【佳學(xué)基因檢測(cè)】基因解碼解讀基因檢測(cè)在臨床精神病診斷治療中的作用

基因解碼導(dǎo)讀：

精神和神經(jīng)系統(tǒng)是人體的基因功能與外界環(huán)境和教育、學(xué)習(xí)、培訓(xùn)相互作用的一個(gè)界面。關(guān)于基因在精神和精神系統(tǒng)中的作用，還有很多專家、病人不愿意承認(rèn)。有些是心理上的，有些是知識(shí)上的。佳學(xué)基因，通過(guò)基因解碼技術(shù)揭示并研究了基因與神經(jīng)、精神系統(tǒng)的功能、有其是基因序列變化與精神病的發(fā)生之間的關(guān)系。佳學(xué)基因希望通過(guò)不斷的努力，最終能夠更清晰地界定影響精神發(fā)生的諸多因素，從而幫助精神疾病的早期診斷和治療，并將現(xiàn)代生殖技術(shù)應(yīng)用于臨床中，消除或減少精神疾病致病基因在人類的存在與傳播。

精神病的基因作用研究概述

許多疾病的遺傳學(xué)和基因組學(xué)的巨大成功為正確醫(yī)學(xué)的發(fā)展提供了基礎(chǔ)。因此，對(duì)與神經(jīng)精神疾病相關(guān)的基因變異的解碼分析及其對(duì)治療作用的研究結(jié)果，使人們?cè)絹?lái)越期望這些發(fā)現(xiàn)能夠很快轉(zhuǎn)化為臨床應(yīng)用，在明確診斷、疾病風(fēng)險(xiǎn)預(yù)測(cè)和藥物治療的個(gè)性化方面發(fā)揮作用。佳學(xué)基因通過(guò)系列文章介紹與精神疾病有關(guān)的基因解碼，并總結(jié)目前在主要精神疾病中的成果。同時(shí)，介紹編碼藥物代謝酶的基因的序列變化與藥物反應(yīng)和毒副作用之間的關(guān)系。通過(guò)評(píng)估這些研究結(jié)果在臨床應(yīng)用中的可行性，增加對(duì)病人診斷與治療的指導(dǎo)作用。

精神疾病的基因解碼

基因解碼一直致力于找出導(dǎo)致精神疾病的潛在分子原因。佳學(xué)基因相信，了解這種疾病的生物學(xué)特性將有助于有效的臨床診斷和風(fēng)險(xiǎn)預(yù)測(cè)，以及更好的個(gè)體治療。因此，從20世紀(jì)60年代起，精神疾病的生物學(xué)假設(shè)主要集中在兒茶酚胺和吲哚胺神經(jīng)遞質(zhì)系統(tǒng)，這些系統(tǒng)通過(guò)間接策略進(jìn)行測(cè)試，如神經(jīng)內(nèi)分泌應(yīng)激，如“通向大腦的窗口”。從80年代中期起，家庭、雙胞胎，收養(yǎng)人群研究為精神疾病的總遺傳效應(yīng)提供了一致的證據(jù)，證明了遺傳因素在精神疾病病因中的重要作用。¹ 大多數(shù)精神疾病的遺傳力估計(jì)值都很高，在0.4到0.8.²之間，這些結(jié)果促使人們努力尋找易患精神疾病的基因序列變化。然而，第一代分子遺傳學(xué)研究基本上沒(méi)有成功。精神疾病的遺傳連鎖研究，在研究前，先假設(shè)存在單一的主基因座或少數(shù)大效應(yīng)基因，所得的結(jié)果大多為陰性，得出結(jié)果不可復(fù)制的結(jié)論。候選基因關(guān)聯(lián)研究主要集中在神經(jīng)遞質(zhì)系統(tǒng)的合成、降解和受體成分上，是有爭(zhēng)議的。^1,3

