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+目錄 遺傳變異分類標準與指南 免責聲明 摘要 Key Words 關鍵詞 1.引言 2.方法 3.總論 3.1 術語 3.2 命名 3.3 文獻及數據庫使用 3.4 生物信息學計算預測程序 4. 序列變異解讀的擬定標準 4.1 PVS1 無功能變異 4.2 PS1 突變?yōu)橥话被?br> 4.3 PS2 PM6 新發(fā)變異 4.4 PS3 BS3 功能研究 4.5 PS4 PM2 BA1 BS1 BS2 變異頻率及對照人群的使用 4.6 PM1 熱點突變和/或關鍵的、得到確認的功能域 4.7 PM3 BP2 順式/反式檢測 4.8 PM4 BP3 由于框內缺失/插入和終止密碼子喪失導致的蛋白長度改變 4.9 PM5 同一位置新的錯義變異 4.10 PP1 BS4 共分離分析 4.11 PP2 BP1 變異譜 4.12 PP3 BP4 生物信息分析數據 4.13 PP4 表型支持 4.14 PP5 BP6 可靠的來源 4.15 BP5 可替代基因座觀察 4.16 BP7 同義變異 5. 序列變異報導 5.1 結果 5.2 解讀 5.3 方法學 5.4 患者維權團體、臨床實驗和研究的獲取 5.5 變異再分析 5.6 變異的驗證 6. 特殊變異 6.1 基于檢測結果對GUS變異的評估和報告 6.2 在健康個體中評估變異或作為偶然發(fā)現 6.3 線粒體變異 6.4 藥物基因組學 6.5 常見復雜疾病 6.6 體細胞變異 7. 醫(yī)療工作者如何使用這些指南和建議 8 參考文獻（略） 圖1 表1 人群數據庫，疾病特異性數據庫和序列數據庫 表2 生物信息分析工具 表3 致病變異分級標準 表4 良性變異分類標準 表5 遺傳變異分類聯合標準規(guī)則 表6 評估人群中變異頻率來策劃變異分類 遺傳變異分類標準與指南 免責聲明 These ACMG Standards and Guidelines were developed primarily as an educational resource for clinical laboratory geneticists to help them provide quality clinical laboratory services. Adherence to these standards and guidelines is voluntary and does not necessarily assure a successful medical outcome. These Standards and Guidelines should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed to obtaining the same results. In determining the propriety of any specific procedure or test, the clinical laboratory geneticist should apply his or her own professional judgment to the specific circumstances presented by the individual patient or specimen. Clinical laboratory geneticists are encouraged to document in the patient’s record the rationale for the use of a particular procedure or test, whether or not it is in conformance with these Standards and Guidelines. They also are advised to take notice of the date any particular guideline was adopted and to consider other relevant medical and scientific information that becomes available after that date. It also would be prudent to consider whether intellectual property interests may restrict the performance of certain tests and other procedures. ACMG制定的標準與指南作為教育資源旨在幫助臨床遺傳學家提供優(yōu)質的臨床檢驗服務。該標準和指南遵循自愿原則，且不一定能夠確保遵循該標準和指南的醫(yī)療結果是成功的。不要認為該標準與指南包含所有合適的流程和檢測，也不排除其他可以獲得相同結果的流程和檢測的合理方法。臨床實驗室遺傳學家應該用自己的專業(yè)知識，依據個體病人或樣本的具體情況來判斷任一具體的流程或檢測的合理性。我們鼓勵臨床實驗室遺傳學家記錄對病人使用的用某一具體流程或檢測的原理，不管這個原理與這些標準與指南是否符合。同時建議臨床實驗室遺傳學家關注指南的采用時間，應考慮到此后更新的一些相關醫(yī)療和科學信息。還需謹慎考慮到知識產權可能會限制某些檢測或流程的使用。 摘要 The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants.1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next-generation sequencing. By adopting and leveraging next-generation sequencing, clinical laboratories are now performing an ever-increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes, and epigenetic assays for genetic disorders. By virtue of increased complexity, this shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context the ACMG convened a workgroup in 2013 comprising representatives from the ACMG, the Association for Molecular Pathology (AMP), and the College of American Pathologists to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP, and College of American Pathologists stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories, including genotyping, single genes, panels, exomes, and genomes. This report recommends the use of specific standard terminology—“pathogenic,” “l(fā)ikely pathogenic,” “uncertain significance,” “l(fā)ikely benign,” and “benign”—to describe variants identified in genes that cause Mendelian disorders. Moreover, this recommendation describes a process for classifying variants into these five categories based on criteria using typical types of variant evidence (e.g., population data, computational data, functional data, segregation data). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a Clinical Laboratory Improvement Amendments–approved laboratory, with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or the equivalent. 美國醫(yī)學遺傳學與基因組學學會（The American College of Medical Genetics and Genomics, ACMG）曾制定過序列變異解讀指南。在過去的十年中，隨著新一代高通量測序的出現，測序技術有了快速發(fā)展。利用新一代測序技術，臨床實驗室檢測遺傳性疾病的產品種類不斷增加，包括基因分型，單基因，基因panel，外顯子組，基因組，轉錄組和表觀遺傳學檢測。隨著技術的復雜性日益增加，基因檢測在測序解讀的方面不斷面臨著新挑戰(zhàn)。因此ACMG在2013年成立了一個工作組來重新審視和修訂序列變異解讀的標準和指南，工作組中包括ACMG、分子病理協會（the Association for Molecular Pathology, AMP）和美國病理學家協會（the College of American Pathologists, CAP）的代表。該工作組由臨床實驗室主任和臨床醫(yī)生組成。本報告代表了工作組中來自ACMG、AMP和CAP的專家意見。本報告提出的建議可應用于臨床實驗室的各種基因檢測方法，包括基因分型、單基因、基因panel、外顯子組和基因組。本報告建議使用特定標準術語來描述孟德爾疾病相關的基因變異——“致病”、“可能致病”、“意義不明確”、“可能良性”和“良性”。此外，本報告描述了對變異進行五級分類的標準過程，該標準需基于變異證據的典型數據類型（如人口數據，計算數據，功能數據，共分離數據）。由于臨床基因檢測分析和解讀中不斷增加的復雜性，ACMG強烈建議臨床分子基因檢測應在符合臨床實驗室改進修正案（CLIA）認證的實驗室中進行，其檢測結果應由通過職業(yè)認證的臨床分子遺傳學家或分子遺傳病理學家或相同職能的專業(yè)人員解讀。 Key Words 關鍵詞 ACMG laboratory guideline; clinical genetic testing; interpretation; reporting; sequence variant terminology; variant reporting ACMG實驗室指南；臨床基因檢測；解讀；報告；序列變異術語；變異報告 1.引言 Clinical molecular laboratories are increasingly detecting novel sequence variants in the course of testing patient specimens for a rapidly increasing number of genes associated with genetic disorders. While some phenotypes are associated with a single gene, many are associated with multiple genes. Our understanding of the clinical significance of any given sequence variant falls along a gradient, ranging from those in which the variant is almost certainly pathogenic for a disorder to those that are almost certainly benign. While the previous American College of Medical Genetics and Genomics (ACMG) recommendations provided interpretative categories of sequence variants and an algorithm for interpretation, the recommendations did not provide defined terms or detailed variant classification guidance.1 This report describes updated standards and guidelines for the classification of sequence variants using criteria informed by expert opinion and empirical data. 由于遺傳疾病患者的樣本檢測基因數目快速增加，臨床分子實驗室也檢測到更多的新的序列變異。某些表型僅與單個基因相關，而多數表型都與多個基因相關。我們對任何給定的序列變異臨床意義的解讀具有梯度性，從幾乎肯定的某個疾病的致病性變異到幾乎肯定的良性變異。雖然ACMG之前的建議提供了序列變異的解讀分類及解讀的算法，但并沒有提供定義的術語或詳細的變異分類指導。本研究依據專家意見和經驗數據，闡述了最新的序列變異分類標準和指南。 2.方法 In 2013 a workgroup consisting of ACMG, Association for Molecular Pathology (AMP), and College of American Pathologists members, representing clinical laboratory directors and clinicians, was formed with the goal of developing a recommendation for the use of standard terminology for classifying sequence variants using available evidence weighted according to a system developed through expert opinion, workgroup consensus, and community input. To assess the views of the clinical laboratory community, surveys were sent to over 100 sequencing laboratories in the United States and Canada that were listed in GeneTests.org, requesting input on terminology preferences and evaluation of evidence for classifying variants. Laboratory testing experience included rare disease as well as pharmacogenomics and somatic cancer testing. The first survey, aimed at assessing terminology preferences, was sent in February 2013, and the results were presented in an open forum at the 2013 ACMG annual meeting including over 75 attendees. Survey respondents represented more than 45 laboratories in North America. The outcome of the survey and open forum indicated that (i) a five-tier terminology system using the terms “pathogenic,” “l(fā)ikely pathogenic,” “uncertain significance,” “l(fā)ikely benign,” and “benign” was preferred and already in use by a majority of laboratories, and (ii) the first effort of the workgroup should focus on Mendelian and mitochondrial variants. 2013年，ACMG、AMP和CAP的成員，代表臨床實驗室主任和臨床醫(yī)生成立了一個工作組，該工作組依據專家建議、工作組共識和公眾反饋開發(fā)了一種可以對現有的證據進行加權的系統，并應用此系統對序列變異進行標準分類。為了評估臨床實驗室的觀點，對列入GeneTests.org上位于美國和加拿大的超過100家的測序實驗室進行了調研，要求各實驗室填寫偏好的術語及變異分類的評估證據。這些實驗室檢測都包括罕見病、藥物基因組學和癌癥體細胞突變檢測。2013年2月開展旨在評估術語喜好第一次調研，在2013年ACMG年會公開論壇上公布了調研結果，年會有超過75個與會者參加。調研實驗室中超過45個在北美，調研和公開論壇的結果表明：(i) 五級術語系統“致病”、“可能致病”、“意義不明確”、“可能良性”和“良性”是優(yōu)選，且已在多數實驗室使用；(ii) 工作組的首要重點應著重于孟德爾疾病和線粒體變異。 In the first survey, laboratories also were asked to provide their protocols for variant assessment, and 11 shared their methods. By analyzing all the protocols submitted, the workgroup developed a set of criteria to weight variant evidence and a set of rules for combining criteria to arrive at one of the five classification tiers. Workgroup members tested the scheme within their laboratories for several weeks using variants already classified in their laboratories and/or by the broader community. In addition, typical examples of variants harboring the most common types of evidence were tested for classification assignment to ensure the system would classify those variants according to current approaches consistently applied by workgroup members. A second survey was sent in August 2013 to the same laboratories identified through GeneTests. org as well as through AMP’s listserv of ~2,000 members, along with the proposed classification scheme and a detailed supplement describing how to use each of the criteria. Laboratories were asked to use the scheme and to provide feedback as to the suitability and relative weighting of each criteria, the ease of use of the classification system, and whether they would adopt such a system in their own laboratory. Responses from over 33 laboratories indicated majority support for the proposed approach, and feedback further guided the development of the proposed standards and guidelines. 在第一次調研中，參與的實驗室被要求提供他們的變異評價方法，最終有11個實驗室提供并分享了他們的變異評估方法。通過分析所有提交的方法，工作組制定了一組準則，包括變異證據評估的加權標準系統和應用這個標準將變異歸類為五類的分類規(guī)則。在幾周的時間里，工作組成員通過使用在實驗室內或其他機構內已進行分類的變異，來檢測這個方案。另外，還將典型變異的常見證據進行分類，來測試現有方法與工作組的分類系統的分類結果是否一致。2013年8月，第二次調研在GeneTests.org上的相同實驗室以及AMP清單上的約2000個單位中進行，同時給各單位提供了分類方案和詳細的方案補充說明。要求各實驗室使用分類方案并對以下內容進行反饋，包括各標準的適宜性和每個標準的相對權重、分類體系的易用性以及他們是否會在自己的實驗室采用這樣的系統。來自超過33個實驗室的答復表明多數實驗室支持所推薦的方案，而且他們的反饋會進一步指導標準和指南的發(fā)展。 In November 2013 the workgroup held a workshop at the AMP meeting with more than 50 attendees, presenting the revised classification criteria and two potential scoring systems. One system is consistent with the approach presented here and the other is a point system whereby each criterion is given a number of points, assigning positive points for pathogenic criteria and negative points for benign criteria, with the total defining the variant class. With an audience-response system, the participants were asked how they would weight each criterion (as strong, moderate or supporting, or not used) during evaluation of variant evidence. Again, the responses were incorporated into the classification system presented here. It should be noted that while the majority of respondents did favor a point system, the workgroup felt that the assignment of specific points for each criterion implied a quantitative level of understanding of each criterion that is currently not supported scientifically and does not take into account the complexity of interpreting genetic evidence. 2013年11月，工作組在AMP會議期間舉行了超過50人參加的研討會，提出了修訂后的分類標準和兩個評分系統。一個系統與這里介紹的方法是一致的，另一個系統則是一個分數系統，每一項標準都有一個分數，正分數為致病標準，負分數為良性標準，根據總分數進行變異分類。參與者使用此系統并進行反饋，回答在評估變異證據過程中他們如何權衡各個標準（如強，中度或支持，或不使用）。參與者的反饋結果經分析后會再次綜合到這里介紹的分類系統中。但要指出的是，雖然大多數調查對象更傾向于分數評價系統，但本工作組認為，每個標準中具體分數的設置量化了對每個標準的理解，但是這一量化指標目前缺乏科學依據，并且沒有考慮解讀遺傳證據解讀時的復雜性。 The workgroup also evaluated the literature for recommendations from other professional societies and working groups that have developed variant classification guidelines for wellstudied genes in breast cancer, colon cancer, and cystic fibrosis and statistical analysis programs for quantitative evaluation of variants in select diseases.While those variant analysis guidelines are useful in a specific setting, it was difficult to apply their proposed criteria to all genes and in different laboratory settings. The variant classification approach described in this article is meant to be applicable to variants in all Mendelian genes, whether identified by single gene tests, multigene panels, exome sequencing, or genome sequencing. We expect that this variant classification approach will evolve as technology and knowledge improve. We should also note that those working in specific disease groups should continue to develop more focused guidance regarding the classification of variants in specific genes given that the applicability and weight assigned to certain criteria may vary by gene and disease. 工作組還評估了文獻中推薦的其他專業(yè)協會和工作組在乳腺癌、結腸癌和囊性纖維化中已制定的變異分類指南，以及在特定疾病中應用統計分析來進行變異定量評估的方法。這些變異分析指南在一定條件下是有效的，但很難將他們推薦的標準應用于所有基因變異和不同的實驗室條件。本文描述的變異分類方法適用于所有孟德爾基因變異，包括單基因測序，多基因panel，外顯子組測序和基因組測序發(fā)現的變異。我們期望這種變異分類方法會隨著技術和知識水平的提高而與時俱進。由于不同基因和不同疾病中的應用和加權評估的標準可能不同，那些特定疾病組的工作應繼續(xù)，以制定更有針對性的具體基因的變異分類指南。 3.總論 3.1 術語 A mutation is defined as a permanent change in the nucleotide sequence, whereas a polymorphism is defined as a variant with a frequency above 1%. The terms “mutation” and “polymorphism,” however, which have been used widely, often lead to confusion because of incorrect assumptions of pathogenic and benign effects, respectively. Thus, it is recommended that both terms be replaced by the term “variant” with the following modifiers: (i) pathogenic, (ii) likely pathogenic, (iii) uncertain significance, (iv) likely benign, or (v) benign. Although these modifiers may not address all human phenotypes, they comprise a five-tier system of classification for variants relevant to Mendelian disease as addressed in this guidance. It is recommended that all assertions of pathogenicity (including “l(fā)ikely pathogenic”) be reported with respect to a condition and inheritance pattern (e.g., c.1521_1523delCTT (p.Phe508del), pathogenic, cystic fibrosis, autosomal recessive). 