Symptom-specific genetics reveal heterogeneity within major depressive disorder

Anastasia A Goula; Floris Huider; Jouke-Jan Hottenga; Joëlle A Pasman; Mariska Bot; M Liset Rietman; Leen M 't Hart; Femke Rutters; Marieke T Blom; Didi Rhebergen; Marjolein Visser; Catharina A Hartman; Albertine J Oldehinkel; Eco J C de Geus; Barbara Franke; H Susan J Picavet; W M Monique Verschuren; Hanna M van Loo; Dorret Ι Boomsma; Brenda W J H Penninx; Yuri Milaneschi

doi:10.64898/2026.03.24.26349158

Symptom-specific genetics reveal heterogeneity within major depressive disorder

Anastasia A Goula
, Floris Huider
, Jouke-Jan Hottenga
, Joëlle A Pasman
, Mariska Bot
, M Liset Rietman
, Leen M 't Hart
, Femke Rutters
, Marieke T Blom
, Didi Rhebergen
, Marjolein Visser
, Catharina A Hartman
, Albertine J Oldehinkel
, Eco J C de Geus
, Barbara Franke
, H Susan J Picavet
, W M Monique Verschuren
, Hanna M van Loo
, Dorret Ι Boomsma
, Brenda W J H Penninx

Yuri Milaneschi^*

^*Corresponding author for this work

Research output: Working paper › Preprint › Academic

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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 (h 2 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.

Original language	English
Publisher	medRxiv
Number of pages	38
DOIs	https://doi.org/10.64898/2026.03.24.26349158
Publication status	Published - 25 Mar 2026

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2026.03.24.26349158v1.fullSubmitted manuscript, 642 KBLicence: CC BY-NC-ND

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Goula, A. A., Huider, F., Hottenga, J.-J., Pasman, J. A., Bot, M., Rietman, M. L., 't Hart, L. M., Rutters, F., Blom, M. T., Rhebergen, D., Visser, M., Hartman, C. A., Oldehinkel, A. J., de Geus, E. J. C., Franke, B., Picavet, H. S. J., Verschuren, W. M. M., van Loo, H. M., Boomsma, D. Ι., ... Milaneschi, Y. (2026). Symptom-specific genetics reveal heterogeneity within major depressive disorder. medRxiv. https://doi.org/10.64898/2026.03.24.26349158

@techreport{639423e5c5614aa4ab93196cb74801a5,

title = "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 (h 2 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.",

author = "Goula, \{Anastasia A\} and Floris Huider and Jouke-Jan Hottenga and Pasman, \{Jo{\"e}lle A\} and Mariska Bot and Rietman, \{M Liset\} and \{'t Hart\}, \{Leen M\} and Femke Rutters and Blom, \{Marieke T\} and Didi Rhebergen and Marjolein Visser and Hartman, \{Catharina A\} and Oldehinkel, \{Albertine J\} and \{de Geus\}, \{Eco J C\} and Barbara Franke and Picavet, \{H Susan J\} and Verschuren, \{W M Monique\} and \{van Loo\}, \{Hanna M\} and Boomsma, \{Dorret Ι\} and Penninx, \{Brenda W J H\} and Yuri Milaneschi",

year = "2026",

month = mar,

day = "25",

doi = "10.64898/2026.03.24.26349158",

language = "English",

publisher = "medRxiv",

type = "WorkingPaper",

institution = "medRxiv",

}

Goula, AA, Huider, F, Hottenga, J-J, Pasman, JA, Bot, M, Rietman, ML, 't Hart, LM, Rutters, F, Blom, MT, Rhebergen, D, Visser, M, Hartman, CA, Oldehinkel, AJ, de Geus, EJC, Franke, B, Picavet, HSJ, Verschuren, WMM, van Loo, HM, Boomsma, DΙ, Penninx, BWJH & Milaneschi, Y 2026 'Symptom-specific genetics reveal heterogeneity within major depressive disorder' medRxiv. https://doi.org/10.64898/2026.03.24.26349158

TY - UNPB

T1 - Symptom-specific genetics reveal heterogeneity within major depressive disorder

AU - Goula, Anastasia A

AU - Huider, Floris

AU - Hottenga, Jouke-Jan

AU - Pasman, Joëlle A

AU - Bot, Mariska

AU - Rietman, M Liset

AU - 't Hart, Leen M

AU - Rutters, Femke

AU - Blom, Marieke T

AU - Rhebergen, Didi

AU - Visser, Marjolein

AU - Hartman, Catharina A

AU - Oldehinkel, Albertine J

AU - de Geus, Eco J C

AU - Franke, Barbara

AU - Picavet, H Susan J

AU - Verschuren, W M Monique

AU - van Loo, Hanna M

AU - Boomsma, Dorret Ι

AU - Penninx, Brenda W J H

AU - Milaneschi, Yuri

PY - 2026/3/25

Y1 - 2026/3/25

N2 - 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 (h 2 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.

AB - 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 (h 2 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.

U2 - 10.64898/2026.03.24.26349158

DO - 10.64898/2026.03.24.26349158

M3 - Preprint

C2 - 41929285

BT - Symptom-specific genetics reveal heterogeneity within major depressive disorder

PB - medRxiv

ER -

Symptom-specific genetics reveal heterogeneity within major depressive disorder

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