“Scientists have characterized the broad genetic patterns that are shared across 14 psychiatric disorders. Could it reframe how mental-health conditions are diagnosed?
Psychiatric disorders disrupt the core functions of the human mind, from perception and cognition to emotion and motivation. Decades of twin and family studies have shown that psychiatric disorders have a large heritable component1, but they are still among the least understood conditions in medicine. Writing in Nature, Grotzinger et al.2 report the analysis of genetic data from more than one million people, examining associations between genetic variants and 14 psychiatric disorders. They reveal that the genetic variation underpinning these conditions clusters into five broad categories, which cut across current diagnostic boundaries.
The ‘genetic architecture’ of a trait refers to the collection of genetic variants that underlie it, how frequently those variants occur in a population, and the sizes and patterns of their effects. Studying the genetic architecture of psychiatric disorders is a natural starting point for investigating how they originate. Genetic factors that affect a person’s susceptibility to certain traits lie at the beginning of the biological cascades that culminate in disease, and can point to molecular pathways, cell types and neural circuits that contribute to risk. Moreover, because the effects of genetic variants are expressed in the context of an environment, they can help to reveal crucial environmental factors (such as traumatic events) and developmental periods (early childhood, for example) that transform vulnerability into illness. Even without knowing how each gene acts biologically, combining their effects into overall risk scores is already helping researchers to study patterns of vulnerability and resilience. This could eventually inform clinical risk prediction.
A large and international group of scientists known as the Psychiatric Genomics Consortium (PGC) has been instrumental in building the extensive data sets required for such studies3. Its open-science model enables researchers to combine results and apply innovative methods. Using these data, Grotzinger and colleagues modelled the genetic risk variants that are distinct, and those that are shared, across 14 disorders. If two traits or diseases share many risk variants, they are said to be highly genetically correlated. On the basis of such high genetic correlations, the authors found that the disorders could be clustered into five ‘genomic factors’ characterized by a high degree of shared risk. Together, these factors accounted for about two-thirds of the disorders’ heritability that is attributable to common genetic variants.
Figure 1 | Genetic correlations between 14 psychiatric disorders. Grotzinger et al.2 characterized the patterns of genetic variation associated with 14 psychiatric disorders: anorexia nervosa (AN), obsessive–compulsive disorder (OCD), Tourette’s syndrome (TS), schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), major depressive disorder (MDD), anxiety disorders (AD), cannabis-use disorder (CUD), alcohol-use disorder (AUD), nicotine dependence (ND) and opioid-use disorder (OUD). Genetic correlation describes the degree to which two traits share the same underlying genetic risk; a higher value means that many risk variants are shared. The authors found that psychiatric conditions cluster into five ‘genomic factors’ with a high degree of shared genetic variation, suggesting that current diagnostic boundaries might not necessarily reflect biology.
Genetic risk for psychiatric disorders does not always correlate negatively with other traits, such as level of educational attainment (EA), cognitive ability (COG), and the non-cognitive skills that contribute to EA, such as motivation and creativity (NCOG).
This suggests that the genetic variation that is associated with mental-health conditions is also a source of valuable neurodiversity.
The ‘compulsive factor’ includes anorexia nervosa, obsessive–compulsive disorder, Tourette’s syndrome and some overlap with anxiety. The ‘schizophrenia–bipolar factor’ includes schizophrenia and bipolar disorder. The ‘neurodevelopmental factor’ includes autism spectrum disorder and attention deficit hyperactivity disorder (ADHD). The ‘internalizing factor’ includes major depressive disorder, post-traumatic stress disorder and anxiety disorders. Finally, the ‘substance-use factor’ includes alcohol, cannabis, nicotine and opioid dependence, with some overlap with ADHD. Few genetic variants are unique to a single diagnosis, suggesting that the categories in the Diagnostic and Statistical Manual (DSM; the conventional tool for diagnosing psychiatric conditions) might be useful clinically but are seemingly arbitrary at a biological level.
Each of these factors is characterized by its own biological signature.
For example, genes in the schizophrenia–bipolar factor are highly expressed in excitatory neurons and brain regions involved in processing reality.
The neurodevelopmental factor overlaps with both the compulsive and the schizophrenia–bipolar factors, reflecting shared developmental pathways. Genes in the internalizing factor are associated with non-neuronal supporting cells called glia, suggesting that mood and anxiety disorders are linked more closely to the brain’s wiring infrastructure than to the cells that transmit signals.
The substance-use factor showed substance-specific signals, such as variants in the genes that encode the enzyme that breaks down alcohol and the receptors that respond to nicotine. The authors found strong associations between this genetic factor and socioeconomic indicators such as income and cognition, suggesting that its genetic signal might be more intertwined with socioenvironmental pathways than are the signals of other factors.
Across all factors, associated genes show peak expression during fetal development, highlighting the importance of early developmental processes in psychiatric risk.
Much of the genetic variation associated with clinical disorders also overlaps with normal-range traits such as cognition, sleep, personality, social behaviour and socioeconomic outcomes. Not all of these relationships are negative.
For example, the schizophrenia–bipolar factor shows a positive association with the non-cognitive component4 of educational attainment, implying that genetic variants that are associated with an increased risk of psychosis might also contribute to creativity, persistence and other traits that promote academic success5.
Associations with normal-range traits — including positive ones — should not be a surprise, because Grotzinger and colleagues’ study focused on genetic variants that are common in the population rather than on rare, disease-causing mutations.
Psychiatric disorders therefore seem to arise more often at the extreme ends of this continuum of genetic variation, when certain combinations of genes and life experiences come together in unfavourable ways. This should reframe mental illness not as defective biology, but as the unfortunate intersection of natural variation and environmental stress.
Such a view has immediate implications. Companies that offer embryo screening as part of in vitro fertilization assess genetic risk for psychiatric disorders, and select against embryos that have a higher risk score6. But if the genetic factors described by Grotzinger and colleagues reflect normal variability that can confer both strengths and vulnerabilities, such selection could inadvertently reduce valuable neurodiversity alongside disease risk. Recognizing these factors as part of natural variation could also motivate the development of quantitative measures that capture the influences of these factors more directly, rather than relying solely on conventional definitions that are based on comparisons of cases and control groups.
Current sample sizes, impressive as they are, remain far below what would be necessary to identify the full set of common genetic variants that contribute to psychiatric disorders. The authors’ analyses of statistical power show that sample sizes would need to be tens to hundreds of times larger than those now available. That would mean the number of study participants required would need to vary from about 12 million for schizophrenia to more than 80 million for major depressive disorder. So far, this level of statistical power has been achieved only for height, a population-wide trait7. Because psychiatric diagnoses are rare by comparison, achieving a similar level of power would require impractically large numbers of diagnosed cases.
However, quantitative measurements that capture the underlying continuous genetic factors could enable more participants to contribute information through population cohorts, which would provide a clearer view of how risk accumulates. Composite measures that combine symptoms, cognition, personality and behaviour could be one way to do this. Studying the full distribution, rather than only clinical extremes, could help to move psychiatric genetics towards the larger sample sizes that are needed to characterize the genetic architecture of mental illness. It would also encourage the recognition that susceptibility to such conditions is part of a natural continuum, rather than a biological defect.” [1]
1. Nature 649, 295-296 (2026) By Abdel Abdellaoui
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