3.2 Assignment/Discussion: Project-Laboratory Tests PowerPoint

E-Mail [email protected]

Editorial

Psychother Psychosom 2016;85:129–135 DOI: 10.1159/000443512

The Limitations of Genetic Testing in Psychiatry

Steven L. Dubovsky

Department of Psychiatry, State University of New York at Buffalo, Buffalo, N.Y. , and Departments of Psychiatry and Medicine, University of Colorado, Denver, Colo. , USA

tocols for them [4] . In moderately differentiated breast cancers, which comprise 50% of breast tumors, gene ex- pression signatures for mitotic index, angiogenic poten- tial, p53 mutational status, and estrogen and progester- one dependence provide better stratification of prognosis than histology [6] . However, despite such advances, there is still not much clear integration between genomics and clinical practice in oncology [7] .

Psychiatric Diagnosis

Numerous markers have been associated with psychi- atric disorders, including genes for BDNF (brain-derived neurotrophic factor), FOS (FBJ murine osteosarcoma vi- ral oncogene homolog), COMT, DRD1, DRD2, DISC1, GABABR1 (γ-aminobutyric acid B receptor 1), NR4A2 (nuclear receptor subfamily 4, group A, member 2), ADORA2A (adenosine A2a receptor), CACNA1C (cal- cium channel gene), sirtuin 1, LHPP, 5HTR1A, RNA- binding proteins, and genes for myelination, glutaminer- gic and GABAergic neurotransmission, oxidative stress, signal transduction, response to the environment, cell survival and proliferation, and cell shrinkage and apop- tosis, among others [8–13] . Yet no genetic marker has yet been shown to be useful in prospectively identifying any specific psychiatric disorder [14] . Because genetic predis-

Despite the burgeoning number of psychiatric treat- ments, we still do not know how to predict which one will work best for which patient. The hope that genetic (single gene effects) and genomic (multiple gene effects) testing might be useful for diagnosis and treatment has been en- couraged by decreased costs of genome sequencing and studies demonstrating an association between mutations in more than 3,000 genes and specific disease phenotypes [1–3] . Are the data as promising in psychiatry as they are in other fields?

Cancer Genomics

Genetic testing has been most promising in oncology. For example, about 10% of cases of breast cancer have an autosomal dominant pattern of transmission, most com- monly mutations in the tumor suppressor genes BRCA1 and BRCA2 [4] . When BRCA1/2 mutations are found, healthy women are offered a very close follow-up, as well as prophylactic antiestrogen therapy or surgery, yet in one study only 9.5% of high-risk women even underwent genetic counseling, let alone testing [5] . Breast cancer risk alleles have also been found for p53, PTEN (phosphate and tensin homolog deleted from chromosome 10), STK11, CDH1 and PALB2; however, these genetic factors are rare, and there is not much research on screening pro-

Received: December 15, 2015 Accepted after revision: December 20, 2015 Published online: April 5, 2016

Steven L. Dubovsky, MD Department of Psychiatry, University at Buffalo 462 Grider Street, Room 1182 Buffalo, NY 14215 (USA) E-Mail dubovsky   @   buffalo.edu

© 2016 S. Karger AG, Basel 0033–3190/16/0853–0129$39.50/0

www.karger.com/pps

Dubovsky

Psychother Psychosom 2016;85:129–135 DOI: 10.1159/000443512

130

position in psychiatry is thinly distributed over thou- sands of loci, each contributing a small effect, with con- siderable overlap of brain systems and shared genetic fac- tors [14] , sample size in most association studies has generally been too small to produce meaningful, replica- ble results [1, 15, 16] . In addition, epigenetic and oth- er factors that alter DNA conformation can determine whether susceptibility genes are expressed or suppressed [10] , complicating analyses of the relationship between genotype and phenotype. Even relevant genetic markers can be difficult to interpret because inherited gene factors appear to interact with each other and the environment to contribute to both illness susceptibility and clinical presentation [17, 18] .

Descriptive diagnoses in psychiatry have multiple do- mains such as age of onset, constellations of specific symptoms, functioning, comorbidity, and evolution over time that assort differently in different patients in the same category to produce functionally different condi- tions [19] . Genetic profiles associated with any one of these features are not likely to predict more global diag- noses. If the direct path from genotype to phenotype ends at discrete endophenotypes such as arousal, anhedonia, information processing, stress responses, inflammation and mood, rather than global diagnosis, attempts to link the latter to specific genes are likely to prove frustrating [20–23] , just as descriptive diagnoses in psychiatry do not adequately consider important subtypes that exhibit dif- ferent assortments of features such as age of onset, sever- ity, progression or functioning.

