Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants
Abstract
:1. Introduction
2. Results
2.1. Baseline Characteristics
2.2. Genes and Variants for Predicting Final Non-Remission after 12 Weeks of Treatment in Patients Exhibiting Poor Early Improvement at 2 Weeks
2.3. Pathway Analyses
3. Methods
3.1. Study Outline
3.2. Participants, Antidepressant Treatment, and Outcomes
3.3. Demographic and Clinical Characteristics
3.4. WES
3.5. Statistical Analysis
3.6. Pathway Analyses
4. Discussion
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Trivedi, M.H.; Rush, A.; Wisniewski, S.R.; Nierenberg, A.A.; Warden, D.; Ritz, L.; Norquist, G.; Howland, R.H.; Lebowitz, B.; McGrath, P.J.; et al. Evaluation of Outcomes with Citalopram for Depression Using Measurement-Based Care in STAR*D: Implications for Clinical Practice. Am. J. Psychiatr. 2006, 163, 28–40. [Google Scholar] [CrossRef] [PubMed]
- Souery, D.; Serretti, A.; Calati, R.; Oswald, P.; Massat, I.; Konstantinidis, A.; Linotte, S.; Bollen, J.; Demyttenaere, K.; Kasper, S.; et al. Switching Antidepressant Class Does Not Improve Response or Remission in Treatment-Resistant Depression. J. Clin. Psychopharmacol. 2011, 31, 512–516. [Google Scholar] [CrossRef] [PubMed]
- Steimer, W.; Müller, B.; Leucht, S.; Kissling, W. Pharmacogenetics: A new diagnostic tool in the management of antidepressive drug therapy. Clin. Chim. Acta 2001, 308, 33–41. [Google Scholar] [CrossRef]
- Crisafulli, C.; Fabbri, C.; Porcelli, S.; Drago, A.; Spina, E.; De Ronchi, D.; Serretti, A. Pharmacogenetics of Antidepressants. Front. Pharmacol. 2011, 2, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tansey, K.; Guipponi, M.; Hu, X.; Domenici, E.; Lewis, G.; Malafosse, A.; Wendland, J.R.; Lewis, C.M.; McGuffin, P.; Uher, R. Contribution of Common Genetic Variants to Antidepressant Response. Biol. Psychiatr. 2013, 73, 679–682. [Google Scholar] [CrossRef]
- Porcelli, S.; Fabbri, C.; Serretti, A. Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with antidepressant efficacy. Eur. Neuropsychopharmacol. 2012, 22, 239–258. [Google Scholar] [CrossRef] [PubMed]
- Niitsu, T.; Fabbri, C.; Bentini, F.; Serretti, A. Pharmacogenetics in major depression: A comprehensive meta-analysis. Prog. Neuro-Psychopharmacol. Biol. Psychiatr. 2013, 45, 183–194. [Google Scholar] [CrossRef]
- Ising, M.; Lucae, S.; Binder, E.B.; Bettecken, T.; Uhr, M.; Ripke, S.; Kohli, M.A.; Hennings, J.M.; Horstmann, S.; Kloiber, S.; et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch. Gen. Psychiatr. 2009, 66, 966–975. [Google Scholar] [CrossRef]
- Uher, R.; Perroud, N.; Ng, M.Y.; Hauser, J.; Henigsberg, N.; Maier, W.; Mors, O.; Placentino, A.; Rietschel, M.; Souery, D.; et al. Genome-Wide Pharmacogenetics of Antidepressant Response in the GENDEP Project. Am. J. Psychiatr. 2010, 167, 555–564. [Google Scholar] [CrossRef]
- Sasayama, D.; Hiraishi, A.; Tatsumi, M.; Kamijima, K.; Ikeda, M.; Umene-Nakano, W.; Yoshimura, R.; Nakamura, J.; Iwata, N.; Kunugi, H. Possible association of CUX1 gene polymorphisms with antidepressant response in major depressive disorder. Pharmacogenomics J. 2012, 13, 354–358. [Google Scholar] [CrossRef] [Green Version]
