Analysis of genetic diversity in patients with major psychiatric disorders versus healthy controls: A molecular-genetic study of 1698 subjects genotyped for 100 candidate genes (549 SNPs)

•A new approach to quantifying genetic diversity enables high-resolution analyses of the variation of genotypic patterns in genes and the correlations between genes.•Significant deviations from “normal” diversity values were found for major depression; Alzheimer's disease; and schizoaffective disorders.•The central finding of this study was the discovery of “singular genes” characterized by distinctive genotypic patterns that appeared exclusively in patients but not in healthy controls.•Neural Net analyses yielded nonlinear classifiers that correctly identified up to 90 % of patients.•Overlaps between diagnostic subgroups on the genotype level suggested that (1) diagnoses-crossing vulnerabilities are likely involved in the pathogenesis of major psychiatric disorders; (2) clinically defined diagnoses may not constitute etiological entities. This study analyzed the extent to which irregularities in genetic diversity separate psychiatric patients from healthy controls. Genetic diversity was quantified through multidimensional “gene vectors” assembled from 4 to 8 polymorphic SNPs located within each of 100 candidate genes. The number of different genotypic patterns observed per gene was called the gene's “diversity index”. The diversity indices were found to be only weakly correlated with their constituent number of SNPs (20.5 % explained variance), thus suggesting that genetic diversity is an intrinsic gene property that has evolved over the course of evolution. Significant deviations from “normal” diversity values were found for (1) major depression; (2) Alzheimer's disease; and (3) schizoaffective disorders. Almost one third of the genes were correlated with each other, with correlations ranging from 0.0303 to 0.7245. The central finding of this study was the discovery of “singular genes” characterized by distinctive genotypic patterns that appeared exclusively in patients but not in healthy controls. Neural Nets yielded nonlinear classifiers that correctly identified up to 90 % of patients. Overlaps between diagnostic subgroups on the genotype level suggested that (1) diagnoses-crossing vulnerabilities are likely involved in the pathogenesis of major psychiatric disorders; (2) clinically defined diagnoses may not constitute etiological entities. Detailed analyses of the variation of genotypic patterns in genes along with the correlation between genes lead to nonlinear classifiers that enable very robust separation between psychiatric patients and healthy c