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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: anna.docherty@utah.edu

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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References

Bigdeli, TB, Lee, D, Webb, BT, Riley, BP, Vladimirov, VI, Fanous, AH, Kendler, KS and Bacanu, SA (2016) A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans. Bioinformatics (Oxford, England) 32, 25982603.CrossRefGoogle ScholarPubMed
Browning, SR and Browning, BL (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. The American Journal of Human Genetics 81, 10841097.CrossRefGoogle ScholarPubMed
Cai, N, Bigdeli, TB, Kretzschmar, WW, Li, Y, Liang, J, Hu, J, Peterson, RE, Bacanu, S, Webb, BT, Riley, B, Li, Q, Marchini, J, Mott, R, Kendler, KS and Flint, J (2017) 11,670 whole-genome sequences representative of the Han Chinese population from the CONVERGE project. Scientific Data 4, 170011.CrossRefGoogle Scholar
CONVERGE Consortium (2015) Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588591.CrossRefGoogle Scholar
Edwards, AC, Docherty, AR, Moscati, A, Bigdeli, TB, Peterson, RE, Webb, BT, Bacanu, SA, Hettema, JM, Flint, J and Kendler, KS (2018) Polygenic risk for severe psychopathology among Europeans is associated with major depressive disorder in Han Chinese women. Psychological Medicine 48, 777789.CrossRefGoogle ScholarPubMed
Fabbri, C, Tansey, KE, Perlis, RH, Hauser, J, Henigsberg, N, Maier, W, Mors, O, Placentino, A, Rietschel, M, Souery, D, Breen, G, Curtis, C, Lee, SH, Newhouse, S, Patel, H, O'Donovan, M, Lewis, G, Jenkins, G, Weinshilboum, RM, Farmer, A, Aitchison, KJ, Craig, I, McGuffin, P, Schruers, K, Biernacka, JM, Uher, R and Lewis, CM (2018) Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: meta-analysis of data from genome-wide association studies. European Neuropsychopharmacology 28, 945954.CrossRefGoogle ScholarPubMed
Howard, DM, Clarke, TK, Adams, MJ, Hafferty, JD, Wigmore, EM, Zeng, Y, Hall, LS, Gibson, J, Boutin, TS, Hayward, C, Thomson, PA, Porteous, DJ, Smith, BH, Murray, AD, MDD Working Group of the PGC, Haley, CS, Deary, IJ, Whalley, HC and McIntosh, AM (2017) The stratification of major depressive disorder into genetic subgroups. bioRxiv 134601.Google Scholar
Li, H, Handsaker, B, Wysoker, A, Fennell, T, Ruan, J, Homer, N, Marth, G, Abecasis, G and Durbin, R (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics (Oxford, England) 25, 20782079.CrossRefGoogle ScholarPubMed
Lunter, G and Goodson, M (2011) Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Research 21, 936939.CrossRefGoogle ScholarPubMed
Major Depressive Disorder Working Group of the PGC, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TMF, Bacanu, SA, Baekvad-Hansen, M, Beekman, AFT, Bigdeli, TB, Binder, EB, Blackwood, DRH, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, TK, Coleman, JIR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Crowley, CA, Dashti, HS, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, CV, Dunn, EC, Eley, TC, Eriksson, N, Escott-Price, V, Kiadeh, FHF, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Giusti-Rodriguez, P, Goes, FS, Gordon, SD, Grove, J, Hall, LS, Hannon, E, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, JJ, Hougaard, DM, Hu, M, Hyde, CL, Ising, M, Jansen, R, Jin, F, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Lane, JM, Li, Y, Li, Y, Lind, PA, Liu, X, Lu, L, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, McGrath, P, McGuffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mill, J, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, O'Reilly, PF, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Purcell, SM, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Saeed