2000年人類基因組計(jì)劃所產(chǎn)生的第一個(gè)人類基因組序列草圖標(biāo)志著一個(gè)新時(shí)代的開(kāi)始，越來(lái)越高效的測(cè)序和基因分型技術(shù)取得了巨大進(jìn)展，使得佳學(xué)基因可以在全基因組范圍內(nèi)評(píng)估人類的基因序列變化的影響。分析可以在全部基因組范圍內(nèi)系統(tǒng)地、更完整地進(jìn)行。對(duì)大量個(gè)體進(jìn)行外顯子組和全基因組分析變得可行。全基因組關(guān)聯(lián)研究（GWAS）是識(shí)別與復(fù)雜疾病相關(guān)的遺傳風(fēng)險(xiǎn)變異的關(guān)鍵工具。這種“反向遺傳學(xué)”方法有助于在沒(méi)有病理生理學(xué)假設(shè)的情況下，鑒定以前從未設(shè)想過(guò)的潛在致病性序列變化。此外，還開(kāi)發(fā)了統(tǒng)計(jì)方法，可以評(píng)估GWAS捕獲的全基因組DNA變異的聚合效應(yīng)，¹例如，通過(guò)計(jì)算共同變異的共同貢獻(xiàn)作為“多基因評(píng)分”。⁴最后，如果沒(méi)有國(guó)際社會(huì)將多個(gè)GWAS研究中的數(shù)據(jù)集結(jié)合起來(lái)，以最大限度地?cái)U(kuò)大樣本量（例如到2019年預(yù)測(cè)10萬(wàn)例精神分裂癥病例）和統(tǒng)計(jì)能力，精神病遺傳學(xué)的進(jìn)展將是不可能的。⁵因此，從2011年起，從精神分裂癥和雙相情感障礙開(kāi)始，可以驗(yàn)證的常見(jiàn)SNP開(kāi)始出現(xiàn)在主要精神疾病的GWAS中。⁷迄今為止，最強(qiáng)的GWAS信號(hào)是精神分裂癥與6號(hào)染色體上主要組織相容性復(fù)合體（MHC）位點(diǎn)的基因序列變化之間的關(guān)聯(lián)。通過(guò)對(duì)這個(gè)復(fù)雜的位點(diǎn)進(jìn)行非常仔細(xì)的分子解剖，6號(hào)染色體上的信號(hào)被追蹤到C4基因。⁸有人認(rèn)為，精神分裂癥患者大腦中C4活性的增加會(huì)導(dǎo)致出生后大腦發(fā)育過(guò)程中突觸生理作用的過(guò)度調(diào)節(jié)。⁸如果這一點(diǎn)得到進(jìn)一步基因解碼的支持，這是極少數(shù)幾個(gè)從GWAS信號(hào)中揭示潛在的生物過(guò)程之一。

主要得益于GWAS中使用的高密度基因組芯片的數(shù)據(jù)，大量新發(fā)和罕見(jiàn)的染色體缺失和重復(fù)，即所謂的拷貝數(shù)變異（CNVs）開(kāi)始被發(fā)現(xiàn)大大增加了精神疾病的風(fēng)險(xiǎn)，特別是孤獨(dú)癥譜系障礙^9,10和精神分裂癥^11,12以及其他疾病例如注意缺陷多動(dòng)障礙（ADHD）。¹³作為基因解碼的基本要求的全外顯子組測(cè)序（WES），是對(duì)人類基因組中所有編碼外顯子的高通量測(cè)序，新穎鑒定并驗(yàn)證了導(dǎo)致自閉癥譜系障礙14-17和精神分裂癥的新發(fā)（基因破壞）編碼突變。^18-20

總的來(lái)說(shuō)，影響精神疾病發(fā)生的基因是由多基因形成的多層次結(jié)構(gòu)，有數(shù)百種甚至數(shù)千種常見(jiàn)的具有微效作用的變異體共同作用而形成（患病的先進(jìn)風(fēng)險(xiǎn)為1.1%到1.2%，而人群風(fēng)險(xiǎn)為-1.0%），從而形成三分之一到二分之一的遺傳效應(yīng)在0.4到0.8之間的群體。這種多基因圖譜對(duì)于大多數(shù)復(fù)雜性狀來(lái)說(shuō)是典型的。此外，具有較大效應(yīng)（效應(yīng)值在2到>20）的罕見(jiàn)和新發(fā)CNV以及罕見(jiàn)和新發(fā)的（破壞性）變異可顯著增加重大精神疾病的風(fēng)險(xiǎn)。然而，這一類的突變的總體占比還不太清楚。

