Characterization of Computational Pipelines for Structural Variant Detection Using Short-Read Sequencing Data in Arctic Charr.

• Main Author: Rehman, Syed Muneeb Ur
• Other Authors: Matthew Peter Kent (Main supervisor), Kristina Severine Rudsjær Stenløkk (Co-supervisor), Célian Diblasi (Co-supervisor)
• Format: Master Thesis
• Language: English
• Published: Norwegian University of Life Sciences 2023
• Subjects: Arctic; Arctic charr; Salvelinus alpinus
• Online Access: https://hdl.handle.net/11250/3125920

Structural variants (SVs) are an important emerging class of genomic variation with pivotal implications for evolution, adaptation, and phenotypic diversity. As a cold-water salmonid fish displaying extensive niche variation and life history plasticity, the Arctic charr (Salvelinus alpinus) serves as an ideal model to elucidate the genomic underpinnings of adaptability. This study performs an integrated analysis to comprehensively characterize the SV landscape across 30 genomes of farmed Arctic charr strains. Using a multi-algorithm approach employing Delly, Manta and Smoove for variant detection, overall 47,966 high-confidence were identified, including deletions, duplications, inversions and translocations. The results show variable numbers of SV’s between individuals, ranging from 71,866 to 128,116 per fish, and reveal that chromosome 36 is enriched for SVs, containing up to 23% of all structural variations. Additional analyses with sequencing coverage data further support the inferences that patterns in chromosome architecture lead to increased structural variation susceptibility. This project substantiates the ability to reliably capture SVs from short-read resequencing but also highlights limitations when using short-read data. By enumerating SVs differentiated among domesticated strains, this study potentiates future research into SVs allele distributions, segregation, and trait associations in selective breeding programs. Overall, the analytical framework and genomic resources developed considerably advance characterization of structural variation spectra in this salmonid species.