Genetic Pharmacokinetic Studies

Since cytochrome P450 (CYP450) 1A2, 2D6, 2C9, 2C19, and 3A4 account for 60% of psychiatric drug me- tabolism [24] , considerable interest has centered on using the CYP450 genotype to predict response to psychotropic medications [3] . However, genotype does not inevitably predict phenotype because multiple copies of a more or less active gene can result in more or less metabolic activ- ity than would be expected from the allele that is identi- fied. In addition, the metabolizer phenotype associated with a particular genotype can be inhibited or enhanced by a number of medications, substances, and foods [25– 30] . In an open study of 900 patients treated with venla- faxine who were both genotyped and phenotyped for CYP2D6, 4% were genotypically poor metabolizers, while 27% were phenotypically poor metabolizers, suggesting that 23% of patients with other genotypes had converted

to a poor metabolizer phenotype as a result of concomi- tant medications [31] .

Even if genotype inevitably predicted phenotype, the correlation is stronger between CYP450 phenotype and drug level than clinical response [32] , which is modified by metabolism of most medications by more than one enzyme, lack of linear kinetics and saturable elimination for many drugs, unclear correlations between blood level and response for many medications, and therapeutic win- dows requiring therapeutic monitoring anyway for some of them [24, 26, 33, 34] . Expression of CYP450 enzymes in the brain, which influences drug effect, may be differ- ent from their expression in the blood, or even in the in- testine and liver [24] .

Drug Transporter Studies

Drug transporters, including P-glycoprotein (P-gp), or- ganic ion transporters, and multidrug and toxin extrusion proteins, modify the effect of CYP450 phenotype on drug levels and drug action because they influence gastrointesti- nal absorption, tissue uptake, and renal elimination as well as transport in and out of the brain [35, 36] . In the iSPOT- D (International Study to Predict Optimized Treatment in Depression), two different MDR1 single nucleotide poly- morphisms (SNPs) of the gene for P-gp (MDR1 or ABCB1) were associated with better responses either to escitalo- pram and sertraline or to venlafaxine, but there was no a priori hypothesis, other relevant factors such as drug me- tabolism, ethnicity, age, specific symptoms, or concomitant illness were not addressed [36] , and DNA was collected af- ter results were known rather than prospectively [37] .

Pharmacodynamic Studies

Both a deletion (short polymorphism or s-allele) and an insertion (long polymorphism or l-allele) have been found in the gene (SCL6A4) for the promoter region (5-HTTLPR) of the gene for the serotonin transporter (SERT) [32] . The short polymorphism (s) decreases and the long polymorphism (l) increases SLC6A4 transcrip- tion rates, resulting in less or more SERT expression, re- spectively [32] . Research on the association of the s/s ge- notype with a lower response rate to serotonin reuptake inhibitors in some ethnic groups has been contradictory [38–40] , and even without correction for multiple statisti- cal tests, the SCL6A4 genotype explains at most 5% of the variance in antidepressant response [30] . Attempts have

Genetics in Psychiatry Psychother Psychosom 2016;85:129–135 DOI: 10.1159/000443512

131

been made to correlate SNPs of the brain-specific voltage- gated rectifier potassium channel (Kv11.1–3.1) and the cardiac specific version (Kv11.1–1A) with risperidone treatment response and changes in cardiac conduction, respectively, in schizophrenia [41] , but the results have not been robust.

Gene Network Studies

Because treatment outcome seems to be influenced by multiple genetic polymorphisms, each with a small effect [3, 21] , research has moved toward analysis of networks of genes in the hope of developing more clinically useful information [42–44] . However, such studies have not produced clinically meaningful results [43, 44] . The Ge- nome-Based Therapeutic Drugs for Depression (GEN- DEP; n = 811) study, a substudy of the Sequenced Treat- ment Alternatives to Relive Depression (STAR * D; n = 1,491) study, and the Munich Antidepressant Response Study (n = 339), did not find any combination of genetic markers that influenced treatment response in depres- sion [1, 44] . Genome-wide association studies did not re- veal any SNPs associated with response or remission of nonbipolar, nonpsychotic, major depressive disorder treated openly with serotonin reuptake inhibitors [45] , and the STAR * D study did not reveal any positive ge- nome-wide association or top 25 SNP associations with treatment response [45] . A genome-wide association study from the Clinical Antipsychotic Trials of Interven- tion Effectiveness (CATIE, n = 738) did not find any com- binations of genetic markers that influenced treatment response in schizophrenia [1, 44] .

