Schizophrenia is a disabling disorder involving genetic predisposition in combination with environmental influences that likely act via dynamic alterations of the epigenome and the transcriptome but its detailed pathophysiology is largely unknown. We performed cell-type specific methylome-wide association study of neonatal blood (N = 333) from individuals who later in life developed schizophrenia and controls. Suggestively significant associations (P < 1.0 × 10−6) were detected in all cell-types and in whole blood with methylome-wide significant associations in monocytes (P = 2.85 × 10−9–4.87 × 10−9), natural killer cells (P = 1.72 × 10−9–7.82 × 10−9) and B cells (P = 3.8 × 10−9). Validation of methylation findings in post-mortem brains (N = 596) from independent schizophrenia cases and controls showed significant enrichment of transcriptional differences (enrichment ratio = 1.98–3.23, P = 2.3 × 10−3–1.0 × 10−5), with specific highly significant differential expression for, for example, BDNF (t = −6.11, P = 1.90 × 10−9). In addition, expression difference in brain significantly predicted schizophrenia (multiple correlation = 0.15–0.22, P = 3.6 × 10−4–4.5 × 10−8). In summary, using a unique design combining pre-disease onset (neonatal) blood methylomic data and post-disease onset (post-mortem) brain transcriptional data, we have identified genes of likely functional relevance that are associated with schizophrenia susceptibility, rather than confounding disease associated artifacts. The identified loci may be of clinical value as a methylation-based biomarker for early detection of increased schizophrenia susceptibility.
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For IRB reasons, sequence information may not be made publicly available but summary statistics from all omic analyses are made available from the authors.
RaMWAS is freely available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/ramwas.html). The RaMWAS script used to perform cell-type specific association studies is available from GitHub: https://github.com/ejvandenoord/celltype_MWAS. In addition, R code to estimate the cell-type proportions by an empirical Bayes approach is also provided on GitHub: https://github.com/ejvandenoord/Empirical-Bayes-estimation-of-cell-type-proportions.
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This work was supported by grant R01MH109525 (PI: Aberg) from the National Institute of Mental Health and the Lundbeck Foundation, Denmark by grant R155-2014-1724. Post-mortem tissue samples were obtained from the Victorian Brain Bank Network, Australia, National Institute of Health Brain Tissue Repository, US, Maryland Brain Bank, US, Stanley Medical Research Institute, US, Harvard Brain Bank, US, Douglas Bell Brain Bank, Canada and King’s College London, England. The sequencing of the transcriptomes was performed at the Genomics Core facility at Virginia Tech. The cell sorting was performed at Aarhus University Hospital. All other lab-technical work, including MBD sequencing was performed at the Center for Biomarker Research and Precision Medicine at Virginia Commonwealth University.
The authors declare no competing interests.
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van den Oord, E.J.C.G., Xie, L.Y., Zhao, M. et al. Genes implicated by a methylome-wide schizophrenia study in neonatal blood show differential expression in adult brain samples. Mol Psychiatry 28, 2088–2094 (2023). https://doi.org/10.1038/s41380-023-02080-5
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