Strategies for mosaic variant calling in brain disorders

Area of Science:

• Genomics
• Neuroscience
• Molecular Biology

Background:

• The human brain exhibits genomic mosaicism due to postzygotic mutations, contributing to neurodevelopmental and neurodegenerative diseases.
• Detecting these ultralow variant allele frequency (VAF) mutations is analytically challenging due to sequencing artifacts.

Purpose of the Study:

• To review current strategies for detecting somatic variants in the brain.
• To emphasize the impact of sequencing depth and algorithm selection on variant detection sensitivity.
• To discuss multitissue sampling and multiomics integration for understanding brain health and disease.

Main Methods:

• Summarizing sampling methods like bulk, laser capture microdissection, and single-cell genomics.
• Evaluating variant calling algorithms (e.g., MuTect2, MosaicForecast) based on VAF detection performance.
• Discussing multitissue sampling approaches and integration with transcriptomic and epigenetic data.

Main Results:

• Mosaic detection sensitivity is fundamentally limited by sequencing depth.
• Algorithm selection should be based on validated VAF detection performance for optimal results.
• Multitissue sampling aids in accurate variant classification through cross-tissue VAF gradients.

Conclusions:

• Optimizing sampling methods and sequencing depth is crucial for detecting brain genomic mosaicism.
• Careful selection of variant calling algorithms tailored to VAF ranges improves detection accuracy.
• Integrating multiomics data with genomic findings will advance the functional understanding of somatic genome's role in brain disorders.