越來(lái)越多的證據(jù)表明，不同的主要精神疾病種類之間存在致病基因重疊，這在許多情況下（盡管不是所有情況下）都可以從其臨床表現(xiàn)中預(yù)測(cè)到。²主要精神疾病具有共同的遺傳變異，^5,21,22第一次GWAS大數(shù)據(jù)分析涉及神經(jīng)元/突觸，免疫和組蛋白途徑。²³同樣，已觀察到罕見(jiàn)和新發(fā)CNVs24和其他編碼序列突變也存在重疊。^19,20疾病之間的遺傳風(fēng)險(xiǎn)的實(shí)質(zhì)性重疊加強(qiáng)了早期基因流行病學(xué)研究的共病證據(jù)，例如，患者親屬患不同精神疾病的風(fēng)險(xiǎn)增加。⁵最近一項(xiàng)漂亮的研究²⁵利用自閉癥、精神分裂癥、雙相情感障礙、抑郁癥和酒精中毒患者大腦皮層的轉(zhuǎn)錄組學(xué)分析，揭示了這些疾病患者共有的和獨(dú)特的基因表達(dá)紊亂模式。基因解碼表明，共同的多基因變異解釋了不同精神疾病存在的多基因序列變化的個(gè)似性。這些結(jié)果強(qiáng)調(diào)精神疾病作為“疾病歷史”的產(chǎn)物并不符合可以明確界定的疾病種類，¹從而對(duì)臨床的診斷和疾病分類提出質(zhì)疑。

從基因解碼結(jié)果到臨床應(yīng)用

隨著基因信息在醫(yī)學(xué)應(yīng)用上的巨大成功，隨著更多的確定的人的基因序列變化與人體疾病之間的關(guān)系的確立，以及癌癥中個(gè)體腫瘤分子圖譜的描繪，使得個(gè)體化診斷和治療成為推動(dòng)正確醫(yī)學(xué)發(fā)展的動(dòng)力。這些發(fā)展主要是由過(guò)去7年中隨著下一代測(cè)序（NGS）的實(shí)施及其所帶來(lái)的巨大技術(shù)進(jìn)步推動(dòng)的。雖然NGS之前的基因檢測(cè)主要是針對(duì)非常罕見(jiàn)的單基因疾病進(jìn)行的，其中許多具有反復(fù)性突變，但NGS的出現(xiàn)允許通過(guò)使用目標(biāo)基因包，WES，或全基因組測(cè)序（WGS）同時(shí)查詢?cè)S多基因及其所有突變。與神經(jīng)精神疾病和治療結(jié)果相關(guān)的基因序列不僅被發(fā)現(xiàn)而且被不斷驗(yàn)證，使人們?cè)絹?lái)越期望這些結(jié)果可以轉(zhuǎn)化為臨床，以改善個(gè)性化診斷和個(gè)體風(fēng)險(xiǎn)預(yù)測(cè)以及治療反應(yīng)，并可以用來(lái)預(yù)測(cè)其他家庭成員的風(fēng)險(xiǎn)。全面的基因檢測(cè)已經(jīng)成為可能，而且通過(guò)不同的商業(yè)模式提供給醫(yī)生和個(gè)人，尤其是通過(guò)“直接對(duì)消費(fèi)者”（DTC）檢測(cè)。因此，現(xiàn)在是時(shí)候討論基因檢測(cè)、基因解碼在精神病的臨床應(yīng)用了。

Prerequisites for genetic testing are analytic validity (does the test accurately detect whether a specific genetic variant is present or absent), and clinical validity (is there adequate scientific evidence to support the correlation between the genetic variant and a specific disease phenotype or risks?). Replication is critical for clinical validity. Clinical utility refers to whether the test can “provide information about diagnosis, treatment, management, or prevention of a disease that is likely to improve patient outcomes” (https://ispg.net/genetic-testing-statement/; http://www.cdc.gov/genomics/gtesting/ACCE/index.htm.). The essential prerequisite is knowledge of the genetic causes of the disorder and robust genotype-phenotype correlations, to enable for instance predictive testing for later onset disorders for family members of affected patients.