突變是指核苷酸序列的永久性改變，而多態(tài)性是指頻率超過1%的變異。雖然術語“突變”和“多態(tài)性”已被廣泛使用，但由于致病性和良性結果的不正確假設往往會被混淆。因此，建議使用“變異”加以下修飾詞替代上述兩個術語：i) 致病性，ii) 可能致病性，iii) 意義不明確，iv) 可能良性，或v) 良性。雖然這些修飾詞不可能適用所有的人類表型，但是正如本指南提出的它包含了孟德爾疾病相關的變異分類五級系統。建議所有致病性（包括可能致病）的結論需要注明疾病及相應的遺傳模式（例如c.1521_1523delCTT (p.Phe508del)，致病性，囊性纖維化，常染色體隱性遺傳）。 It should be noted that some laboratories may choose to have additional tiers (e.g., subclassification of variants of uncertain significance, particularly for internal use), and this practice is not considered inconsistent with these recommendations. It should also be noted that the terms recommended here differ somewhat from the current recommendations for classifying copy-number variants detected by cytogenetic microarray.6 The schema recommended for copy-number variants, while also including five tiers, uses “uncertain clinical significance— likely pathogenic” and “uncertain clinical significance—likely benign.” The majority of the workgroup was not supportive of using “uncertain significance” to modify the terms “l(fā)ikely pathogenic” or “l(fā)ikely benign” given that it was felt that the criteria presented here to classify variants into the “l(fā)ikely” categories included stronger evidence than outlined in the copy-number variant guideline and that combining these two categories would create confusion for the health-care providers and individuals receiving clinical reports. However, it was felt that the use of the term “l(fā)ikely” should be restricted to variants where the data support a high likelihood that it is pathogenic or a high likelihood that it is benign. Although there is no quantitative definition of the term “l(fā)ikely,” guidance has been proposed in certain variant classification settings. A survey of the community during an ACMG open forum, however, suggested a much wider range of uses of the term “l(fā)ikely.” Recognizing this, we propose that the terms “l(fā)ikely pathogenic” and “l(fā)ikely benign” be used to mean greater than 90% certainty of a variant either being diseasecausing or benign to provide laboratories with a common, albeit arbitrary, definition. Similarly, the International Agency for Research on Cancer guideline supports a 95% level of certainty of pathogenicity, but the workgroup (confirmed by feedback during the ACMG open forum) felt that clinicians and patients were willing to tolerate a slightly higher chance of error, leading to the 90% decision. It should also be noted that at present most variants do not have data to support a quantitative assignment of variant certainty to any of the five categories given the heterogeneous nature of most diseases. It is hoped that over time experimental and statistical approaches to objectively assign pathogenicity confidence to variants will be developed and that more rigorous approaches to defining what the clinical community desires in terms of confidence will more fully inform terminologies and likelihoods. 應當注意的是，一些實驗室可能選擇其他等級（例如，意義不明確的變異的子分類，特別是內部使用時），并且這種做法被認為與這些建議一致。還應當指出的是，某種程度上本指南推薦的術語與細胞遺傳學基因芯片檢測的拷貝數變異分類不同。雖然拷貝數變異分類系統也包括五級分類標準，但是它使用“臨床意義不明確-可能致病性”和“臨床意義不明確-可能良性”。這里提出的“可能性”變異分類標準，應包括比拷貝數變異指南概述中更有力的證據，并且合并這兩個“可能性”分類會使醫(yī)療工作者和臨床報告接收者產生混淆，因此大多數工作組不支持使用“意義不明確”來修飾“可能致病性”或“可能良性”。然而，有人認為“可能性”一詞的使用應限于有數據支持其極高可能為致病性或良性的變異。雖然對“可能性”一詞沒有量化界定，但是在某些變異分類中提出了指導。然而，在ACMG開放論壇的一項調查中，建議術語“可能性”具有更廣泛的使用。認識到這一點，我們建議術語“可能致病”和“可能良性”用來說明一個變異具有大于90%的可能引起致病或者是良性，盡管比較隨意，但是給實驗室提供一種常見的定義。同樣，國際癌癥機構指南支持致病性的確定水平為95%，但是工作組（通過ACMG公開論壇期間的反饋確認）認為，臨床醫(yī)生和患者愿意容忍略高的錯誤機會，從而做出確定性為90%的決定。還應當指出的是，考慮到多數疾病的異質性，目前大多數變異沒有數據能支持將它們列入上述五個變異類別。希望隨著時間的推移，能夠建立實驗和統計方法來客觀地賦予變異的致病可信度，以及有更嚴格的方法來定義臨床領域所期望的可信度，充分說明術語及其可能性。 The use of new terminologies may require education of the community. Professional societies are encouraged to engage in educating all laboratories as well as health-care providers on the use of these terms, and laboratories also are encouraged to directly educate their ordering physicians. 新術語的使用可能需要該領域的培訓，鼓勵專業(yè)團隊對所有實驗室和醫(yī)療工作者進行這些術語的培訓，也鼓勵實驗室直接對其主治醫(yī)生進行培訓教育。 3.2 命名 A uniform nomenclature, informed by a set of standardized criteria, is recommended to ensure the unambiguous designation of a variant and enable effective sharing and downstream use of genomic information. A standard gene variant nomenclature (http://www./mutnomen) is maintained and versioned by the Human Genome Variation Society (HGVS), and its use is recommended as the primary guideline for determining variant nomenclature except as noted. Laboratories should note the version being used in their test methods. Tools are available to provide correct HGVS nomenclature for describing variants (https://). Clinical reports should include sequence reference(s) to ensure unambiguous naming of the variant at the DNA level, as well as to provide coding and protein nomenclature to assist in functional interpretations (e.g., “g.” for genomic sequence, “c.” for coding DNA sequence, “p.” for protein, “m.” for mitochondria). 建議通過一套規(guī)范的標準制定的統一命名來確保變異的明確定義，并實現基因組信息的有效共享和下游使用。標準的基因變異命名由人類基因組變異協會（the Human Genome Variation Society, HGVS）維護和版本化，除非另有說明，一般推薦該命名法作為確定變異命名的首要準則。實驗室應該注意他們在實驗方法中所使用的版本?？衫霉ぞ咛峁┱_的HGVS命名來描述變異 (http://)。臨床報告應該包含參考序列以確保該變異在DNA水平上的明確命名，并提供編碼和蛋白質命名法來協助功能注釋（例如，“g”為基因組序列，“c”為編碼DNA序列，“p”為蛋白質，“m”為線粒體）。 The coding nomenclature should be described using the “A” of the ATG translation initiation codon as position number 1. Where historical alternate nomenclature has been used, current nomenclature should be used with an additional notation of the historical naming. The reference sequence should be complete and derived from either the National Center for Biotechnology Information RefSeq database (http://www.ncbi.nlm./RefSeq/) with the version number or the Locus Reference Genomic database (http:// www.). Genomic coordinates should be used and defined according to a standard genome build (e.g., hg19) or a genomic reference sequence that covers the entire gene (including the 5′ and 3′ untranslated regions and promoter). A reference transcript for each gene should be used and provided in the report when describing coding variants. The transcript should represent either the longest known transcript and/or the most clinically relevant transcript. Communitysupported reference transcripts can often be identified through Locus Reference Genomic, the Consensus CDS Database, the Human Gene Mutation Database (http://www.hgmd. ), ClinVar (http://www.ncbi.nlm./clinvar), or a locus-specific database. However, laboratories should evaluate the impact of the variant on all clinically relevant transcripts, including alternate transcripts that contain additional exons or extended untranslated regions, when there are known variants in these regions that are clinically interpretable. 編碼命名應該使用翻譯起始密碼子ATG中的“A”作為位置編號1來描述。在歷史性替換命名已被使用的地方，當今命名應該對歷史命名進行額外注釋。參考序列應該是完整的，并來源于具有版本號的生物技術信息參考序列數據庫美國中心（http://www.ncbi.nlm./Refseq/）或LRG數據庫（http://www.）。根據標準基因組構建（如hg19）或覆蓋整個基因（包括5'和3'非翻譯區(qū)以及啟動子）的基因組來使用和定義基因組坐標。當描述編碼變異時，應該在報告中使用和提供每個基因的一個參考轉錄本。該轉錄本應該是最長的已知轉錄本或者是最具臨床相關性的轉錄本。協會支持的參考轉錄本通?？梢酝ㄟ^LRG數據庫、CDS共識數據庫、人類基因突變數據庫(http://www.hgmd.)、ClinVar(http://www.ncbi.nlm./clinvar)或特異基因座數據庫來確定。然而，當這些區(qū)域發(fā)生臨床可解釋的已知變異時，實驗室應該評估該變異對所有臨床相關的轉錄本的影響，包括含有其他外顯子或非翻譯區(qū)延伸的可變剪切轉錄本。 Not all types of variants (e.g., complex variants) are covered by the HGVS recommendations, but possible descriptions for complex variants have been reported. In addition, this ACMG recommendation supports three specific exceptions to the HGVS nomenclature rules: (i) “X” is still considered acceptable for use in reporting nonsense variants in addition to the current HGVS recommendation of “*” and “Ter”; (ii) it is recommended that exons be numbered according to the chosen reference transcript used to designate the variant; and (iii) the term “pathogenic” is recommended instead of “affects function” because clinical interpretation is typically directly evaluating pathogenicity. HGVS并未覆蓋所有類型的變異（如復雜變異），但是復雜變異的可能描述也已被報道。此外，ACMG支持HGVS命名規(guī)則之外的三種特殊例外：(i) 除了當今HGVS推薦的“*”和“Ter”，“X”仍然被認為用于報告無義變異；(ii) 建議根據指定變異選擇的參考轉錄本對外顯子進行編號；(iii) 通常因為臨床解釋直接評估致病性，所以推薦使用術語“致病性”而不是“影響功能”。 3.3 文獻及數據庫使用 A large number of databases contain a growing number of variants that are continuously being discovered in the human genome. When classifying and reporting a variant, clinical laboratories may find valuable information in databases, as well as in the published literature. As noted above, sequence databases can also be used to identify appropriate reference sequences. Databases can be useful for gathering information but should be used with caution. 目前存在著大量與遺傳疾病相關的數據庫，而這些數據庫中也包含著越來越多不斷在人類基因組中發(fā)現的變異。當臨床實驗室需要對一個變異進行分類和出具報告時，可能會在已有的數據庫（或發(fā)表的文獻）中尋找到有價值的參考信息。在上述情況中，也可以通過使用序列數據庫獲得合適的參考序列。總體說來，數據庫的使用極大的方便了人們獲取有用信息，但在使用中需要保持謹慎的態(tài)度。 Population databases (Table 1) are useful in obtaining the frequencies of variants in large populations. Population databases cannot be assumed to include only healthy individuals and are known to contain pathogenic variants. These population databases do not contain extensive information regarding the functional effect of these variants or any possible associated phenotypes. When using population databases, one must determine whether healthy or disease cohorts were used and, if possible, whether more than one individual in a family was included, as well as the age range of the subjects. 人口數據庫（表1）當需要獲得大規(guī)模人口中某變異發(fā)生的頻率時，使用人口數據庫是非常適宜的。需要注意的是，人口數據庫不是僅僅指的是健康人口，也包括有致病性變異的人口。另外，在人口數據庫中，不包含這些變異造成的相關功能改變信息，或一些可能相關的表型信息。在參考使用人口數據庫時，必須確保以下幾點：①是否使用了病例-對照研究；②（如果可能）是否有的數據是一個家庭中有超過1個受試者的情況存在，以及是否有受試者的年齡信息。 Disease databases (Table 1) primarily contain variants found in patients with disease and assessment of the variants’ pathogenicity. Disease and gene-specific databases often contain variants that are incorrectly classified, including incorrect claims published in the peer-reviewed literature, because many databases do not perform a primary review of evidence. When using disease databases, it is important to consider how patients were ascertained, as described below. 疾病數據庫（表1）疾病數據庫主要由兩部分組成，一部分是在疾病患者中發(fā)現的變異，另一部分是通過診斷確認的致病性變異。通常，疾病數據庫和特殊基因數據庫會包含一些錯誤分類的變異，包括發(fā)表在同行評審文獻中的錯誤論證，這是因為很多數據庫并不會對論證數據進行初步審核。所以，在使用疾病數據庫時，考慮病人到變異信息是如何被確定的非常重要，如下所述： When using databases, clinical laboratories should (i) determine how frequently the database is updated, whether data curation is supported, and what methods were used for curation;(ii) confirm the use of HGVS nomenclature and determine the genome build and transcript references used for naming variants; (iii) determine the degree to which data are validated for analytical accuracy (e.g., low-pass nextgeneration sequencing versus Sanger-validated variants) and evaluate any quality metrics that are provided to assess data accuracy, which may require reading associated publications; and (iv) determine the source and independence of the observations listed. 當使用數據庫時，臨床實驗室應做到以下幾點：(i) 確定數據庫的更新頻率，確定數據管理的運行，以及確定使用了什么方法來進行數據管理；(ii) 確認使用HGVS命名，并確定將其用于基因組和轉錄本序列中的變異命名；(iii) 確定數據的分析精度（比如，變異是通過低通量（low-pass）的新一代測序技術確定，還是Sanger測序技術確定），并確定用于數據精度評估中的度量指標，要獲得這些信息可能需要閱讀相關的文獻；(iv) 確定數據信息來源及其獨立性。 Variant assessment also includes searching the scientific and medical literature. Literature using older nomenclature and classification or based on a single observation should be used with caution. When identifying individuals and families with a variant, along with associated phenotypes, it is important to consider how patients were ascertained. This caveat is important when assessing data from publications because affected individuals and related individuals are often reported multiple times, depending on the context and size of the study. This may be due to authorship overlap, interlaboratory collaborations, or a proband and family members being followed across different clinical systems. This may mistakenly lead to duplicate counting of affected patients and a false increase in variant frequency. Overlapping authorship or institutions is the first clue to the potential for overlapping data sets. 在變異解讀中，除了使用疾病數據庫外，也可以通過檢索科學和醫(yī)學文獻。在使用舊的命名和分類系統的文獻，以及基于單一觀察的文獻時要慎重。當鑒定具有相關聯表型的個人和家系的變異時，非常重要的是要考慮患者的變異是如何被確定。當依據發(fā)表的文獻數據時進行判斷時，這一觀念就很重要，因為基于研究內容和規(guī)模大小的差異，受累的患者和相關人員常常會被多次報道。這可能是由于作者有重疊、實驗室間有合作、或先證者及其家庭成員同時被不同臨床系統隨訪，這可能會導致患者錯誤被的重復計數，變異頻率虛增加。作者或其研究機構互相重疊是導致數據集重復的首要潛在因素。 