Comprehensive identification of somatic nucleotide variants in human brain tissue
Brain Somatic Mosaicism Network, Yifan Wang, Taejeong Bae, Jeremy Thorpe, Maxwell A. Sherman, Attila Jones, Sean Cho, Kenneth Daily, Yanmei Dou, Javier Ganz, Alon Galor, Irene Lobón, Reenal Pattni, Chaggai Rosenbluh, Simone Tomasi, Livia Tomasini, Xiaoxu Yang, Bo Zhou, Schahram Akbarian, Laurel Ball, Sara Bizzotto, Sarah B. Emery, Ryan N. Doan, Liana Fasching, Yeongjun Jang, David Juan, Esther Lizano, Lovelace J. Luquette, John B. Moldovan, Rujuta Narurkar, Matthew T. Oetjens, Rachel E. Rodin, Shobana Sekar, Joo Heon Shin, Eduardo Soriano, Richard E. Straub, Weichen Zhou, Andrew Chess, Joseph G. Gleeson, Tomàs Marquès‐Bonet, Peter J. Park, Mette A. Peters, Jonathan Pevsner, Christopher A. Walsh, Daniel R. Weinberger, Flora M. Vaccarino, John V. Moran, Alexander E. Urban, Jeffrey M. Kidd, Ryan E. Mills, Alexej Abyzov
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.