As outlined above, major adult psychiatric disorders are generally not caused by a single gene or variant, nor do they have a rare Mendelian subform as many other complex disorders do, eg, the adult-onset neurodegenerative disorders such as Alzheimer disease. On the contrary, they are complex, highly polygenic disorders involving numerous genes and variants that have only a modest impact on risk and are neither necessary nor sufficient to cause disease. This makes a clinical interpretation of the present findings at the individual level extremely difficult, if not impossible. Thus, despite tremendous progress in recent years, psychiatric genetics has, with few exceptions, not yet sufficiently advanced to be able to deduce concrete recommendations, or even clinical guidelines, for the use of genetic testing for diagnostic purposes and risk prediction. This applies in particular to major psychiatric disorders which typically begin in adult life, such as depression, bipolar disorder, substance dependence, and schizophrenia (see also https://ispg.net/genetic-testing-statement/; the 'Genetic Testing Statement' of the International Society of Psychiatric Genetics (ISPG) is being periodically updated as research progresses).

There are, however, a few circumstances where genetic testing may be useful in various clinical settings. These pertain to the analysis of variants of strong effect, such as rare or de novo CNVs and disrupting mutations, prevalent in individuals with autism spectrum disorders (ASD), schizophrenia, or other psychiatric disorders, especially when accompanied by intellectual disability. ASD not only has shared phenotypic overlap with many syndromic forms, such as Down syndrome, Prader-Willi/Angelman syndrome and Fragile X-linked intellectual disability (about 4% to 5% of ASD),26 but is also one of the disorders for which rare variants have been demonstrated to have strong effect. The potential detection of such rare variants has made it amenable to genetic testing in one form or another. Microdeletion 22qll.l syndrome is typically caused by a recurrent 3 MB deletion of 40 genes, including TBX1. Twenty to 50% of patients with this deletion develop ASD,27 but the deletion is also found in approximately 1% of people with schizophrenia and also in patients with bipolar disorder and idiopathic Parkinson disease.28,29 Current microarrays detect an ASD-associated CNV in 7% to 10 % of cases.30 There are now more than 50 ASD-associated CNVs and at least 61 ASD-risk genes, 18 of which have recently been identified in a comprehensive study using WGS of trios.31 Of the 61 ASD-associated genes, 36 (59%) are associated with known syndromes/ phenotypes in OMIM (Online Mendelian Inheritance in Man, www.omim.org), with CHD8, SHANK2, and NLGN3 associated only with ASD. Many of the identified ASD-risk genes converge into shared biological pathways and networks, including synaptic and neuronal adhesion (SHANK3, SCN2A, GRIN2B, SYNGAP1, ANK2), axonal guidance, transcriptional regulation (eg, NF1, PTEN and SYNGAP1) and chromatin remodeling (eg, MECP2, MBD5, CHD8, ADNP, ARID1B and TBR1)?1,32 Sixteen genes contain subdomains that could be targeted by pharmaceutical interventions and specific drug-gene interactions are known for seven genes.31 For example, individuals with pathogenic variants in SCN2A are potential candidates for drug trials involving allosteric modulators of GABA receptors.33

