Establishing pangenome graph as a framework for the analysis of the impact of structural variation on gene expression

Abstract

This thesis addresses key challenges and opportunities in structural variation (SV) detection, genotyping, and downstream analyses using pangenome variation graphs for Brassica napus. By integrating different sequencing technologies and available bioinformatics tools, it demonstrates strategies to optimize the analysis of complex plant genomes, which are typically large, repetitive, and polyploid. Popular mapping and SV calling pipelines were evaluated using both simulated and real datasets from major crops, including rapeseed, tomato, maize, and soybean, across low to medium sequencing depths. The results demonstrate the feasibility of cost-effective SV detection, identifying the most efficient aligners and callers that achieve robust performance even at low coverage (≥5×). These findings provide a practical framework for population-scale crop studies, where sequencing costs and coverages are often a limiting factor. A graph-based pangenome approach was developed by combining long-read SV discovery with pangenome reference, allowing comparison with existing references to assess and reduce reference bias. This strategy enabled the identification of SVs, the construction of graph-based pangenomes, and subsequent SV genotyping in larger populations using short-read data, eliminating the need for costly de novo assemblies. The approach is therefore scalable, accessible, and suitable for high-throughput crop genomics. Importantly, integration with gene expression data revealed that many SVs, particularly those linked to transposable elements, significantly affect gene regulation and may underlie key agronomic traits. Overall, this thesis demonstrates that SVs are not only a major source of genetic diversity but also critical drivers of gene regulatory variation in crops. By providing benchmarking guidelines, novel graph-based pipelines, and functional insights into SVs, it lays the foundation for incorporating structural variation into future genome-informed breeding and trait discovery, ultimately supporting the development of more resilient and productive crop varieties.