- Myung, W.; Kim, J.; Lim, S.-W.; Shim, S.; Won, H.-H.; Kim, S.; Kim, S.; Lee, M.-S.; Chang, H.S.; Kim, J.-W.; et al. A genome-wide association study of antidepressant response in Koreans. Transl. Psychiatr. 2015, 5, e633. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- GENDEP Investigators; MARS Investigators; STAR*D Investigators. Common Genetic Variation and Antidepressant Efficacy in Major Depressive Disorder: A Meta-Analysis of Three Genome-Wide Pharmacogenetic Studies. Am. J. Psychiatr. 2013, 170, 207–217. [Google Scholar] [CrossRef] [PubMed]
- Biernacka, J.M.; Sangkuhl, K.; Jenkins, G.; Whaley, R.M.; Barman, P.; Batzler, A.; Altman, R.B.; Arolt, V.; Brockmoller, J.; Chen, C.H.; et al. The International SSRI Pharmacogenomics Consortium (ISPC): A genome-wide association study of antidepressant treatment response. Transl. Psychiatr. 2015, 5, e553. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tammiste, A.; Jiang, T.; Fischer, K.; Magi, R.; Krjutškov, K.; Pettai, K.; Esko, T.; Li, Y.; Tansey, K.; Carroll, L.S.; et al. Whole-exome sequencing identifies a polymorphism in the BMP5 gene associated with SSRI treatment response in major depression. J. Psychopharmacol. 2013, 27, 915–920. [Google Scholar] [CrossRef] [PubMed]
- Wong, M.; Dong, C.; Flores, D.L.; Ehrhart-Bornstein, M.; Bornstein, S.; Arcos-Burgos, M.; Licinio, J. Clinical outcomes and genome-wide association for a brain methylation site in an antidepressant pharmacogenetics study in Mexican Americans. Am. J. Psychiatr. 2014, 171, 1297–1309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Szegedi, A.; Jansen, W.T.; Van Willigenburg, A.P.P.; Van Der Meulen, E.; Stassen, H.H.; Thase, M.E. Early improvement in the first 2 weeks as a predictor of treatment outcome in patients with major depressive disorder: A meta-analysis including 6562 patients. J. Clin. Psychiatr. 2009, 70, 344–353. [Google Scholar] [CrossRef]
- Wagner, S.; Engel, A.; Engelmann, J.; Herzog, D.P.; Dreimüller, N.; Müller, M.B.; Tadić, A.; Lieb, K. Early improvement as a resilience signal predicting later remission to antidepressant treatment in patients with Major Depressive Disorder: Systematic review and meta-analysis. J. Psychiatr. Res. 2017, 94, 96–106. [Google Scholar] [CrossRef]
- Uher, R.; Mors, O.; Rietschel, M.; Rajewska-Rager, A.; Petrović, A.; Zobel, A.; Henigsberg, N.; Mendlewicz, J.; Aitchison, K.J.; Farmer, A.; et al. Early and Delayed Onset of Response to Antidepressants in Individual Trajectories of Change During Treatment of Major Depression: A secondary analysis of data from the Genome-Based Therapeutic Drugs for Depression (GENDEP) study. J. Clin. Psychiatr. 2011, 72, 1478–1484. [Google Scholar] [CrossRef]
- Gorwood, P.A.; Bayle, F.; Vaiva, G.; Courtet, P.; Corruble, E.; Llorca, P.-M. Is it worth assessing progress as early as week 2 to adapt antidepressive treatment strategy? Results from a study on agomelatine and a global meta-analysis. Eur. Psychiatr. 2013, 28, 362–371. [Google Scholar] [CrossRef]
