Transcriptional profile (qPCR) response to treatment
The 50 selected candidate transcripts (hereafter “genes”) were tested to determine which genes showed a transcription response to either of the treatments. Two genes (cfap58 , ubr4 ) were dropped from the analysis due to failure of PCR amplification for most of the samples, thus 48 candidate genes were included for the rest of the study. To reduce the number of independent variables and to avoid over fitting the models, we used Principal Component Analyses (PCA) on the qPCR data for the 48 selected genes using “prcomp” (which is a part of the R statistical analysis package) and factoextra package (1.0.7) (Kassambara and Mundt, 2017) in R (version 4.1.1). Based on a threshold of Eigenvalue > 1, and % variance explained > 2%, the first nine PC axes were selected. We used Linear mixed models (LMM) (lmerTest package (v3.1.3)) (Kuznetsova et al., 2017) in R with the selected PC axes to test for the effect of treatment (fixed effect), and the random effects of dam, sire, fish body weight, tank ID and chip effect, with all interaction terms for fixed and random factors on gene transcription patterns. Chip ID, body weight, dam, treatment×dam, treatment×sire effects were nonsignificant before FDR correction and were removed from the model. When any of the nine PCs were found to exhibit significant effects with any of the independent variables (treatments, dam, sire, body weight, tank ID, or chip effect), we examined the individual gene transcription loading values. We used fviz_contrib within the factoextra package (1.0.7) to identify genes with contributions to the PC greater than expected (Kassambara and Mundt, 2017). The identified genes were included in a second analysis that used LMM with the ΔCT values for the selected genes and the same independent variables (treatment, dam, sire, body weight, tank ID and chip effect), including all interaction terms for fixed and random factors. Nonsignificant factors (Chip ID, body weight, dam, and all interactions) were removed from the model and the analysis was re-run. Lastly, a sequential Bonferroni P value correction was applied for multiple testing correction (Rice, 1989).