Multiple, rare CNVs have been associated with schizophrenia, all of which encompass many genes and are also common to other psychiatric and neurological disorders.34 Approximately 2.5% of schizophrenia patients will carry one of the associated CNVs, and many more genes may be associated through more powerful sequencing studies in the near future.35 The use of patient-parent trios to identify potentially harmful “de novo” variants has been applied to schizophrenia in a number of studies.18-20,36 Each of these studies demonstrated an excess of damaging de novo variants in schizophrenia, particularly in glutamatergic postsynaptic proteins and proteins whose messenger RNAs are targets of the Fragile X-linked mental retardation protein, FMRP. A subsequent, combined whole-exome sequencing case-control analysis in 4264 patients, 9343 controls and 1077 trios from previous studies revealed a significant excess of very rare, gene-disrupting variants in the SETD1A gene in patients (0.19%). This was the first statistically significant association between schizophrenia and a single candidate gene,37 although pathogenic SETD1A variants are also found in patients with more severe developmental and physical abnormalities. SETDIA is involved in histone methylation, substantiating the report that common risk variants for psychiatric disorders may aggregate on histone methylation pathways.23

Although individually rare, the net effects of CNVs across psychiatric disorders are substantial. Specifically, the net effects of the more frequent CNVs on a broad range of psychiatric and intellectual disability- syndromes have already been sufficiently well-assessed by Malhotra et al38 and Gershon and Alliey-Rodriguez.39 A recent review of CNVs in schizophrenia in over 41 000 subjects by Marshall et al34 largely confirmed previous reports of CNV associations in schizophrenia, adding suggestive evidence for six novel CNVs and providing analyses of the genes involved and of the net effects of these CNVs on schizophrenia. Although the majority of adult patients would not be expected to carry a large CNV and such CNVs mostly lack diagnostic specificity-, the identification of an inherited or de novo CNV in a known high-risk region for one of the major psychiatric disorders in such patients, may help diagnose a rare condition that has important medical and psychiatric implications for the patient and their family. Patients who carry such CNVs may find it easier to accept their diagnosis and adhere to treatment when presented with an objective “laboratory test.”39 Siblings and offspring could be offered genetic testing and might be reassured if they do not carry the same CNV as their mentally ill relative;39 (https://ispg.net/genetic-testing-statement/). The identification of de novo CNVs could be useful in the management of severe psychiatric disorders, especially those that present atypically or in the context of intellectual disability or certain medical syndromes (https://ispg.net/genetic-testing-statement/) .

The analysis of genes involved in variable drug response

The pharmacological treatment of psychiatric disorders has been severely hampered by a large inter-individual variation in drug response and/or severe side effects, often leading to painful, frustrating and inefficient trial-and-error-based changes of treatment regimens. This variation is to a large extent due to genetic factors, with an estimated heritability h2 of ~0.6 - 0.8.40 Thus, numerous studies attempted to detect gene variants associated with individually different drug responsiveness or serious side effects. Their motivation was to identify pharmacogenetic biomarkers for drug efficacy and safety, which would allow prediction of an individual's response to drug therapy and facilitate individually tailored treatment. These studies focused primarily on the analysis of candidate genes including (i) genes involved in drug metabolism (pharmacokinetics); (ii) genes encoding the specific target molecules mediating drug action (pharmacodynamics); and (iii) genes mediating severe side effects. Typically, a few up to hundreds of SNPs within these genes were genotyped in cases and controls. Furthermore, GWAS were applied to scan the genome for variants predisposing to differential drug response “hypothesis-free,” allowing detection of yet unknown genes or biological mechanisms. In view of the immense literature, we will prioritize those results which proved to be most consistent and therefore merit further consideration for potential translation in the clinic. We will focus on the pharmacogenomics of antidepressants and antipsychotics. The results essentially refer to drug-gene relationships.

