Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery. (September 2016)

• Record Type: Journal Article
• Authors: Breen, Michael S.; Stein, Dan J.; Baldwin, David S.
• Is Part Of: Human psychopharmacology. Volume 31:Number 5(2016:Jul.)
• Page Start: 373
• Page End: 381
• Publication Date: 2016-09
• Subjects: blood‐based biomarker -- gene expression -- machine learning -- gene network analysis -- prediction -- diagnosis; Psychopharmacology -- Periodicals; Psychotropic drugs -- Periodicals; Psychopharmacology -- Periodicals; Psychotropic Drugs -- pharmacology -- Periodicals; 615.78
• DOI: 10.1002/hup.2546
• Languages: English
• ISSNs: 0885-6222

Abstract

Abstract : Introduction: The utility of blood for genome‐wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood‐based biomarker discovery, validation, and testing are needed. Methods: We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders. Results: A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher‐order systems‐level perspective to identify gene module‐based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100. Conclusions: Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood‐based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next‐generation technologies set the stage for moving the field forwards. Copyright © 2016 John Wiley & Sons, Ltd.