- Kang, H.-J.; Park, Y.; Yoo, K.-H.; Kim, K.-T.; Kim, E.-S.; Kim, J.-W.; Kim, S.-W.; Shin, I.-S.; Yoon, J.-S.; Kim, J.-M.; et al. Sex differences in the genetic architecture of depression. Sci. Rep. 2020, 10, 9927. [Google Scholar] [CrossRef]
- Pitychoutis, P.M.; Zisaki, A.; Dalla, C.; Papadopoulou-Daifoti, Z. Pharamacogenetic Insight into depression and antidepressant response: Does sex matter? Curr. Pharm. Des. 2010, 16, 2214–2223. [Google Scholar] [CrossRef] [PubMed]
- Lee, K.H.; Baik, S.Y.; Lee, S.Y.; Park, C.H.; Park, P.J.; Kim, J.H. Genome Sequence Variability Predicts Drug Precautions and Withdrawals from the Market. PLoS ONE 2016, 11, e0162135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Corponi, F.; Fabbri, C.; Pae, C.-U. Pharmacogenetics and Depression: A Critical Perspective. Psychiatr. Investig. 2019, 16, 645–653. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Boison, D. Adenosine as a modulator of brain activity. Drug News Perspect. 2007, 20, 607. [Google Scholar] [CrossRef] [PubMed]
- Boison, D. Adenosine as a neuromodulator in neurological diseases. Curr. Opin. Pharmacol. 2008, 8, 2–7. [Google Scholar] [CrossRef] [Green Version]
- Kang, H.-J.; Kim, J.-W.; Kim, S.-Y.; Kim, S.-W.; Shin, H.-Y.; Shin, M.-G.; Kim, J.-M. The MAKE Biomarker Discovery for Enhancing anTidepressant Treatment Effect and Response (MAKE BETTER) Study: Design and Methodology. Psychiatr. Investig. 2018, 15, 538–545. [Google Scholar] [CrossRef]
- Sheehan, D.V.; Lecrubier, Y.; Sheehan, K.H.; Amorim, P.; Janavs, J.; Weiller, E.; Hergueta, T.; Baker, R.; Dunbar, G.C. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatr. 1998, 59, S22–S33. [Google Scholar]
- Hamilton, M. A RATING SCALE FOR DEPRESSION. J. Neurol. Neurosurg. Psychiatr. 1960, 23, 56–62. [Google Scholar] [CrossRef] [Green Version]
- Cowie, M.R. National Institute for Health and Care Excellence. Eur. Hear. J. 2015, 36. [Google Scholar]
- American Psychiatric Association. Treating Major Depressive Disorder; Practice Guideline for the Treatment of Patients with Major Depressive Disorder. Available online: http://psychiatryonline.org/pb/assets/raw/sitewide/practive_guidellines/guidelines/mdd.pdf. (accessed on 6 June 2017).
- deVries, A.N.; Roest, A.M.; Bos, E.H.; Burgerhof, J.G.M.; van Loo, H.M.; de Jonge, P. Predicting antidepressant Response by monitoring early improvement of individual symptoms of depression:Indivdual patient data meta-analysis. Br. J. Psychiatr. 2019, 214, 4–10. [Google Scholar] [CrossRef]
- Zigmond, A.S.; Snaith, R.P. The Hospital Anxiety and Depression Scale. Acta Psychiatr. Scand. 1983, 67, 361–370. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Overall, J.E.; Gorham, D.R. The brief psychiatric rating scale. Psychol. Rep. 1962, 10, 799–812. [Google Scholar] [CrossRef]
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed.; American Psychiatric Press Inc.: Washington, DC, USA, 2000. [Google Scholar]
- Oh, S.M.; Min, K.J.; Park, D.B. A study on the standardization of the hospital anxiety and depression scale for Koreans: A comparison of normal, depressed and anxious groups. J. Korean Neuropsychiatr. Assoc. 1999, 38, 289–296. [Google Scholar]