Prospective Treatment Studies

Only a small number of reports have involved the pro- spective use of genotyping to make treatment decisions. An open study of 58 depressed inpatients reported that genotyping for ABCB1 was associated with a shorter hos- pital stay because patients with the TT/GG genotype were more likely to have an increase in the dose of an antide- pressant that was a P-gp substrate, although changing to a non-P-gp substrate did not affect outcome [46] . The study was not randomized, and numerous intervening variables, including pharmacokinetics, comorbidity, and history, were not considered.

Four studies have been supported by the manufactur- er of a proprietary survey (GeneSight) of CYP2D6, 2C19,

2C9, and 1A2, SLC6A4, and 5HTR2A genotypes that gen- erates a ‘composite report’ classifying antidepressants and antipsychotic drugs used in the treatment of depres- sion into three categories: ‘use as directed’, ‘use with cau- tion’, and ‘use with caution and with more frequent mon- itoring’. An 8-week open study of 44 patients assigned in a nonrandom manner to treatment guided by the com- posite report (guided treatment) or nonguided treatment by the same clinicians, who were involved with the prod- uct, reported that patients in the guided group were less likely to receive medications in the ‘use with caution and with more frequent monitoring’ category, presumably because of reluctance by the guided clinicians to prescribe medications that required more monitoring [47] . Al- though improvement of depression was similar for the first 4 weeks in both groups, a single measure at 8 weeks indicated increased depression scores for the nonguided but not the guided group. No explanation was offered for the final increase in depression scores in the nonguided group, when multiple earlier ratings demonstrated a steady decrease in scores. Improvement in the guided group was not impressive, and it is impossible to know whether comorbid factors, concomitant medications, treatment adherence, patient enthusiasm, substance use, adjunctive psychotherapy, clinician knowledge of treat- ment condition, and open ratings affected the conduct of treatment or the outcome assessment.

A second open, nonrandomized study conducted by the same group in 227 mildly-moderately depressed pa- tients, 165 of whom completed 8 weeks of treatment, re- ported that patients in the guided group were twice as likely to respond [48] . Since clinicians reported substan- tial levels of confidence in the genetic reports, it is possi- ble that they worked more vigorously with patients in the guided group, that patients in the guided group were more adherent with a treatment approach they thought would be more effective, or that they reported better re- sults to please the investigators.

In a double-blind, randomized, controlled trial of GeneSight, 25 depressed patients were assigned to treat- ment as usual and 26 to guided treatment [49] . Improve- ment was numerically greater in patients in the guided than in the treatment-as-usual group, but none of the group differences were statistically significant. In a fourth report from the same company [50] , 97 patients with a depressive or anxiety disorder treated openly by a single psychiatrist with one of the medications in the genetic sur- vey were followed openly for 1 year. The 9 patients taking at least one medication in the ‘use with caution’ category had significantly more total health care visits and nonpsy-

Dubovsky

Psychother Psychosom 2016;85:129–135 DOI: 10.1159/000443512

132

chiatric medical visits than the other subjects and had a higher average cost of care. However, these patients also took more medications than the other subjects, and there was a significant correlation between the number of med- ications taken and the two outcome variables. When dif- ferent statistical analyses were performed on the same data (e.g. analysis of variance and t tests), some were sig- nificant and some were not, and no correction was made for multiple statistical tests. Although the authors con- tended that their genetic analysis could save health care costs, this hypothesis was not actually tested. Since no data were available on medical comorbidity and severity of psychiatric illness, the possibility was not considered that the small number of patients in the ‘use with caution’ cat- egory had more health care visits and took more medi- cations because they were sicker psychiatrically or medi- cally.

Gene Expression

Current genotyping approaches in psychiatry consider the presence or absence of a particular allele or group of alleles, but expression of those genes may be suppressed, modified, or enhanced by a number of factors, including circadian transcription patterns, epistasis (gene interac- tions), regulatory regions, epigenetic factors, and noncod- ing RNA [14] . Histone modification and DNA methyla- tion in response to experience, inflammation, the illness, and the medications used to treat it can induce or suppress multiple genes, and genotype itself can affect methylation of regulatory sites that leads to epigenetic changes in brain development [51] . Micro-RNAs and short interfering RNAs are short, noncoding posttranslational regulators of gene expression that target hundreds of mRNA transcripts to influence gene networks [27, 52, 53] . The expression of genes for CYP450 enzymes is altered by promoter meth- ylation, micro-RNAs associated with inflammation and other illnesses [27] , and some medications [54] , resulting in an altered CYP450 phenotype.

