Improving Genetic Predictions Using Multiple Salmon Populations

Jenn Hoskins
16th May, 2024

Improving Genetic Predictions Using Multiple Salmon Populations

Image Source: Natural Science News, 2024

Key Findings

  • The study by Nofima focused on improving breeding values for AGD resistance in Atlantic salmon using multi- and across-population genomic predictions
  • Combining data from multiple salmon populations generally improved the accuracy of breeding values for AGD resistance
  • The improvement was more significant when using a selected subset of SNPs and nonlinear prediction models
Atlantic salmon breeding programs often face challenges related to the accuracy of genomic predictions, which are crucial for selecting traits such as disease resistance. One such disease, amoebic gill disease (AGD), significantly impacts salmon health and the aquaculture industry. A recent study conducted by Nofima[1] evaluated the potential of multi- and across-population genomic prediction to improve the accuracy of breeding values for AGD resistance in Atlantic salmon. Genomic prediction involves using dense single nucleotide polymorphisms (SNPs) to estimate the genetic merit of individuals for specific traits. The accuracy of these predictions largely depends on the size of the reference population, which is a group of individuals with known genotypes and phenotypes. Larger reference populations usually provide more accurate predictions[2]. In Atlantic salmon breeding, four parallel populations exist, presenting an opportunity to combine them and increase the reference population size, potentially reducing costs and welfare issues related to trait recording. The study by Nofima examined the accuracy of genomic predictions using both all SNPs on a 55K chip and a selected subset of SNPs. The selection was based on the signs of allele substitution effect estimates across populations. Two types of genomic prediction models were used: linear and nonlinear. The researchers also investigated the genetic distance, genetic correlation, and persistency of linkage disequilibrium (LD) phase across the populations. Linkage disequilibrium (LD) refers to the non-random association of alleles at different loci. When a genetic marker and a quantitative trait locus (QTL) are in LD in one population, they may not be in LD in another population, or their LD phase may be reversed[3]. The persistence of LD phase across populations is crucial for the success of multi-population genomic predictions. The study found that the genetic distance and correlation between the populations affected the accuracy of genomic predictions. This finding aligns with previous research indicating that combining data sets from multiple populations can increase reliabilities, especially when marker density is high and populations are not too diverged[4]. The results showed that multi-population predictions generally improved the accuracy of breeding values for AGD resistance compared to single-population predictions. This improvement was more pronounced when using the selected subset of SNPs and nonlinear prediction models. The findings suggest that incorporating genetic information from multiple populations can enhance the reliability of genomic predictions, even for traits with complex genetic architectures like disease resistance. Moreover, the study highlighted the importance of maintaining a high marker density to ensure the persistence of LD phase across populations. This requirement echoes previous findings that a higher marker density is needed for accurate genomic predictions across diverged breeds[3]. By leveraging the genetic diversity and relationships between the four Atlantic salmon populations, the study demonstrated a practical approach to improving the efficiency and effectiveness of breeding programs. In conclusion, the study by Nofima provides valuable insights into the benefits of multi-population genomic prediction for AGD resistance in Atlantic salmon. By combining reference populations and using advanced prediction models, breeders can achieve more accurate and reliable breeding values, ultimately enhancing the health and productivity of salmon stocks. This research builds on earlier findings[2][3][4] and underscores the potential of genomic selection to revolutionize aquaculture breeding programs.

GeneticsAnimal ScienceMarine Biology

References

Main Study

1) Accuracy of genomic prediction using multiple Atlantic salmon populations

Published 15th May, 2024

https://doi.org/10.1186/s12711-024-00907-5

Related Studies

2) Invited review: Genomic selection in dairy cattle: progress and challenges.

https://doi.org/10.3168/jds.2008-1646

3) Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle.

https://doi.org/10.1534/genetics.107.084301

4) Reliability of genomic predictions across multiple populations.

https://doi.org/10.1534/genetics.109.104935


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