Identification of Trait-Associated Molecular Signatures for Precision Psychiatry Using Deep Phenotyping
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| Year of publication | 2022 |
| Type | Conference abstract |
| Citation | |
| Description | Background: Mental disorders (MDx) comprise a heterogeneous group of conditions collectively characterized by abnormal patterns of feelings, thoughts, and behavior. Differential diagnosis is complicated by their varied and overlapping clinical presentations, which are shaped by a concerted interplay between hereditary risks and environmental exposures. Suggestive of interconnective etiologies, many identified risks are non-specifically associated with a range of MDx, and patients often undergo a number of diagnostic categories over their life course. Collectively, this challenges the validity of the current categorical classifications of MDx. Identification of trait-associated molecular signatures is thus paramount for patient stratification and the implementation of precision psychiatry. Methods: Using a deep phenotyping approach, we intend to identify quantifiable traits that are associated with MDx genetic risk. Particularly, in addition to assessing putative trait correlates of genome-wide MDx polygenic risk scores (PRS), our analyses will focus on MDx biology-weighted, pathway-specific PRS (e.g., gene sets implicated with cell-specific biologies or particular molecular networks). Building upon data from a COST action aimed at delineating the neural architecture of consciousness (CA18106), our deep phenotyping resource will combine SNP genotyping with behavioral and questionnaire-based characterization (20h+), extensive brain imaging (1h+) and high-resolution mass spectrometry-based fasting-state blood metabolomics in 1000+ young, medication-free research participants. Results: At present, data has been collected and genotypes are being constructed for ~600 participants, while ~200 bio-samples have been submitted to metabolomic profililng. The participants are primarily in the age group of 20-25 years old with equal representation of genders. Of note, ~12% of participants have previously been diagnosed with one or more MDx (e.g., depression (8%)) and >25% report of diagnosed MDx among first degree relatives. Discussion: There is an urgent need to translate psychiatric genetic insight into improvements in the prevention, diagnosis, and treatment of MDx. Here we present the development of a comprehensive deep phenotyping resource that can be used to probe for associations between genetic risk profiles and a wide range of MDx-relevant phenotypes. We envision that our research will contribute to the implementation of precision medicine in psychiatry, by providing a tool for improved patient stratification and risk prediction. Importantly, as we are able to re-contact participants for up to seven years from enrollment, our setup allows for prospective assessment of the clinical utility of our findings. Disclosure: Nothing to disclose. |