- Yi, J.S.; Bae, S.O.; Ahn, Y.M.; Park, D.B.; Noh, K.S.; Shin, H.K.; Woo, H.W.; Lee, H.S.; Han, S.I.; Kim, Y.S. Validity and reliability of the Korean version of the Hamilton Depression Rating Scale (K-HDRS). J. Korean Neuropsychiatr. Assoc. 2005, 44, 456–465. [Google Scholar]
- Kim, M.-K.; Lee, B.-K.; Jeon, Y.-W. Reliability of Korean Brief Psychiatric Rating Scale(BPRS)—Comparison of interrater reliability between the two rating methods and correlation of BPRS and SCL-90 self-report test. Korean. J. Clin. Psychol. 2003, 22, 685–698. [Google Scholar]
- McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, H.; Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010, 26, 589–595. [Google Scholar] [CrossRef] [Green Version]
- DePristo, M.A.; Banks, E.; Poplin, R.; Garimella, K.V.; Maguire, J.R.; Hartl, C.; Philippakis, A.A.; Del Angel, G.; Rivas, M.A.; Hanna, M.; et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 2011, 43, 491–498. [Google Scholar] [CrossRef]
- Van Der Auwera, G.A.; Carneiro, M.O.; Hartl, C.; Poplin, R.; Del Angel, G.; Levy-Moonshine, A.; Jordan, T.; Shakir, K.; Roazen, D.; Thibault, J.; et al. From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline. Curr. Protoc. Bioinform. 2013, 43, 11.10.1–11.10.33. [Google Scholar] [CrossRef]
- Cingolani, P.; Platts, A.E.; Wang, L.L.; Coon, M.; Nguyen, T.; Wang, L.; Land, S.J.; Lu, X.; Ruden, D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 2012, 6, 80–92. [Google Scholar] [CrossRef] [Green Version]
- Cingolani, P.; Patel, V.M.; Coon, M.; Nguyen, T.; Land, S.J.; Ruden, D.M.; Lu, X. Using Drosophila melanogaster as a Model for Genotoxic Chemical Mutational Studies with a New Program, SnpSift. Front. Genet. 2012, 3, 35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ng, P.C.; Henikoff, S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003, 31, 3812–3814. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Seo, H.; Kwon, E.J.; You, Y.-A.; Park, Y.; Min, B.J.; Yoo, K.; Hwang, H.-S.; Kim, J.; Kim, Y.J. Deleterious genetic variants in ciliopathy genes increase risk of ritodrine-induced cardiac and pulmonary side effects. BMC Med. Genom. 2018, 11, 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Park, Y.; Kim, H.; Choi, J.Y.; Yun, S.; Min, B.-J.; Seo, M.-E.; Im, H.J.; Kang, H.J.; Kim, J.H. Star Allele-Based Haplotyping versus Gene-Wise Variant Burden Scoring for Predicting 6-Mercaptopurine Intolerance in Pediatric Acute Lymphoblastic Leukemia Patients. Front. Pharmacol. 2019, 10, 654. [Google Scholar] [CrossRef] [Green Version]
- Kim, J.-M.; Kim, S.-W.; Stewart, R.J.; Kim, S.-Y.; Yoon, J.-S.; Jung, S.-W.; Lee, M.-S.; Yim, H.-W.; Jun, T.-Y. Predictors of 12-week remission in a nationwide cohort of people with depressive disorders: The CRESCEND study. Hum. Psychopharmacol. Clin. Exp. 2011, 26, 41–50. [Google Scholar] [CrossRef]
- Lee, S.; Wu, M.C.; Lin, X. Optimal tests for rare variant effects in sequencing association studies. Biostatics 2012, 13, 762–775. [Google Scholar] [CrossRef] [Green Version]
- The 1000 Genomes Project Consortium A global reference for human genetic variation. Nature 2015, 526, 68–74. [CrossRef] [Green Version]
