Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies

Abstract

Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.

Figure 1.. Dimensional and Transdiagnostic GWAS Phenotypes Across the Lifespan

Note: Psychiatric phenotypes demonstrate temporal features such as age of onset and developmental trajectories, ‘state’ fluctuations around the ‘stable’ severity levels, recurrences, and treatment response. Many dimensional, transdiagnostic, and other non-diagnostic phenotypes are well-suited to measuring such temporal features. Higher-order phenotypes such as the general factor of psychopathology (p-factor) can ‘lump’ symptom burden at a particular point in time. The most optimal sources of information may vary across the lifespan, e.g., externalizing behaviors might be assessed via informant-reports in childhood, and via self-reports and cotinine in adulthood.

Figure 2 –. “Lumping” and “Splitting” of Dimensional GWAS Phenotypes

Note: Dimensional and transdiagnostic phenotypes can be measured using various sources of information, such as interview, self-reports, tissue samples, passive sensors, and other biological and behavioral markers. Variance (either phenotypic, or genetic if using genomic SEM) that is common to all measured phenotypes can be statistically “lumped” into a single higher-order phenotype, which can enhance GWAS power and result in discovery of pleiotropic SNPs. Simultaneously, variance that is unique to the measured phenotype can be statistically “split” into a lower-order phenotype, which can result in additional SNP discoveries with higher phenotype-specificity.