Biology subjects

Boison, S.

Publications and source records attributed to Boison, S..

3 recordsLinked to original sources

bioRxiv2026

Imputed graph-genotyped structural variants identify regulatory haplotypes associated with gene expression in Atlantic salmon

Structural variants (SVs) can affect gene regulation, but they are difficult to include in expression genetic studies when large RNA-seq cohorts lack whole-genome sequencing. This is common in non-human and non-model systems, where whole-genome sequencing at population scale remains costly. As a result, expression quantitative trait locus (eQTL) studies often rely on single nucleotide polymorphism (SNP) markers. These analyses can identify expression-associated regions, but often provide limited biological interpretation of the underlying regulatory mechanisms. Here, we used Atlantic salmon as a study system to test whether graph-genotyped SVs can be imputed into a SNP-array-genotyped RNA-seq cohort and used to interpret regulatory haplotypes. SVs were discovered from two long-read-sequenced individuals, supplemented with short-read SV and SNP calls from a 112-individual whole-genome-sequenced reference panel, graph-genotyped, jointly phased with SNPs, and imputed into 906 offspring with gill RNA-seq and SNP-array genotypes. After size filtering, the imputed SV catalogue contained 100,269 variants and showed nonuniform genomic distributions associated with sex-specific recombination landscapes. Association testing identified 51 SV-eQTL candidates, including 35 cis and 16 trans associations. These candidates were enriched for short-read-derived variants, indicating that short-read supplementation can recover regulatory variants missed by small-scale long-read discovery. SV-eQTL candidates were more strongly tagged by nearby SNPs than non-associated variants generally, but individual SNP lead markers often failed to capture the same eQTL signals in conditional regression. Retained candidates after the conditional analysis included target-gene-overlapping deletions, nearby local variants without target-gene overlap, trans associations, and short insertions with opposite effects on gene expression. These results show that imputed graph-genotyped SVs can add biological interpretation to possible regulatory haplotypes.

genomics↗

bioRxiv2025

Optimization of reference population for imputation of low-density SNPs panel for genomic prediction in Atlantic salmon.

In recent years many advances have been made towards developing cost-efficient low-density genomic tools for a wider implementation of genomic selection in aquaculture breeding programmes. Genotype imputation from very low-density (LD) SNP panels of just a few hundred markers to high-density (HD) SNP panels has become a promising strategy to reduce the cost of genotyping while maintaining accurate genomic prediction. The objective of this study is to assess the impact of the makeup of HD-genotyped reference populations on i) the accuracy of imputation for LD-genotyped individuals and ii) the accuracy of genomic prediction for three traits of importance in Atlantic salmon production: growth, resistance to cardiomyopathy syndrome and resistance to pancreas disease. An Atlantic salmon population genotyped with a 47K SNP array was used for the study, along with an in silico LD panel of 554 SNPs. Five reference population scenarios for imputation were tested, which could include only the parents of the candidates for selection, a combination of parents and candidates, or just candidates. All scenarios resulted in highly accurate imputation rates (over 80%) except when the HD reference population was only composed of selection candidates. Nonetheless, the accuracy of imputation barely had an impact on the accuracy of genomic prediction, as the imputed datasets performed very similarly to the HD-panel. Adding a proportion of the offspring to the reference population, in addition to the parents, did not result in any benefit in terms of genomic prediction. Imputation is a cost-effective and robust option for genomic selection in aquaculture.

genetics↗

bioRxiv2024

Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon

Wild Atlantic salmon migrate to sea following completion of a developmental process known as parr - smolt transformation (PST), which establishes a seawater (SW) tolerant phenotype. Effective imitation of this aspect of anadromous life-history is a crucial aspect of commercial salmon production, with current industry practice being marred by significant losses during transition from the freshwater (FW) to SW phase of production. The natural photoperiodic control of PST can be mimicked by exposing farmed juvenile fish to a reduced duration photoperiod for at least 6 weeks before increasing the photoperiod in the last 1 - 2 months before SW transfer. While it is known that variations in this general protocol affect subsequent SW performance, there is no uniformly accepted industry standard; moreover, reliable prediction of SW performance from fish attributes in the FW phase remains a major challenge. Here we describe an experiment in which we took gill biopsies 1 week prior to SW transfer from 3000 individually tagged fish raised on 3 different photoperiod regimes during the FW phase. Biopsies were subjected to RNA profiling by Illumina sequencing, while individual fish growth and survival was monitored over 300 days in a SW cage environment, run as a common garden experiment. Using a random forest machine learning algorithm, we developed gene expression-based predictive models for initial survival and stunted growth in SW. Stunted growth phenotypes could not be predicted based on gill transcriptomes, but survival the first 40 days in SW could be predicted with moderate accuracy. While several previously identified marker genes contribute to this model, a surprisingly low weighting is ascribed to sodium potassium ATPase subunit genes, contradicting advocacy for their use as SW readiness markers. However, genes with photoperiod-history sensitive regulation were highly enriched among the genes with highest importance in the prediction model. This work opens new avenues for understanding and exploiting developmental changes in gill physiology during smolt development.

genomics↗