See related article, p 1056

Technical and informatic advances have led to increased availability and reduced cost of whole-exome sequencing (WES) and whole-genome sequencing for clinical purposes.¹ Simultaneously, adoption of standards for classification of variants according to their potential pathogenicity has helped to facilitate the interpretation of identified variants for both the clinician and patient. Using this technology as part of the workup for monogenic causes of stroke and cardiometabolic disease would, therefore, appear to be a reasonable application, but there remains considerable uncertainty about the diagnostic yield of this approach, as well as the actionability of the returned results.² Many single gene defects have been reported in association with stroke, often detected in families or patients who experience stroke at an early age. European stroke registries have suggested that ≈10% of stroke patients present at an age ≤55 years, and almost half of all patients with stroke have a positive family history, making indiscriminate large-scale application of sequencing infeasible.³ Furthermore, genome-wide association and WES studies have demonstrated that monogenic syndromes do not make up a substantial proportion of sporadic stroke, and so rational approaches are needed to determine the optimal age range and degree of familial clustering to justify the use of WES/whole-genome sequencing to search for monogenic stroke syndromes in individual patients.^4,5

A recent large-scale effort queried the Online Inheritance in Man database for stroke associations and cross-referenced the results against peer-reviewed reports validating their association in PubMed. Combining these findings with other monogenic syndromes associated with stroke-related diseases and genome-wide association study results, the authors identified 214 genes plausibly associated with monogenic stroke, along with their mechanistic subtypes.⁶ The resulting monogenic stroke gene panel was not intended to be deterministic, as variants found in these genes could be nonpathogenic, and the genes themselves may not be necessary and sufficient to cause stroke. However, reducing the dimensionality of the genome down to 214 genes of high pretest probability of harboring variants that could cause monogenic stroke was a step toward making the task of WES/whole-genome sequencing interpretation more practical.

Building on these efforts in the current issue of Stroke, Ilinca et al⁷ performed WES in 22 ischemic stroke patients, all aged ≤55 years at the time of their event, with familial clustering of stroke and without a previously established monogenic disorder. The included patients were selected from the Lund Stroke Register and the SECRETO study (Searching for Explanations for Cryptogenic Stroke in the Young: Revealing the Etiology, Triggers, and Outcome) of cryptogenic stroke in the young, with an average of more than 4 stroke-affected members per family cluster. WES was performed via Ion Torrent, which is a rapid and cost-efficient approach with the tradeoffs of short reads and relatively higher error rates. Annotation was performed via ANNOVAR, a functional annotation tool designed for rare diseases where the causal variant is expected to be deleterious. The aforementioned monogenic stroke gene panel was used to filter results. In total, rare variants in genes from the monogenic stroke panel compatible with the clinical stroke subtype were identified in 27% of cases (6/22). For several of these variants, follow-up genetic examination of affected and unaffected family members demonstrated a lack of segregation by stroke status, suggesting that some variants are unlikely to result in a true monogenic cause of stroke. Two additional individuals had variants identified via WES that were not validated by Sanger sequencing, demonstrating the importance of quality control and validation procedures in clinical sequencing protocols.

At first blush, these would appear to be disheartening results. The included patients were indeed young and had multiple stroke-affected family members, and many clinicians would reasonably suspect an underlying genetic cause. However, there are multiple reasons why a convincing monogenic cause was not identified in the majority of these patients. First, stroke is a common disease, and shared environmental and behavioral risk factors combined with random clustering could have conspired to suggest genetic segregation where none existed. Furthermore, it is possible that the included patients and families were super-selected due to the requirement that no preexisting monogenic disorder be present, leaving more diagnostically challenging cases. Rare pathogenic variants in alternative genes may be sufficient to cause stroke syndromes but are not included in existing monogenic stroke panels, and some genetic causes of familial stroke may actually be oligogenic. In contrast, the appearance of suspected causal variants in unaffected family members raises concern that either the prediction of pathogenicity for some of the variants is unreliable or that true pathogenic variation within the gene itself is incompletely penetrant or insufficient to cause stroke without other coexposures. Finally, genetic variants outside of single nucleotide polymorphisms, such as repeat expansions or structural variants would not be reliably detected using the employed WES approach. Stroke clinicians are not alone in facing these genetic diagnostic challenges.⁸

This study by Ilinca et al⁷ is notable for the relatively large number of patients and family members studied in the context of a monogenic stroke workup, with harmonized application of WES methodologies and variant interpretation. Examination of identified variants in affected as well as unaffected family members provided the important opportunity to test for pathogenicity and penetrance for the purported causal monogenic variants. Finally, information about stroke subtypes allowed the authors to test whether identified putative causal variants acted through stroke mechanisms consistent with the patients’ presentations. Relative weaknesses include the lack of clinical or genetic information available for some family members, in addition to the potential for selection bias in the included cases, as mentioned above. Finally, the employed WES approach provides no information about noncoding alleles falling outside of the exome which could certainly be causal for stroke, but determination of pathogenicity of variants in the noncoding genome remains a major challenge and starting with WES for the purposes of this analysis seems readily defensible.⁹