- Kuleshov, M.V.; Jones, M.R.; Rouillard, A.; Fernandez, N.F.; Duan, Q.; Wang, Z.; Koplev, S.; Jenkins, S.L.; Jagodnik, K.M.; Lachmann, A.; et al. Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016, 44, W90–W97. [Google Scholar] [CrossRef] [Green Version]
- Harris, M.A.; Clark, J.; Ireland, A.; Lomax, J.; Ashburner, M.; Foulger, R.; Eilbeck, K.; Lewis, S.; Marshall, B.; Mungall, C.; et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004, 32, D258–D261. [Google Scholar]
- Mi, H.; Thomas, P. PANTHER Pathway: An Ontology-Based Pathway Database Coupled with Data Analysis Tools. Methods Mol. Biol. 2009, 563, 123–140. [Google Scholar] [CrossRef]
- Joshi-Tope, G.; Gillespie, M.; Vastrik, I.; D’Eustachio, P.; Schmidt, E.; de Bono, B.; Jassal, B.; Gopinath, G.R.; Wu, G.R.; Mattews, L.; et al. Reactome: A knowledgebase of biological pathways. Nucleic Acids Res. 2005, 33, D428–D432. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Romero, P.; Wagg, J.; Green, M.L.; Kaiser, D.; Krummenacker, M.; Karp, P.D. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2004, 6, R2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Menke, A.; Klengel, T.; Binder, E.B. Epigenetics, depression and antidepressant treatment. Curr. Pharm. Des. 2012, 18, 5879–5889. [Google Scholar] [CrossRef] [PubMed]
- Price, J.B.; Bronars, C.; Erhardt, S.; Cullen, K.R.; Schwieler, L.; Bert, M.; Walder, K.; McGee, S.L.; Frye, M.; Tye, S.J. Boenergetics and synaptic plasticity as potential targets for individualizing treatment for depression. Neurosci. Biobehav. Rev. 2018, 90, 212–220. [Google Scholar] [CrossRef] [PubMed]
- Tadić, A.; Wachtlin, D.; Berger, M.; Braus, D.F.; Van Calker, D.; Dahmen, N.; Dreimüller, N.; Engel, A.; Gorbulev, S.; Helmreich, I.; et al. Randomized controlled study of early medication change for non-improvers to antidepressant therapy in major depression—The EMC trial. Eur. Neuropsychopharmacol. 2016, 26, 705–716. [Google Scholar] [CrossRef] [PubMed]
- Sramek, J.J.; Murphy, M.F.; Cutler, N.R. Sex differenxces in the psychopharmacological treatment of depression. Dialogues Clin. Neurosci. 2016, 18, 447–457. [Google Scholar]
- Fabbri, C.; Zohar, J.; Serretti, A. Pharmacogenetic tests to guide drug treatment in depression: Comparison of the available testing kits and clinical trials. Prog. Neuro-Psychopharmacol. Biol. Psychiatr. 2018, 86, 36–44. [Google Scholar] [CrossRef]
- Lenox, R.H.; Frazer, A. Mechanism of action of antidepressants and mood stabilizers. In Neuropsychopharmacology: The Fifth Generation of Progress; Davis, C.D., Coyle, K.I., Nemeroff, J.T., Eds.; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2002; pp. 1139–1163. [Google Scholar]
- Tsai, S.-J.; Gau, Y.-T.A.; Hong, C.-J.; Liou, Y.-J.; Yu, Y.W.-Y.; Chen, T.-J. Sexually dimorphic effect of catechol-O-methyltransferase val158met polymorphism on clinical response to fluoxetine in major depressive patients. J. Affect. Disord. 2009, 113, 183–187. [Google Scholar] [CrossRef]
- Nackley, A.G.; Shabalina, S.A.; Lambert, J.E.; Conrad, M.S.; Gibson, D.G.; Spiridonov, A.N.; Satterfield, S.K.; Diatchenko, L. Low Enzymatic Activity Haplotypes of the Human Catechol-O-Methyltransferase Gene: Enrichment for Marker SNPs. PLoS ONE 2009, 4, e5237. [Google Scholar] [CrossRef] [Green Version]
