Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities

Abstract

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of psychopathology versus rarer forms of serious mental illness. Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest that the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind rather than merely in degree.

Graphical abstract

Figure 1

Univariate association results for lifetime measures of mood disturbance and psychosis (A–C) Manhattan plots and a quantile-quantile plots for (A) depressive, (B) manic, and (C) psychotic symptoms. In the Manhattan plots, the x axis refers to chromosomal position, the y axis refers to the significance on a -log₁₀ scale, the horizontal dashed line denotes genome-wide significance (p = 5 × 10⁻⁸), and the horizontal dotted line marks suggestive significance (p = 5 × 10⁻⁵). In the quantile-quantile plots, the x axis refers to expected p value, while the y axis refers to the observed p value. For each plot, the nearest gene for the lead SNP in the top five genome-wide significant loci is labeled.

Figure 2

Relationships between eight psychiatric symptoms and disorders (A) Matrix of bivariate genetic-correlation estimates, where the diagonal elements correspond to SNP h² and the off-diagonal elements correspond to genetic correlations. Estimates that are non-significant are crossed out. (B) Scatterplot of standardized factor loadings from the exploratory factor analysis. (C) Path diagram for the final confirmatory factor model with standardized parameter estimates.

Figure 3

Multivariate association results for the two transdiagnostic latent genetic factors (A and B) Miami plots for (A) F1 and (B) F2. The top of each Miami plot corresponds to the significance of SNP effects on each latent factor, as traditionally conveyed in a Manhattan plot, while the bottom corresponds to the significance of heterogeneity tests for SNP effects (Q_SNP; i.e., the degree to which SNP effects are not mediated by F1 or F2). For each plot, the x axis refers to chromosomal position, the y axis refers to the significance on a -log₁₀ scale, the horizontal dashed line denotes genome-wide significance (p = 5 × 10⁻⁸), and the horizontal dotted line marks suggestive significance (p = 5 × 10⁻⁵). For each plot, the nearest gene for the lead SNP in the top five genome-wide significant loci is labeled. (C and D) UpSet plots illustrating the intersection of the five gene-mapping methods, ranked by degree of overlap.

Figure 4

Biological annotation of the two transdiagnostic latent genetic factors (A) Manhattan plots for local genetic correlation, covariance, and variance for F1 and F2. Black bars indicate significant local genetic correlation. (B) Scatterplot of gene set enrichment results illustrating convergence and divergence across the latent genetic factors with accompanying histograms for the top 10 gene sets for each factor. (C) Scatterplot of neuroimaging genetic-correlation results with accompanying figures where the −log₁₀ p values are mapped across the cortex, as parcellated in the Desikan-Killiany-Tourville atlas. (D) Smoothed line plots of gene set expression across developmental time in the PsychENCODE dataset for prioritized genes with transcriptomic profiles that are spatially similar to the neuroimaging genetic correlation maps for F1 and F2 (as indexed in the Allen Human Brain Atlas). For all plots, the dashed black line corresponds to the Bonferroni-corrected significance threshold when applicable.

Figure 5

Genetic-correlation and phenome-wide association results for the two transdiagnostic latent genetic factors (A) Scatterplot of genetic correlations (r_g) with marginal histograms. (B) Scatterplot of partial genetic correlations (b_g) with marginal histograms. For both plots, phenotypes are grouped into one of four broad domains: (1) demography and socioeconomic status, (2) health and disease, (3) personality and risky behavior, and (4) psychopathology and cognition. A line of best fit (with 95% confidence interval) is fit for all 92 data points. Points are colored burgundy if significant only for F1, violet if significant only for F2, black if significant for both, and faded gray if non-significant for both. The standard errors (SEs) for point estimates are plotted for both factors. (C) Rotated Miami plot for (left) F1 and (right) F2, where the y axis refers to the ICD-10 code category, the x axis refers to the significance on a −log₁₀ scale, the vertical light red line denotes phenome-wide significance (p = 3.27 × 10⁻⁵) following Bonferroni correction, and the vertical light blue line marks nominal significance (p = 0.05). The direction of the triangle refers to the direction of effect. Phecodes closely resembling Genomic SEM model phenotypes are bolded and italicized for emphasis.