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Updated: Aug 18, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI)
Published on: November 27, 2019
Meta-analysis in psychiatric genetics
1Department of Psychiatry, University of Pennsylvania School of Medicine, 3535 Market Street, Room 4006, Philadelphia, PA 19104, USA. DFL@mail.med.upenn.edu
Abstract:
The article reviews literature on methods for meta-analysis of genetic linkage and association studies, and summarizes and comments on specific meta-analysis findings for psychiatric disorders. The Genome Scan Meta-Analysis and Multiple Scan Probability methods assess the evidence for linkage across studies. Multiple Scan Probability analysis suggested linkage of two chromosomal regions (13q and 22q) to schizophrenia and bipolar disorder, whereas Genome Scan Meta-Analysis on a larger sample identified at least 10 schizophrenia linkage regions, but none for bipolar disorder. Meta-analyses of pooled ORs support association of schizophrenia to the Ser311Cys polymorphism in DRD2 and the T102C polymorphism in HTR2A, and of attention deficit hyperactivity disorder to the 48-bp repeat in DRD4. The 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4) may contribute to the risk of bipolar disorder, suicidal behavior, and neuroticism, but association to the lifetime risk of major depression has not been shown. Meta-analyses support linkage of schizophrenia to regions where replicable associations to candidate genes have been identified through positional cloning methods. There are additional supported regions where susceptibility genes are likely to be identified. Linkage meta-analysis has had less clear success for bipolar disorder based on a smaller dataset. Meta-analysis can guide the prioritization of regions for study, but proof of association requires biological confirmation of hypotheses about gene actions. Elucidation of causal mechanisms will require more comprehensive study of sequence variation in candidate genes, better statistical and meta-analytic methods to take all variation into account, and biological strategies for testing etiologic hypotheses.
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