Huge genetics study shows what really divides and unites 14 psychiatric disorders

A sweeping genomic analysis reveals how psychiatric disorders cluster in five biological families, exposing common pathways and identifying where their genetic roots diverge.

Study: Mapping the genetic landscape in 14 psychiatric disorders. Image credit: GrAl / Shutterstock

In a recent study published in the journal Naturescientists in the Psychiatric Genomics Consortium Cross Disorder Working Group (CDG3) analyzed genetic data from 14 psychiatric disorders to assess genetic risk shared between disorders relative to degree of disorder-relatedness.

They identified five major underlying factors that explain, on average, about two-thirds of the genetic variance in each disorder, although some conditions, such as Tourette syndrome, retain significant variance for the disorder, and found 238 loci associated with at least one of the disorder factors, including 27 loci shared by two or more factors.

The analysis also identified hundreds of loci differentiating pairs of disorders, particularly those from different genomic factors, with disorders within the same factor showing very few differentiating loci, consistent with strong within-factor similarity.

Their findings offer insights into more biologically based psychiatric classification and treatment.

High comorbidity and unclear diagnoses

Psychiatric disorders are extremely common, with about half of people meeting diagnostic criteria for one or more conditions during their lifetime. Many individuals experience multiple disorders, and high rates of comorbidity make it difficult to draw clear boundaries between diagnostic categories. Because diagnoses are based on symptoms rather than biological mechanisms, the underlying causes remain poorly understood.

Advances in psychiatric genomics have revealed hundreds of associated genetic variants, several of which affect multiple disorders simultaneously. These findings highlight important genetic associations between the conditions, suggesting common biological bases.

Designing genomic analysis of disorders

Compared to previous cross-disorder efforts, this analysis benefited from much larger sample sizes and the inclusion of substance use disorders. Because ancestry diversity varied widely between datasets, primary analyzes were restricted to participants of similar genetic ancestry to Europe, with additional crossover controls often not possible and therefore interpreted cautiously.

The researchers compiled a genome-wide association study (GWAS) summary statistics for 14 psychiatric disorders, derived from manual-based diagnostic criteria and from GWAS datasets supported by these criteria.

These included updated results for eight disorders from previous Cross Disorder Group analyses, namely anorexia nervosa, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder (OCD), schizophrenia and Tourette syndrome and six recently added disorders (alcohol, cannabis and opioid use disorders, anxiety disorders, post-traumatic stress disorder (PTSDand nicotine addiction).

Sample sizes varied, and most analyzes were restricted to individuals of similar genetic ancestry to Europe to ensure statistical comparability. CDG3 represents a substantial improvement in power and disturbance statistical coverage compared to earlier CDG1 and CDG2 analyses.

Various analytical frameworks were used. Linkage disequilibrium score regression (LDSC) was used to estimate genome-wide genetic associations between disorders. Popcorn assessed genetic correlations between ancestors to assess generalizability. MiXeR, a bivariate causal mixture model, quantified the total number of common causal variants, independent of the direction of the effect.

Genome Structural Equation Modeling (genomic SEM) identified latent genetic factors underlying shared risk among the disorders. This approach evaluated multiple model constructs, including a correlated five-factor model and a hierarchical p-factor model representing general psychopathology. Local covariance correlation analysis (LAVA) examined regional genetic associations among 1,093 linkage disequilibrium (LD)- independent genomic regions, identifying hotspots in which multiple disorders share the local genetic architecture.

The study also used a case-by-case basis GWAS (CC GWAS) to identify disorder-discriminating loci, with almost all disorder-discriminating loci occurring between disorders attributed to different genomic factors and almost none occurring between disorders within the same factor, supporting factor structure.

Together, these methods triangulated genetic overlap from global, regional, functional, and local perspectives.

Common and specific genetic risk for disorders

Genome-wide LDSC analyzes showed broad genetic overlap among the 14 disorders, forming clusters of particularly strong association, such as major depression with anxiety and PTSDand schizophrenia with bipolar disorder.

Cross-origin analyzes showed that some findings, such as schizophrenia, appeared more consistently in European and East Asian-like data sets. On the contrary, others, such as PTSD and major depression, showed weaker cross-population consistency and remain limited due to insufficient statistical power.

MiXeR analyzes revealed that the disorders shared more causal variation than implied LDSC correlations, suggesting that most common variants influence the disorders in the same direction.

Genomic SEM identified five latent genetic factors, compulsive (anorexia nervosa, OCDTourette’s), schizophrenia, bipolar, neurodevelopmental (autism, ADHDTourette’s), internalizing (major depression, PTSDanxiety) and substance use disorders (SUD) (alcohol, cannabis, opioid use, nicotine dependence, and less cross-loading than ADHD).

These factors account for most of the heritability of any disorder attributable to single nucleotide polymorphisms (SNPs), although Tourette syndrome showed significant disorder-specific genetic variation.

A higher-order p factor explained common variance across all five factors, loading most strongly on internalizing disorders but with significant heterogeneity between SNPsindicating that factor-specific signals remain necessary to account for aberrant genetic effects and that factor p alone is insufficient to represent the genetic architecture of psychopathology.

Correlations between factors and externalizing traits showed significant patterns, including strong associations with neuroticism, stress sensitivity, and suicidality, as well as distinct associations with cognitive performance and socioeconomic characteristics for some factors.

LAVA analyzes identified 101 genomic hotspots where multiple disorders shared significant local associations, with particularly dense overlap between major depression, anxiety, major depression, PTSDand bipolar, schizophrenia.

Towards Biologically Based Psychiatry

This large-scale analysis shows that psychiatric disorders share substantial genetic underpinnings, with five general genomic factors explaining much of their heritable risk. The strongest shared architecture was observed for schizophrenia, bipolar disorder, and internalizing disorders, which had very few specific loci in CC GWAS analyses, reinforcing their high degree of genetic similarity.

Biological analyzes demonstrated distinct cellular pathways underpinning different factors, such as the involvement of excitatory neurons in schizophrenia and bipolar disorder and oligodendrocyte-related processes in internalizing disorders.

These findings support a move toward a more biologically informed psychiatric classification system that complements rather than replaces existing symptom-based diagnoses.

Strengths include an unprecedented sample size, diverse analytical methods, and the integration of genomic, regional, and functional insights.

Limitations include unequal representation of ancestry, which necessitated restricting most analyzes to European-like datasets. significant variation in GWAS Sample sizes. the possibility of multiple attributes by inflating coupling associations. diagnostic misclassification. and variable diagnostic accuracy between studies.

Despite these limitations, the work provides a comprehensive map of the shared genetic architecture and identifies promising targets for future mechanistic research and therapeutic development.