Psychiatric genetics and the structure of psychopathology

Abstract

For over a century, psychiatric disorders have been defined by expert opinion and clinical observation. The modern DSM has relied on a consensus of experts to define categorical syndromes based on clusters of symptoms and signs, and, to some extent, external validators, such as longitudinal course and response to treatment. In the absence of an established etiology, psychiatry has struggled to validate these descriptive syndromes, and to define the boundaries between disorders and between normal and pathologic variation. Recent advances in genomic research, coupled with large-scale collaborative efforts like the Psychiatric Genomics Consortium, have identified hundreds of common and rare genetic variations that contribute to a range of neuropsychiatric disorders. At the same time, they have begun to address deeper questions about the structure and classification of mental disorders: To what extent do genetic findings support or challenge our clinical nosology? Are there genetic boundaries between psychiatric and neurologic illness? Do the data support a boundary between disorder and normal variation? Is it possible to envision a nosology based on genetically informed disease mechanisms? This review provides an overview of conceptual issues and genetic findings that bear on the relationships among and boundaries between psychiatric disorders and other conditions. We highlight implications for the evolving classification of psychopathology and the challenges for clinical translation.

Figure 1.. Methods commonly used to evaluate genetic overlap between phenotypes.

A. At the DNA variant level, individual loci (e.g. SNPs, rare mutations, or CNVs) may show evidence of pleiotropic association with two (e.g. P₁, P₂) or more phenotypes. At the level of biological pathways, gene sets assigned to a pathway may be enriched in association signals beyond chance expectation across multiple phenotype (e.g. P₁, P₂, P₃). B. Genetic risk scores (or polygenic risk scores, PRS) are developed in a “discovery“ GWAS sample and computed for each individual in an independent “target” sample. The genetic risk score for each individual (i) in the target sample is computed as the product of the number of risk alleles (X) at each SNP (j) multiplied by that SNP’s association effect size (β_j) and summing over all SNPs. The left hand plot shows the distribution of genetic risk scores in cases and controls in an independent target set for the same phenotype as that of the discovery sample. To examine cross-phenotype overlap, the discovery risk score is applied to target samples of other phenotypes. The proportion of variance explained by the discovery GWAS (R²) for each target phenotype (e.g. P₂, P₃, P₄). is shown in the plot on the right hand side. C. Genetic correlation between phenotypes (ranging from −1.0 to +1.0) can be estimated using multiple methods that compare genetic and phenotypic similarity among unrelated individuals. The figure shows a hypothetical genetic correlation matrix between multiple pairs of phenotypes (P₁ – P₈).