In summary, Ilinca et al⁷ demonstrate that WES of patients with young-onset stroke and strong familial clustering has a low diagnostic yield, and identified variants lying within purported monogenic stroke genes may also segregate with unaffected family members, further limiting the utility of the approach. Future work is needed to identify stroke-related phenotypes associated with novel rare pathogenic variants, which will be facilitated by increasing availability of large well-phenotyped biobanks and hospital registries for adequately powered analyses. Additionally, efforts to understand how variants interact with each other and in concert with nongenetic exposures will be critically important for future effects to return accurate and actionable diagnostic genetic testing results for patients with young and familial stroke syndromes.

Dr Anderson receives sponsored research support from the National Institutes of Health of the United States, the American Heart Association, Massachusetts General Hospital, and Bayer AG, and has consulted for ApoPharma, Inc.

The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.

请参阅相关文章，第1056页

技术和信息方面的进步已导致增加了可用性，并降低了用于临床目的的全外显子测序（WES）和全基因组测序的成本。¹同时，根据变体的潜在致病性对变体进行分类的标准的采用有助于简化对临床医生和患者的已识别变体的解释。因此，使用该技术作为中风和心脏代谢疾病单基因病因的检查工作的一部分，似乎是合理的应用，但该方法的诊断结果以及返回结果的可操作性仍存在很大不确定性。²已报道许多与中风有关的单基因缺陷，通常在家庭或较早经历中风的患者中发现。欧洲中风登记机构建议，约10％的中风患者年龄≤55岁，几乎所有中风患者中有一半具有阳性家族史，因此不加选择地大规模应用测序是不可行的。³此外，全基因组关联和WES研究表明，单基因综合征在散发性中风中所占比例不大，因此需要合理的方法来确定最佳年龄范围和家族聚类程度，以证明使用WES /全基因组测序以寻找单个患者的单基因卒中综合征。^4,5

最近的一项大规模工作是查询笔画关联的“在线人类继承”数据库，并将结果与​​经过同行评审的报告进行交叉引用，以验证他们在PubMed中的关联。将这些发现与与中风相关疾病相关的其他单基因综合征以及全基因组关联研究结果相结合，作者确定了与单基因中风合理相关的214个基因，以及它们的机制亚型。⁶所得的单基因卒中基因组无意是确定性的，因为在这些基因中发现的变体可能是非致病性的，并且基因本身可能不是引起卒中的必要和充分条件。但是，将基因组的维数减少到214种具有携带可能导致单基因中风的变异的高预测试概率的基因，是使WES /全基因组测序解释的任务更加实际的一步。

基于当前《中风》杂志的这些努力，Ilinca等人⁷在22例缺血性卒中患者中进行了WES治疗，事件发生时年龄均≤55岁，伴有家族性卒中聚集，且无先前确定的单基因疾病。纳入的患者选自Lund脑卒中寄存器和SECRETO研究（寻找年轻人中隐源性卒中的解释：揭示病因，中风和结局），平均年龄超过4次，每个家庭类别的受影响成员。WES通过Ion Torrent执行，这是一种快速且具有成本效益的方法，但需要权衡短读和相对较高的错误率。通过ANNOVAR进行注释，ANNOVAR是一种功能注释工具，专为因果变异有害的稀有疾病而设计。前述的单基因中风基因组用于过滤结果。总体而言，在27％的病例中，从与临床卒中亚型兼容的单基因卒中组中发现了罕见的基因变异（6/22）。对于这些变体中的几个，对患病和未患病家庭成员进行的后续遗传检查显示，缺乏中风状态的隔离，表明某些变体不太可能导致中风的真正单基因原因。另外两个人通过WES鉴定了未通过Sanger测序验证的变体，证明了质量控制和验证程序在临床测序方案中的重要性。对于这些变体中的几个，对患病和未患病家庭成员进行的后续遗传检查显示，缺乏中风状态的隔离，表明某些变体不太可能导致中风的真正单基因原因。另外两个人通过WES鉴定了未通过Sanger测序验证的变体，证明了质量控制和验证程序在临床测序方案中的重要性。对于这些变体中的几种，对患病和未患病家庭成员进行的后续遗传检查显示，缺乏中风状态的隔离，表明某些变体不太可能导致中风的真正单基因原因。另外两个人通过WES鉴定了未通过Sanger测序验证的变体，证明了质量控制和验证程序在临床测序方案中的重要性。