Limitations of Pharmacogenetic Testing in

Psychiatry

No matter how much we may want to translate direct- ly to clinical diagnosis reports of an association between a diagnosis and a genetic marker, the nature of the current level of knowledge does not permit this application. Sam- ple sizes in most existing studies have been too small to

produce meaningful, replicable results because of the clinical and genetic heterogeneity of psychiatric disorders [55] , and the combined influence of multiple genes, each with a small effect size [1, 15, 16] . Most studies have uti- lized retrospective or post hoc analyses rather than pro- spective a priori hypotheses [56] , and statistical signifi- cance is often inflated by lack of correction for multiple statistical tests [16] . The majority of studies lack replica- tion in independent samples, especially by different in- vestigators [16] . Even robust findings would not be clini- cally applicable until a prospective study demonstrated their ability to preferentially predict one diagnosis or even clinically relevant feature over another.

Using genotype to predict response to medications is even more problematic. Pharmacogenetic studies have been conducted in normal subjects or patients who are not taking other medications and who do not have other illnesses, limiting extrapolation to most clinical settings [33, 34] . Most studies do not control for the effect on the expression of CYP450 and other genes of age [27] , ethnic- ity [30] , smoking [30, 34] , and use of substances such as alcohol, hormones, St. John’s wort, caffeine, cabbage, and grapefruit juice [30] . Genetic studies of treatment out- come have not measured nonadherence [57] , but as the rate of nonadherence increases in any population, statis- tical power to detect a genotype effect decreases substan- tially [1] . For medications that are chiral mixtures of en- antiomers with different actions, the metabolism of each enantiomer may be by different enzymes [58] . Active me- tabolites with their own metabolic pathways may en- hance or interfere with therapeutic or toxic effects pre- dicted by the presumed metabolism of the parent drug [59, 60] . In most instances, more than one genetic factor affects drug levels and disposition [3] , and interactions between these factors can be difficult to predict.

A clear demonstration of a genotype/blood level rela- tionship in a single dose or 8-week study may not corre- late with chronic treatment, in which compensatory changes in secondary metabolic pathways and drug trans- porters, gene up- or downregulation, saturation pharma- cokinetics and other factors may modify the impact of oxidative enzyme polymorphisms on final drug level [24, 59] . With chronic treatment, some psychotropic drug metabolites form complexes with P450 enzymes that alter or even reverse the acute effect on metabolism [25] . Long- term changes in P450 enzymes also occur in the brain, with further unpredictable effects, not only on the sub- strate drug, but on neurotransmitters and neurosteroids metabolized by the same enzymes on which the medica- tion may act [25] . Another complicating factor is that the

Genetics in Psychiatry Psychother Psychosom 2016;85:129–135 DOI: 10.1159/000443512

133

illness, as well as medications used to treat it, can alter the relationship between pharmacologic genotype and phe- notype. For example, many proinflammatory cytokines and acute-phase proteins that are associated with mood and anxiety disorders [61] act on transcription or post- translational protein modification to downregulate some CYP450 genes and upregulate others [62] . At the same time, suppression of cytokines by antidepressants can al- ter gene expression in directions that antagonize im- provement of depression [25] . The impact of evolution of the illness and its response to different treatments in modifying therapeutic strategies during the course of treatment of cancer is relatively straightforward to study by virtue of methodologies for examining genotype and phenotype of cellular clones, but it is still difficult to de- velop the correct approach to well-characterized tumors [63] . The absence of such measures in psychiatric diagno- ses makes this prospect considerably more difficult.

Where Do We Go from Here?

Psychiatrists, whose work frequently involves ambigu- ous clinical problems, and who must often consider con- tradictory elements of patient presentations and avoid premature closure, can have a remarkably low tolerance for ambiguity, conflict, and delayed gratification when it comes to the latest laboratory study. The hope that phar- macogenetic testing will result in unambiguous ‘person- alized psychiatry’ should not lead to quick adoption of

technologies that have not yet been demonstrated to reli- ably predict a specific course or a need for a specific med- ication, the choice of which remains largely empirical. Af- ter all, genetic associations are statistical, but medical practice is personal [14] . Yet there is tremendous pressure to translate each new report of such associations to our patients, not only from our own need to appear ‘scien- tific’ and from industry marketing of proprietary tests, but from the marketing of ideas by thought leaders with an intellectual attachment to the latest conceptualization of genetic causality [64] .