Clinical laboratories should implement an internal system to track all sequence variants identified in each gene and clinical assertions when reported. This is important for tracking genotype–phenotype correlations and the frequency of variants in affected and normal populations. Clinical laboratories are encouraged to contribute to variant databases, such as ClinVar, including clinical assertions and evidence used for the variant classification, to aid in the continued understanding of the impact of human variation. Whenever possible, clinical information should be provided following Health Insurance Portability and Accountability Act regulations for privacy. Clinical laboratories are encouraged to form collaborations with clinicians to provide clinical information to better understand how genotype influences clinical phenotype and to resolve differences in variant interpretation between laboratories. Because of the great potential to aid clinical laboratory practice, efforts are underway for clinical variant databases to be expanded and standardized. Standardization will provide easier access to updated information as well as facilitate submission from the clinical laboratory. For example, the ClinVar database allows for the deposition of variants with clinical observations and assertions, with review status tracked to enable a more transparent view of the levels of quality of the curation. 臨床實驗室應建立一個內部系統去跟蹤每個基因上的所有序列變異及其臨床和評估。這對于追蹤基因型-表型之間的相關性，以及該變異在患者和正常人群中的發(fā)生頻率非常重要的。我們鼓勵臨床實驗室提交變異數據到各種數據庫庫如ClinVar，所提供的數據包括用于不同分類的臨床評估和證據，以幫助人類可以不斷加深對人類遺傳變異的理解。在任何可能的時候，所提供的臨床數據應盡可能的遵循“醫(yī)療保險可移植和責任法案（HIPAA）”對個人隱私的保護。我們鼓勵臨床實驗室與臨床醫(yī)生合作，獲得臨床信息，以便更好地理解基因型是如何影響臨床表型的，并且化解不同實驗室對遺傳變異解讀的差異。因為臨床變異數據庫對臨床實驗室的實踐具有極大的潛在幫助，并且正努力進行不斷的擴展和標準化。標準化更加便于臨床實驗室獲取數據庫信息，也便于他們提交最新的變異檢測信息到數據庫。例如，ClinVar數據庫可以不斷積累臨床觀察診斷的變異解讀數據，且能跟蹤提交信息的審核狀態(tài)，確保以更透明的方式進行質量管理。 3.4 生物信息學計算預測程序 A variety of in silico tools, both publicly and commercially available, can aid in the interpretation of sequence variants. The algorithms used by each tool may differ but can include determination of the effect of the sequence variant at the nucleotide and amino acid level, including determination of the effect of the variant on the primary and alternative gene transcripts, other genomic elements, as well as the potential impact of the variant on the protein. The two main categories of such tools include those that predict whether a missense change is damaging to the resultant protein function or structure and those that predict whether there is an effect on splicing (Table 2). Newer tools are beginning to address additional noncoding sequences. 各種公用和商業(yè)化計算機工具可以輔助解讀序列變異。每種工具使用的算法可能有差異，但工具都對序列變異在核苷酸及氨基酸水平上的影響進行判斷，包括變異對初級轉錄本，可變轉錄本，其他基因組元件的影響，以及變異對蛋白質可能的影響。這些工具主要分為兩種：一種可以預測錯義變異是否會毀壞其所產生的蛋白質的結構或功能；另一種可以預測是否影響剪接（表2）。新的工具已可以定位額外的非編碼序列。 The impact of a missense change depends on criteria such as the evolutionary conservation of an amino acid or nucleotide, the location and context within the protein sequence, and the biochemical consequence of the amino acid substitution. The measurement of one or a combination of these criteria is used in various in silico algorithms that assess the predicted impact of a missense change. Several efforts have evaluated the performance of available prediction software to compare them with each other and to assess their ability to predict “known” disease-causing variants. In general, most algorithms for missense variant prediction are 65–80% accurate when examining known disease variants. Most tools also tend to have low specificity, resulting in overprediction of missense changes as deleterious, and are not as reliable at predicting missense variants with a milder effect.18 The in silico tools more commonly used for missense variant interpretation in clinical laboratories include PolyPhen2, SIFT, and MutationTaster. A list of in silico tools used to predict missense variants can be found in Table 2. 判斷錯義突變的結果有些標準，，如一個氨基酸或核苷酸的進化保守性，其在蛋白質序列中的位置和其上下游序列，以及氨基酸改變導致的生化結果。在各種計算機算法中所應用的一個或幾個準則進行測定，從而對錯義突變的影響進行評估。已經有一些工作通過預測軟件之間的相互比較并評估他們預測已知致病突變的能力來評估預測軟件的性能。一般情況下，多數算法預測已知致病的錯義突變的準確率能達到65-80%。但是大多數工具特異性較低，導致有些錯義突變被過預測為有害突變，而且對于中性變異的預測也并不可靠。臨床實驗室常用的錯義變異解讀工具有PolyPhen 2、SIFT和MutationTaster。用于預測錯義變異的生物信息分析工具見表2。 Multiple software programs have been developed to predict splicing as it relates to the creation or loss of splice sites at the exonic or intronic level. In general, splice site prediction tools have higher sensitivity (~90–100%) relative to specificity (~60–80%) in predicting splice site abnormalities. Some of the in silico tools commonly used for splice site variant interpretation are listed in Table 2. 現在已經有許多用于預測剪接的軟件，因其與內含子或外顯子水平上剪接位點的丟失或產生有關。一般情況下，剪接位點預測工具在預測剪接位點異常方面有較高的敏感性（~90-100%），其預測特異性只有~60-80% 。一些常用的剪接位點變異解讀預測的分析計算工具見表2。 While many of the different software programs use different algorithms for their predictions, they have similarities in their underlying basis; therefore, predictions combined from different in silico tools are considered as a single piece of evidence in sequence interpretation as opposed to independent pieces of evidence. The use of multiple software programs for sequence variant interpretation is also recommended because the different programs each have their own strengths and weaknesses, depending on the algorithm; in many cases performance can vary by the gene and protein sequence. These are only predictions, however, and their use in sequence variant interpretation should be implemented carefully. It is not recommended that these predictions be used as the sole source of evidence to make a clinical assertion. 雖然許多程序使用不同的算法進行預測，但他們的基本原理均相似；因此，在序列解讀中，組合不同軟件工具的預測結果被視為單一證據而不是相互獨立的證據。不過仍然建議使用多種軟件進行序列變異解讀，因為每個軟件擁有自己獨特的優(yōu)點及缺點，這取決于他們使用的算法；很多情況下，預測性能可因基因和蛋白質序列的不同而有差異。然而，這些只是預測，他們在序列變異解讀中的應該慎用。我們不建議僅使用這些預測結果作為唯一證據來源去做臨床判斷。 4. 序列變異解讀的擬定標準 The following approach to evaluating evidence for a variant is intended for interpretation of variants observed in patients with suspected inherited (primarily Mendelian) disorders in a clinical diagnostic laboratory setting. It is not intended for the interpretation of somatic variation, pharmacogenomic (PGx) variants, or variants in genes associated with multigenic non- Mendelian complex disorders. Care must be taken when applying these rules to candidate genes (“genes of uncertain significance” (GUS)) in the context of exome or genome studies (see Special Considerations below) because this guidance is not intended to fulfill the needs of the research community in its effort to identify new genes in disease. 以下評估變異證據的方法是用了解釋在臨床診斷實驗室中具有疑似遺傳（主要指孟德爾遺傳）疾病患者的變異。并不適用于解讀體細胞變異、藥物基因組（PGx）變異、或者是多基因非孟德爾復雜疾病相關的基因變異。在外顯子組或基因組研究中，對候選基因（意義不明確的基因（GUS））應用這些準則時應當謹慎（見下面注意事項），因為本指南目的不是滿足研究社群在疾病中鑒定新致病基因的需求。 Although these approaches can be used for evaluating variants found in healthy individuals or secondary to the indication for testing, further caution must be used, as noted in several parts of the guideline, given the low prior likelihood that most variants unrelated to the indication are pathogenic. Although we expect that, in general, these guidelines will apply for variant classification regardless of whether the variant was identified through analysis of a single gene, gene panel, exome, genome, or transcriptome, it is important to consider the differences between implicating a variant as pathogenic (i.e., causative) for a disease and a variant that may be predicted to be disruptive/ damaging to the protein for which it codes, but is not necessarily implicated in a disease. These rules are intended to determine whether a variant in a gene with a definitive role in a Mendelian disorder may be pathogenic for that disorder. Pathogenicity determination should be independent of interpreting the cause of disease in a given patient. For example, a variant should not be reported as pathogenic in one case and not pathogenic in another simply because the variant is not thought to explain disease in a given case. Pathogenicity should be determined by the entire body of evidence in aggregate, including all cases studied, arriving at a single conclusion. 雖然這些方法可用于評估在健康個體中發(fā)現的變異或繼發(fā)于測試指征的變異，但是如在指南的幾個部分中所述，對于與指征無關的致病性變異在給予較低的先驗可能性時需更加謹慎。雖然我們通常期望這些指南可以適用于變異分類，-無論其是通過分析單基因，一組基因，外顯子組，基因組或者轉錄組而鑒定的。重要的是要關注一種區(qū)別，即與疾病有關的致病變異和雖然被預測為破壞\損傷其編碼蛋白但卻與疾病無充分關聯的變異之間的區(qū)別。這些規(guī)則旨在確定在孟德爾遺傳病中有明確作用的基因的變異是否對該遺傳疾病是致病的。在某些病人中，致病性判定應該獨立于對疾病病因的解讀。例如，某變異在一個案例中被評估為“致病的”，而在另一個案例中，由于不能解釋該疾病，就對這個位點不給出“致病的”的評價，這樣的情況是絕對不允許的。確定致病性需要由全部證據，結合所有的案例分析，最終得到一個獨立的結論。 This classification approach may be somewhat more stringent than laboratories have applied to date. They may result in a larger proportion of variants being categorized as uncertain significance. It is hoped that this approach will reduce the substantial number of variants being reported as “causative” of disease without having sufficient supporting evidence for that classification. It is important to keep in mind that when a clinical laboratory reports a variant as pathogenic, health-care providers are highly likely to take that as “actionable” and to alter the treatment or surveillance of a patient or remove such management in a genotype-negative family member, based on that determination (see How Should Health-Care Providers Use These Guidelines and Recommendations, below). 此指南的分類方法可能比目前實驗室應用的標準更為嚴格。這將導致很大一部分的變異被歸類為“意義不明確”。希望這種方法可以大量減少那些沒有足夠分類證據支持而報告為致病原因的變異。重要的是要記住，當臨床實驗室報告一個變異為“致病”時，醫(yī)療單位很可能把其當作“可行的”，基于這個判斷，從而會改變對患者的治療、監(jiān)測，或去除基因型陰性的家庭成員的治療、監(jiān)測（參見下面的醫(yī)療工作者應該如何使用這些指南和建議）。 We have provided two sets of criteria: one for classification of pathogenic or likely pathogenic variants (Table 3) and one for classification of benign or likely benign variants (Table 4). Each pathogenic criterion is weighted as very strong (PVS1), strong (PS1–4); moderate (PM1–6), or supporting (PP1–5), and each benign criterion is weighted as stand-alone (BA1), strong (BS1– 4), or supporting (BP1–6). The numbering within each category does not convey any differences of weight and is merely labeled to help refer to the different criteria. For a given variant, the user selects the criteria based on the evidence observed for the variant. The criteria then are combined according to the scoring rules in Table 5 to choose a classification from the five-tier system. The rules apply to all available data on a variant, whether gathered from the current case under investigation or from well-vetted previously published data. Unpublished case data may also be obtained through public resources (e.g., ClinVar or locus specific databases) and from a laboratory’s own database. To provide critical flexibility to variant classification, some criteria listed as one weight can be moved to another weight using professional judgment, depending on the evidence collected. For example, rule PM3 could be upgraded to strong if there were multiple observations of detection of the variant in trans (on opposite chromosomes) with other pathogenic variants (see PM3 BP2 cis/trans Testing for further guidance). By contrast, in situations when the data are not as strong as described, judgment can be used to consider the evidence as fulfilling a lower level (e.g., see PS4, Note 2 in Table 3). If a variant does not fulfill criteria using either of these sets (pathogenic or benign), or the evidence for benign and pathogenic is conflicting, the variant defaults to uncertain significance. The criteria, organized by type and strength, is shown in Figure 1. Please note that expert judgment must be applied when evaluating the full body of evidence to account for differences in the strength of variant evidence. 我們提供了兩套標準：一是用于對致病或可能致病的變異進行分類（表3），另一是用于對良性或可能良性的變異進行分類（表4）。致病變異標準可分為非常強（very strong，PVS1），強（strong，PS1-4）；中等（moderate，PM1–6），或輔助證據（supporting，PP1-5）。良性變異證據可分為獨立（stand-alone，BA1），強（strong，BS1–4），或輔助證據（BP1–6）。其中，數字只是作為有助于參考的分類標注，不具有任何意義。每個類別中的數字不表示分類的任何差異，僅用來標記以幫助指代不同的規(guī)則。對于一個給定的變異，用戶基于觀察到的證據來選擇標準。根據表5的評分規(guī)則把標準組合起來進而從5級系統中選擇一個分類。這些規(guī)則適用于變異上的所有可用數據，無論是基于調查現有案例獲得的數據，還是來源于先前公布的數據。未發(fā)表的數據也可以通過公共數據庫（例如，ClinVar或位點特異數據庫）和實驗室自有數據庫獲得。為了對變異分類具有較好靈活性，基于收集的證據和專業(yè)判斷，可以把某些位于一種加權中的轉換到另一標準。例如，如果有多個反式變異（位于相對的染色體）檢測為致病變異，PM3可以上調到強（進一步指導見PM3 BP2順/反式檢測）。相反，在數據并不像描述的那么強的情況下，可以判斷變異為一個較低的水平（見表3注2 PS4）。如果一個變異不符合分類標準（致病或良性），或良性和致病的證據是相互矛盾的，則默認該變異為“意義不確定”。程度判斷評價標準如圖1所示。請注意，當評估全部的證據以解釋變異證據強度的差異時，必須進行專家判斷。 The following is provided to more thoroughly explain certain concepts noted in the criteria for variant classification (Tables 3 and 4) and to provide examples and/or caveats or pitfalls in their use. This section should be read in concert with Tables 3 and 4. 下面提供更徹底地解釋在變異分類標準（表3和4）中提及的某些概念，并提供實際使用中的實例和/或警告或缺陷。這部分應該與表3及4一塊閱讀。 4.1 PVS1 無功能變異 Certain types of variants (e.g., nonsense, frameshift, canonical ±1 or 2 splice sites, initiation codon, single exon or multiexon deletion) can often be assumed to disrupt gene function by leading to a complete absence of the gene product by lack of transcription or nonsense-mediated decay of an altered transcript. One must, however, exercise caution when classifying these variants as pathogenic by considering the following principles: 某些變異類型可以導致無法進行轉錄或由于無義突變導致轉錄終止從而導致基因功能破壞。例如，無義突變、移碼突變、經典剪接位點±1或2點突變、起始密碼子變異、單個或多個外顯子缺失，這些突變因此也會被稱為無功能突變。然而，我們將這些變異分類為致病突變時必須考慮以下原則： (i) When classifying such variants as pathogenic, one must ensure that null variants are a known mechanism of pathogenicity consistent with the established inheritance pattern for the disease. For example, there are genes for which only heterozygous missense variants cause disease and null variants are benign in a heterozygous state (e.g., many hypertrophic cardiomyopathy genes). A novel heterozygous nonsense variant in the MYH7 gene would not be considered pathogenic for dominant hypertrophic cardiomyopathy based solely on this evidence, whereas a novel heterozygous nonsense variant in the CFTR gene would likely be considered a recessive pathogenic variant. (i) 當將這些變異分類為致病時，必須確定這些有害變異有已知明確的致病機制，并且該突變的遺傳方式與該疾病的遺傳模式一致。