Genomic and Transcriptomic Analysis of Amoebic Gill Disease Resistance in Atlantic Salmon (Salmo salar L.)

Abstract

Amoebic gill disease (AGD) is one of the most important parasitic diseases of farmed Atlantic salmon. It is a source of major economic loss to the industry and poses significant threats to animal welfare. Previous studies have shown that resistance against this disease has a moderate, heritable genetic component, although the genes and the genetic pathways that contribute to this process have yet to be elucidated. In this study, to identify the genetic mechanisms of AGD resistance, we first investigated the molecular signatures of AGD infection in Atlantic salmon through a challenge model, where we compared the transcriptome profiles of the naïve and infected animals. We then conducted a genome-wide association analysis with 1,333 challenged tested fish to map the AGD resistance genomic regions, supported by the results from the transcriptomic data. Further, we investigated the potential of incorporating gene expression analysis results in genomic prediction to improve prediction accuracy. Our data suggest thousands of genes have modified their expression following infection, with a significant increase in the transcription of genes with functional properties in cell adhesion and a sharp decline in the abundance of various components of the immune system genes. From the genome-wide association analysis, QTL regions on chromosomes ssa04, ssa09, and ssa13 were detected to be linked with AGD resistance. In particular, we found that QTL region on ssa04 harbors members of the cadherin gene family. These genes play a critical role in target recognition and cell adhesion. The QTL region on ssa09 also is associated with another member of the cadherin gene family, protocadherin Fat 4. The associated genetic markers on ssa13 span a large genomic region that includes interleukin-18-binding protein, a gene with function essential in inhibiting the proinflammatory effect of cytokine IL18. Incorporating gene expression information through a weighted genomic relationship matrix approach decreased genomic prediction accuracy and increased bias of prediction. Together, these findings help to improve our breeding programs and animal welfare against AGD and advance our knowledge of the genetic basis of host-pathogen interactions.

Keywords: AGD; Atlantic salmon; GWAS; amoebic gill disease; genome-wide association study; genomic predictions; transcriptome.

Figure 1

(A) Principle component of gene expression. (B) Clustering of the gene expression data between the naïve, animals during the first infection with scores 2 or 3 and animals during the second infection with scores 2 or 3. The scorings are based on the scheme outlined by Taylor et al. (2009a,b).

Figure 2

Heatmap of the standardized FPKM gene expression, showing relative levels of abundance of differentially regulated immune related transcripts between (A) Naïve and animals during the first infection and (B) Naïve and animals during the second infection. Numbers following an underscore represent putative duplicated genes.

Figure 3

Heatmap of the standardized FPKM gene expression, showing relative levels of abundance of differentially regulated transcripts with functional properties in “cell-adhesion” between naïve and animals during the 2nd infection. Numbers following an underscore represent putative duplicated genes.

Figure 4

Manhattan plot from the genome-wide marker analysis of resistance to AGD. The red and blue represent Bonferroni and chromosome-wide significance thresholds.

Figure 5

Estimate of heritability for different λ (blending of two genomic relationship matrices) values. The two genomic relationship matrices were generated for SNPs that were either within significant differentially expressed (DE) genes in gene expression analysis or not. Markers within significant DE genes were also weighted with the log2ΔF of that gene.

Figure 6

Accuracy of selection of genomic predictions for different λ (blending of two genomic relationship matrices) values. The two genomic relationship matrices were generated for SNPs that were either within significant genes in gene expression analysis or not. Markers within significant DE genes were also weighted with the log₂ΔF of that gene.

Figure 7

Regression coefficient of adjusted phenotype on genomic breeding values for different λ (blending of two genomic relationship matrices) values. The two genomic relationship matrices were generated for SNPs that were either within significant genes in gene expression analysis or not. Markers within significant DE genes were also weighted with the log₂ΔF of that gene.