Examining the causes and consequences of sub-types of depression across the life course

Background:

Depression is a complex and multi-faceted disorder, associated with concurrent and long-lasting social, psychological and physiological impairments (1). It is critical to identify individuals in whom active management or effective prevention strategies could mitigate their symptoms and improve health and wellbeing. However, depression rates continue to rise. Currently, our understanding of depression is limited by several interrelated challenges that must be addressed to improve mitigation strategies for depression.

First, depression is highly heterogeneous and not the same thing for everyone. For example, research is beginning to show that there are key sub-types of depression, which vary by the number and combination of symptoms, duration of symptoms, age of onset and severity (2). This makes treatment and prevention challenging as different sub-types may have more circumscribed aetiologies or differential outcomes that are not fully captured by a one size fits all approach.

Secondly, different sub-types of depression are likely to reflect varying aetiologies comprised of multiple genetic and environmental antecedents (3). Untangling this requires large datasets combined with innovative approaches. Furthermore, the causal role of these antecedents for depression sub types, either in isolation, or in combination is far from understood. There is also no definitive answer as to which risk factors (or combinations of) are most important and thus we do not know what treatments to priortise for what sub-type of depression.

Finally, depression does not neccesarily mean the same thing across the life course. Different sub-types of depression may manifest differtially and have varying aetiologies at different timings throughout development (4). What may underpin depression in youth may be very different to what underpins depression in later life. Therefore it is important to identify timing specific sub-types of depression and their antecedents to ensure we know what may help the right person at the right time.

Aims:

This PhD will use newly available large datsets to identify sub-types of of depression, and examine how a combination of genetic risk, environmental risk factords and biomarker data could underpin different sub-types of depression. The data included are UK Biobank (UKB, n=500K), Generation Scotland (GS, n=20K) and the Avon Longitudinal Study of Parents and Children (ALSPAC; n=30K).

Key aims include:

1. Stratify depression into clinicially meaningful sub-types using cross-sectional and longitudinal data (using data driven approaches such as latent class analysis and growth mixture modelling)

2. Explore associations between these sub-types of depression and a range of genetic, environmental, biomarker and imaging data using traditional (regression and correlational) analysis and non-traditional (machine learning, prediction) analysis.

3. Conduct genome-wide association studies (GWAS) on the different subtypes of depression and explore genetic correlations and causal relationships (using Mendelian Randomization) with other disorders/traits.

4. Explore how this varies across the life-course by examining age effects of sub-types of depression and leveraging methods and results from the aims above.

Training outcomes:

This proposal benefits greatly from expertise in psychiatric epidemiology, genomics, data science, longitudinal modelling, neuroimaging and quantitative and clinical psychology and builds on an ongoing collaboration across the University of Edinburgh, the Karolinska Institute and Glasgow University.

Extensive bespoke training will be offered across a variety of essential skills and across institutions with opportunities for lab visits, including: genomic/epigenetics analysis, quantitative trajectory modelling, epidemiology, MRI image processing and data science. External and internal training courses will also be available for the student. Statistical packages like R and Python will be used, with an emphasis on reproducibility, open science and the potential for co-production with people with lived experience. This project provides an exciting opportunity to work on several novel datasets which will provide the student with a unique set of precision medicine skills that are transferable to a number of disciplines.

References:

1. Marx, W., Penninx, B.W.J.H., Solmi, M. et al. Major depressive disorder. Nat Rev Dis Primers 9, 44 (2023). https://doi.org/10.1038/s41572-023-00454-1

2. Na Cai, Karmel W Choi, Eiko I Fried, Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies, Human Molecular Genetics, Volume 29, Issue R1, 15 September 2020, Pages R10–R18, https://doi.org/10.1093/hmg/ddaa115

3. Nguyen TD, Harder A, Xiong Y, Kowalec K, Hägg S, Cai N, Kuja-Halkola R, Dalman C, Sullivan PF, Lu Y. (2022) Genetic heterogeneity and subtypes of major depression. Molecular Psychiatry, Epub 08 January 2022. PMID: 34997191.

4. Musliner, K. L., Munk-Olsen, T., Eaton, W. W., & Zandi, P. P. (2016). Heterogeneity in long-term trajectories of depressive symptoms: Patterns, predictors and outcomes. Journal of affective disorders, 192, 199–211. https://doi.org/10.1016/j.jad.2015.12.030