Two genes of central importance in the metabolism of antidepressants and antipsychotics are those encoding cytochrome P450 (CYP) monooxygenase system enzymes, CYP2D6 and CYP2C19.41,42 Variants in these genes can cause different pharmacokinetic phenotypes in individuals treated with the same dose of a drug: “ultrarapid metabolizers” (UM), characterized by significantly- reduced drug concentrations, hence decreased drug effect or non-response; “extensive metabolizers” (EM) representing the “normal” phenotype; “intermediate metabolizers” (IM), characterized by drug concentrations that are higher compared to EM; and “poor metabolizers” (PM) having the highest drug concentrations at all, resulting potentially in drug-related toxicity due to overdosing.41 Thus, UM and PM appear to represent the clinically most relevant phenotypes/genotypes. In effect, comprehensive systematic literature reviews have substantiated evidence for lower plasma levels and an increased risk for non-response to tricyclic antidepressant treatment in UM as well as an increased risk for severe side effects in PM.43-45 The same applied to antidepressant treatment with selective serotonin reuptake inhibitors (SSRI).43,46 Regarding treatment with antipsychotics, the studies show a significantly increased risk for tardive dyskinesias in particular for CYP2D6-PM, while CYP2D6-UM overall does not appear to have a significant influence on antipsychotic drug response. Furthermore, a potential influence of CYP1A2 and CYP3A4 variants, other pharmacokinetic candidates of importance, on antipsychotic response has remained inconclusive.40,42,43,45 Importantly, the altered activity CYP2D6 variants have been reported to exhibit substantial population differences in comprehensive global surveys.47-49 Based on the first global data,48 Europeans showed the highest fraction of CYP2D6-PM (8%) and ~3% CYP2D6-UM, while for instance 40% of the population were CYP2D6-UM in North Africa. Thus, knowledge of ethnic background is of critical clinical relevance for the development of personalized pharmacological treatment strategies. The classification of pharmacokinetic phenotypes described above is subject to constant efforts towards further standardization. Although well-established, it does not yet represent the entirety- of genetic variation, or allelic combinations. A meta-analysis of population scale sequencing projects integrating whole-genome and exome NGS data from 56 945 individuals of five major populations, demonstrated that the previous pharmacokinetic phenotype predictions from genotype data may have underestimated the prevalence of CYP2D6-PM and -IM subjects substantially.49 Between 25.3% and 70.3% of analyzed CYP alleles contained variant combinations with no or reduced functional activity. This trend was further substantiated in a comprehensive literature review.47 Another gene of potential importance for the pharmacokinetics of many antidepressants and some antipsychotics encodes the ATP Binding Cassette (ABC) Subfamily B Member 1 (ABCB1); this ABC transporter gene is expressed at blood-brain barrier (BBB) sites. Its membrane-associated gene product, P Glycoprotein, also known as Multidrug-Resistance Protein 1, transports various substances across the BBB out of the brain. Meta-analyses have shown associations of two (out of several) SNPs with antidepressant response.50,51 Overall, however, the role of genetic variation in ABCB1 in variable antidepressant response has remained controversial and will require further examination.

Concerning the analysis of pharmacodynamic candidate genes involved in antidepressant response, a large number of studies have addressed the gene encoding the serotonin transporter (SCL6A4), a direct target for most prescribed antidepressants. The functional insertion-deletion polymorphism located in the promoter region, 5-HTTLPR, possibly was the most studied variant in relation to antidepressant response at all. Significant associations between this polymorphism and antidepressant response and remission rates were described in major meta-analyses.52,53 Particularly-, a higher probability of response and remission to SSRI treatment was observed in Caucasian carriers of the long (“1”) allele, although its influence on SSRI efficacy was of modest effect.52 Inversely, Caucasian patients with the short (“s”) allele were found to have difficulties to achieve remission and showed a reduced response to SSRI52,53 as well as an increased risk for side effects.54 Overall, however, the results are still inconsistent, precluding the use of 5-HTTLPR as a predictor of antidepressant response at present.42 Condensing other candidate gene data of note, a comprehensive meta-analysis by51 has suggested a significant association of variants in the serotonin 2A receptor gene (HTR2A) with antidepressant response; the same was true for variants in the gene encoding the FK506-binding protein 5 (FKBPS), which is involved in the regulation of stress hormones. Furthermore, this meta-analysis substantiated evidence that heterozygous carriers of the rs6265 polymorphism (Val66Met) in the brain-derived neurotrophic factor gene (BDNF) respond best to SSRI, particularly Asian patients.51 Numerous other plausible candidate genes have been investigated, with controversial results and modest effect sizes overall.42

