Genetic Architecture of Depression: Where Do We Stand Now?

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)

BACKGROUND

This study analyzed the extent to which irregularities in genetic diversity separate psychiatric patients from healthy controls.

METHODS

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".

RESULTS

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.

CONCLUSION

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 controls on the genotype level.

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy

OBJECTIVE

The primary objective of this study is to address the challenge posed by the increasing number of variants of unknown clinical significance (VUS) within the GABRD gene, which encodes the δ subunit of γ-Aminobutyric acid type A receptors. The focus is on predicting the most pathogenic GABRD VUS to enhance precision medicine and improve our understanding of relevant pathophysiology.

METHODS

The study employs a combination of in silico algorithms to analyze 82 variants of unknown clinical significance of GABRD gene sourced from the ClinVar database. Initially, separate algorithms based on sequence homology are utilized to assess this variant set. Subsequently, consensus variants predicted as pathogenic undergo further evaluation through a web server employing an algorithm based on structural homology. The resulting 11 variants are then validated using in silico tools that assess variant effects based on genetic and molecular data. The evaluation includes consideration of disease association and protein stability due to amino acid substitutions.

RESULTS

The study identifies specific variants (L111R, R114C, D123N, G150S, and L243P) in the coding region of the GABRD gene, which are predicted as deleterious by multiple algorithms. These variants are evolutionarily conserved, mapped onto the extracellular domain of the δ subunit, and associated with idiopathic generalized epilepsy.

CONCLUSIONS

The findings suggest structural or functional consequences that lead to pathogenicity, offering valuable insights for wet-lab experimentation. Besides, the findings contribute to the validation of clinically significant genetic variants in the GABRD gene, which is critical for epilepsy precision medicine.