It is a continuing challenge to examine new genetic findings critically without applying them immediately in the clinic. When adequately powered studies that address gene number and expression and that control for real-life factors that affect outcome such as comorbidity, poly- pharmacy, environmental exposure, age, gender, ethnic- ity, substance use, and treatment adherence emerge [44] , clinicians who have not put new information into action before integrating it with emerging knowledge about di- agnosis, neurobiology, and the evolution of complex dis- orders will be ready to apply them effectively.

Disclosure Statement

Dr. Dubovsky has received research support from Janssen, Ot- suka, Sumitomo, Neurocrine, Tower Foundation, Wendt Founda- tion, Oshei Foundation and Patrick Lee Foundation. The author has no other conflicts of interest to disclose.

References

1 Malhotra AK, Zhang J-P, Lencz T: Pharmaco- genetics in psychiatry: translating research into clinical practice. Mol Psychiatry 2012; 17: 760–769.

2 Cox DBT, Platt RJ, Zhang F: Therapeutic ge- nome editing: prospects and challenges. Nat Med 2015; 21: 121–131.

3 Trembplay J, Hamet P: Role of genomics on the path to personalized medicine. Metabo- lism 2013; 62:S2–S5.

4 Stuckey AR, Onstad MA: Hereditary breast cancer: an update on risk assessment and ge- netic testing in 2015. Am J Obstet Gynecol 2015; 213: 161–165.

5 Bellcross CA, Peipins LA, McCarty FA, Ro- driguez JL, Hawkins NA, Hensley Alford S, Leadbetter S: Characteristics associated with genetic counseling referral and BRCA1/2 test- ing among women in a large integrated health system. Genet Med 2015; 17: 43–50.

6 Ivshina AV, George J, Senko O, Mow B, Putti TC, Smeds J, Lindahl T, Pawitan Y, Hall P, Nordgren H, Wong JEL, Liu ET, Bergh J, Kuznetsove VA, Miller LD: Genetic reclassi- fication of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res 2006; 66: 10292–10301.

7 Anonymous: The future of cancer genomics. Nat Med 2015; 21: 99.

8 Le-Niculescu H, Balaraman Y, Patel SD, Ayalew M, Gupta J, Kuczenski R, Shekhar A, Shekhar A, Schork N, Geyer MA, Niculescu AB: Convergent functional genomics of anxi- ety disorders: translational identification of genes, biomarkers, pathways and mecha- nisms. Transl Psychiatry 2011; 1:e9.

9 Carrard A, Salzmann A, Malafosse A, Karege F: Increased DNA methylation status of the serotonin receptor 5HTR1A gene promoter in schizophrenia and bipolar disorder. J Af- fect Disord 2011; 132: 450–453.

10 Mill J, Tang T, Kaminsky Z, Khare T, Yaz- danpanah S, Bouchard L, Jia P, Assadzadeh A, Flanagan J, Schumacher A, Wang SC, Petro- nis A: Epigenomic profiling reveals DNA- methylation changes associated with major psychosis. Am J Hum Genet 2008; 82: 696– 711.

11 Fullerton JM, Tiwari Y, Agahi G, Heath A, Berk M, Mitchell PB, Schofield PR: Assessing oxidative pathway genes as risk factors for bi- polar disorder. Bipolar Disord 2010; 12: 550– 556.

12 Hoenicka J, Garrido E, Ponce G, Rodriguez- Jimenez R, Martinez I, Rubio G, Jimenez-Ar- riero MA, Palomo T: Sexually dimorphic in- teraction between the DRD1 and COMT genes in schizophrenia. Am J Med Genet 2010; 153B:984–954.

Dubovsky

Psychother Psychosom 2016;85:129–135 DOI: 10.1159/000443512

134

13 Hong CJ, Liou YJ, Bai Y, Chen TT, Wang Y, Tsai SJ: Dopamine receptor D2 gene is associ- ated with weight gain in schizophrenia pa- tients under long-term atypical antipsychotic treatment. Pharmacogenet Genomics 2010; 20: 359–366.

14 Li JZ: Circadian rhythms and mood: opportu- nities for multi-level analyses in genomics and neuroscience. Bioessays 2013; 36: 305– 315.

15 Genetics of Personality Consortium: Meta- analysis of genome-wide association studies for neuroticism, and the polygenic associa- tion with major depression. JAMA Psychiatry 2015; 72: 642–650.

16 Gillis NK, Innocenti F: Evidence required to demonstrate clinical utility of pharmacoge- netic testing: the debate continues. Clin Phar- macol Ther 2014; 96: 655–657.