Concerning pharmacodynamic candidate genes involved in antipsychotic treatment response, the most consistent results have been obtained for genes of the dopaminergic and serotonergic systems.40,42 Thus, an insertion deletion (Ins/Del) polymorphism of the dopamine D2 receptor gene (DRD2) was found significantly associated with antipsychotic drug response, Del allele carriers exhibiting a poorer response rate than patients with the Ins/Ins genotype.55 Moreover, a Ser9Gly polymorphism of the dopamine D3 receptor gene (DRD3) showed a consistent, though not significant trend for the Ser-allele and reduced clozapine response.56 Also, two polymorphisms in the IITR2A gene (His452Tyr and T102C) were found associated with clozapine response.57 Another receptor gene of the serotonergic system (HTR2C) contained a C759T polymorphism, the C-allele of which conferred a significantly increased risk for antipsychotic-induced weight gain, one of the most consistent associations observed in antipsychotics pharmacogenetics.58,59 Strong candidates known to be involved in the genetics of obesity, the melanocortin 4 receptor (MC4R) and leptin genes, were also suggested to be prominent risk factors predisposing to this serious adverse effect of antipsychotics. 58,59 Finally, several polymorphisms of the HLA-system, specifically of HLA-B38, DR4 and DQw360 and HLA-DQB1 and HLA-B61 were found associated with clozapine-induced agranulocytosis, another serious side effect of antipsychotics. For a detailed summary of the genetics of common antipsychotic-induced adverse effects see also MacNeil and Müller.62 Numerous studies were performed with candidate genes potentially involved in lithium response, which all were inconclusive, in part also due to its unresolved underlying biology.42

Translating pharmacogenomics to clinical practice

Pharmacogenomic studies aimed to improve individual psychiatric drug treatment through pre-emptive genotyping, which would allow adjustment of dosages to reduce the risk of overdosing and serious side effects, or a change of drug. In sum, the scientific evidence to support the clinical validity of pharmacogenetic testing is still insufficient for most gene-drug pairs. Moreover, the clinical utility of specific gene-drug pairs has not yet been clearly demonstrated in adequately powered, double-blind clinical trials, which need to be conducted to clarify whether patients benefit substantially from genotype-guided treatment compared to “treatment as usual.” Also other factors that influence treatment response such as co-medication, age, gender, disease symptoms/comorbidity, smoking and diet and, importantly, ethnic background, need to be taken into account and studied further. Despite these limitations, CYP2D6 and CYP2C19 testing has already been recommended for clinical use,63 and guidelines for using and generating genetic information have been outlined.64 First implementation studies using CYP2D6 and CYP2C19 genotype information in clinical practice indicated that pharmacogenetic testing was very well accepted by both physicians and patients, could particularly be beneficial for non-extensive metabolizing patients, and hold great potential for optimization of drug treatment in psychiatry.45,65 Recently, the Individualized Medicine: Pharmacogenetics Assessment and Clinical Treatment (IMPACT) study was launched to demonstrate the feasibility- and utility of pharmacogenetic testing on a large scale and facilitate implementation of this testing in routine health care practice.66

The implementation of pharmacogenomics in the clinic is supported by the establishment of comprehensive resources such as the Pharmacogenomic Knowledge Base (PharmGKB) (https://www.pharmgkb.org), and international expert groups that enable objective and transparent assessment of existing pharmacogenetic studies to derive clinical recommendations, such as the Clinical Pharmacogenomics Implementation Consortium (CPIC). Accordingly, CPIC performs a systematic review/evaluation of the comprehensive literature curated in PharmGKB to develop peer-reviewed gene-drug guidelines that are published and updated periodically (for further information on pharmacogenomics resources see Pouget et al40 and Müller et al).42 Thus, CPIC guidelines for CYP2D6 and CYP2C19 genotype-directed dosing of tricyclic antidepressants as well as SSRIs44,46 have been published. These guidelines provide concrete information for the interpretation of genetic tests, that is, a list of existing genotypes with their “likely (pharmacokinetic) phenotypes” assigned and corresponding dosing recommendations or alternative therapeutic recommendations (suggesting selection of a drug not primarily metabolized by CYP2D6). The expert groups' recommendations are further translated by national or cross-national regulatory agencies. Thus, the US Food and Drug Administration (FDA) and other agencies distinguish for instance four categories, “required,” “recommended,” “actionable,” and “informative,” this classification of gene-drug pairs often varying between agencies and countries.

