Identifying genomic and phenotypic risk factors for the clinical progression of depressive symptoms

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Molecular. Genetics & Pop Health

Abstract

Major depressive disorder (MDD) is one of the commonest psychiatric illnesses and one of the world's leading causes of morbidity. It is a heritable condition, with a heritability estimated from twin studies at approximately 37% (1). The number of associations detected by genome-wide association studies (GWAS) is gradually increasing with larger sample sizes. MDD treatment response (2) and severity (3) also have an important genetic component, but the specific loci have been rarely studied. This project aims to initiate the identification of the risk factors and genetic loci conferring increased severity and clinical progression of MDD using electronic health record data from Generation Scotland (GS) and UK Biobank (UKB), in collaboration with the Karolinska Institute and the Psychiatric Genomics Consortium (PGC) MDD Working Group.

GS is a family-based population study comprising data from more than 20,000 individuals from across Scotland. UKB is a health resource consisting in 500,000 individuals from all over Britain. In both cohorts, records from several health-related complex traits (including depression status) are available, together with lifestyle ("environmental") data, data on physical co-morbidity, cognition and other relevant traits.

Aims: Firstly, using GS, we will use a variance-components approach (4) to estimate the genetic (common SNP), pedigree-associated and share environmental contributions to depression severity and progression from primary to secondary care management. Wherever possible, we will replicate these analyses in UKB, and other datasets from the PGC.

Secondly, we will conduct genome-wide association analyses of depression severity and/or the progression of MDD management from primary to secondary care. Wherever possible, we will include further GWAS analyses of other markers of MDD severity, such as treatment with electroconvulsive therapy (ECT).

We will estimate the heritability of MDD-severity from available GWAS data, identify phenotypic and genetic correlations with other relevant phenotypes, and infer directional and causal associations between these variables and MDD-severity using Mendelian Randomisation.

The objectives of this project are: (1) to gauge the extent of variation in MDD-severity accounted for by genetic and environmental actors (2) identify specific loci conferring these genetic effects and (3) use these discoveries and other available GWAS data to make stronger directional/causal associations.

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