ENHANCING UNDERSTANDING OF MENTAL DISORDERS THROUGH INTEGRATION OF GENOMIC AND LIFE-COURSE DATA

There has been substantial progress into the genomic underpinnings of mental disorders, such as depression, which have led to the identification of hundreds of key genomic loci, enhanced understanding of causal pathways and improved direction for drug targets. However, despite such progress, our actual understanding of the genetic mechanisms underlying these mental disorders, and therefore our ability to develop better treatments is still limited.

One reason for this research has tended to focus on lifetime or cross-sectional effects, but mental disorders are not static, they change over time and may have periods where genetic effects are more prominent. Similarly, life-course approaches postulate that repeated information from the same person over time are likely to be highly correlated and leveraging that correlation can reduce measurement error and improve signal. Combing life-course and genomic (genome-wide association studies [GWAS], polygenic risk scores [PRS], mendelian randomization [MR]) approaches are becoming more common with the emergence of large-scale longitudinal biobanks, availability of genetic data and new statistical methods. This integration of both these approaches could lead to an enhanced understanding of the genomics of mental disorder more broadly, but also how genetics influence the onset, course and persistence of mental disorders across the life-course.

In this symposium, we will highlight emerging work from this field that demonstrates the utility of combining genomic and life-course methods for improved understanding of mental disorders.

First, Alex Kwong (Edinburgh) will show that leveraging repeated assessments of depression from large datasets such as UK Biobank improve the signal in GWAS, resulting in a greater number of genome-wide significant hits, more mapped genes, higher single nucleotide polymorphism (SNP) based heritability and higher amount of variance explained in PRS analysis. Importantly, this approach identifies greater GWAS signal in non-European ancestries, providing a framework for potentially improving gene discovery in future studies.

Next, Poppy Grimes (Edinburgh) will present the latest PGC-MDD and EAGLE consortia results from an adolescent onset depression GWAS using repeated data from prospective and retrospective cohorts. These results show good genetic correlation between prospective and retrospective cohorts, but importantly also identifies novel GWAS hits, some of which are common or unique to adult MDD.

Following, Robyn Wootton (Oslo) will demonstrate how MR can be implemented within longitudinal studies to examine the causal effect of predictors on specific periods of depression development, rather than just on averaged lifetime effects. Using data spanning over 30 years from the ALSPAC study, these results show how predictors such as BMI and educational attainment may be causal of later depression development.

Finally, Ruby Tsang (Bristol) will show how a spectrum of psychiatric genetic risk are associated with more severe depression trajectories, across 30 years of data in the ALSPAC study. Importantly, these results highlight that genetic risk of psychiatric illnesses are not uniform, some are more important that others and have much greater effects across various stages of the life-course.

Michel Nivard (Bristol) will summarise these studies and provide a discussion on what future longitudinal and genetic studies should do to ensure this becomes a fruitful area of research.