Bioinformatics pipeline to guide post-GWAS studies in Alzheimer's: A new catalogue of disease candidate short structural variants

BackgroundShort structural variants (SSVs), including insertions/deletions (indels), are common in the human genome and impact disease risk. The role of SSVs in late-onset Alzheimer's disease (LOAD) has been understudied. In this study, we developed a bioinformatics pipeline of SSVs within LOAD-genome-wide association study (GWAS) regions to prioritize regulatory SSVs based on the strength of their predicted effect on transcription factor (TF) binding sites. MethodsThe pipeline utilized publicly available functional genomics data sources including candidate cis-regulatory elements (cCREs) from ENCODE and single-nucleus (sn)RNA-seq data from LOAD patient samples. ResultsWe catalogued 1581 SSVs in candidate cCREs in LOAD GWAS regions that disrupted 737 TF sites. That included SSVs that disrupted the binding of RUNX3, SPI1, and SMAD3, within the APOE-TOMM40, SPI1, and MS4A6A LOAD regions. ConclusionsThe pipeline developed here prioritized non-coding SSVs in cCREs and characterized their putative effects on TF binding. The approach integrates multiomics datasets for validation experiments using disease models.

Automated summary

In this study, a bioinformatics pipeline was developed to prioritize short structural variants (SSVs), including insertions/deletions (indels), within late-onset Alzheimer's disease (LOAD) genome-wide association study (GWAS) regions based on their predicted effects on transcription factor (TF) binding. By using publicly available functional genomics data sources, including candidate cis-regulatory elements (cCREs) from ENCODE and single-nucleus (sn)RNA-seq data from LOAD patient samples, a total of 1581 SSVs were identified in candidate cCREs that disrupted 737 TF binding sites. The pipeline provides a way to prioritize non-coding SSVs and assess their potential effects on TF binding, and can be validated using disease models in future experiments.