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Pathway-based polygene risk for severe depression implicates drug metabolism in CONVERGE

Published online by Cambridge University Press:  02 April 2019

Anna R. Docherty*
Affiliation:
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA
Arden Moscati
Affiliation:
Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA Department of Psychiatry, Mount Sinai School of Medicine, New York, NY, USA
Tim B. Bigdeli
Affiliation:
Department of Psychiatry, SUNY Downstate, Brooklyn, NY, USA
Alexis C. Edwards
Affiliation:
Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA
Roseann E. Peterson
Affiliation:
Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA
Daniel E. Adkins
Affiliation:
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA Department of Sociology, University of Utah, Salt Lake City, UT, USA
John S. Anderson
Affiliation:
Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
Jonathan Flint
Affiliation:
Center for Neurobehavioral Genetics, UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA Department of Psychiatry and Biobehavioral Sciences, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
Kenneth S. Kendler
Affiliation:
Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA
Silviu-Alin Bacanu
Affiliation:
Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA
*
Author for correspondence: Anna R. Docherty, E-mail: [email protected]

Abstract

Background

The Psychiatric Genomics Consortium (PGC) has made major advances in the molecular etiology of MDD, confirming that MDD is highly polygenic. Pathway enrichment results from PGC meta-analyses can also be used to help inform molecular drug targets. Prior to any knowledge of molecular biomarkers for MDD, drugs targeting molecular pathways (MPs) proved successful in treating MDD. It is possible that examining polygenicity within specific MPs implicated in MDD can further refine molecular drug targets.

Methods

Using a large case–control GWAS based on low-coverage whole genome sequencing (N = 10 640) in Han Chinese women, we derived polygenic risk scores (PRS) for MDD and for MDD specific to each of over 300 MPs previously shown to be relevant to psychiatric diagnoses. We then identified sets of PRSs, accounting for critical covariates, significantly predictive of case status.

Results

Over and above global MDD polygenic risk, polygenic risk within the GO: 0017144 drug metabolism pathway significantly predicted recurrent depression after multiple testing correction. Secondary transcriptomic analysis suggests that among genes in this pathway, CYP2C19 (family of Cytochrome P450) and CBR1 (Carbonyl Reductase 1) might be most relevant to MDD. Within the cases, pathway-based risk was additionally associated with age at onset of MDD.

Conclusions

Results indicate that pathway-based risk might inform etiology of recurrent major depression. Future research should examine whether polygenicity of the drug metabolism gene pathway has any association with clinical presentation or treatment response. We discuss limitations to the generalizability of these preliminary findings, and urge replication in future research.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2019

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