Mirza, S, Saxena, R, Schoevers, R, Schulte, EC, Shen, L, Shi, J, Shyn, SI, Sigurdsson, E, Sinnamon, GBC, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Stockmeier, CA, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Tian, C, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, DI, Cichon, S, Dannlowski, U, de Geus, ECJ, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Hinds, DA, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PFA, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Muller-Myhsok, B, Nordentoft, M, Nothen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, B, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Volzke, H, Weissman, MM, Werge, T, Winslow, AR, Lewis, CM, Levinson, DF, Breen, G, Borglum, AD and Sullivan, PF (2018) Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature Genetics 50, 668681.CrossRefGoogle ScholarPubMed
McKenna, A, Hanna, M, Banks, E, Sivachenko, A, Cibulskis, K, Kernytsky, A, Garimella, K, Altshuler, D, Gabriel, S, Daly, M and DePristo, MA (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research 20, 12971303.CrossRefGoogle ScholarPubMed
Milaneschi, Y, Lamers, F, Peyrot, WJ, Baune, BT, Breen, G, Dehghan, A, Forstner, AJ, Grabe, HJ, Homuth, G, Kan, C, Lewis, C, Mullins, N, Nauck, M, Pistis, G, Preisig, M, Rivera, M, Rietschel, M, Streit, F, Strohmaier, J, Teumer, A, Van der Auwera, S, Wray, NR, Boomsma, DI and Penninx, B (2017) Genetic association of major depression with atypical features and obesity-related immunometabolic dysregulations. JAMA Psychiatry 74, 12141225.CrossRefGoogle ScholarPubMed
Peterson, RE, Cai, N, Bigdeli, TB, Li, Y, Reimers, M, Nikulova, A, Webb, BT, Bacanu, SA, Riley, BP, Flint, J and Kendler, KS (2017) The genetic architecture of major depressive disorder in Han Chinese women. JAMA Psychiatry 74, 162168.CrossRefGoogle ScholarPubMed
Peterson, RE, Cai, N, Dahl, AW, Bigdeli, TB, Edwards, AC, Webb, BT, Bacanu, SA, Zaitlen, N, Flint, J and Kendler, KS (2018) Molecular genetic analysis subdivided by adversity exposure suggests etiologic heterogeneity in major depression. The American Journal of Psychiatry 175, 545554.CrossRefGoogle ScholarPubMed
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421427.CrossRefGoogle Scholar
Sim, SC, Nordin, L, Andersson, TM, Virding, S, Olsson, M, Pedersen, NL and Ingelman-Sundberg, M (2010) Association between CYP2C19 polymorphism and depressive symptoms. The American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 153b, 11601166.Google ScholarPubMed
Singh, AK, Zajdel, J, Mirrasekhian, E, Almoosawi, N, Frisch, I, Klawonn, AM, Jaarola, M, Fritz, M and Engblom, D (2017) Prostaglandin-mediated inhibition of serotonin signaling controls the affective component of inflammatory pain. The Journal of Clinical Investigation 127, 13701374.CrossRefGoogle ScholarPubMed
The 1000 Genomes Project Consortium, Abecasis, GR, Auton, A, Brooks, LD, DePristo, MA, Durbin, RM, Handsaker, RE, Kang, HM, Marth, GT and McVean, GA (2012) An integrated map of genetic variation from 1092 human genomes. Nature 491, 5665.Google ScholarPubMed
Vilhjalmsson, BJ, Yang, J, Finucane, HK, Gusev, A, Lindstrom, S, Ripke, S, Genovese, G, Loh, PR, Bhatia, G, Do, R, Hayeck, T, Won, HH, Kathiresan, S, Pato, M, Pato, C, Tamimi, R, Stahl, E, Zaitlen, N, Pasaniuc, B, Belbin, G, Kenny, EE, Schierup, MH, De Jager, P, Patsopoulos, NA, McCarroll, S, Daly, M, Purcell, S, Chasman, D, Neale, B, Goddard, M, Visscher, PM, Kraft, P, Patterson, N and Price, AL (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. The American Journal of Human Genetics 97, 576592.CrossRefGoogle ScholarPubMed
Wang, Y, Lu, J, Yu, J, Gibbs, RA and Yu, F (2013) An integrative variant analysis pipeline for accurate genotype/haplotype inference in population NGS data. Genome Research 23, 833842.CrossRefGoogle ScholarPubMed
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