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Liver weights. Ap: Common Pekin duck; Cm: Muscovy duck; mu: Mule duck; hi: Hinny duck. (DOCX 24 kb)

Additional file 1 Additional file 1: Supplementary figures (S1-S26) and tables (S1-S15).

Clusters of enriched GO terms associated to differentially expressed genes defined by semantic similarity. (PDF 2017 kb)

Background Bacterial cold-water disease, which is caused by Flavobacterium psychrophilum, is one of the major diseases that affect rainbow trout (Oncorhynchus mykiss) and a primary concern for trout farming. Better knowledge of the genetic basis of resistance to F. psychrophilum would help to implement this trait in selection schemes and to investigate the immune mechanisms associated with resistance. Various studies have revealed that skin and mucus may contribute to response to infection. However, previous quantitative trait loci (QTL) studies were conducted by using injection as the route of infection. Immersion challenge, which is assumed to mimic natural infection by F. psychrophilum more closely, may reveal different defence mechanisms. Results Two isogenic lines of rainbow trout with contrasting susceptibilities to F. psychrophilum were crossed to produce doubled haploid F2 progeny. Fish were infected with F. psychrophilum either by intramuscular injection (115 individuals) or by immersion (195 individuals), and genotyped for 9654 markers using RAD-sequencing. Fifteen QTL associated with resistance traits were detected and only three QTL were common between the injection and immersion. Using a model that accounted for epistatic interactions between QTL, two main types of interactions were revealed. A “compensation-like” effect was detected between several pairs of QTL for the two modes of infection. An “enhancing-like” interaction effect was detected between four pairs of QTL. Integration of the QTL results with results of a previous transcriptomic analysis of response to F. psychrophilum infection resulted in a list of potential candidate immune genes that belong to four relevant functional categories (bacterial sensors, effectors of antibacterial immunity, inflammatory factors and interferon-stimulated genes). Conclusions These results provide new insights into the genetic determinism of rainbow trout resistance to F. psychrophilum and confirm that some QTL with large effects are involved in this trait. For the first time, the role of epistatic interactions between resistance-associated QTL was evidenced. We found that the infection protocol used had an effect on the modulation of defence mechanisms and also identified relevant immune functional candidate genes. Electronic supplementary material The online version of this article (10.1186/s12711-018-0431-9) contains supplementary material, which is available to authorized users.

Additional file 3: Figure S2. Evolution of the rate of hererozygosity along chromosomes (mean values for the 24 individuals with an overall rate of heterozygosity higher than 1%). Description: Metacentric chromosomes (a): under the hypothesis of spontaneous retention of the second polar body during meiosis, the rate of heterozygosity is expected to be lower around the centromere (—) than in telomeric regions (- - - -). Acrocentric chromosomes (b): under the hypothesis of spontaneous retention of the second polar body during meiosis, the rate of heterozygosity is expected to increase along the chromosome from the centromeric region to the telomere. Data are illustrated for 15 chromosomes.

Additional file 5: Figure S4. Final survival rate according to the allele origin at pairs of epistatic QTL for resistance to infection with F. psychrophilum. Description: For each figure, abscissa corresponds to the combination of favourable (R) and unfavourable (S) alleles with the grandparent origin in colour (green for B57 and red for AP2) for each pair of epistatic QTL OmyA_OmyB. Survival rates (in ordinate) with similar letters are not significantly different (Fisher exact test P ≤ 0.05 and Benjamini–Hochberg correction for multiple testing of stat package from R software).

Additional file 1: Fig. S1. Global Jaccard index for our RNA-Seq data calculated with various threshold values using the R software HTSfilter [32]. This figure shows on the left, count data for long noncoding RNAs; and on the right, count data for protein-coding genes. Count data were normalized by TMM methods [81]. For each type of gene, the data-based threshold corresponds to the red cross and red dotted line. For the long noncoding genes (left), the curve shape of the Jaccard index (that gives a threshold—at the maximum of the curve—equal to one read) is not consistent with the expected index curve shape, in contrast to the protein-coding genes (right) that behave correctly, with a maximum of approximately 32 reads.

Additional file 5: Fig. S2. Visualization by IGV of the lncLSTR, APOA1-AS and HULC loci in the chicken genome. IGV: Integrative Genomics Viewer from the Broad Institute. LncLSTR [60] was expected between HMCN1 and Ivns1abp genes localized on Scaffold JH375182.1. HULC long noncoding gene was expected between the OFCC1 and SLC35B3 genes localized on chromosome 2, and APOA1-AS was expected to overlap with APOA1 localized on chromosome 24. Expected locations are in red squares. The liver RNA-Seq data used here are a merge of the .bam files of the 16 samples. The chicken reference genome was the Ensembl Galgal4, and the annotation version was Ensembl v84.4.

Additional file 6. Command lines and input files used to run the different analyses in the current study.

Additional file 2. CGH protocol. In this document, a detailed CGH protocol is provided.

Additional file 7: Fig. S3. Boxplot and distribution across the 16 replicates of the “no gene” set representing the noise signal. This figure shows on the left the boxplots of FPKM-UQ for 10 “no gene” sample sets (see “Methods” section) in white compared to those for lncRNAs (red) and mRNAs (blue); and on the right, the distribution across the 16 replicates of the “no gene” set (green) that satisfied the FPKM-UQ threshold of 0.1. This distribution is very different from those observed for lncRNAs (red) or mRNAs (blue).

Additional file 6. Details on the immune genes induced by F. psychrophilum infection that map within resistance-associated QTL.