17 Fanous AH, Kendler KS: Genetic heterogene- ity, modifier genes, and quantitative pheno- types in psychiatric illness: searching for a framework. Mol Psychiatry 2005; 10: 6–13.

18 Patel SD, Le-Niculescu H, Koller DL, Green SD, Lahiri DK, McMahon FJ, Nurnberger JIJ, Niculescu AB: Coming to grips with complex disorders: genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics. Am J Med Genet 2010; 153B:850–877.

19 Fava GA, Guidi J, Grandi S, Hasler G: The missing link between clinical states and bio- markers in mental disorders. Psychother Psy- chosom 2014; 83: 136–141.

20 Van Borkulo C, Boschioo L, Borsboom D, Pennix BWJH, Waldorp LJ, Schoevers RA: Association of symptom network structure with the course of longitudinal depression. JAMA Psychiatry 2015; 72: 1219–1226.

21 Keers R, Aitchison KJ: Pharmacokinetics of antidepressant response. Expert Rev Neu- rother 2011; 11: 101–125.

22 Domschke K: Clinical and molecular genetics of psychotic depression. Schizophr Bull 2013; 39: 766–775.

23 Fabbri C, Porcelli S, Serretti A: From pharma- cogenetics to pharmacogenomics: the way to- ward the personalization of antidepressant treatment. Can J Psychiatry 2014; 59: 62–75.

24 Preissner SC, Hoffmann MF, Preissner R, Dunkel M, Gewiess A, Preissner S: Polymor- phic cytochrome P450 enzymes (CYPs) and their role in personalized therapy. PLoS One 2013; 8:e82562.

25 Wladyslawa AD: The influence of long-term treatment with psychotropic drugs on cyto- chrome P450: the involvement of different mechanisms. Expert Opin Drug Metab Toxi- col 2005; 1: 203–217.

26 Crews KR, Gaedigk A, Dunnenberger HM, Leeder JS, Klein TE, Caudle KE, Haidar CE, Shen DD, Callaghan JT, Sadhasivam S, Prows CA, Kharasch ED, Skaar TC: Clinical Phar- macogenetics Implementation Consortium guidelines for cytochrome P450 2D6 geno- type and codeine therapy: 2014 update. Clin Pharmacol Ther 2014; 95: 376–382.

27 Zanger UM, Klein K, Thomas M, Rieger JK, Tremmel R, Kandel BA, Klein BA, Klein M, Magdy T: Genetics, epigenetics, and regulation of drug-metabolizing cytochrome p450 en- zymes. Clin Pharmacol Ther 2014; 95: 258–261.

28 Misaka S, Kawabe K, Onoue S, Werba JP, Gi- roli M, Tamaki S, Kan T, Kimura J, Watanabe H, Yamada S: Effects of green tea catechins on cytochrome P450 2B6, 2C8, 2C19, 2D6 and 3A activities in human liver and intestinal mi- crosomes. Drug Metab Pharmacokinet 2013; 28: 244–249.

29 Yamasaki I, Yamada M, Uotsu N, Teramoto S, Takayanagi R, Yamada Y: Inhibitory effects of kale ingestion on metabolism by cyto- chrome P450 enzymes. Biomed Res 2012; 33: 235–242.

30 Jones DS, Perlis RH: Pharmacogenetics, race, and psychiatry: prospects and challenges. Harvard Rev Psychiatry 2006; 14: 92–108.

31 Preskorn SH, Kane CP, Lobello K, Nichols AI, Fayyad R, Buckley G, Focht K, Guico-Pabia CJ: Cytochrome P450 2D6 phenoconversion is common in patients being treated for de- pression: implications for personalized medi- cine. J Clin Psychiatry 2013; 74: 614–621.

32 Alter CN, Hornberger J, Shewade A, Cruz V, Garrison J, Mrazek D: Clinical validity of cy- tochrome P450 metabolism and serotonin gene variants in psychiatric pharmacothera- py. Int Rev Psychiatry 2013; 25: 509–533.

33 De Leon J, Arranz GR: Pharmacogenetic test- ing in psychiatry: a review of features and clin- ical realities. Clin Lab Med 2008; 28: 599–617.

34 Kirchheiner J, Brosen K, Dahl ML, Gram LF, Kasper S, Roots I, Sjoqvist F, Spina E, Brock- moller J: CYP2D6 and CYP2C19 genotype- based dose recommendations for antidepres- sants: a first step towards subpopulation-spe- cific dosages. Acta Psychiatr Scand 2001; 104: 173–192.

