Projects per year
Abstract
Background: Both genetic and environmental factors contribute to risk of depression, but estimates of their rela- tive contributions are limited. Commonalities between clinically-assessed major depressive disorder (MDD) and self-declared depression (SDD) are also unclear.
Methods: Using data from a large Scottish family-based cohort (GS:SFHS, N = 19,994), we estimated the genetic and environmental variance components for MDD and SDD. The components representing the genetic effect as- sociated with genome-wide common genetic variants (SNP heritability), the additional pedigree-associated ge- netic effect and non-genetic effects associated with common environments were estimated in a linear mixed model (LMM).
Findings: Both MDD and SDD had significant contributions from components representing the effect from com- mon genetic variants, the additional genetic effect associated with the pedigree and the common environmental effect shared by couples. The estimate of correlation between SDD and MDD was high (r = 1.00, se = 0.20) for common-variant-associated genetic effect and lower for the additional genetic effect from the pedigree (r = 0.57, se = 0.08) and the couple-shared environmental effect (r = 0.53, se = 0.22).
Interpretation: Both genetics and couple-shared environmental effects were major factors influencing liability to depression. SDD may provide a scalable alternative to MDD in studies seeking to identify common risk variants. Rarer variants and environmental effects may however differ substantially according to different definitions of depression.
Methods: Using data from a large Scottish family-based cohort (GS:SFHS, N = 19,994), we estimated the genetic and environmental variance components for MDD and SDD. The components representing the genetic effect as- sociated with genome-wide common genetic variants (SNP heritability), the additional pedigree-associated ge- netic effect and non-genetic effects associated with common environments were estimated in a linear mixed model (LMM).
Findings: Both MDD and SDD had significant contributions from components representing the effect from com- mon genetic variants, the additional genetic effect associated with the pedigree and the common environmental effect shared by couples. The estimate of correlation between SDD and MDD was high (r = 1.00, se = 0.20) for common-variant-associated genetic effect and lower for the additional genetic effect from the pedigree (r = 0.57, se = 0.08) and the couple-shared environmental effect (r = 0.53, se = 0.22).
Interpretation: Both genetics and couple-shared environmental effects were major factors influencing liability to depression. SDD may provide a scalable alternative to MDD in studies seeking to identify common risk variants. Rarer variants and environmental effects may however differ substantially according to different definitions of depression.
| Original language | English |
|---|---|
| Pages (from-to) | 161-167 |
| Journal | EBioMedicine |
| Volume | 14 |
| Early online date | 4 Nov 2016 |
| DOIs | |
| Publication status | Published - 14 Dec 2016 |
Keywords / Materials (for Non-textual outputs)
- Major Depressive Disorder
- Self-declared depression
- SNP heritability
- couple effect
- family environment
- linear mixed modeling
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Dive into the research topics of 'Shared genetics and couple-associated environment are major contributors to the risk of both clinical and self-declared depression'. Together they form a unique fingerprint.Projects
- 4 Finished
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Stratifying Resilience and Depression Longitudinally
McIntosh, A. (Principal Investigator), Deary, I. (Co-investigator), Evans, K. (Co-investigator), Haley, C. (Co-investigator) & Porteous, D. (Co-investigator)
1/01/15 → 30/06/21
Project: Research
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RA2661 Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2. Main Budget.
Deary, I. (Principal Investigator), Gale, C. (Co-investigator), Holmes, M. (Co-investigator), Logie, P. (Co-investigator), Maclullich, A. (Co-investigator), Porteous, D. (Co-investigator), Seckl, J. (Co-investigator), Starr, J. (Co-investigator), Wardlaw, J. (Co-investigator) & Okely, J. (Researcher)
1/09/13 → 31/08/19
Project: Research
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ISP1: Analysis and prediction in complex animal systems
Tenesa, A. (Principal Investigator), Archibald, A. (Co-investigator), Beard, P. (Co-investigator), Bishop, S. (Co-investigator), Bronsvoort, M. (Co-investigator), Burt, D. (Co-investigator), Freeman, T. (Co-investigator), Haley, C. (Co-investigator), Hocking, P. (Co-investigator), Houston, R. (Co-investigator), Hume, D. (Co-investigator), Joshi, A. (Co-investigator), Law, A. (Co-investigator), Michoel, T. (Co-investigator), Summers, K. (Co-investigator), Vernimmen, D. (Co-investigator), Watson, M. (Co-investigator), Wiener, P. (Co-investigator), Wilson, A. (Co-investigator), Woolliams, J. (Co-investigator), Ait-Ali, T. (Researcher), Barnett, M. (Researcher), Carlisle, A. (Researcher), Finlayson, H. (Researcher), Haga, I. (Researcher), Karavolos, M. (Researcher), Matika, O. (Researcher), Paterson, T. (Researcher), Paton, B. (Researcher), Pong-Wong, R. (Researcher), Robert, C. (Researcher) & Robertson, G. (Researcher)
1/04/12 → 31/03/17
Project: Research