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ARS Home » Northeast Area » Leetown, West Virginia » Cool and Cold Water Aquaculture Research » Research » Publications at this Location » Publication #406772
Research Project: Integrated Research Approaches for Improving Production Efficiency in Rainbow Trout

Location: Cool and Cold Water Aquaculture Research

Title: Genetic architecture and accuracy of predicted genomic breeding values for sea lice resistance in the St John River aquaculture strain of North American Atlantic salmon

Author
item Vallejo, Roger
item Pietrak, Michael
item Milligan, Melissa
item Gao, Guangtu
item TSURUTA, SHOGO - University Of Georgia
item FRAGOMENI, BRENO - University Of Connecticut
item Long, Roseanna
item Peterson, Brian
item Palti, Yniv

Submitted to: Aquaculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/11/2024
Publication Date: 3/16/2024
Citation: Vallejo, R.L., Pietrak, M.R., Milligan, M.T., Gao, G., Tsuruta, S., Fragomeni, B.O., Long, R., Peterson, B.C., Palti, Y. 2024. Genetic architecture and accuracy of predicted genomic breeding values for sea lice resistance in the St John River aquaculture strain of North American Atlantic salmon. Aquaculture. 586:740819. https://doi.org/10.1016/j.aquaculture.2024.740819.
DOI: https://doi.org/10.1016/j.aquaculture.2024.740819

Interpretive Summary: Infections of sea lice cause major economic losses for Atlantic salmon across the globe. Genetic improvement of the resistance of salmon to sea lice infection is one aspect of the multifaceted approach taken by the industry to remediate the problem. In this report we used a newly-developed, high-density genotyping array to assess the genetic architecture of sea lice resistance and the potential for genetic improvement in Atlantic salmon of North American origin in the USDA-ARS breeding program. Our research demonstrated that substantial genetic gains can be achieved through traditional pedigree-based selective breeding. However, genomic-enabled methods using high-density genotypic data did not provide better prediction accuracy, and thus are not expected to improve the rate of genetic improvement compared to traditional selective breeding, given the sample size used in the current study. It is likely that a larger number of families and fish per family will be needed to utilize the full potential of genomic-enabled models for improving resistance to sea lice infection. This research provides important practical information for genetic improvement of sea lice resistance in Atlantic salmon of North American origin.

Technical Abstract: Sea lice infections cause significant economic losses in Atlantic salmon (Salmo salar) farming. The objectives of this study were to (1) estimate the heritability of resistance to sea lice in the USDA’s breeding program for North American (N.A.) Atlantic salmon; (2) elucidate the genetic architecture of sea lice resistance; and (3) assess the accuracy and bias of predicted breeding values for sea lice resistance using cross-validation analysis (CVA) and progeny testing of selection candidates (PTSC). Fish from year-class (YC) 2014 (n=967) and YC 2018 (n=941) were challenged with sea lice (Lepeophtheirus salmonis) and genotyped with the new 50K SNP chip developed for the N.A. Atlantic salmon. Heritability estimates for sea lice density in the two year-classes were between 0.18–0.20. GWAS with weighted ssGBLUP (wssGBLUP) highlighted the polygenic architecture of the trait. Similar accuracy was observed with CVA for the genomic prediction methods (mean=0.72) and the pedigree-based method (mean=0.70). With the PTSC approach, the pedigree-based method PBLUP had higher estimated accuracy than the genomic prediction methods (0.47 vs. 0.42). Bias of BV predictions with CVA (mean=1.14; range 0.83–1.43) was lower than with PTSC (mean=4.32; range 2.78–6.21). Our retrospective assessment of the accuracy of indirect sib-based selection to improve sea lice resistance in the St John River aquaculture stock that is commonly used for farming of Atlantic salmon in North America demonstrated that substantial genetic gains can be obtained. Due to the polygenic architecture of the trait, the estimated accuracy of the ssGBLUP model was better than the wssGBLUP model. In our assessment, a larger training sample size will be needed to achieve optimal results with the whole genome-enabled selective breeding method due to the polygenic architecture of sea lice resistance coupled with moderate heritability of the trait.