- Lee, S.G.; Joo, Y.; Kim, B.; Chung, S.; Kim, H.L.; Lee, I.; Choi, B.; Kim, C.; Song, K. Association of Ala72Ser polymorphism with COMT enzyme activity and the risk of schizophrenia in Koreans. Hum. Genet. 2005, 116, 319–328. [Google Scholar] [CrossRef]
- Chen, C.-Y.; Yeh, Y.-W.; Kuo, S.-C.; Ho, P.-S.; Liang, C.-S.; Yen, C.-H.; Lu, R.-B.; Huang, S.-Y. Catechol-O-methyltransferase gene variants may associate with negative symptom response and plasma concentrations of prolactin in schizophrenia after amisulpride treatment. Psychoneuroendocrinology 2016, 65, 67–75. [Google Scholar] [CrossRef] [PubMed]
- Tunbridge, E.M.; Harrison, P.J.; Warden, D.R.; Johnston, C.; Refsum, H.; Smith, A. Polymorphisms in the catechol-O-methyltransferase (COMT) gene influence plasma total homocysteine levels. Am. J. Med Genet. Part B: Neuropsychiatr. Genet. 2008, 147, 996–999. [Google Scholar] [CrossRef] [PubMed]
- Jiang, H.; Xie, T.; Ramsden, D.B.; Ho, S.-L. Human catechol-O-methyltransferase down-regulation by estradiol. Neuropharmacology 2003, 45, 1011–1018. [Google Scholar] [CrossRef]
- Linnér, L.; Arborelius, L.; Nomikos, G.G.; Bertilsson, L.; Svensson, T.H. Locus coeruleus neuronal activity and noradrenaline availability in the frontal cortex of rats chronically treated with imipramine: Effect of α2-adrenoceptor blockade. Biol. Psychiatr. 1999, 46, 766–774. [Google Scholar] [CrossRef]
- Szegedi, A.; Rujescu, D.; Tadic, A.; Muller, M.J.; Kohnen, R.; Stassen, H.H.; Dahmen, N. The catechol-O-methyltransferase Val108/158Met polymorphism affects short-term treatment response to mirtazapine, but not to paroxetine in major depression. Pharmacogenomics J. 2005, 5, 49–53. [Google Scholar] [CrossRef] [PubMed]
- Leuchter, A.F.; Cook, I.A.; Marangell, L.B.; Gilmer, W.S.; Burgoyne, K.S.; Howland, R.H.; Trivedi, M.H.; Zisook, S.; Jain, R.; McCracken, J.T.; et al. Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: Results of the BRITE-MD study. Psychiatr. Res. 2009, 169, 124–131. [Google Scholar] [CrossRef] [PubMed]
- Browning, M.; Kingslake, J.; Dourish, C.; Goodwin, G.M.; Harmer, C.; Dawson, G.R. Predicting treatment response to antidepressant medication using early changes in emotional processing. Eur. Neuropsychopharmacol. 2019, 29, 66–75. [Google Scholar] [CrossRef]
HUGO Gene Symbol | Description | Statistical Analysis | Response | Model | ||
---|---|---|---|---|---|---|
ER(−)/REM(−) Vs. ER(+)/REM(+) | Total | Male | Female | |||
PRNP | Prion protein [HGNC:9449] | MLR | V | V | ||
COMT | Catechol-O-methyltransferase [HGNC:2228] | V | V | |||
BRPF3 | Bromodomain and PHD finger containing 3 [HGNC:14256] | V | V | |||
SLC25A40 | Solute carrier family 25 member 40 [HGNC:29680] | V | V | |||
ST3GAL5 | ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 5 [HGNC:10872] | V | V | |||
CGREF1 | Cell growth regulator with EF-hand domain 1 [HGNC:16962] | SKAT-O | V | V | ||
PPFIBP1 | PPFIA binding protein 1 [HGNC:9249] | V | V | |||
LZTS3 | Leucine zipper tumor suppressor family member 3 [HGNC:30139] | V | V | |||
