Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Abstract

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Keywords: GWAS; Psychiatric genetics; cross-disorder genetics; functional genomics; gene expression; genetic architecture; genetic correlation; neurodevelopment; pleiotropy; psychiatric disorders.

Figure 1.. Genetic relationships between eight psychiatric disorders.

A) SNP-based genetic correlations (r_g) were estimated between eight neuropsychiatric disorders using LDSC. The size of the circles scales with the significance of the p-values. The darker the color, the larger the magnitude of r_g. Star sign (*) indicates statistical significance after Bonferroni correction. (B) SNP-based genetic correlations between eight disorders were depicted using an in-directed graph to reveal complex genetic relationships. Only significant genetic correlations after Bonferroni correction in (A) were displayed. Each node represents a disorder, with edges indicating the strength of the pairwise correlations. The width of the edges increases, while the length decreases, with the absolute values of r_g. (C) Based on the results of an exploratory factor analysis of the genetic correlation matrix produced from multivariable LD-score regression, a confirmatory factor model with three correlated genetic factors was specified using Genomic SEM and estimated with the weighted least squares algorithm. In this solution, each common genetic factor (i.e., F1_g, F2_g, F3_g) represents variation in genetic liability that is shared across the disorders that load on it. These common factors are specified so as to account for the genetic covariation among the psychiatric disorders. For example, F1_g represents shared genetic liability among disorders characterized by compulsive behaviors (AN, OCD and TS). One-headed arrows connecting the common genetic factors to the individual disorders represent standardized loadings, which can be interpreted as coefficients from a regression of the true genetic liability for the disorder on the common factor. Two-headed arrows connecting the three factors to one another represent their correlations. Two-headed arrows connecting the genetic components of the individual psychiatric disorders to themselves represent residual genetic variances and correspond to the proportion of heritable variation in liability to each individual psychiatric disorder that is unexplained by the three factors. Standardized parameters are depicted with their standard errors in parentheses. Paths labeled 1 with no standard errors reported are fixed parameters, which are used for scaling.

Figure 2.. Results of cross-disorder meta-analysis and candidate gene mapping.

(A) Quantile-quantile (QQ) plot displaying the observed meta-analysis statistics vs. the expected statistics under the null model of no associations in the -log₁₀(p-value) scale. Although a marked departure is notable between the two statistics, the estimated lambda₁₀₀₀ and the estimated LD Score regression intercept indicate that the observed inflation is mainly due to polygenic signals rather than major confounding factors including population stratification. (B) Gene prioritization strategies for significantly associated loci. Candidate genes were mapped on each locus if the index SNP and credible SNPs reside within a protein-coding gene, are eQTL markers of the gene in the brain tissue, or interact with promoter regions of the gene based on brain Hi-C data. (C) Manhattan plot displaying the cross-disorder meta-analysis results highlighting candidate genes mapped to top pleiotropic regions. When multiple genes were mapped to the same locus, genes encompassing the index SNP or genes with the largest number of evidences were displayed for clarity. Candidate genes that have not previously implicated in individual disorder GWAS are marked with an asterisk.

Figure 3.. Profile of disorder associations for illustrative pleiotropic loci: (A) rs8084351 on 18q21.2; (B) rs7193263 on 16p13.3; (C) rs117956829 on 11q14.3; and (D) rs10265001 on 7q34.

For each locus, disorder-specific effects of the index SNP are shown using ForestPMPlot. The first panel is the forest plot, displaying disorder-specific association p-value, log odds ratios (ORs), and standard errors of the SNP. The meta-analysis p-value and the corresponding summary statistic are displayed on the top and the bottom of the forest plot, respectively. The second panel is the PM-plot in which X-axis represents the m-value, the posterior probability that the effect eixsts in each disorder, and the Y-axis represents the disorder-specific association p-value as -log₁₀(p-value). Disorders are depicted as a dot whose size represents the sample size of individual GWAS. Disorders with estimated m-values of at least 0.9 are colored in red, while those with m-values less than 0.9 are marked in green.

Figure 4.. Eleven loci with opposite directional effects.

The radius of each wedge corresponds to the absolute values of the Z-scores (log(Odds ratios)/S.E) obtained from association tests of the SNP for eight disorders. The color indicates whether the examined SNP carries risk (red) or protective effects (green) for each disorder. The dotted line around the center indicates statistically significant SNP effects that account for multiple testing of 206 SNPs the q-value of 0.001.

Figure 5.. Results of functional genomics data analysis for pleiotropic vs. disorder-specific loci.

(A) GTEX tissue-specific enrichment results for 146 risk loci associated with at least one of eight neuropsychiatric disorders. GTEX tissues were classified as 9 distinct categories, of which the brain tissues were colored in blue. The dotted red line indicates a statistically significant p-value after conducting Bonferroni correction for multiple testing. Psychiatric disorder-associated loci show significant enrichment in genes expressed in pituitary and all brain tissues except nerve_tibal. (B) Brain developmental expression trajectory displayed for the three groups of genes based on (Kang et al., 2011) The 146 genome-wide significant loci from the cross-disorder meta analysis were clustered into three groups based on predicted disorder-specific associations: (1) no-pleiotopy; (2) pleiotropy=2; and (3) pleiotropy>2. The “no-pleiotropy” group included 37 loci that showed a single-disorder-specific association, while the “pleiotropy=2” and “pleiotropy>2” groups included 60 and 49 loci that were associated with two and more than two disorders, respectively. (C) In the adult cortex, genes mapped to pleiotropic loci were enriched for frontal cortex specific genes, while genes mapped to non-pleiotropic loci are enriched for occipical cortex specific genes. (D) Genes mapped to 146 risk loci show higher expression values in neurons and oligodendrocytes, with much higher neuronal specificity for pleiotropic loci.