Symptom-specific genetics reveal heterogeneity within major depressive disorder

Abstract Background Major Depressive Disorder (MDD) is clinically and biologically heterogeneous. Here, we leveraged the genetics of individual depressive symptoms to dissect the disorder’s underlying heterogeneity. Methods We utilized the BIObanks Netherlands Internet Collaboration (BIONIC). A series of genome-wide association studies (effective- N range: 14,407 - 47,110) compared controls (N=48,286) with partially different subsets of lifetime MDD cases (range: 3,892–15,577), each endorsing one of 12 individual DSM-based depressive symptoms. Results were combined in genetic correlations that informed factor analyses with Genomic Structural Equation Modeling, decomposing underlying MDD liability dimensions. The identified factors were assessed and further characterized using multivariate regression of neurodevelopmental/psychiatric and cardiometabolic traits. Results All symptoms demonstrated substantial SNP-based heritability ( h² SNP : 0.088 – 0.127). Despite high between-symptom genetic correlations, factor analyses yielded two highly correlated ( rg =0.85) but still distinct latent factors: factor 1 (F1), capturing appetite/weight loss, insomnia, guilt/worthlessness, psychomotor slowing and suicidality, and factor 2 (F2), reflecting concentration problems, anhedonia, depressed mood, appetite/weight gain and fatigue. Overall, F1 had a stronger genetic overlap with neurodevelopmental/psychiatric phenotypes (e.g., autism: standardized estimate β =0.45, p =4.49×10⁻⁴; schizophrenia: β =0.40, p =1.73×10⁻⁴), while F2 significantly overlapped with cardiometabolic traits (e.g., metabolic syndrome: β =0.44, p =8.69×10⁻⁴; coronary artery disease: β =0.31, p =0.009). Conclusions We identified two genetic dimensions of MDD, each linked to partially distinct clinical manifestations and underlying biology, with one reflecting neurodevelopmental/psychiatric liabilities and the other capturing a strong cardiometabolic vulnerability. Disentangling such distinct dimensions may help guide patient stratification and targeted treatment, thereby advancing precision psychiatry.