Scalable approaches for functional analyses of whole-genome sequencing non-coding variants

Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.

Automated summary

Non-coding genetic variants outside of protein-coding genome regions play a crucial role in genetic and epigenetic regulation. Understanding their roles has become increasingly significant as these variants often dominate the findings of genome-wide association studies. This review focuses on the latest approaches for annotating and prioritizing non-coding variants discovered in whole-genome sequencing analyses, covering scalable annotation tools, databases, functional genomic resources, and machine learning-based predictive models. The review also discusses future research directions to enhance our understanding of disease etiology through the effective functional analysis of WGS-identified variants.