舉例來說，有些基因僅雜合錯義突變即可致病，而有些疾病雜合無功能變異則是屬于良性突變（如許多肥厚性心肌病基因）。對顯性肥厚性心肌病來說，不能僅僅因為在MYH7基因上發(fā)現了一個雜合無義突變就認定是致病的，而當一個新的雜合無義變異出現在 CFTR 基因上則可以考慮它是一個隱性致病變異。 (ii) One must also be cautious when interpreting truncating variants downstream of the most 3′ truncating variant established as pathogenic in the literature. This is especially true if the predicted stop codon occurs in the last exon or in the last 50 base pairs of the penultimate exon, such that nonsense-mediated decay would not be predicted, and there is a higher likelihood of an expressed protein. The length of the predicted truncated protein would also factor into the pathogenicity assignment, however, and such variants cannot be interpreted without a functional assay. (ii)當文獻中將3’遠端下游致病截短變異注釋成致病突變時，要特別小心。特別是當所預測的終止密碼子出現在末端外顯子，或者出現在倒數第二個外顯子的最后50個堿基對時，這種無義突變介導的轉錄衰減可能不會發(fā)生，這個蛋白很可能會表達。據此所預測的截短蛋白的長度也是致病性評估的因素，但這些變異未經功能分析是無法進行判定的。 (iii) For splice-site variants, the variant may lead to exon skipping, shortening, or inclusion of intronic material as a result of alternative donor/acceptor site usage or creation of new sites. Although splice-site variants are predicted to lead to a null effect, confirmation of impact requires functional analysis by either RNA or protein analysis. One must also consider the possibility of an in-frame deletion/insertion, which could retain the critical domains of the protein and hence lead to either a mild or neutral effect with a minor length change (PM4) or a gain-of-function effect. (iii) 剪接位點變異可能導致外顯子丟失、縮短，也可能會導致外顯子部分包含內含子序列，這是由于外顯子剪切位點的供體/受體位點改變了或產生了新的剪切位點。雖然剪切位點變異可能被預測為無功能變異，然而該變異類型造成的影響需要通過RNA或蛋白質功能分析確認。還必須考慮閱讀框內缺失/插入的可能性，其可以保留蛋白質的關鍵結構域，并且因此導致輕微或中性效應，并且其長度變化較?。≒M4）或功能獲得效應。 (iv) Considering the presence of alternate gene transcripts and understanding which are biologically relevant, and in which tissues the products are expressed, are important. If a truncating variant is confined to only one or not all transcripts, one must be cautious about overinterpreting variant impact given the presence of the other protein isoforms. (iv) 基因會有不同的轉錄本，哪一種轉錄本與生物學功能相關，在哪些組織會表達哪些轉錄本，這些都是需要進行重點考慮的。如果一個截短變異只限于一個或并非所有轉錄本，則必須謹慎考慮到可能存在其它同功型蛋白質，防止過度解釋。 (v) One must also be cautious in assuming that a null variant will lead to disease if found in an exon where no other pathogenic variants have been described, given the possibility that the exon may be alternatively spliced. This is particularly true if the predicted truncating variant is identified as an incidental finding (unrelated to the indication for testing), given the low prior likelihood of finding a pathogenic variant in that setting. (v) 如果發(fā)現一個無功能變異位于某個外顯子上，而該外顯子先前無致病變異報道，那么該外顯子可能被選擇性剪切了，此時需要謹慎考慮該變異的致病性。當預測的截短變異是偶然發(fā)現時（與檢測指標無關）則應特別小心，在這種情況下該位點致病的可能性非常低。 4.2 PS1 突變?yōu)橥话被?br> In most cases, when one missense variant is known to be pathogenic, a different nucleotide change that results in the same amino acid (e.g., c.34G>C (p.Val12Leu) and c.34G>T (p.Val12Leu)) can also be assumed to be pathogenic, particularly if the mechanism of pathogenicity occurs through altered protein function. However, it is important to assess the possibility that the variant may act directly through the specific DNA change (e.g., through splicing disruption as assessed by at least computational analysis) instead of through the amino acid change, in which case the assumption of pathogenicity may no longer be valid. 多數情況下，當一個錯義變異明確是致病的，另一個變異導致的突變?yōu)橄嗤被岣淖儠r（如，c.34G>C (p.Val12Leu) 和c.34G>T (p.Val12Leu) ），一般認為該變異是致病的，特別是當致病機制是蛋白質功能的改變。然而，需考慮變異不是通過氨基酸水平改變，而是通過特定DNA改變來發(fā)揮作用的可能性（如剪接位點破壞，至少要計算機軟件分析評估），在這種情況下致病性的假設是不成立的。 4.3 PS2 PM6 新發(fā)變異 A variant observed to have arisen de novo (parental samples testing negative) is considered strong support for pathogenicity if the following conditions are met: (i) Both parental samples were shown through identity testing to be from the biological parents of the patient. Note that PM6 applies if identity is assumed but not confirmed. (ii) The patient has a family history of disease that is consistent with de novo inheritance (e.g., unaffected parents for a dominant disorder). It is possible, however, that more than one sibling may be affected because of germ-line mosaicism. (iii) The phenotype in the patient matches the gene’s disease association with reasonable specificity. For example, this argument is strong for a patient with a de novo variant in the NIPBL gene who has distinctive facial features, hirsutism, and upper-limb defects (i.e., Cornelia de Lange syndrome), whereas it would be weaker for a de novo variant found by exome sequencing in a child with nonspecific features such as developmental delay. 新發(fā)變異是指患者自身攜帶的變異，父母樣本檢測都為陰性。當我們將一個新發(fā)變異歸類為致病時，需要滿足以下條件：(i)父母樣本通過身份檢驗表明是患者的生物學父母。注意如果身份檢驗是假定的而沒有被證實，則判定為PM6； (ii)新發(fā)變異符合當前的家族史。例如，新發(fā)變異為顯性遺傳病且父母未患病。也可能存在生殖細胞嵌合現象，但此種情況一般有多個兄弟患?。?iii) 患者的表型與基因相匹配。例如，某個患者具有特殊面容、多毛和上肢缺陷（即Cornelia de Lange綜合征），檢測到NIPBL基因的新生突變即為強致病證據，而另一個患者僅發(fā)育遲緩無明顯特征，通過外顯子組測序發(fā)現的新發(fā)變異，則判斷此變異致病性較低。 4.4 PS3 BS3 功能研究 Functional studies can be a powerful tool in support of pathogenicity; however, not all functional studies are effective in predicting an impact on a gene or protein function. For example, certain enzymatic assays offer well-established approaches to assess the impact of a missense variant on enzymatic function in a metabolic pathway (e.g., α-galactosidase enzyme function). On the other hand, some functional assays may be less consistent predictors of the effect of variants on protein function. To assess the validity of a functional assay, one must consider how closely the functional assay reflects the biological environment. For example, assaying enzymatic function directly from biopsied tissue from the patient or an animal model provides stronger evidence than expressing the protein in vitro. Likewise, evidence is stronger if the assay reflects the full biological function of the protein (e.g., substrate breakdown by an enzyme) compared with only one component of function (e.g., adenosine triphosphate hydrolysis for a protein with additional binding properties). Validation, reproducibility, and robustness data that assess the analytical performance of the assay and account for specimen integrity, which can be affected by the method and time of acquisition, as well as storage and transport, are important factors to consider. These factors are mitigated in the case of an assay in a Clinical Laboratory Improvement Amendments laboratory–developed test or commercially available kit. Assays that assess the impact of variants at the messenger RNA level can be highly informative when evaluating the effects of variants at splice junctions and within coding sequences and untranslated regions, as well as deeper intronic regions (e.g., messenger RNA stability, processing, or translation). Technical approaches include direct analysis of RNA and/or complementary DNA derivatives and in vitro minigene splicing assays. 功能研究是判斷致病性的有力工具，然而并非所有的功能研究都能有效預測基因或蛋白功能。舉例來說，某些酶的功能實驗研究可以很好的評估錯義變異對酶在代謝途徑中的功能影響（如α-半乳糖苷酶的功能）。而另一方面，某些功能實驗在評估變異對蛋白質功能影響時缺乏一致性。評估功能測定是否準確，必須考慮功能實驗有多大程度上反映了生物環(huán)境。例如，與離體蛋白相比，直接從患者或動物模型的活檢組織做酶的功能實驗提供了更有力的證據。同樣，可以反映蛋白質的全部生物學功能（如酶分解底物功能）的證據則比僅反映一部分功能（如一種蛋白有附帶水解ATP的功能）證據性更強。樣本的完整性影響了功能實驗的準確性、重復性和穩(wěn)定性，所以需重點關注樣本的采集方法、時間、存儲和運輸等影響樣本性能和完整性的因素。臨床實驗室改進的實驗室開發(fā)的檢測或市售的試劑盒會減少這些因素對實驗的影響。評估變異在剪接位點、編碼序列、非翻譯區(qū)以及更深內含子區(qū)域（如信使RNA穩(wěn)定性，加工或翻譯）的致病性時，對變異在信使RNA水平進行評估，可獲得更有價值的信息。相關的技術方法包括RNA和/或互補DNA衍生物直接分析以及體外微小基因剪接分析。 4.5 PS4 PM2 BA1 BS1 BS2 變異頻率及對照人群的使用 Assessing the frequency of a variant in a control or general population is useful in assessing its potential pathogenicity. This can be accomplished by searching publicly available population databases (e.g., 1000 Genomes Project, National Heart, Lung, and Blood Institute Exome Sequencing Project Exome Variant Server, Exome Aggregation Consortium; Table 1), as well as using race-matched control data that often are published in the literature. The Exome Sequencing Project data set is useful for Caucasian and African American populations and has coverage data to determine whether a variant is absent. Although the 1000 Genomes Project data cannot be used to assess the absence of a variant, it has a broader representation of different racial populations. The Exome Aggregation Consortium more recently released allele frequency data from >60,000 exomes from a diverse set of populations that includes approximately two-thirds of the Exome Sequencing Project data. In general, an allele frequency in a control population that is greater than expected for the disorder (Table 6) is considered strong support for a benign interpretation for a rare Mendelian disorder (BS1) or, if over 5%, it is considered as stand-alone support (BA1). Furthermore, if the disease under investigation is fully penetrant at an early age and the variant is observed in a well-documented healthy adult individual for a recessive ( homozygous), dominant (heterozygous), or X-linked ( hemizygous) condition, then this is considered strong evidence for a benign interpretation (BS2). If the variant is absent, one should confirm that the read depth in the database is sufficient for an accurate call at the variant site. If a variant is absent from (or below the expected carrier frequency if recessive) a large general population or a control cohort (>1,000 individuals) and the population is race-matched to the patient harboring the identified variant, then this observation can be considered a moderate piece of evidence for pathogenicity (PM2). Many benign variants are “private” (unique to individuals or families), however, and therefore absence in a race-matched population is not considered sufficient or even strong evidence for pathogenicity. 評估變異在對照人群或普通人群中的攜帶頻率有助于評估其潛在致病性。通過搜索公共人群數據庫（如千人數據庫，ESP數據庫，EAC數據庫；表1），以及已發(fā)表文獻中相同種族的對照數據可獲得變異頻率。ESP數據庫覆蓋了白種人和非裔美國人群的變異頻率。千人數據庫盡管不能被用來評估變異致病性，但它囊括了具有更廣泛代表性的不同種族的人群。EAC數據庫近期發(fā)布了一組來源于不同人群的6萬多個外顯子組的等位基因頻率數據，包括了大約三分之二的ESP的數據。一般情況下，某一等位基因在對照人群的頻率大于疾病預期（表6）時，可認為是罕見孟德爾疾病良性變異的強證據（BS1），或者如果頻率超過5%時，可認為是良性變異的獨立證據（BA1）。此外，如果疾病發(fā)生在早期，且變異在健康成人中以隱性（純合子）、顯性（雜合子）或X-連鎖（半合子）的狀態(tài)存在，那么這就是良性變異的強證據（BS2）。如果某一變異在數據庫中不存在，應該確認數據庫測序深度是否足以檢測該變異位點。如果在一個大樣本的普通人群或對照人群數據庫（>1000人）中變異不存在（或隱性遺傳的突變頻率是低頻），并且攜帶此變異的患者與對照人群為同一種族，那么可以認為該變異是致病性的中等證據（PM2）。許多良性變異是“私人的”（個人或家系獨有），因此即使在相同種族的人群中缺乏也不能作為致病性的充足甚至強的證據。 The use of population data for case–control comparisons is most useful when the populations are well phenotyped, have large frequency differences, and the Mendelian disease under study is early onset. Patients referred to a clinical laboratory for testing are likely to include individuals sent to “rule out” a disorder, and thus they may not qualify as well-phenotyped cases. When using a general population as a control cohort, the presence of individuals with subclinical disease is always a possibility. In both of these scenarios, however, a case–control comparison will be underpowered with respect to detecting a difference; as such, showing a statistically significant difference can still be assumed to provide supportive evidence for pathogenicity, as noted above. By contrast, the absence of a statistical difference, particularly with extremely rare variants and less penetrant phenotypes, should be interpreted cautiously. 當孟德爾遺傳病表型顯著、頻率差異大且是早期發(fā)病時，使用通過“變異－對照”人群研究獲得的變異數據庫進行變異分析是最有效的。臨床實驗室檢測的患者可能包括 “排除”某一疾病的個體，因此他們可能不能作為表型顯著的病例；當使用普通人群作為對照群體時，具有亞臨床疾病的個體總是可能存在的。在這兩種情況下，認為檢測出的變異致病性證據不充分。變異頻率有統計學顯著差異可以假定為致病性的支持證據。與此相反，對于統計差異不顯著，特別是極為罕見變異和不明顯的表型，應謹慎解釋。 Odds ratios (ORs) or relative risk is a measure of association between a genotype (i.e., the variant is present in the genome) and a phenotype (i.e., affected with the disease/ outcome) and can be used for either Mendelian diseases or complex traits. In this guideline we are addressing only its use in Mendelian disease. While relative risk is different from the OR, relative risk asymptotically approaches ORs for small probabilities. An OR of 1.0 means that the variant does not affect the odds of having the disease, values above 1.0 mean there is an association between the variant and the risk of disease, and those below 1.0 mean there is a negative association between the variant and the risk of disease. In general, variants with a modest Mendelian effect size will have an OR of 3 or greater, whereas highly penetrant variants will have very high ORs; for example, APOE E4/E4 homozygotes compared with E3/E3 homozygotes have an OR of 13 (https://www.tgen. org/home/education-outreach/past-summer-interns/2012- summer-interns/erika-kollitz.aspx#.VOSi3C7G_vY). However, the confidence interval (CI) around the OR is as important as the measure of association itself. If the CI includes 1.0 (e.g., OR = 2.5, CI = 0.9–7.4), there is little confidence in the assertion of association. In the above APOE example the CI was ~10–16. Very simple OR calculators are available on the Internet (e.g., http://www./ConfidOR.htm/ and http:///statistics/odds-ratio.php/). 