Abstract: Neuropsychiatric diseases ranging from schizophrenia to affective disorders and autism are heritable, highly complex and heterogeneous conditions, diagnosed purely clinically, with no supporting biomarkers or neuroimaging criteria. Relying on these “umbrella diagnoses”, genetic analyses, including genome-wide association studies (GWAS), were undertaken but failed to provide insight into the biological basis of these disorders. “Risk genotypes” of unknown significance with low odds ratios of mostly <1.2 were extracted and confirmed by including ever increasing numbers of individuals in large multicenter efforts. Facing these results, we have to hypothesize that thousands of genetic constellations in highly variable combinations with environmental co-factors can cause the individual disorder in the sense of a final common pathway. This would explain why the prevalence of mental diseases is so high and why mutations, including copy number variations, with a higher effect size than SNPs, constitute only a small part of variance. Elucidating the contribution of normal genetic variation to (disease) phenotypes, and so re-defining disease entities, will be extremely labor-intense but crucial. We have termed this approach PGAS (“phenotype-based genetic association studies”). Ultimate goal is the definition of biological subgroups of mental diseases. For that purpose, the GRAS (Göttingen Research Association for Schizophrenia) data collection was initiated in 2005. With >3000 phenotypical data points per patient, it comprises the world-wide largest currently available schizophrenia database (N > 1200), combining genome-wide SNP coverage and deep phenotyping under highly standardized conditions. First PGAS results on normal genetic variants, relevant for e.g., cognition or catatonia, demonstrated proof-of-concept. Presently, an autistic subphenotype of schizophrenia is being defined where an unfortunate accumulation of normal genotypes, so-called pro-autistic variants of synaptic genes, explains part of the phenotypical variance. Deep phenotyping and comprehensive clinical data sets, however, are expensive and it may take years before PGAS will complement conventional GWAS approaches in psychiatric genetics.
Keywords: mental disease; diagnosis; classification systems; GWAS; monozygotic twins; deep phenotyping; Manhattan plot; genotype; SNP; GRAS data collection
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Ehrenreich, H.; Nave, K.-A. Phenotype-Based Genetic Association Studies (PGAS)—Towards Understanding the Contribution of Common Genetic Variants to Schizophrenia Subphenotypes. Genes 2014, 5, 97-105.
Ehrenreich H, Nave K-A. Phenotype-Based Genetic Association Studies (PGAS)—Towards Understanding the Contribution of Common Genetic Variants to Schizophrenia Subphenotypes. Genes. 2014; 5(1):97-105.
Ehrenreich, Hannelore; Nave, Klaus-Armin. 2014. "Phenotype-Based Genetic Association Studies (PGAS)—Towards Understanding the Contribution of Common Genetic Variants to Schizophrenia Subphenotypes." Genes 5, no. 1: 97-105.