Summary
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/894675 |
| Start date: | 01-02-2021 |
| End date: | 31-01-2023 |
| Total budget - Public funding: | 214 158,72 Euro - 214 158,00 Euro |
Cordis data
Original description
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.Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
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