Comprehensive identification of somatic nucleotide variants in human brain tissue
Name
13059_2021_Article_2285.pdf
Size
2.03 MB
Format
Adobe PDF
Checksum (MD5)
fec6bf2d2d279ecdb8049d120d0bf2a5
Author(s)
Wang, Yifan
Bae, Taejeong
Thorpe, Jeremy
Sherman, Maxwell A
Jones, Attila G
Cho, Sean
Daily, Kenneth
Dou, Yanmei
Ganz, Javier
Galor, Alon
Date Issued
March 29, 2021
Publisher
BioMed Central
Citation
Genome Biology. 2021 Mar 29;22(1):92
Version
Final published version
Abstract
Abstract
Background
Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells.
Results
Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees.
Conclusions
This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13059-021-02285-3
