Analysis of Genetic Diversity in Patients with Major Psychiatric Disorders versus Healthy Controls: A molecular-genetic study of 1,698 subjects genotyped for 100 candidate genes (549 SNPs)

Stassen, Hans; Bachmann, Silke; Bridler, René; Cattapan, Katja; Hartmann, Annette; Rujescu, Dan; Seifritz, Erich; Weisbrod, Matthias; Scharfetter, Christian (October 2022). Analysis of Genetic Diversity in Patients with Major Psychiatric Disorders versus Healthy Controls: A molecular-genetic study of 1,698 subjects genotyped for 100 candidate genes (549 SNPs) Research Square 10.21203/rs.3.rs-2222846/v1

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Text Analysis_of_Genetic_Diversity_in_Patients_with_Major_Psychiatric_Disorders_versus_Healthy_Controls-_A_molecular-genetic_study_of_1_698_subjects_genotyped_for_100_candidate_genes__549_SNPs____Research_Square.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (8MB) | Preview

In this study we addressed the question of the extent to which irregularities in genetic diversity might separate patients with major psychiatric disorders from healthy controls. Genetic diversity was quantified per gene through multidimensional "gene vectors" assembled from 4-8 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". Our sample was comprised of 1,698 subjects from Central Europe (1,431 psychiatric patients, 267 healthy controls), all genotyped for 549 specifically selected SNPs.

The evaluation of the diversity indices of the 100 candidate genes resulted in a mean value of 109.4±82.8, ranging from 18 to 476. Highly significant deviations from "normal" diversity values were detected for (1) major depression (n=596): a significant reduction (p<0.0001); (2) Alzheimers disease (n=75): a significant reduction (p<0.0001); and (3) schizoaffective disorders (n=64): a significant increase (p<0.0001). 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. In each ofthe diagnostic subgroups under study, there were no less than 45%-55% of patients who exhibited genotypic patterns of singular genes that did not at all show up in the healthy controls. Neural Net (NN) analyses enabled the construction of nonlinear classifiers that correctly identified up to 90% of patients in comparisons with healthy controls at false-positive error rates of zero percent. The NN analyses revealed considerable overlaps an the genotype level between the various clinically defined diagnostic subgroups, suggesting that diagnosis-crossing, unspecific vulnerabilities are likely involved in the pathogenesis of major psychiatric disorders.

Clinical applications of the proposed method are immediately possible and will facilitate the early detection of latent psychiatric disorders among risk cases, so that early interventions can be started before clinically relevant symptoms develop. A larger number of hospitalizations could be prevented in this way.

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