Integrating structural variants into genome-wide association studies – PanSV

Structural variants (SVs) are defined as genomic variations involving 50 nucleotides or more. They can take multiple forms, from simple deletions and insertions to complex rearrangements formed by multiple SV types like inversions, translocations and duplications. Most SVs are under-studied because of the technical challenges to identify them. Their impact is also more difficult to predict, as they often affect multiple functional elements or can trigger epigenetic changes.
Although the sequencing of short DNA fragments of about 300 nucleotides (short read sequencing) is widely used today, it cannot identify many types of SVs. Long read sequencing, in contrast, can sequence longer DNA fragments (thousands of nucleotides) and detect SVs at much higher resolution, but remains costly. We have shown that short read sequencing data can be analyzed with pangenomes to accurately genotype SVs that had been discovered with long read sequencing. A pangenome represents multiple genomes and provides a framework to enhance a reference genome by integrating known variants.
I propose to reanalyze sequencing data from large cohorts, like the UK Biobank, to test the association between SVs and disease traits in human. By including SVs in these genome-wide association studies, the aim is to identify novel disease genetic factors or identify the causal variant of known disease associations.
The specialized computational genomics tools developed for this project will be combined with experimental assays to validate the findings. I propose to first validate this approach on hemochromatosis, a disease characterized by an excess of iron and with a known genetic risk factor but incomplete penetrance. With positive replication and experimental validation, this project would lay the groundwork for future studies on other diseases. The integration of variants that couldn't be studied before will help explain a fraction of the "missing heritability", observed for numerous traits.