MAP1A | Microtubule-associated protein 1A [HGNC:6835] | V | V | |||
MEPCE | Methylphosphate capping enzyme [HGNC:20247] | V | V | |||
PFAS | Phosphoribosylformylglycinamidine synthase [HGNC:8863] | V | V |
Analysis | MLR Result | SKAT-O | |||||||
---|---|---|---|---|---|---|---|---|---|
Group | Gene Symbol | p-Value | FDR q Value | OR (95% CI) | GVB (NR) | GVB (PR) | p-Value | FDR q Value | |
ER(−)/REM(−) vs. ER(+)/REM(+) | Total patients | ST3GAL5 | NA | NA | NA | 1.0 ± 0 | 1.0 ± 0 | 5.4 × 10−5 | 0.205 * |
Male patients | BRPF3 | 0.00056 | 0.2459 * | 1.71 (3.41−8.63) | 0.76 ± 0.4 | 0.91 ± 0.27 | NA | NA | |
COMT | 0.00054 | 0.2459 * | 8.72 (2.55−29.79) | 0.59 ± 0.50 | 0.80 ± 0.40 | NA | NA | ||
SLC25A40 | 0.00050 | 0.2459 * | 14.84 (3.25−67.82) | 0.70 ± 0.43 | 0.91 ± 0.27 | NA | NA | ||
PRNP | 0.00035 | 0.2459 * | 9.43 (2.76−32.23) | 0.62 ± 0.48 | 0.85 ± 0.35 | NA | NA | ||
CGREF1 | 0.5250 | 1.0000 | 1.77 (0.12−4.73) | 0.52 ± 0.47 | 0.47 ± 0.45 | 4.8 × 10−5 | 0.125 * | ||
Female patients | PPFIBP1 | 0.00782 | 0.3822 | 13.49 (1.98−91.79) | 0.98 ± 0.18 | 0.98 ± 0.12 | 9.3 × 10−6 | 0.018 * | |
LZTS3 | NA | NA | NA | 1.0 ± 0 | 1.0 ± 0 | 5.7 × 10−5 | 0.078 * | ||
MEPCE | NA | NA | NA | 1.0 ± 0 | 1.0 ± 0 | 2.2 × 10−4 | 0.152 * | ||
MAP1A | 0.60242 | 1.0000 | 1.45 (0.62−4.24) | 0.50 ± 0.29 | 0.52 ± 0.29 | 2.3 × 10−6 | 0.009 * | ||
PFAS | 0.87284 | 1.0000 | 0.99 (0.5−1.72) | 0.37 ± 0.39 | 0.39 ± 0.37 | 1.9 ×10−4 | 0.150 * |
Analysis | Additional FET Results | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Group | Gene Symbol | Position | Alt | rsID | SIFT | CADD | NR | PR | p-Value | OR (95% CI) | EAS AF | 1 KG AF | |||||
Ref | Het | Hom | Ref | Het | Hom | ||||||||||||
Male patients | ER(−)/REM(−) vs. ER(+)/REM(+) | PRNP | chr20:4680521 | G > A | rs1800014 | 0.03 | 14.46 | 26 | 9 | 1 | 108 | 11 | 0 | 0.00982 | 3.78 (1.45−9.84) | 0.025 | 0.016 |
COMT | chr22:19950263 | G > T | rs6267 | 0.01 | 24.1 | 21 | 12 | 3 | 95 | 23 | 1 | 0.01492 | 2.83 (1.27−6.29) | 0.035 | 0.013 | ||
BRPF3 | chr6:36168614 | G > A | rs200565609 | 0.27 | 15.42 | 33 | 3 | 0 | 119 | 0 | 0 | 0.01173 | Inf (NA-Inf) | 0.001 | 0.00019 | ||
SLC25A40 | chr7:87476339 | T > G | rs3213633 | 0.07 | 23.1 | 27 | 9 | 0 | 108 | 10 | 1 | 0.02157 | 3.27 (1.23−8.69) | 0.052 | 0.011 |
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Kang, H.-J.; Kim, K.-T.; Yoo, K.-H.; Park, Y.; Kim, J.-W.; Kim, S.-W.; Shin, I.-S.; Kim, J.H.; Kim, J.-M. Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants. Int. J. Mol. Sci. 2020, 21, 4884. https://doi.org/10.3390/ijms21144884
Kang H-J, Kim K-T, Yoo K-H, Park Y, Kim J-W, Kim S-W, Shin I-S, Kim JH, Kim J-M. Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants. International Journal of Molecular Sciences. 2020; 21(14):4884. https://doi.org/10.3390/ijms21144884
Chicago/Turabian StyleKang, Hee-Ju, Ki-Tae Kim, Kyung-Hun Yoo, Yoomi Park, Ju-Wan Kim, Sung-Wan Kim, Il-Seon Shin, Ju Han Kim, and Jae-Min Kim. 2020. "Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants" International Journal of Molecular Sciences 21, no. 14: 4884. https://doi.org/10.3390/ijms21144884