【佳学基因检测】基因解码解读基因检测在临床精神病诊断治疗中的作用

基因解码导读：

精神和神经系统是人体的基因功能与外界环境和教育、学习、培训相互作用的一个界面。关于基因在精神和精神系统中的作用，还有很多专家、病人不愿意承认。有些是心理上的，有些是知识上的。佳学基因，通过基因解码技术揭示并研究了基因与神经、精神系统的功能、有其是基因序列变化与精神病的发生之间的关系。佳学基因希望通过不断的努力，最终能够更清晰地界定影响精神发生的诸多因素，从而帮助精神疾病的早期诊断和治疗，并将现代生殖技术应用于临床中，消除或减少精神疾病致病基因在人类的存在与传播。

精神病的基因作用研究概述

许多疾病的遗传学和基因组学的巨大成功为正确医学的发展提供了基础。因此，对与神经精神疾病相关的基因变异的解码分析及其对治疗作用的研究结果，使人们越来越期望这些发现能够很快转化为临床应用，在明确诊断、疾病风险预测和药物治疗的个性化方面发挥作用。佳学基因通过系列文章介绍与精神疾病有关的基因解码，并总结目前在主要精神疾病中的成果。同时，介绍编码药物代谢酶的基因的序列变化与药物反应和毒副作用之间的关系。通过评估这些研究结果在临床应用中的可行性，增加对病人诊断与治疗的指导作用。

精神疾病的基因解码

基因解码一直致力于找出导致精神疾病的潜在分子原因。佳学基因相信，了解这种疾病的生物学特性将有助于有效的临床诊断和风险预测，以及更好的个体治疗。因此，从20世纪60年代起，精神疾病的生物学假设主要集中在儿茶酚胺和吲哚胺神经递质系统，这些系统通过间接策略进行测试，如神经内分泌应激，如“通向大脑的窗口”。从80年代中期起，家庭、双胞胎，收养人群研究为精神疾病的总遗传效应提供了一致的证据，证明了遗传因素在精神疾病病因中的重要作用。¹ 大多数精神疾病的遗传力估计值都很高，在0.4到0.8.²之间，这些结果促使人们努力寻找易患精神疾病的基因序列变化。然而，第一代分子遗传学研究基本上没有成功。精神疾病的遗传连锁研究，在研究前，先假设存在单一的主基因座或少数大效应基因，所得的结果大多为阴性，得出结果不可复制的结论。候选基因关联研究主要集中在神经递质系统的合成、降解和受体成分上，是有争议的。^1,3

2000年人类基因组计划所产生的第一个人类基因组序列草图标志着一个新时代的开始，越来越高效的测序和基因分型技术取得了巨大进展，使得佳学基因可以在全基因组范围内评估人类的基因序列变化的影响。分析可以在全部基因组范围内系统地、更完整地进行。对大量个体进行外显子组和全基因组分析变得可行。全基因组关联研究（GWAS）是识别与复杂疾病相关的遗传风险变异的关键工具。这种“反向遗传学”方法有助于在没有病理生理学假设的情况下，鉴定以前从未设想过的潜在致病性序列变化。此外，还开发了统计方法，可以评估GWAS捕获的全基因组DNA变异的聚合效应，¹例如，通过计算共同变异的共同贡献作为“多基因评分”。⁴最后，如果没有国际社会将多个GWAS研究中的数据集结合起来，以最大限度地扩大样本量（例如到2019年预测10万例精神分裂症病例）和统计能力，精神病遗传学的进展将是不可能的。⁵因此，从2011年起，从精神分裂症和双相情感障碍开始，可以验证的常见SNP开始出现在主要精神疾病的GWAS中。⁷迄今为止，最强的GWAS信号是精神分裂症与6号染色体上主要组织相容性复合体（MHC）位点的基因序列变化之间的关联。通过对这个复杂的位点进行非常仔细的分子解剖，6号染色体上的信号被追踪到C4基因。⁸有人认为，精神分裂症患者大脑中C4活性的增加会导致出生后大脑发育过程中突触生理作用的过度调节。⁸如果这一点得到进一步基因解码的支持，这是极少数几个从GWAS信号中揭示潜在的生物过程之一。

主要得益于GWAS中使用的高密度基因组芯片的数据，大量新发和罕见的染色体缺失和重复，即所谓的拷贝数变异（CNVs）开始被发现大大增加了精神疾病的风险，特别是孤独症谱系障碍^9,10和精神分裂症^11,12以及其他疾病例如注意缺陷多动障碍（ADHD）。¹³作为基因解码的基本要求的全外显子组测序（WES），是对人类基因组中所有编码外显子的高通量测序，新颖鉴定并验证了导致自闭症谱系障碍14-17和精神分裂症的新发（基因破坏）编码突变。^18-20

总的来说，影响精神疾病发生的基因是由多基因形成的多层次结构，有数百种甚至数千种常见的具有微效作用的变异体共同作用而形成（患病的先进风险为1.1%到1.2%，而人群风险为-1.0%），从而形成三分之一到二分之一的遗传效应在0.4到0.8之间的群体。这种多基因图谱对于大多数复杂性状来说是典型的。此外，具有较大效应（效应值在2到>20）的罕见和新发CNV以及罕见和新发的（破坏性）变异可显著增加重大精神疾病的风险。然而，这一类的突变的总体占比还不太清楚。

越来越多的证据表明，不同的主要精神疾病种类之间存在致病基因重叠，这在许多情况下（尽管不是所有情况下）都可以从其临床表现中预测到。²主要精神疾病具有共同的遗传变异，^5,21,22第一次GWAS大数据分析涉及神经元/突触，免疫和组蛋白途径。²³同样，已观察到罕见和新发CNVs24和其他编码序列突变也存在重叠。^19,20疾病之间的遗传风险的实质性重叠加强了早期基因流行病学研究的共病证据，例如，患者亲属患不同精神疾病的风险增加。⁵最近一项漂亮的研究²⁵利用自闭症、精神分裂症、双相情感障碍、抑郁症和酒精中毒患者大脑皮层的转录组学分析，揭示了这些疾病患者共有的和独特的基因表达紊乱模式。基因解码表明，共同的多基因变异解释了不同精神疾病存在的多基因序列变化的个似性。这些结果强调精神疾病作为“疾病历史”的产物并不符合可以明确界定的疾病种类，¹从而对临床的诊断和疾病分类提出质疑。

从基因解码结果到临床应用

随着基因信息在医学应用上的巨大成功，随着更多的确定的人的基因序列变化与人体疾病之间的关系的确立，以及癌症中个体肿瘤分子图谱的描绘，使得个体化诊断和治疗成为推动正确医学发展的动力。这些发展主要是由过去7年中随着下一代测序（NGS）的实施及其所带来的巨大技术进步推动的。虽然NGS之前的基因检测主要是针对非常罕见的单基因疾病进行的，其中许多具有反复性突变，但NGS的出现允许通过使用目标基因包，WES，或全基因组测序（WGS）同时查询许多基因及其所有突变。与神经精神疾病和治疗结果相关的基因序列不仅被发现而且被不断验证，使人们越来越期望这些结果可以转化为临床，以改善个性化诊断和个体风险预测以及治疗反应，并可以用来预测其他家庭成员的风险。全面的基因检测已经成为可能，而且通过不同的商业模式提供给医生和个人，尤其是通过“直接对消费者”（DTC）检测。因此，现在是时候讨论基因检测、基因解码在精神病的临床应用了。

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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