35 Rosenhagen MC, Uhr M: The clinical impact of ABCB1 polymorphisms in the treatment of psychiatric diseases. Curr Pharm Des 2011; 17: 2843–2851.

36 Goswami S, Yee SW, Stocker S, Mosley JD, Kubo M, Castro R, Mefford JA, Wen C, Liang X, Witte J, Brett C, Maeda S, Simpson MD, Hedderson MM, Davis RL, Roden DM, Gia- comini KM, Savic RM: Genetic variants in transcription factors are associated with the pharmacokinetics and pharmacodynamics of metformin. Clin Pharmacol Ther 2014; 96: 370–370.

37 Schatzberg AF, DeBattista C, Lazzeroni LC, Etkin A, Murphy GM, Williams LM: ABCB1 genetic effects on antidepressant outcomes: a report from the iSPOT-D trial. Am J Psychia- try 2015; 172: 751–759.

38 Porcelli S, Fabbri C, Serretti A: Meta-analysis of serotonin transporter gene promoter poly- morphism (5-HTTLPR) association with an- tidepressant efficacy. Eur Neuropsychophar- macol 2012; 22: 239–258.

39 Taylor MJ, Sen S, Bhagwagar Z: Antidepres- sant response and the serotonin transporter- linked polymorphic region. Biol Psychiatry 2010; 68: 536–543.

40 Kang HJ, Kim JM, Stewart R, Kim SY, Bae KY, Kim SW, Shin MG, Yoon JS: Association of SLC6A4 methylation with early adversity, characteristics and outcomes in depression. Prog Neuropsychopharmacol Biol Psychiatry 2013; 44: 23–28.

41 Heide J, Zhang F, Bigos KL, Mann SA, Carr VJ, Weickert CS, Green MJ, Weinberger DR, Vandenberg JI: Differential response to risperidone in schizophrenia patients by KCNH2 genotype and drug metabolizer sta- tus. Am J Psychiatry 2016; 173: 53–59.

42 Costa e Silva JA: Personalized medicine in psychiatry: new technologies and approaches. Metabolism 2013; 62:S40–S44.

43 Singh AB, Bousman CA, Ng C, Berk M: Anti- depressant pharmacogenetics. Curr Opin Psychiatry 2014; 27: 43–51.

44 Hamilton SP: The promise of psychiatric pharmacogenomics. Biol Psychiatry 2015; 77: 29–35.

45 Ji Y, Biernacka JM, Hebbring S, Jenkins GD, Batzler A, Snyder KA, Drews MS, Desta Z, Flockart D, Mushiroda T, Kubo M, Naka- mura Y, Kamatani N, Schaid D, Weinshil- boum RM, Mrazek D: Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: ge- nome-wide associations and functional ge- nomics. Pharmacogenomics J 2013; 13: 456– 463.

46 Breitenstein B, Scheuer S, Pfister H, Uhr M, Lucae S, Holsboer F, Ising M, Bruckl T: The clinical application of ABCB1 genotyping in antidepressant treatment: a pilot study. CNS Spectr 2014; 19: 165–175.

47 Hall-Flavin DK, Winner JG, Allen J, Jordan JJ, Nesheim RS, Snyder KA, Drews MS, Eister- hold LL, Biernacka JM, Mrazek D: Using a pharmacogenomic algorithm to guide the treatment of depression. Transl Psychiatry 2012; 2:e172.

48 Hall-Flavin DK, Winner JG, Allen J, Carhart JM, Proctor B, Snyder KA, Drews MS, Eister- hold LL, Geske J, Mrazek D: Utility of inte- grated pharmacogenomic testing to support the treatment of major depressive disorder in a psychiatric outpatient setting. Pharmaco- genet Genomics 2013; 23: 535–548.

49 Winner JG, Carhart JM, Alter CN, Allen J, Dechairo BM: A prospective, randomized, double-blind study assessing the clinical im- pact of integrated pharmacogenomic testing for major depressive disorder. Discov Med 2013; 16: 219–227.

50 Winner JG, Allen J, Altar CA, Spahic-Miha- jlovic A: Psychiatric pharmacogenomics pre- dicts health resource utilization of outpatients with anxiety and depression. Transl Psychia- try 2013; 3:e242.

Genetics in Psychiatry Psychother Psychosom 2016;85:129–135 DOI: 10.1159/000443512

135

51 Hannon E, Spiers H, Viana J, Pidsley R, Bur- rage J, Murphy TM, Troakes C, Turecki G, O’Donovan MC, Schalkwyk LC, Bray NJ, Mill J: Methylation QTLS in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci 2016; 19: 48–54.