比值比（OR）或相對風險用于衡量基因型（即存在于基因組中的變異）和表型（即所患疾病/結果）之間的關聯，適用于任何孟德爾疾病或復雜疾病。本指南只涉及其在孟德爾疾病中的使用。相對風險與OR不同，但概率較小時相對風險近似等于OR。OR值為1.0意味著該變異與疾病風險不相關，大于1.0意味著變異與疾病風險正相關，小于1.0意味著變異與疾病風險負相關。一般情況下，具有孟德爾中等效應的變異，其OR值為3或者更大，高度外顯的變異具有非常高的OR值，例如，APOE基因 E4/E4純合子與E3/E3純合子相比，OR值為13 (https://www./home/education-outreach/past-summer-interns/2012-summer-interns/erika-kollitz.aspx#.VOSi3C7G_vY)。OR值的置信區(qū)間（confidence interval, CI）也是一個重要的衡量工具。如果CI中包括1.0（如OR = 2.5, CI = 0.9-7.4），則關聯的可信度很小。在上面APOE的例子中，CI為10-16。在線可獲得簡單的OR值計算器（http://www./ConfidOR.htm/and http:///statistics/odds-ratio.php/）。 4.6 PM1 熱點突變和/或關鍵的、得到確認的功能域 Certain protein domains are known to be critical to protein function, and all missense variants in these domains identified to date have been shown to be pathogenic. These domains must also lack benign variants. In addition, mutational hotspots in less well-characterized regions of genes are reported, in which pathogenic variants in one or several nearby residues have been observed with greater frequency. Either evidence can be considered moderate evidence of pathogenicity. 某些結構域對蛋白質的功能起到了關鍵作用，若在這些結構域上發(fā)現的所有錯義突變均已被證實為致病突變，且這些結構域中一定沒有已知的良性突變，那么這就能作為致病的中等證據。若突變發(fā)生在基因突變熱點上，且一個或多個鄰近殘基中存在已知致病突變，那么這也能作為致病的中等證據。 4.7 PM3 BP2 順式/反式檢測 Testing parental samples to determine whether the variant occurs in cis (the same copy of the gene) or in trans (different copies of the gene) can be important for assessing pathogenicity. For example, when two heterozygous variants are identified in a gene for a recessive disorder, if one variant is known to be pathogenic, then determining that the other variant is in trans can be considered moderate evidence for pathogenicity of the latter variant (PM3). In addition, this evidence could be upgraded to strong if there are multiple observations of the variant in trans with other pathogenic variants. If the variant is present among the general population, however, a statistical approach would be needed to control for random co-occurrence. By contrast, finding the second variant in cis would be supporting, though not definitive, evidence for a benign role (BP2). In the case of uncertain pathogenicity of two heterozygous variants identified in a recessive gene, then the determination of the cis versus trans nature of the variants does not necessarily provide additional information with regard to the pathogenicity of either variant. However, the likelihood that both copies of the gene are impacted is reduced if the variants are found in cis. 檢測雙親樣本以確定突變在基因上是順式序列（位于基因的同一拷貝）還是反式序列（位于基因的不同拷貝），這對評估突變的致病性非常重要。例如，當兩個雜合突變發(fā)生在隱性遺傳病的致病基因上時，如果已知其中一個突變?yōu)橹虏⊥蛔?，那么當另一個突變與其反式排列（in trans）時，這可以作為后者的中等致病證據（PM1），另外，若后者與多個已知致病突變呈反式排列，則該證據可升為強致病證據，但是，若后者在普通人群中存在，則需要用統計學手段判斷該現象是否僅為隨機共發(fā)生事件；相反，當已知致病突變與另一突變呈順式排列（in cis）時，這可以作為后者的支持良性證據（BP2）。如果兩個發(fā)生在隱性遺傳病的致病基因上的雜合突變的致病性都未知，那么判斷它們是順式排列還是反式排列并不能為判斷其中任一變異的致病性提供更多信息，但是，如果兩者是順式排列（in cis）的話，該基因兩個拷貝均受影響的可能性將會降低。 In the context of dominant disorders the detection of a variant in trans with a pathogenic variant can be considered supporting evidence for a benign impact (BP2) or, in certain well-developed disease models, may even be considered standalone evidence, as has been validated for use in assessing CFTR variants. 對于顯性遺傳病而言，若突變與致病突變反式排列（in trans），則可作為該突變的支持良性證據（BP2），對于某些研究成熟的疾病模型，甚至可以考慮將其作為獨立良性證據，例如CFTR相關的突變。 4.8 PM4 BP3 由于框內缺失/插入和終止密碼子喪失導致的蛋白長度改變 The deletion or insertion of one or more amino acids as well as the extension of a protein by changing the stop codon to an amino acid codon (e.g., a stop loss variant) is more likely to disrupt protein function compared with a missense change alone as a result of length changes in the protein. Therefore, in-frame deletions/insertions and stop losses are considered moderate evidence of pathogenicity. The larger the deletion, insertion, or extension, and the more conserved the amino acids are in a deleted region, the more substantial is the evidence to support pathogenicity. By contrast, small in-frame deletions/insertions in repetitive regions, or regions that are not well conserved in evolution, are less likely to be pathogenic. 相較于單一的錯義突變所導致的蛋白質長度變化，一個或多個氨基酸的缺失或插入、以及由終止密碼子變?yōu)榉g氨基酸的密碼子（如終止密碼子丟失）而導致的蛋白質延長更可能破壞蛋白質功能。因此，框內缺失/插入以及終止密碼子丟失可作為中等致病證據。缺失、插入或延伸范圍越大，缺失區(qū)域的氨基酸越保守，則支持致病的證據越強。相反，在重復區(qū)域或在進化中不是很保守的區(qū)域中的小的框內缺失/插入的致病的可能性較小。 4.9 PM5 同一位置新的錯義變異 A novel missense amino acid change occurring at the same position as another pathogenic missense change (e.g., Trp38Ser and Trp38Leu) is considered moderate evidence but cannot be assumed to be pathogenic. This is especially true if the novel change is more conservative compared with the established pathogenic missense variant. Also, the different amino acid change could lead to a different phenotype. For example, different substitutions of the Lys650 residue of the FGFR3 gene are associated with a wide range of clinical phenotypes: p.Lys650Gln or p.Lys650Asn causes mild hypochondroplasia; p.Lys650Met causes severe achondroplasia with developmental delay and acanthosis nigricans; and thanatophoric dysplasia type 2, a lethal skeletal dysplasia, arises from p.Lys650Glu. 如果一個新發(fā)錯義突變導致的氨基酸改變與同一位置上的已知致病突變導致的氨基酸改變相同（如Trp38Ser和Trp38Leu），那么可作為中等致病證據（但不能假定一定是致病的），尤其當新的突變比已知致病錯義突變更保守時。此外，不同的氨基酸變化可能導致不同的表型。例如，FGFR3基因編碼的Lys650殘基的不同變化與不同的臨床表型相關：p.Lys650Gln或p.Lys650Asn會導致輕度軟骨發(fā)育不良；p.Lys650Met會導致嚴重的軟骨發(fā)育不全伴發(fā)育遲緩和黑棘皮??；p.Lys650Glu會導致2型發(fā)育異常及致命的骨骼發(fā)育不良。 4.10 PP1 BS4 共分離分析 Care must be taken when using segregation of a variant in a family as evidence for pathogenicity. In fact, segregation of a particular variant with a phenotype in a family is evidence for linkage of the locus to the disorder but not evidence of the pathogenicity of the variant itself. A statistical approach has been published with the caveat that the identified variant may be in linkage disequilibrium with the true pathogenic variant in that family. Statistical modeling takes into account age-related penetrance and phenocopy rates, with advanced methods also incorporating in silico predictions and co-occurrence with a known pathogenic variant into a single quantitative measure of pathogenicity. Distant relatives are important to include because they are less likely to have both the disease and the variant by chance than members within a nuclear family. Full gene sequencing (including entire introns and 5′ and 3′ untranslated regions) may provide greater evidence that another variant is not involved or identify additional variants to consider as possibly causative. Unless the genetic locus is evaluated carefully, one risks misclassifying a nonpathogenic variant as pathogenic. 在使用家系中變異的共分離現象作為致病性證據時需謹慎。事實上，一個與某種表型相關的特定變異在某一家系中的共分離現象是位點與疾病連鎖的證據，而不是變異本身致病性的證據。一個已經發(fā)表的統計方法顯示，在某個家系中鑒定的變異可能與真正的致病變異是連鎖不平衡的。統計模型考慮到了年齡相關的外顯率和擬表型率，同時也將生物信息分析預測以及與已知致病突變共存作為致病性的單獨定量指標。將遠親納入統計之中是很重要的，因為與核心家系成員相比，他們不太可能同時有該疾病和變異。全基因測序（包括整個內含子和5'和3'非編碼區(qū)）可在沒有另一個變異的情況下提供更多的證據，或者是鑒定額外的可能致病的變異。除非仔細評估基因位點，否則非致病變異可能被錯誤的認為是致病變異。 When a specific variant in the target gene segregates with a phenotype or disease in multiple affected family members and multiple families from diverse ethnic backgrounds, linkage disequilibrium and ascertainment bias are less likely to confound the evidence for pathogenicity. In this case, this criterion may be taken as moderate or strong evidence, depending on the extent of segregation, rather than supporting evidence. 當目標基因的特定變異在多個患病的家系成員中以及不同種族背景的多個家系中與表型或疾病共分離時，則其作為致病的證據不太會受到連鎖不平衡和確認偏倚的影響。在這種情況下，該標準可以作為中等或強致病證據而不是支持性證據，其強度取決于共分離的程度。 On the other hand, lack of segregation of a variant with a phenotype provides strong evidence against pathogenicity. Careful clinical evaluation is needed to rule out mild symptoms of reportedly unaffected individuals, as well as possible phenocopies (affected individuals with disease due to a nongenetic or different genetic cause). Also, biological family relationships need to be confirmed to rule out adoption, nonpaternity, sperm and egg donation, and other nonbiological relationships. Decreased and age-dependent penetrance also must be considered to ensure that asymptomatic family members are truly unaffected. 另一方面，一個變異與表型并不共分離時，為其非致病的強證據。需要進行仔細的臨床評估來排除正常個體的輕度癥狀和可能的擬表型（患者表型由非遺傳或不同的遺傳原因引起）。此外，需確認生物學家庭關系來排除收養(yǎng)、非生父、精子和卵子捐獻以及其他非生物學關系。同時,也必須考慮外顯率下降和年齡依賴性的外顯率也必須考慮，以確保無癥狀家系成員是真正的無癥狀。 Statistical evaluation of cosegregation may be difficult in the clinical laboratory setting. If appropriate families are identified, clinical laboratories are encouraged to work with experts in statistical or population genetics to ensure proper modeling and to avoid incorrect conclusions of the relevance of the variant to the disease. 在臨床實驗室進行共分離的統計評估可能并不容易，當鑒定了合適的家系時，為了確保建模合適,并避免得出變異與疾病相關性的錯誤結論,鼓勵臨床實驗室與統計或群體遺傳學專家合作。 4.11 PP2 BP1 變異譜 Many genes have a defined spectrum of pathogenic and benign variation. For genes in which missense variation is a common cause of disease and there is very little benign variation in the gene, a novel missense variant can be considered supporting evidence for pathogenicity (PP2). By contrast, for genes in which truncating variants are the only known mechanism of variant pathogenicity, missense variants can be considered supporting evidence for a benign impact (BP1). For example, truncating variants in ASPM are the primary type of pathogenic variant in this gene, which causes autosomal recessive primary microcephaly, and the gene has a high rate of missense polymorphic variants. Therefore missense variants in ASPM can be considered to have this line of supporting evidence for a benign impact. 許多基因具有明確的致病變異和良性變異譜。在某些基因中，錯義突變是導致疾病的常見原因，且該基因上的良性突變非常少，那么這種基因上的新發(fā)錯義突變可作為致病變異的支持證據（PP2）。相反，有些基因致病的唯一已知變異是截短突變，該基因上的新發(fā)錯義突變可作為良性的支持證據（BP1）。例如，ASPM基因的截短變異是該基因引起常染色體隱性遺傳小頭畸形的主要致病變異類型，且該基因發(fā)生錯義多態(tài)性突變的頻率高，因此ASPM基因上的錯義變異可認為是良性影響的支持證據。 4.12 PP3 BP4 生物信息分析數據 Not overestimating computational evidence is important, particularly given that different algorithms may rely on the same (or similar) data to support predictions and most algorithms have not been validated against well-established pathogenic variants. In addition, algorithms can have vastly different predictive capabilities for different genes. If all of the in silico programs tested agree on the prediction, then this evidence can be counted as supporting. If in silico predictions disagree, however, then this evidence should not be used in classifying a variant. The variant amino acid change being present in multiple nonhuman mammalian species in an otherwise well-conserved region, suggesting the amino acid change would not compromise function, can be considered strong evidence for a benign interpretation. One must, however, be cautious about assuming a benign impact in a nonconserved region if the gene has recently evolved in humans (e.g., genes involved in immune function). 對生物信息分析證據不能過高估計，特別是在已知不同的算法對于致病的預測可能依賴于相同的或類似的數據來支持預測結果，且多數算法未經已知致病變異的驗證。此外，針對不同的基因,相同的算法可能具有完全不同的預測能力。如果所有的生物信息分析預測結果一致，那么這可以作為支持證據。然而如果生物信息分析預測結果不一致，則此證據不應用于變異分類。若某一變異引起的氨基酸改變在多個非人哺乳動物物種不太保守的區(qū)域中出現，說明該變異可能不會損害功能，可以作為良性解讀的強的證據。然而，如果某基因已在人類中發(fā)生進化（如參與免疫功能的基因），我們在判定該基因在非保守區(qū)域中發(fā)生的變異為良性時必須小心。 4.13 PP4 表型支持 In general, the fact that a patient has a phenotype that matches the known spectrum of clinical features for a gene is not considered evidence for pathogenicity given that nearly all patients undergoing disease-targeted tests have the phenotype in question. If the following criteria are met, however, the patient’s phenotype can be considered supporting evidence: (i) the clinical sensitivity of testing is high, with most patients testing positive for a pathogenic variant in that gene; (ii) the patient has a welldefined syndrome with little overlap with other clinical presentations (e.g., Gorlin syndrome including basal cell carcinoma, palmoplantar pits, odontogenic keratocysts); (iii) the gene is not subject to substantial benign variation, which can be determined through large general population cohorts (e.g., Exome Sequencing Project); and (iv) family history is consistent with the mode of inheritance of the disorder. 考慮到幾乎所有接受疾病針對性測試的患者都有題目中的表型，通常，不將患者表型與某個基因臨床特征譜匹配作為判斷致病的證據。但是，如果滿足以下條件，患者的表型可作為支持證據：(i) 臨床檢測的靈敏度高，大多數帶有該基因致病突變的患者都檢測為陽性；(ii) 患者癥狀明確，與其他臨床表現幾乎無重疊（如戈爾林綜合征包括基底細胞癌、掌跖坑和牙源性角化）；(iii) 該基因沒有大量的通過普通群體研究（如外顯子組測序項目）確定的良性變異；(iv) 家族史與疾病遺傳方式一致。 4.14 PP5 BP6 可靠的來源 There are increasing examples where pathogenicity classifications from a reputable source (e.g., a clinical laboratory with long-standing expertise in the disease area) have been shared in databases, yet the evidence that formed the basis for classification was not provided and may not be easily obtainable. In this case, the classification, if recently submitted, can be used as a single piece of supporting evidence. However, laboratories are encouraged to share the basis for classification as well as communicate with submitters to enable the underlying evidence to be evaluated and built upon. If the evidence is available, this criterion should not be used; instead, the criteria relevant to the evidence should be used. 