In sum, there is very good consensus concerning the pharmacogenetic testing of CYP2D6, which is “recommended” for therapy with tricyclic antidepressants with particular reference to the increased risk for serious side effects in patients with PM-status. Also the testing of CYP2C19 is considered “particularly clinically relevant.” Beyond avoiding harm, testing both CYPs is considered to improve therapy through selection of alternative drugs and provide useful information for many other diseases. Agencies such as the FDA have begun to include pharmacogenomics information in drug labeling and recommend genetic testing for now 25 psychiatric drugs.42 As emphasized in the Genetic Testing Statement released by the ISPG, clinicians are encouraged to consider such recommendations in their treatment decisions and to “stay current on changes to drug labeling and adverse event reports” (https://ispg. net/genetic-testing-statement/). The FDA and other agencies “require” genetic testing in patients of Asian ancestry before carbamazepine treatment; carriers of the major histocompatibility allele HLA-B*15:02 are at highly increased risk to develop Stevens-Johnson syndrome (SJS), a potentially lethal skin disease. The only other “required” genetic test concerns children and adult patients who receive pimozide, an antipsychotic, to prevent side effects in CYP2D6-PM.

Conclusions and outlook

Psychiatric genetics has generated very promising results in terms of risk variants associated with major psychiatric disorders and treatment outcome. Despite these successes, psychiatry still lags behind other fields in medicine in terms of translation of existing knowledge into diagnostic genetic tests that could facilitate early diagnosis and accurate classification of disorders. The nature of genotype-phenotype-relationships has remained largely elusive, and the “fundamental biology” of psychiatric disorders has yet to be revealed.1,5 Significant progress can be expected from several lines of technological advancement/development. For example, there is reason to be excited about the prospect of WGS being increasingly implemented as the assay of choice for both gene discovery and diagnostic testing in highly heterogeneous disorders. Advantages of WGS include its comprehensiveness, with the analysis of coding and non-coding sequence, the improved coverage of sequences, and in fact, of whole genes that were previously not easily sequenced, as well as the detection of all types of genetic variation. This also promises to increase diagnostic yield. Moreover, it will allow establishment of a catalogue of non-coding variation, which is assumed to contribute substantially to the development of psychiatric disorders. One could envisage a comprehensive, genome -wide panel assay, where one assesses all known variants with proven associations to psychiatric disorders in an individual patient. Since these disorders, as well as individual drug response, are complex traits which can be influenced by multiple genes, further progress can be expected through assessment of gene-gene interactions, gene networks and the application of systems approaches.67 Complex traits are also significantly influenced by environmental factors. Thus, the analysis of the epigenome as the interface between genome and environment is expected to contribute key insights into the development of psychiatric disorders.68,69 True genome-wide assessments of epigenetic marks, such as DNA methylation, or chromatin modifications have become possible, mainly also through progress in second-generation DNA sequencing methods.69 Furthermore, the inaccessibility of the human brain can now be overcome by stem cell approaches, which make it possible to study (pluripotent stem cell-derived) neurons from patients “in a dish.”70 The generation of CNS organoids as model systems may open new avenues towards precision drug treatment. Beyond technological advancements, a reconsideration/rethinking of previous research concepts could critically move the field forward. As outlined by Kapur et al,71 to achieve clinical utility of diagnostic genetic testing may require a new approach. Rather than comparing prototypic patients to healthy controls, the field should focus on “identifying biologically homogeneous subtypes that cut across phenotypic diagnosis.” Validating such biomarker/genetically-defined subtypes will require longitudinal studies of individual patients, providing the “natural basis for a 'stratified' psychiatry that will improve clinical outcomes across conventional diagnostic boundaries,” ultimately more compatible with the major goal of precision medicine71—and the findings obtained to date.

Selected abbreviations and acronyms

REFERENCES

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