52 Geaghan M, Cairns MJ: MicroRNA and post- transcriptional dysregulation in psychiatry. Biol Psychiatry 2015; 78: 231–239.

53 Xu Q, Wu X, Xiong Y, Xing Q, He L, Qin S: Pharmacogenomics can improve antipsy- chotic treatment in schizophrenia. Front Med 2013; 7: 180–190.

54 Gardiner E, Carroll A, Tooney PA, Cairns MJ: Antipsychotic drug-associated gene-miRNA interaction in T-lymphocytes. Int J Neuro- psychopharmacol 2014; 17: 929–943.

55 Le-Niculescu H, Kurian SM, Yehyawi N, Dike C, Patel SD, Edenberg HJ, Tsuang MT, Salo- mon DR, Nurnberger JIJ, Niculescu AB: Iden- tifying blood biomarkers for mood disorders using convergent functional genomics. Mol Psychiatry 2009; 14: 156–174.

56 Janssens ACJW, Deverka PA: Useless until proven effective: the clinical utility of pre- emptive pharmacogenetic testing. Clin Phar- macol Ther 2014; 96: 652–654.

57 Melfi CA, Chawla AJ, Croghan TW, Hanna MP, Kennedy S, Sredl K: The effects of adher- ence to antidepressant treatment guidelines on relapse and recurrence of depression. Arch Gen Psychiatry 1998; 55: 1128–1132.

58 Crettol S, de Leon J, Hiemke C, Eap CB: Phar- macogenomics in psychiatry: from therapeu- tic drug monitoring to genomic medicine. Clin Pharmacol Ther 2014; 95: 254–257.

59 Kirchheiner J, Nickchen K, Bauer M, Wong M-L, Roots I, Brockmoller J: Pharmacogenet- ics of antidepressants and antipsychotics: the contribution of allelic variations to the phe- notype of drug response. Mol Psychiatry 2004; 9: 442–473.

60 Seeringer A, Kirchheiner J: Pharmacogenet- ics-guided dose modifications of antidepres- sants. Clin Lab Med 2008; 28: 619–626.

61 Young JJ, Bruno D, Pomara N: A review of the relationship between proinflammatory cyto- kines and major depressive disorder. J Affect Disord 2014; 169: 15–20.

62 Harvey RD, Morgan ET: Cancer, inflamma- tion, and therapy: effects on cytochrome p450-mediated drug metabolism and impli- cations for novel immunotherapeutic agents. Clin Pharmacol Ther 2014; 96: 449–457.

63 Michor F, Beal K: Improving cancer treat- ment via mathematical modeling: surmount- ing the challenges is worth the effort. Cell 2015; 163: 1059–1063.

64 Dubovsky SL, Dubovsky AN: Psychotropic Drug Prescriber’s Survival Guide: Ethical Pre- scribing in the Age of Big Pharma. New York, Norton, 2007.

1. CitRef_1:
2. CitRef_2:
3. CitRef_3:
4. CitRef_4:
5. CitRef_5:
6. CitRef_6:
7. CitRef_7:
8. CitRef_8:
9. CitRef_9:
10. CitRef_10:
11. CitRef_11:
12. CitRef_12:
13. CitRef_13:
14. CitRef_14:
15. CitRef_15:
16. CitRef_16:
17. CitRef_17:
18. CitRef_18:
19. CitRef_19:
20. CitRef_20:
21. CitRef_21:
22. CitRef_22:
23. CitRef_23:
24. CitRef_24:
25. CitRef_25:
26. CitRef_26:
27. CitRef_27:
28. CitRef_28:
29. CitRef_29:
30. CitRef_30:
31. CitRef_31:
32. CitRef_32:
33. CitRef_33:
34. CitRef_34:
35. CitRef_35:
36. CitRef_36:
37. CitRef_37:
38. CitRef_38:
39. CitRef_39:
40. CitRef_40:
41. CitRef_41:
42. CitRef_42:
43. CitRef_43:
44. CitRef_44:
45. CitRef_45:
46. CitRef_46:
47. CitRef_47:
48. CitRef_48:
49. CitRef_49:
50. CitRef_50:
51. CitRef_51:
52. CitRef_52:
53. CitRef_53:
54. CitRef_54:
55. CitRef_55:
56. CitRef_56:
57. CitRef_57:
58. CitRef_58:
59. CitRef_59:
60. CitRef_60:
61. CitRef_61:
62. CitRef_62:
63. CitRef_63: