Genetics, Geography and the Intergenerational Transmission of Maternal Depression Risk

Projektbeschreibung

Genetische und geografische Determinanten von Depression

Mütterliche Depression (MD) ist die häufigste Ursache für perinatale Mortalität und macht 20 % aller postpartalen Todesfälle aus. Obwohl Depressionen bis zum Jahr 2030 weltweit die voraussichtlich wichtigste Ursache für die Krankheitslast sein werden, fehlen uns grundlegende Kenntnisse über deren Ätiologie. Um dieses Problem anzugehen, wird das EU-finanzierte Projekt GenGeoRisk Daten von MoBa, der weltweit größten Kohortenstudie zu Schwangerschaften in der norwegischen Bevölkerung, verwenden, um genetische Varianten zu identifizieren, die für mütterliche Depression spezifisch sind. Ziel ist es, die allgemeine Veranlagung für Psychopathologien zu bestimmen sowie geografische Parameter zu identifizieren, die das Risiko mütterlicher Depression beeinflussen können. Angesichts der hohen Prävalenz von Depressionen und deren weit verbreiteten und verheerenden Auswirkungen sollen die Projektergebnisse dazu beitragen, die Behandlung der Krankheit zu verbessern.

Ziel

We can all name a family member, close friend, colleague or acquaintance who has suffered from depression. It is a common, pervasive and devastating illness characterised by persistent episodes of low mood. Depression affects individuals across all geographical locations and by the year 2030 it will be the largest contributor to disease burden worldwide. Depression that occurs in women during pregnancy and post-partum is referred to as maternal depression (MD). MD is the leading cause of perinatal mortality and it accounts for ∼20% of all postpartum deaths. Despite this profound individual and societal burden, the aetiology of depression remains poorly understood in part, due to three research barriers: 1) lack of success in identifying genetic variants specific to MD 2) inadequate account of environmental risk factors in genetic research and 3) heterogeneity. GenGeoRisk will address each of these research barriers in the following ways: 1) I will use aggregates of genetic variants (polygenic scores) derived from other successful studies of psychiatric traits to calculate genetic p—a general dimension, which sits at the top of a hierarchical structure of psychopathological dimensions and captures one’s general liability to psychopathology. Genetic p will be used to predict MD symptom risk and resilience in the world’s largest (N= 240 000) pregnancy cohort (MoBa; the Norwegian, mother, father and offspring cohort study) 2) I will use geographical location data from the entire Norwegian population (> 7 000 000) that has been recently linked to MoBa data to illuminate how MD symptoms and genetic p vary across neighbourhoods and diverse Norwegian municipalities 3) I will focus on MD, a less heterogeneous form of major depression, and use genetic p to discover MD subtypes. GenGeoRisk will be the first intergenerational, multi-environmental and DNA based investigation of MD to date.