Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies

• Authors: Aaron M. Holleman, Emory University; K. Alaine Broadaway, Emory University; Richard Duncan, Emory University; Andrei Todor, Emory University; Lynn M. Almli, Emory University; Bekh Bradley-Davino, Emory University; Kerry Ressler, Emory University; Debashis Ghosh, Colorado School of Public Health; Jennifer Mulle, Emory University; Michael Epstein, Emory University
• Journal Title: Scientific Reports
• Volume: Volume 9, Number 1
• Publisher: Nature Research (part of Springer Nature): Fully open access journals | 2019-05-17, Pages 7523-7523
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1038/s41598-019-44046-0
• Subjects: Biology, Genetics; Psychology, Psychobiology; Psychology, Clinical
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; POSTTRAUMATIC-STRESS-DISORDER; GENOME-WIDE ASSOCIATION; RISK; DEPRESSION; TRAUMA; PHENOTYPES; EXPOSURE; ABUSE; TWIN

Abstract

Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom data from questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate questionnaire data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom data collectively using a statistical framework that compares similarity in multivariate symptom-scale data from questionnaires to similarity in common genetic variants across a gene. We use simulated data to demonstrate this strategy provides substantially increased power over standard approaches that collapse questionnaire data into a single surrogate outcome. We also illustrate our approach using GWAS data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom data of arbitrary dimension.