Background The presence of somatic variation in the human brain has been elucidated over the past several decades. Most recently, single-cell whole-genome sequencing studies have provided unequivocal evidence that individual human brain cells harbor unique genetic variants. The functional significance of this phenomenon is unclear, but some studies have suggested that it contributes to risk of neuropsychiatric disease. Currently, cost and labor requirement render difficult the sequencing of individual genomes at the scale that will likely be necessary to fully uncover the extent and role of somatic variation in the brain. A scalable alternative, however, has been developed in the field of cancer genomics wherein somatic variants are identified by comparing the frequencies of variant alleles in tumor and normal bulk tissue specimens from the same individual. Here, we apply this approach to investigate the role of somatic variation in schizophrenia in a pilot study of 9 individuals (5 schizophrenia cases and 4 unaffected controls). Methods Specimens from the prefrontal cortex and temporal muscle of 5 schizophrenia cases and 4 controls were obtained from the Mount Sinai NIH Brain and Tissue Repository. Deep whole-exome sequencing (250X coverage) was performed using DNA extracted from neuronal and non-neuronal nuclei, which were isolated from cortical specimens by fluorescent-activated nuclear sorting. We followed standard protocols for mapping and filtering reads, then called somatic Single Nucleotide Variants (sSNVs) using a suite of 6 calling algorithms. For each individual, the calling algorithms were utilized to call variants in the exomes of both the neuronal and non-neuronal cells of the brain by comparing them to a matched “normal” sample from the same individual (i.e., the temporal muscle). Variants were then filtered using an in-house quality control pipeline, and a subset of the remaining calls were validated with digital PCR (dPCR) and parallel sequencing of multiple clones (clone-seq). Results We identified 26 sSNVs in neuronal and non-neuronal cells from the brains of 9 individuals. We chose 10 of 26 for experimental validation using dPCR and clone-seq, observing a validation rate of 80%. We noted that 13 of the 26 sSNVs were found in a single schizophrenia case; as such, 4 of the 10 sSNVs included in validation experiments were from this individual, 3 of which were successfully validated. For 5 sSNVs, only the neuronal exome of the individual had the variant, for 17 only the non-neuronal exome and for 4 both the neuronal and non-neuronal exomes. At least 1 sSNV was observed in all 5 of the cases and in 2 of the 4 controls. We noted that 2 of the 23 somatic SNVs in cases fell within one of the 16 copy number variants implicated in schizophrenia by the largest study of copy number variation in schizophrenia to date. Several of the sSNVs observed in our cohort fall within genes known to function in determining cellular fate and development, including WNT10B, SMAD1 and FGF1. Discussion Here, we present a pipeline suitable for the large-scale investigation of somatic variation in the human brain. We show evidence that somatic variation occurs in a cell-type specific manner in the human brain. Genes affected by sSNVs in the brain have known roles in determining cell fate. Greater number of sSNVs in schizophrenia cases compared to controls was observed, and multiple case sSNVs fall within known schizophrenia CNVs. Larger sample sizes are needed so as to further elucidate the extent and role of somatic variation in the human brain.