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

  • Anna R. Docherty (a1) (a2), Arden Moscati (a2) (a3), Tim B. Bigdeli (a4), Alexis C. Edwards (a2), Roseann E. Peterson (a2), Daniel E. Adkins (a1) (a5), John S. Anderson (a1), Jonathan Flint (a6) (a7), Kenneth S. Kendler (a2) and Silviu-Alin Bacanu (a2)...



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.


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.


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.


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.


Corresponding author

Author for correspondence: Anna R. Docherty, E-mail:


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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.
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.
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.
CONVERGE Consortium (2015) Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588591.
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.
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.
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.
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.
Lunter, G and Goodson, M (2011) Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Research 21, 936939.
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.
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.
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.
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.
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.
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421427.
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.
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.
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.
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.
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.


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

  • Anna R. Docherty (a1) (a2), Arden Moscati (a2) (a3), Tim B. Bigdeli (a4), Alexis C. Edwards (a2), Roseann E. Peterson (a2), Daniel E. Adkins (a1) (a5), John S. Anderson (a1), Jonathan Flint (a6) (a7), Kenneth S. Kendler (a2) and Silviu-Alin Bacanu (a2)...


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