現在有越來越多可靠來源（如長期專注于某一疾病領域的臨床實驗室）的致病性分類信息被分享在數據庫中，然而，形成分類判斷依據的證據并沒有被提供或者很難獲取。在這種情況下，近期提交的分類可以作為一個單獨的支持證據。然而，還是鼓勵實驗室共享分類的判斷依據，并與提交者進行溝通以評估和創(chuàng)建潛在的分類證據。如果能獲得證據，不應使用該標準；反之，應該使用證據相關的標準。 4.15 BP5 可替代基因座觀察 When a variant is observed in a case with a clear alternate genetic cause of disease, this is generally considered supporting evidence to classify the variant as benign. However, there are exceptions. An individual can be a carrier of an unrelated pathogenic variant for a recessive disorder; therefore, this evidence is much stronger support for a likely benign variant classification in a gene for a dominant disorder compared with a gene for a recessive disorder. In addition, there are disorders in which having multiple variants can contribute to more severe disease. For example, two variants, one pathogenic and one novel, are identified in a patient with a severe presentation of a dominant disease. A parent also has mild disease. In this case, one must consider the possibility that the novel variant could also be pathogenic and contributing to the increased severity of disease in the proband. In this clinical scenario, observing the novel variant as the second variant would not support a benign classification of the novel variant (though it is also not considered support for a pathogenic classification without further evidence). Finally, there are certain diseases in which multigenic inheritance is known to occur, such as Bardet-Beidel syndrome, in which case the additional variant in the second locus may also be pathogenic but should be reported with caution. 一般情況下，當某一變異是在一個具有明確的可替代遺傳病因的疾病患者中觀察到時，可作為該變異良性解讀的證據。不過，也有例外。某一個體可以是某一不相關隱性遺傳疾病致病變異的攜帶者，因此本證據與隱性遺傳性疾病相比，更支持顯性遺傳性疾病基因良性變異的分類。此外，有些疾病當具有多個變異可以導致更嚴重的疾病。例如，在一個具有嚴重表型的顯性遺傳患者中鑒定了兩個變異，一個是致病的，一個是新的變異，父母中的一個也有輕微的疾病，這種情況下，必須考慮新的變異致病的可能性，且新的變異使先證者表型加重。在這種臨床情況下，觀察到的第二個新的變異不應分類為良性變異，（盡管在無進一步證據的前提下也不認為該變異是致病的）。最后，有些疾病已知為多基因遺傳模式，如Bardet-Beidel綜合征，在第二個基因座位上的額外變異也有可能是致病的，但應謹慎進行報告。 4.16 BP7 同義變異 There is increasing recognition that splicing defects, beyond disruption of the splice consensus sequence, can be an important mechanism of pathogenicity, particularly for genes in which loss of function is a common mechanism of disease. Therefore, one should be cautious in assuming that a synonymous nucleotide change will have no effect. However, if the nucleotide position is not conserved over evolution and splicing assessment algorithms predict neither an impact to a splice consensus sequence nor the creation of a new alternate splice consensus sequence, then a splicing impact is less likely. Therefore, if supported by computational evidence (BP4), one can classify novel synonymous variants as likely benign. However, if computational evidence suggests a possible impact on splicing or there is raised suspicion for an impact (e.g., the variant occurs in trans with a known pathogenic variant in a gene for a recessive disorder), then the variant should be classified as uncertain significance until a functional evaluation can provide a more definitive assessment of impact or other evidence is provided to rule out a pathogenic role. 人們逐漸認識到剪接缺陷，除了破壞剪接一致序列，還可能是重要的致病機制，特別是對那些功能喪失是常見致病機制的基因。因此，在認為同義核苷酸改變沒有影響時應持謹慎態(tài)度。然而如果核苷酸位置進化不保守，且剪接評估算法預測其對剪接一致序列沒有影響，也不會產生新的剪接一致序列，那么剪接影響的可能性就比較小。因此，如果生物信息分析證據支持（BP4），可將新發(fā)同義變異分類為可能良性。然而，如果生物信息分析證據表明剪接可能有影響或懷疑有影響（例如，發(fā)生在隱性遺傳病致病基因上，且與已知致病突變呈反式排列的突變），那么該變異應該被歸類為意義不明確，除非功能評估可以提供更確切的對影響的評估或者得到其他可排除該變異致病作用的證據。 5. 序列變異報導 Writing succinct yet informative clinical reports can be a challenge as the complexity of the content grows from reporting variants in single genes to multigene panels to exomes and genomes. Several guidance documents have been developed for reporting, including full sample reports of the ACMG clinical laboratory standards for next-generation sequencing guidance. Clinical reports are the final product of laboratory testing and often are integrated into a patient’s electronic health record. Therefore, effective reports are concise, yet easy to understand. Reports should be written in clear language that avoids medical genetics jargon or defines such terms when used. The report should contain all of the essential elements of the test performed, including structured results, an interpretation, references, methodology, and appropriate disclaimers. These essential elements of the report also are emphasized by Clinical Laboratory Improvement Amendments regulations and the College of American Pathologists laboratory standards for next-generation sequencing clinical tests. 編寫簡明而內容豐富的臨床報告是充滿挑戰(zhàn)性的，因為從檢測單個基因，到多基因小組，再到外顯子組和基因組，變異情況的報告內容復雜程度會大大增加。為規(guī)范報告內容已出臺了一些指南文件，包括符合ACMG臨床實驗室標準的新一代測序檢測完整報告示例。臨床報告是實驗室檢測結果的最終體現，通常會放入到患者的電子健康檔案中。因此，有效的報告應該是簡明扼要且易于理解的。報告應該使用清晰的語言書寫，避免使用醫(yī)學遺傳學術語，當必須要使用時需指明所用術語的定義。報告應包含所有的檢測基本要素，包括結構化的結果、解釋、參考文獻、檢測方法和適當的免責聲明。美國病理學家學會在針對新一代測序臨床實驗標準的《臨床實驗室改進法案》（CLIA）中，也對上述基本要素予以了強調。 5.1 結果 The results section should list variants using HGVS nomenclature (see Nomenclature). Given the increasing number of variants found in genetic tests, presenting the variants in tabular form with essential components may best convey the information. These components include nomenclature at both the nucleotide (genomic and complementary DNA) and protein level, gene name, disease, inheritance, exon, zygosity, and variant classification. An example of a table to report structured elements of a variant is found in the Supplementary Appendix S1 online. Parental origin may also be included if known. In addition, if specific variants are analyzed in a genotyping test, the laboratory should specifically note the variants interrogated, with their full description and historical nomenclature if it exists. Furthermore, when reporting results from exome or genome sequencing, or occasionally very large disease-targeted panels, grouping variants into categories such as “Variants in Disease Genes with an Established Association with the Reported Phenotype,” “Variants in Disease Genes with a Likely Association with the Reported Phenotype,” and (where appropriate) “Incidental (Secondary) Findings” may be beneficial. 結果部分應根據HGVS命名規(guī)則（見命名部分）列出變異檢測結果?？紤]到在基因檢測中發(fā)現的變異結果數目越來越多，以包含基本內容的表格形式呈現變異檢測結果可能是傳達信息的最好方法。這些基本內容包括在核苷酸（基因組和互補DNA）和蛋白質雙重水平的命名、基因名稱、涉及的疾病、遺傳方式、外顯子、合子類型及變異的分類 。用于報告變異檢測結果的結構性要素表格見附錄S1。親本來源如果已知也可包括在內。此外如果在基因分型檢測中分析某一特定變異時，實驗室應特別注意所分析變異的完整描述及曾用名。當報告外顯子組或全基因組測序結果,或偶爾報告涵蓋基因數目巨大的的疾病特異性panel檢測結果時，將變異檢測結果進行分類分組是有益的。推薦將變異分類成“疾病基因變異與對應已報道的表型有確切關聯”，“疾病基因變異與對應已報道的表型可能存在關聯”，和（在適當情況下）“附帶（次要）發(fā)現”。 5.2 解讀 The interpretation should contain the evidence supporting the variant classification, including its predicted effect on the resultant protein and whether any variants identified are likely to fully or partially explain the patient’s indication for testing. The report also should include any recommendations to the clinician for supplemental clinical testing, such as enzymatic/ functional testing of the patient’s cells and variant testing of family members, to further inform variant interpretation. The interpretation section should address all variants described in the results section but may contain additional information. It should be noted whether the variant has been reported previously in the literature or in disease or control databases. The references, if any, that contributed to the classification should be cited where discussed and listed at the end of the report. The additional information described in the interpretation section may include a summarized conclusion of the results of in silico analyses and evolutionary conservation analyses. However, individual computational predictions (e.g., scores, terms such as “damaging”) should be avoided given the high likelihood of misinterpretation by health-care providers who may be unfamiliar with the limitations of predictive algorithms (see In Silico Predictive Programs, above). A discussion of decreased penetrance and variable expressivity of the disorder, if relevant, should be included in the final report. Examples of how to describe evidence for variant classification on clinical reports are found in the Supplementary Appendix S1 online. 解讀應包含對變異檢測結果進行分類的證據，包括變異之后基因編碼蛋白的功能預測，以及檢測所發(fā)現的變異是否可能全部或部分地解釋患者的病例跡象 。報告也應包括一些對臨床醫(yī)生的建議，這些建議包括一些補充的臨床檢測，如對患者細胞進行的酶學/功能檢測，以及對患者家系其他成員進行的變異檢測，以便為進一步解讀變異檢測結果提供支持。解讀部分應當包括檢測結果部分描述的全部變異，以及其他附加信息。對于各個變異需要注明是否已經在先前的文獻、疾病病例或對照數據庫中有過報道。在對變異檢測結果分類時所引用的全部參考文獻和信息，在報告結尾處都需要列出。解讀部分其他的附加信息可以包括對變異位點進行進化保守性分析的結果總結。然而，由于醫(yī)療工作者可能不熟悉預測算法的局限性（見上文“3.4 生物信息學計算預測程序” ），因此應該避免報告對個體進行生物信息學預測的計算結果（如分數，諸如“破壞性”之類的術語），以免造成醫(yī)療工作者對報告產生誤解。如果有相關的外顯率下降和疾病表現多樣性分析討論，也需要包含在最終的報告中。在臨床報告中如何描述對變異檢測結果進行分類所用證據的示例見附錄S1。 5.3 方法學 The methods and types of variants detected by the assay and those refractory to detection should be provided in the report. Limitations of the assay used to detect the variants also should be reported. Methods should include those used to obtain nucleic acids (e.g., polymerase chain reaction, capture, wholegenome amplification), as well as those to analyze the nucleic acids (e.g., bidirectional Sanger sequencing, next-generation sequencing, chromosomal microarray, genotyping technologies), because this may provide the health-care provider with the necessary information to decide whether additional testing is required to follow up on the results. The methodology section should also give the official gene names approved by the Human Genome Organization Gene Nomenclature Committee, RefSeq accession numbers for transcripts, and genome build, including versions. For large panels, gene-level information may be posted and referenced by URL. The laboratory may choose to add a disclaimer that addresses general pitfalls in laboratory testing, such as sample quality and sample mix-up. 報告中應說明使用的實驗方法、檢測所涉及的變異類型、檢測過程的難點，以及變異檢測方法的局限性。需要說明的實驗方法應包括核酸獲取方法（如聚合酶鏈式反應、捕獲、全基因組擴增等）以及核酸測序方法（如雙向Sanger測序、新一代測序、染色體基因芯片、基因分型技術等），這些信息可以為醫(yī)療工作者提供必要的信息，以幫助其決定是否需要追加實驗來跟進這些檢測結果。方法部分還應包括人類基因組組織（HUGO）基因命名委員會批準的正式基因名稱、轉錄產物的RefSeq登錄號和所參考的基因組版本。對于大的Panel，基因水平的信息可以通過引用URL來加以說明。實驗室還可以選擇增加對檢測過程中常見問題（如樣本質量問題、樣品混合污染等）的免責聲明。 5.4 患者維權團體、臨床實驗和研究的獲取 Although specific clinical guidance for a patient is not recommended for laboratory reports, provision of general information for categories of results (e.g., all positives) is appropriate and helpful. A large number of patient advocacy groups and clinical trials are now available for support and treatment of many diseases. Laboratories may choose to add this information to the body of the report or attach the information so it is sent to the health-care provider along with the report. Laboratories may make an effort to connect the health-care provider to research groups working on specific diseases when a variant’s effect is classified as “uncertain,” as long as Health Insurance Portability and Accountability Act patient privacy requirements are followed. 盡管不提倡在實驗室報告中對患者提供具體臨床指導，但是在報告中提供對于檢測結果分類的總體信息（如全部陽性檢測結果）是恰當且有益的。大量患者團體和臨床試驗現在可用于多種疾病的支持和治療。實驗室可以選擇將此信息添加到報告的正文或附加信息，并且與報告一起發(fā)送給醫(yī)療工作者。在遵守醫(yī)療保險可攜性和責任法案（HIPAA）保護患者隱私的前提下，當某一變異檢測結果被歸為意義不明確時，實驗室可嘗試幫助醫(yī)療工作者和特定的疾病研究小組建立聯系。 5.5 變異再分析 As evidence on variants evolves, previous classifications may later require modification. For example, the availability of variant frequency data among large populations has led many uncertain significance variants to be reclassified as benign, and testing additional family members may result in the reclassification of variants. 隨著針對特定變異結果的證據信息的更新和增補，既往的分類結果可能需要修改。例如，當大樣本人群變異頻率數據被報道之后，許多原本意義不明確的變異信息能夠被重新歸類為良性，而對家系其他成員的補充檢測也可能會導致變異檢測結果的重新分類。 As the content of sequencing tests expands and the number of variants identified grows, expanding to thousands and millions of variants from exome and genome sequencing, the ability for laboratories to update reports as variant knowledge changes will be untenable without appropriate mechanisms and resources to sustain those updates. To set appropriate expectations with health-care providers and patients, laboratories should provide clear policies on the reanalysis of data from genetic testing and whether additional charges for reanalysis may apply. Laboratories are encouraged to explore innovative approaches to give patients and providers more efficient access to updated information. 隨著測序檢測內容的擴大和檢測鑒定出的變異信息數量的增加，外顯子組和基因組測序可以檢測到數以百萬計的的變異信息。實驗室如果沒有適當的機制和資源作為支撐，將無法根據新報道的變異支持信息來更新之前的報告內容。為了給醫(yī)療工作者和患者建立適當的期望，實驗室應該提供明晰的基因檢測數據的再分析政策，并明確再分析是否需要額外費用。實驗室應該被鼓勵開發(fā)新方法來幫助患者和醫(yī)療工作者更有效訪問最新信息。 For reports containing variants of uncertain significance in genes related to the primary indication, and in the absence of updates that may be proactively provided by the laboratory, it is recommended that laboratories suggest periodic inquiry by health-care providers to determine whether knowledge of any variants of uncertain significance, including variants reported as likely pathogenic, has changed. By contrast, laboratories are encouraged to consider proactive amendment of cases when a variant reported with a near-definitive classification (pathogenic or benign) must be reclassified. Regarding physician responsibility, see the ACMG guidelines on the duty to recontact. 如果報告中有一些和患者主要病例跡象有關的變異檢測結果被分類為意義不明確，當實驗室無法主動提供更新信息時，實驗室應該建議醫(yī)療工作者定期查詢，以確定任何被分類為意義不明確及可能致病的變異其分類是否發(fā)生更改。