Identification of Trait-Associated Molecular Signatures for
Precision Psychiatry Using Deep Phenotyping

a 2022

Identification of Trait-Associated Molecular Signatures for Precision Psychiatry Using Deep Phenotyping

QVIST, Per, Julie DONSKOV, Simon DURAND, Justyna HOBOT, Lenka JURKOVIČOVÁ et. al.

Základní údaje

Originální název

Identification of Trait-Associated Molecular Signatures for Precision Psychiatry Using Deep Phenotyping

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Vydání

European Neuropsychopharmacology, 2022

Další údaje

Typ výsledku

Konferenční abstrakt

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 5.600

ISSN

DOI

http://dx.doi.org/10.1016/j.euroneuro.2022.07.500

Změněno: 6. 10. 2022 11:08, Mgr. Lenka Jurkovičová

Anotace

V originále

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.

Citovat

QVIST, Per, Julie DONSKOV, Simon DURAND, Justyna HOBOT, Lenka JURKOVIČOVÁ, Renate RUTIKU, Jonas BYBJERG-GRAUHOLM, Francesco RUSSO, Madeleine ERNST, Michal WIERZCHOŃ, Milan BRÁZDIL, Kristian SANDBERG a Anders BØRGLUM. Identification of Trait-Associated Molecular Signatures for Precision Psychiatry Using Deep Phenotyping. In European Neuropsychopharmacology. 2022. ISSN 0924-977X. Dostupné z: https://dx.doi.org/10.1016/j.euroneuro.2022.07.500.

@proceedings{2222862,
   author = {Qvist, Per and Donskov, Julie and Durand, Simon and Hobot, Justyna and Jurkovičová, Lenka and Rutiku, Renate and BybjergandGrauholm, Jonas and Russo, Francesco and Ernst, Madeleine and Wierzchoń, Michal and Brázdil, Milan and Sandberg, Kristian and Børglum, Anders},
   booktitle = {European Neuropsychopharmacology},
   doi = {http://dx.doi.org/10.1016/j.euroneuro.2022.07.500},
   title = {Identification of Trait-Associated Molecular Signatures for Precision Psychiatry Using Deep Phenotyping},
   year = {2022}
}

TY  - CONF
ID  - 2222862
AU  - Qvist, Per - Donskov, Julie - Durand, Simon - Hobot, Justyna - Jurkovičová, Lenka - Rutiku, Renate - Bybjerg-Grauholm, Jonas - Russo, Francesco - Ernst, Madeleine - Wierzchoń, Michal - Brázdil, Milan - Sandberg, Kristian - Børglum, Anders
PY  - 2022
TI  - Identification of Trait-Associated Molecular Signatures for Precision Psychiatry Using Deep Phenotyping
N2  - 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.
ER  -

QVIST, Per, Julie DONSKOV, Simon DURAND, Justyna HOBOT, Lenka JURKOVIČOVÁ, Renate RUTIKU, Jonas BYBJERG-GRAUHOLM, Francesco RUSSO, Madeleine ERNST, Michal WIERZCHO$\backslash$'N, Milan BRÁZDIL, Kristian SANDBERG a Anders BØRGLUM. Identification of Trait-Associated Molecular Signatures for Precision Psychiatry Using Deep Phenotyping. In \textit{European Neuropsychopharmacology}. 2022. ISSN~0924-977X. Dostupné z: https://dx.doi.org/10.1016/j.euroneuro.2022.07.500.

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