乍一看，这些结果似乎令人沮丧。纳入的患者确实年轻，并且有多个中风患者家庭成员，许多临床医生会合理地怀疑潜在的遗传原因。但是，有多种原因导致在大多数这些患者中未发现令人信服的单基因病因。首先，中风是一种常见疾病，环境和行为的共同危险因素与随机聚类相结合可能会暗示不存在遗传隔离。此外，由于要求不存在既存的单基因疾病，因此有可能对患者和家属进行了超选，从而留下了更具诊断挑战性的病例。替代基因中罕见的致病变异可能足以引起中风综合征，但不包括在现有的单基因中风组中，家族性中风的某些遗传原因实际上可能是寡聚的。相反，在未受影响的家庭成员中出现可疑的因果变体引起了人们的担忧，即某些变体的致病性预测不可靠，或者基因本身内的真正致病性变体不完全渗透或在没有其他共同暴露的情况下不足以引起中风。最后，使用所采用的WES方法无法可靠地检测到单核苷酸多态性之外的遗传变异，例如重复扩增或结构变异。中风临床医生并不是唯一面对这些基因诊断挑战的人。家族性中风的某些遗传原因实际上可能是寡聚的。相反，在未受影响的家庭成员中出现可疑的因果变异引起人们的担忧，即某些变异的致病性预测不可靠，或者基因本身内的真实致病变异不完全渗透或在没有其他共同暴露的情况下不足以引起中风。最后，使用所采用的WES方法无法可靠地检测到单核苷酸多态性以外的遗传变异，例如重复扩增或结构变异。中风临床医生并不是唯一面对这些基因诊断挑战的人。家族性中风的某些遗传原因实际上可能是寡聚的。相反，在未受影响的家庭成员中出现可疑的因果变体引起了人们的担忧，即某些变体的致病性预测不可靠，或者基因本身内的真正致病性变体不完全渗透或在没有其他共同暴露的情况下不足以引起中风。最后，使用所采用的WES方法无法可靠地检测到单核苷酸多态性之外的遗传变异，例如重复扩增或结构变异。中风临床医生并不是唯一面对这些基因诊断挑战的人。在未受影响的家庭成员中出现可疑的因果变体引起了人们的担忧，即某些变体的致病性预测不可靠，或者基因本身内的真正致病性变体不完全渗透或不足以在没有其他共同暴露的情况下引起中风。最后，使用所采用的WES方法无法可靠地检测到单核苷酸多态性之外的遗传变异，例如重复扩增或结构变异。中风临床医生并不是唯一面对这些遗传诊断挑战的人。在未受影响的家庭成员中出现可疑的因果变体引起了人们的担忧，即某些变体的致病性预测不可靠，或者基因本身内的真正致病性变体不完全渗透或不足以在没有其他共同暴露的情况下引起中风。最后，使用所采用的WES方法无法可靠地检测到单核苷酸多态性之外的遗传变异，例如重复扩增或结构变异。中风临床医生并不是唯一面对这些基因诊断挑战的人。使用所采用的WES方法将无法可靠地检测到诸如重复扩展或结构变异之类的信息。中风临床医生并不是唯一面对这些基因诊断挑战的人。使用所采用的WES方法将无法可靠地检测到诸如重复扩展或结构变异之类的信息。中风临床医生并不是唯一面对这些基因诊断挑战的人。⁸

Ilinca等人的这项研究⁷值得注意的是，在单基因卒中检查的背景下研究了相对大量的患者和家属，并统一应用了WES方法和变体解释。对患病及未患病家庭成员中鉴定出的变异进行检查提供了重要的机会，以测试所声称的因果单基因变异的致病性和穿透性。最后，关于中风亚型的信息使作者能够测试确定的推定因果变异是否通过与患者表现相符的中风机制起作用。如上所述，相对的弱点包括某些家庭成员缺乏临床或遗传信息，此外在上述案例中可能存在选择偏见。最后，⁹

综上所述，Ilinca等人⁷证明具有年轻家族性卒中和家族性强簇的患者的WES诊断率较低，并且在所声称的单基因卒中基因中发现的变异也可能与未受影响的家庭成员隔离，进一步限制了该方法的实用性。需要进一步的工作来确定与新型罕见病原体变异相关的中风相关表型，这将通过增加大型的具有良好表型的生物库和医院登记处进行足够有力的分析的方法来促进。此外，了解变体如何相互作用以及如何与非基因暴露相结合，对于将来的效果至关重要，对于为年轻的和家族性中风综合征的患者返回准确，可行的诊断基因检测结果，这一点至关重要。

安德森博士获得了美国国立卫生研究院，美国心脏协会，麻省总医院和拜耳公司的赞助研究支持，并曾为ApoPharma，Inc.提供咨询服务。

本文表达的观点不一定是编辑者或美国心脏协会的观点。