相比之下，當某一變異報導為近似確切的變異（致病性或良性）時必須重新分類，鼓勵實驗室主動修改。醫(yī)生的具體責任，可詳見ACMG指南。 5.6 變異的驗證 Recommendations for the confirmation of reported variants is addressed elsewhere. Except as noted, confirmation studies using an orthogonal method are recommended for all sequence variants that are considered to be pathogenic or likely pathogenic for a Mendelian disorder. These methods may include, but are not limited to, re-extraction of the sample and testing, testing of parents, restriction enzyme digestion, sequencing the area of interest a second time, or using an alternate genotyping technology. 本指南在別的地方說明了證實變異信息的推薦方法。除非另有說明，建議對于孟德爾疾病的致病或可能致病變異分類使用正交法進行驗證。具體方法包括但不限于：重新取樣和檢測、檢測父母的變異情況、限制性內切酶消化、對于目標區(qū)域重新測序或使用另一種基因分型技術。 6. 特殊變異 6.1 基于檢測結果對GUS變異的評估和報告 Genome and exome sequencing are identifying new genotype– phenotype connections. When the laboratory finds a variant in a gene without a validated association to the patient’s phenotype, it is a GUS. This can occur when a gene has never been associated with any patient phenotype or when the gene has been associated with a different phenotype from that under consideration. Special care must be taken when applying the recommended guidelines to a GUS. In such situations, utilizing variant classification rules developed for recognized genotype– phenotype associations is not appropriate. For example, when looking across the exome or genome, a de novo observation is no longer strong evidence for pathogenicity given that all individuals are expected to have approximately one de novo variant in their exome or 100 in their genome. Likewise, thousands of variants across a genome could segregate with a significant logarithm of the odds (LOD) score. Furthermore, many deleterious variants that are clearly disruptive to a gene or its resultant protein (nonsense, frameshift, canonical ±1,2 splice site, exonlevel deletion) may be detected; however, this is insufficient evidence for a causative role in any given disease presentation. 基因組和外顯子組測序正在不斷鑒定出新的基因型-表型關聯。當實驗室發(fā)現某個基因的變異與病人的表型不具有已經驗證的關聯時，該變異是為GUS變異。當一個基因從未與任何病人表型相關聯時，或者當這個基因正被考慮與其它不同表型相關聯時，會出現這種情況。當推薦指南應用于GUS時必須特別注意。在這種情況下，使用用于識別基因型-表型關聯的變異分類規(guī)則是不合適的。例如，縱觀外顯子組或基因組，考慮到所有個體的外顯子組中預計約有1個新發(fā)變異或基因組中約有100個新發(fā)變異，新發(fā)變異的發(fā)現不再是致病性的強有力的證據。同樣地，整個基因組中成千上萬個變異可與顯著的LOD值共分離。此外，許多明顯破壞基因或其合成蛋白的有害變異（無義、移碼、典型±1,2剪接位點、外顯子水平缺失）可能被檢測出來，然而，在任何給出的疾病解釋中，這些變異都是不充分的致病證據。 Variants found in a GUS may be considered as candidates and reported as “variants in a gene of uncertain significance.” These variants, if reported, should always be classified as uncertain significance. Additional evidence would be required to support the gene’s association to disease before any variant in the gene itself can be considered pathogenic for that disease. For example, additional cases with matching rare phenotypes and deleterious variants in the same gene would enable the individual variants to be classified according to the recommendations presented here. GUS中發(fā)現的變異可作為候選，并報告為“意義不明確的基因變異”。如果報道這些變異，應該一直被分類為意義不明確。在任何基因變異可被考慮為疾病的致病原因之前，都需要附加的證據支持基因與疾病的關聯。例如，與罕見表型匹配和在相同基因上存在有害變異的其他病例將能夠根據此處提出的建議對個體變異進行分類。 6.2 在健康個體中評估變異或作為偶然發(fā)現 Caution must be exercised when using these guidelines to evaluate variants in healthy or asymptomatic individuals or to interpret incidental findings unrelated to the primary indication for testing. In these cases the likelihood of any identified variant being pathogenic may be far less than when performing disease-targeted testing. As such, the required evidence to call a variant pathogenic should be higher, and extra caution should be exercised. In addition, the predicted penetrance of pathogenic variants found in the absence of a phenotype or family history may be far less than predicted based on historical data from patients ascertained as having disease. 當評估健康或無癥狀個體的變異或者解釋與主要檢測指征無關的偶然發(fā)現時，必須謹慎使用這些指南。在這些情況下，任何識別出的變異為致病變異的可能性可能都會遠遠低于疾病靶向性檢測。正因為如此，判定這些變異為致病變異需要更高的證據，且需額外謹慎。此外，與基于具有確定疾病患者的歷史數據預測的外顯率相比，在無相關表型或家族史的個體中發(fā)現的致病變異預測的外顯率可能要低很多。 6.3 線粒體變異 The interpretation of mitochondrial variants other than well-established pathogenic variants is complex and remains challenging; several special considerations are addressed here. 除了明確的致病變異，線粒體變異的解讀是復雜且依舊充滿挑戰(zhàn)的，此處提出了一些特殊的考慮。 The nomenclature differs from standard nomenclature for nuclear genes, using gene name and m. numbering (e.g., m.8993T>C) and p. numbering, but not the standard c. numbering (see also Nomenclature). The current accepted reference sequence is the Revised Cambridge Reference Sequence of the Human Mitochondrial DNA: GenBank sequence NC_012920 gi:251831106. 線粒體變異的命名法與核基因的標準命名法不同，使用基因名和m.編號（例如，m.8993T > C）和p.編號，而不是標準的c.編號（見命名法）。目前公認的參考序列是人類線粒體DNA修訂版劍橋參考序列：基因庫序列NC_012920 gi:251831106。 Heteroplasmy or homoplasmy should be reported, along with an estimate of heteroplasmy of the variant if the test has been validated to determine heteroplasmy levels. Heteroplasmy percentages in different tissue types may vary from the sample tested; therefore, low heteroplasmic levels also must be interpreted in the context of the tissue tested, and they may be meaningful only in the affected tissue such as muscle. Over 275 mitochondrial DNA variants relating to disease have been recorded (http:///bin/view.pl/MITOMAP/ WebHome). MitoMap is considered the main source of information related to mitochondrial variants as well as haplotypes. Other resources, such as frequency information (http://www. mtdb.igp./), secondary structures, sequences, and alignment of mitochondrial transfer RNAs (http://mamittrna. /), mitochondrial haplogroups (http://www. /)and other information (http://www.mtdnacommunity. org/default.aspx), may prove useful in interpreting mitochondrial variants. 如果已通過檢測對異質性水平進行確定，應該對異質性或同質性，以及變異異質性的評估進行報道。不同組織類型的異質性百分比因檢測樣本的不同而有所改變，因此，低異質性水平也必須結合檢測組織進行解讀，且它們可能僅在受影響的組織如肌肉中才是有意義的。超過275個與疾病相關的線粒體DNA變異已被記錄（http:///bin/view.pl/MITOMAP/WebHome）。 MitoMap是線粒體變異及單倍型相關信息的主要來源。其它資源，例如頻率信息（http://www.mtdb.igp./）、二級結構、序列和線粒體轉運RNA的比對（http://mamittrna./）、線粒體單倍群（http://www./)和其他信息（http://www./default.aspx），可能在解讀線粒體變異時是有用的。 Given the difficulty in assessing mitochondrial variants, a separate evidence checklist has not been included. However, any evidence needs to be applied with additional caution. The genes in the mitochondrial genome encode for transfer RNA as well as for protein; therefore, evaluating amino acid changes is relevant only for genes encoding proteins. Similarly, because many mitochondrial variants are missense variants, evidence criteria for truncating variants likely will not be helpful. Because truncating variants do not fit the known variant spectrum in most mitochondrial genes, their significance may be uncertain. Although mitochondrial variants are typically maternally inherited, they can be sporadic, yet de novo variants are difficult to assess because of heteroplasmy that may be below an assay’s detection level or different between tissues. The level of heteroplasmy may contribute to the variable expression and reduced penetrance that occurs within families. Nevertheless, there remains a lack of correlation between the percentage of heteroplasmy and disease severity. Muscle, liver, or urine may be additional specimen types useful for clinical evaluation. Undetected heteroplasmy may also affect outcomes of case, case–control, and familial concordance studies. In addition, functional studies are not readily available, although evaluating muscle morphology may be helpful (i.e., the presence of ragged red fibers). Frequency data and published studies demonstrating causality may often be the only assessable criteria on the checklist. An additional tool for mitochondrial diseases may be haplogroup analysis, but this may not represent a routine method that clinical laboratories have used, and the clinical correlation is not easy to interpret. 鑒于線粒體變異評估的難度，本指南并未包括單獨的證據清單。然而，任何證據的應用均需要格外謹慎。線粒體基因組中的基因編碼轉運RNA和蛋白質，因此，評估氨基酸的變化僅與蛋白質的編碼基因有關。同樣地，因為很多線粒體變異是錯義突變，截短突變的證據標準可能并不適用。由于截短突變并不符合多數線粒體基因的已知變異譜，其意義可能是不確定的。盡管線粒體變異是典型的母系遺傳，它們也可以散發(fā)的。然而由于異質性可能低于試驗檢測水平或組織間的差異，新發(fā)變異是難以評估的。異質性水平可能是家族內表達差異和外顯率降低的原因。盡管如此，異質性百分比和疾病嚴重程度之間仍缺乏相關性。肌肉、肝臟或尿液可以作為附加樣本類型用于臨床評估。未檢測到的異質性也可能影響病例、病例對照和家系一致性研究的結果。此外，沒有現成的功能研究方法，盡管評估肌肉形態(tài)可能會有所幫助（即破碎紅纖維的存在）。頻率數據和已發(fā)表的證明因果關系的研究往往是檢測報告上唯一的評估標準。單倍群分析可以作為線粒體疾病的另一個工具，但可能不是臨床實驗室已使用的常規(guī)方法，而且臨床相關性難以解釋。 Consideration should be given to testing nuclear genes associated with mitochondrial disorders because variants in nuclear genes could be causative of oxidative disorders or modulating the mitochondrial variants. 因為核基因變異也可能是氧化疾病的致病原因或起著調節(jié)線粒體變異的作用，因此應考慮檢測與線粒體疾病相關的核基因。 6.4 藥物基因組學 Establishing the effects of variants in genes involved with drug metabolism is challenging, in part because a phenotype is only apparent upon exposure to a drug. Still, variants in genes related to drug efficacy and risk for adverse events have been described and are increasingly used in clinical care. Gene summaries and clinically relevant variants can be found in the Pharmacogenomics Knowledge Base (http://www.pharmgkb. org/). Alleles and nomenclature for the cytochrome P450 gene family is available at http://www.cypalleles./.Although the interpretation of PGx variants is beyond the scope of this document, we include a discussion of the challenges and distinctions associated with the interpretation and reporting of PGx results. 證實基因變異在藥物代謝中的作用是充滿挑戰(zhàn)的，部分原因在于表型只有在服藥后才明顯表現出來。不過，與藥物療效和副作用風險相關的基因變異已被描述且越來越多地應用于臨床治療中?；蛄斜砗团R床相關變異可以在藥物基因組學知識庫中找到（http://www./）。細胞色素P450基因家族的等位基因和命名可查詢http://www.cypalleles./。盡管PGx變異的解讀超出了本文的范圍，我們對PGx結果解讀和報告相關的挑戰(zhàn)和區(qū)別進行了討論。 The traditional nomenclature of PGx alleles uses star (*) alleles, which often represent haplotypes, or a combination of variants on the same allele. Traditional nucleotide numbering using outdated reference sequences is still being applied. Converting traditional nomenclature to standardized nomenclature using current reference sequences is an arduous task, but it is necessary for informatics applications with next-generation sequencing. 傳統的PGx等位基因的命名使用星號（*）標記等位基因，通常代表單倍型或相同等位基因變異的組合。采用舊的參考序列的傳統核苷酸編號仍被應用。采用最新參考序列將傳統命名轉換為標準命名是一項艱巨的任務，但這對于下一代測序的信息學應用是必需的。 Many types of variants have been identified in PGx genes, such as truncating, missense, deletions, duplications (of functional as well as nonfunctional alleles), and gene conversions, resulting in functional, partially functional (decreased or reduced function), and nonfunctional (null) alleles. Interpreting sequence variants often requires determining haplotype from a combination of variants detected. Haplotypes are typically presumed based on population frequencies and known variant associations rather than testing directly for chromosomal phase (molecular haplotyping). PGx基因上已經識別了許多種變異類型，如截短、錯義、缺失、重復（功能及非功能等位基因）以及基因轉換，導致功能性的、部分功能性的（減少或降低的功能）和非功能性的（無效的）等位基因。解讀序列變異常常需要從檢測到的變異的組合來確定單倍型。單倍型通常是基于人群頻率和已知變異關聯分析來假定的，而不是染色體片段（分子單倍型）的直接檢測。 In addition, for many PGx genes (particularly variants in genes coding for enzymes), the overall phenotype is derived from a diplotype, which is the combination of variants or haplotypes on both alleles. Because PGx variants do not directly cause disease, using terms related to metabolism (rapid, intermediate, poor); efficacy (resistant, responsive, sensitive); or “risk,” rather than pathogenic, may be more appropriate. Further nomenclature and interpretation guidelines are needed to establish consistency in this field. 此外，對于許多PGx基因（特別是編碼酶的基因上的變異），整體的表型取決于二倍型，即兩個等位基因上的變異或單倍型的組合。由于PGx變異并不直接引起疾病，使用代謝（快速、中等、弱）、功效（耐藥、響應、敏感）或“風險”相關的術語，可能比使用“致病”更合適。這一領域的術語和解讀指南需要進一步建立一致性。 6.5 常見復雜疾病 Unlike Mendelian diseases, the identification of common, complex disease genes, such as those contributing to type 2 diabetes, coronary artery disease, and hypertension, has largely relied on population-based approaches (e.g., genome-wide association studies) rather than family-based studies. Currently, numerous genome-wide association study reports have resulted in the cataloguing of over 1,200 risk alleles for common, complex diseases and traits. Most of these variants are in nongenic regions, however, and additional studies are required to determine whether any of the variants are directly causal through effects on regulatory elements, for example, or are in linkage disequilibrium with causal variants. 與孟德爾疾病以家系為基礎的研究不同，常見復雜疾?。ㄈ?型糖尿病、冠心病和高血壓）相關基因的鑒定，在很大程度上依賴于以人群為基礎的方法（如全基因組關聯分析）。目前，大量的全基因組關聯研究報告已對1200余種常見復雜疾病和性狀的風險等位基因進行了編目。這些變異大多數位于基因間區(qū)，但仍需要進一步的研究來確定這些變異是否為致病的直接原因，例如，是否通過影響調控因子而致病，又或者與致病變異處于連鎖不平衡狀態(tài)。 