence variants in multi-line genomic predictions. Weights from BayesR did not help improve the performance of ssGBLUP. This study revealed limited benefits of using preselected whole-genome sequence variants for multi-line genomic predictions, even when tens of thousands of animals had imputed sequence data. Correctly accounting for line differences with UPG or MF in MLE is essential to obtain predictions similar to SLE; however, the only observed benefit of an MLE is to have comparable predictions across lines. Further investigation into the amount of data and novel methods to preselect whole-genome causative variants in combined populations would be of significant interest.

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Can multi-subpopulation reference sets improve the genomic predictive ability for pigs?1

Cited by 6 publications

References 22 publications

Genomic data reveals large similarities among Canadian and French maternal pig lines

Genomic data reveals large similarities among Canadian and French maternal pig lines

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

Multi-line ssGBLUP evaluation using preselected markers from whole-genome sequence data in pigs

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