Genetic structure of major depression symptoms across clinical and community cohorts

Author:

Adams Mark JORCID,Thorp Jackson GORCID,Jermy Bradley S,Kwong Alex S FORCID,Kõiv Kadri,Grotzinger Andrew D,Nivard Michel G,Marshall SallyORCID,Milaneschi Yuri,Baune Bernhard T,Müller-Myhsok BertramORCID,Penninx Brenda WJH,Boomsma Dorret IORCID,Levinson Douglas F,Breen Gerome,Pistis Giorgio,Grabe Hans J,Tiemeier Henning,Berger Klaus,Rietschel Marcella,Magnusson Patrik K,Uher Rudolf,Hamilton Steven P,Lucae Susanne,Lehto Kelli,Li Qingqin S,Byrne Enda M,Hickie Ian B,Martin Nicholas G,Medland Sarah E,Wray Naomi RORCID,Tucker-Drob Elliot M,Lewis Cathryn MORCID,McIntosh Andrew MORCID,Derks Eske MORCID, ,

Abstract

AbstractDiagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor forAppetite/Weightsymptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

Publisher

Cold Spring Harbor Laboratory

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