Common, complex risk alleles typically confer low relative risk and are meager in their predictive power. To date, the utility of common, complex risk allele testing for patient care has been unclear, and models to combine multiple markers into a cumulative risk score often are flawed and are usually no better than traditional risk factors such as family history, demographics, and nongenetic clinical phenotypes. Moreover, in almost all of the common diseases the risk alleles can explain only up to 10% of the variance in the population, even when the disease has high heritability. Given the complexity of issues, this recommendation does not address the interpretation and reporting of complex trait alleles. We recognize, however, that some of these alleles are identified during the course of sequencing Mendelian genes, and therefore guidance on how to report such alleles when found incidentally is needed. The terms “pathogenic” and “l(fā)ikely pathogenic” are not appropriate in this context, even when the association is statistically valid. Until better guidance is developed, an interim solution is to report these variants as “risk alleles” or under a separate “other reportable” category in the diagnostic report. The evidence for the risk, as identified in the case–control/ genome-wide association studies, can be expressed by modifying the terms, such as “established risk allele,” “l(fā)ikely risk allele,” or “uncertain risk allele,” if desired. 常見復雜風險等位基因通常被賦予較低的相對風險，且預測能力薄弱。迄今為止，常見復雜風險等位基因檢測對于患者治療的效用尚不清楚，將多個指標組合起來進行累計風險評估的模型往往是有缺陷的，通常并不優(yōu)于家族史、人口統計資料和非遺傳性臨床表型等傳統風險因素。另外，在幾乎所有的常見疾病中，風險等位基因僅可解釋至多10%的群體變異，即使當疾病有高度遺傳性時也是如此?？紤]到問題的復雜性，本建議并不涉及復雜性狀的等位基因的解讀和報告。然而我們認識到，在對孟德爾基因進行測序時可以識別這些等位基因中的一部分，因此需要有偶然發(fā)現這些等位基因時如何進行報告的指南。這種情況下，術語“致病的”和“可能致病的”并不適用，即使關聯在統計學上是有效的。在建立更好的指南之前，臨時的解決辦法是將這些變異報告為“風險等位基因”，或在診斷報告中設立一個單獨的“其它報告”類別。同病例對照/全基因組關聯研究鑒定一樣，風險證據可以通過修改術語來表達，如“確定風險等位基因”，“可能風險等位基因”或“不確定風險等位基因”。 6.6 體細胞變異 The description of somatic variants, primarily those observed in cancer cells, includes complexities not encountered with constitutional variants, because the allele ratios are highly variable and tumor heterogeneity can cause sampling differences. Interpretation helps select therapy and predicts treatment response or the prognosis of overall survival or tumor progression–free survival, further complicating variant classification. For the interpretation of negative results, understanding the limit of detection of the sequencing assay (at what allele frequency the variant can be detected by the assay) is important and requires specific knowledge of the tumor content of the sample. Variant classification categories are also different, with somatic variants compared with germ-line variants, with terms such as “responsive,” “ resistant,” “driver,” and “passenger” often used. Whether a variant is truly somatic is confirmed by sequence analysis of the patient’s germ-line DNA. A different set of interpretation guidelines is needed for somatic variants, with tumor-specific databases used for reference, in addition to databases used for constitutional findings. To address this, a workgroup has recently been formed by the AMP. 在描述主要在癌細胞中觀察到的那些體細胞變異時，具有原發(fā)性變異所沒有的復雜性，因為其等位基因比值是高度可變的，且腫瘤異質性也可導致樣本的差異。變異的解讀有助于選擇治療方案、預測治療效果或整體的生存預后或腫瘤的無進展生存期預后，使得變異的分類更加復雜化。對于陰性結果的解讀，了解測序分析的檢測局限性（變異可在何種等位基因頻率時被檢測到）至關重要，且需要了解樣本中腫瘤含量的特定信息。與胚系變異相比，體細胞變異的分類類別也不同，經常使用“敏感”、“拮抗”、“驅動”和“伴隨”等術語。一個變異是否是體細胞變異需要通過患者胚系DNA的序列分析來證實。除了用于原發(fā)性突變的數據庫以外，體細胞變異還需要一組不同的解讀指南，以腫瘤特異性數據庫作為參考。為了解決這個問題，AMP最近已經成立了一個工作組。 7. 醫(yī)療工作者如何使用這些指南和建議 The primary purpose of clinical laboratory testing is to support medical decision making. In the clinic, genetic testing is generally used to identify or confirm the cause of disease and to help the health-care provider make individualized treatment decisions including the choice of medication. Given the complexity of genetic testing, results are best realized when the referring health-care provider and the clinical laboratory work collaboratively in the testing process. 臨床實驗室檢測的主要目的是為醫(yī)療決策提供依據。在臨床上，基因檢測一般用于識別或確認疾病的原因，并幫助醫(yī)療工作者做出個性化的治療決策，包括用藥的選擇。鑒于基因檢測的復雜性，檢測過程中需相關醫(yī)療工作者和臨床實驗室協作才能得到最佳結果。 When a health-care provider orders genetic testing, the patient’s clinical information is integral to the laboratory’s analysis. As health-care providers increasingly utilize genomic (exome or genome) sequencing, the need for detailed clinical information to aid in interpretation assumes increasing importance. For example, when a laboratory finds a rare or novel variant in a genomic sequencing sample, the director cannot assume it is relevant to a patient just because it is rare, novel, or de novo. The laboratory must evaluate the variant and the gene in the context of the patient’s and family’s history, physical examinations, and previous laboratory tests to distinguish between variants that cause the patient’s disorder and those that are incidental (secondary) findings or benign. Indeed, accurate and complete clinical information is so essential for the interpretation of genome-level DNA sequence findings that the laboratory can reasonably refuse to proceed with the testing if such information is not provided with the test sample. 當醫(yī)療工作者提出基因檢測需求時，患者的臨床信息對實驗室分析是不可或缺的。由于醫(yī)療工作者越來越多地利用基因組（外顯子組或基因組）測序，對有助于解讀的詳細的臨床信息的需要與日俱增。例如，當一個實驗室在基因組測序樣品中發(fā)現一個罕見或新發(fā)的變異時，實驗室負責人不能僅因為該變異是罕見的、新奇的或者新發(fā)的來假定它與患者有關。該實驗室必須通過患者的背景、家族史、體格檢查和前期實驗室檢查對變異和基因進行評估，進而區(qū)分致病變異和其他偶然（次要）發(fā)現或良性變異。事實上，準確和完整的臨床信息對于基因組水平DNA序列檢測結果的解讀是不可或缺的，對不提供測試樣品此類信息的，實驗室可以合理拒絕繼續(xù)進行檢測。 For tests that cover a broad range of phenotypes (large panels, exome and genome sequencing) the laboratory may find candidate causative variants. Further follow-up with the health-care provider and patient may uncover additional evidence to support a variant. These additional phenotypes may be subclinical, requiring additional clinical evaluation to detect (e.g., temporal bone abnormalities detected by computed tomography in a hearing-impaired patient with an uncertain variant in SLC26A4, the gene associated with Pendred syndrome). In addition, testing other family members to establish when a variant is de novo, when a variant cosegregates with disease in the family, and when a variant is in trans with a pathogenic variant in the same recessive disease-causing gene is valuable. Filtering out or discounting the vast majority of variants for dominant diseases when they can be observed in healthy relatives is possible, making the interpretation much more efficient and conclusive. To this end, it is strongly recommended that every effort be made to include parental samples along with that of the proband, so-called “trio” testing (mother, father, affected child), in the setting of exome and genome sequencing, particularly for suspected recessive or de novo causes. Obviously this will be easier to achieve for pediatric patients than for affected adults. In the absence of one or both parents, the inclusion of affected and unaffected siblings can be of value. 對于覆蓋了廣泛表型的檢測（大的panels、外顯子組和基因組測序），實驗室可能會發(fā)現候選致病變異。對醫(yī)療工作者和患者后續(xù)的隨訪可能會發(fā)現更多的證據來支持某一變異。這些額外的表型可能是亞臨床癥狀，需要附加的臨床評估來檢測（例如，一個在SLC26A4基因（與Pendred綜合征相關的基因）上有不確定變異的聽力受損患者，CT檢查提示顳骨異常）。此外，當一個變異是新發(fā)變異，或者當一個變異在家系中與表型共分離，或者在相同隱性致病基因中，一個變異與一個致病變異處于反式位置時，有必要在其他家系成員中進行驗證。當顯性疾病可在健康親屬中觀察到時，對其絕大部分變異過濾或刪減是可行的，使得解讀更加有效和準確。為此，我們強烈建議在開展外顯子組或基因組測序時，盡力做到“核心家系”檢測（即母親、父親、患病兒童），尤其是對懷疑有隱性遺傳或新發(fā)變異的患者。與成人患者相比，這顯然在兒科患者中更易實現。在沒有父母一方或雙方時，納入患病和正常的兄弟姐妹也是有意義的。 Many genetic variants can result in a range of phenotypic expression (variable expressivity), and the chance of disease developing may not be 100% (reduced penetrance), further underscoring the importance of providing comprehensive clinical data to the clinical laboratory to aid in variant interpretation. Ideally, it is recommended that clinical data be deposited into, and shared via, centralized repositories as allowable by Health Insurance Portability and Accountability Act and institutional review board regulations. Importantly, referring health-care providers can further assist clinical laboratories by recruiting DNA from family members in scenarios where their participation will be required to interpret results, (e.g., when evaluating cosegregation with disease using affected family members, genotyping parents to assess for de novo occurrence and determining the phase of variants in recessive disorders using first-degree relatives). 許多遺傳變異會導致一系列表型 (表達多樣性)，疾病發(fā)生的機率也可能不是100% (外顯率降低)，這些均進一步強調了向臨床實驗室提供全面的臨床數據來幫助解讀變異的重要性。在理想的情況下，建議臨床數據應依據醫(yī)療保險可攜性和責任法案（HIPAA）和機構審查委員會條例的許可存入并通過集中存儲庫共享。重要的是，當家庭成員的參與對于解讀結果是必需的時候，相關醫(yī)療工作者可以進一步幫助臨床實驗室收集家庭成員的DNA（例如，當評估家系患者與疾病共分離時，父母的基因型分析可用來評估新發(fā)變異的發(fā)生，一級親屬可用來確定隱性遺傳疾病的變異階段)。 A key issue for health-care providers is how to use the evidence provided by genetic testing in medical management decisions. Variant analysis is, at present, imperfect, and the variant category reported does not imply 100% certainty. In general, a variant classified as pathogenic using the proposed classification scheme has met criteria informed by empirical data such that a health-care provider can use the molecular testing information in clinical decision making. Efforts should be made to avoid using this as the sole evidence of Mendelian disease; it should be used in conjunction with other clinical information when possible. Typically, a variant classified as likely pathogenic has sufficient evidence that a health-care provider can use the molecular testing information in clinical decision making when combined with other evidence of the disease in question. For example, in the prenatal setting an ultrasound may show a key confirmatory finding; in postnatal cases, other data such as enzyme assays, physical findings, or imaging studies may conclusively support decision making. However, it is recommended that all possible follow-up testing, as described above, be pursued to generate additional evidence related to a likely pathogenic variant because this may permit the variant to be reclassified as pathogenic. A variant of uncertain significance should not be used in clinical decision making. Efforts to resolve the classification of the variant as pathogenic or benign should be undertaken. While this effort to reclassify the variant is underway, additional monitoring of the patient for the disorder in question may be prudent. A variant considered likely benign has sufficient evidence that a health-care provider can conclude that it is not the cause of the patient’s disorder when combined with other information, for example, if the variant does not segregate in an affected family member and complex inheritance patterns are unlikely. A variant considered benign has sufficient evidence that a health-care provider can conclude that it is not the cause of the patient’s disorder. 醫(yī)療工作者的一個關鍵問題是如何使用基因檢測提供的證據進行醫(yī)療管理決策。目前變異分析是不完善的，報道的變異分類也并不是100%確定的。一般來說，根據推薦的分類方法劃分為致病性的變異符合經驗數據形成的標準，所以醫(yī)療工作者可以在臨床決策時采用分子檢測信息。應盡力避免使用此類信息作為孟德爾疾病的唯一證據，在可能的情況下應與其他臨床資料相結合。通常情況下，一個有足夠的證據被劃分為可能致病的變異，當與可疑疾病的其它證據相結合時，醫(yī)療工作者可以使用分子檢測信息進行臨床決策的制定。例如，產前超聲可能顯示關鍵的證實結果，對于產后的病例，其他數據如酶檢測、體格檢查，或影像學研究可能最終支持臨床決策。然而，推薦進行所有如上所述的可能的后續(xù)檢測，追蹤可能致病變異相關的附加證據的產生，因為這有可能將可能致病變異重新歸類為致病變異。意義不明確的變異不宜應用于臨床決策。應努力將變異分類為致病性或良性。雖然變異的重新分類正在進行，對可疑致病的患者進行額外的監(jiān)測應審慎。一個有足夠證據被考慮為可能良性的變異，醫(yī)療工作者可以結合其它信息，推斷此變異不是該患者致病的原因，例如，變異在患病的家族成員中不分離且可排除復雜遺傳模式。一個有足夠證據被考慮為良性的變異，醫(yī)療工作者可以得出此變異不是該患者致病原因的結論。 How the genetic testing evidence is used is also dependent on the clinical context and indication for testing. In a prenatal diagnostic case where a family is considering irrevocable decisions such as fetal treatment or pregnancy termination, the weight of evidence from the report and other sources such as fetal ultrasound needs to be considered before action is taken. When a genetic test result is the only evidence in a prenatal setting, variants considered likely pathogenic must be explained carefully to families. It is therefore critical for referring healthcare providers to communicate with the clinical laboratory to gain an understanding of how variants are classified to assist in patient counseling and management. 基因檢測的證據如何使用也依賴于臨床背景和檢測指示。在產前診斷的病例中，如果該家庭正在考慮不可逆的宮內治療或終止妊娠等決定時，需要在采取行動之前慎重考慮報告中證據的份量和胎兒超聲等其它信息。當基因檢測結果是產前檢查的唯一證據時，必須向相關家庭認真解釋可能致病的變異。所以相關醫(yī)療工作者應與臨床實驗室溝通，以了解變異是如何分類的，進而協助病人咨詢和健康管理，這是至關重要的。 8 參考文獻（略） 圖1   表1 人群數據庫，疾病特異性數據庫和序列數據庫  人群數據庫 Exome Aggregation Consortium http://exac./ 本數據庫中的變異信息是通過對61486個獨立個體進行全外顯子測序獲得。同時也是多種特殊疾病和群體遺傳學研究中的一部分。庫中不包括兒科疾病患者及其相關人群。 Exome Variant Server http://evs.gs./EVS本數據庫中的變異信息是通過對幾個歐洲和非洲裔大規(guī)模人群的全外顯子測序獲得。當缺乏變異信息時，庫中以覆蓋數據替代默認該數據已覆蓋。 1000 Genomes Project http://browser.本數據庫中的變異信息是通過對26個種群進行低覆蓋度的全基因組測序和高覆蓋度的靶序列測序獲得。本庫所提供的信息比Exome Variant Server更具多樣性，但也包含有低質量的數據，有些群體中還包含有關聯性個體在內。 dbSNP http://www.ncbi.nlm./snp本數據庫由多種來源獲得的短片段遺傳變異（通常≤50bp）信息組成。庫中可能缺乏溯源性研究的細節(jié)，也可能包含致病性突變在內。 dbVar http://www.ncbi.nlm./dbvar本數據庫由多種來源獲得的基因結構變異（通常>50bp）信息組成。 疾病數據庫 ClinVar http://www.ncbi.nlm./clinvar對變異與表型和臨床表型之間的關聯進行確定的數據庫。 OMIM http://www.本數據庫所含人類基因和相關遺傳背景，同時具有疾病相關基因遺傳變異的代表性樣本收錄與與遺傳疾病典型相關的樣本變異信息。 Human Gene Mutation Database http://www.本數據庫中的變異注釋有文獻發(fā)表。庫中大部分內容需付費訂閱。 其他特殊數據庫 Human Genome Variation Society http://www./dblist/dblist.html本數據庫由人類基因組變異協會（HGVS）開發(fā)，提供數千種專門針對人群中的特殊變異進行的注釋。數據庫很大一部分是基于Leiden Open Variation Database system建立。 Leiden Open Variation Database http://www. DECIPHER http://decipher.使用Ensemble基因組瀏覽器，將基因芯片數據和臨床表型進行關聯，便于臨床醫(yī)生和研究人員使用的細胞分子遺傳學數據庫。 序列數據庫 NCBI Genome http://www.ncbi.nlm./genome 人類全基因組參考序列的來源 RefSeqGene http://www.ncbi.nlm./refseq/rsg醫(yī)學相關基因參考序列 Locus Reference Genomic (LRG) http://www. MitoMap http://www./MITOMAP/HumanMitoSeq對“劍橋版-人類線粒體DNA參考序列”進行修訂后形成 表2 生物信息分析工具  分類名稱網站依據 錯義預測Consurf http://consurftest. 進化保守性 FATHMMhttp://fathmm.進化保守性 MutationAsses http:// 進化保守性 PANTHER http://www./tools/csnpScoreForm.jsp進化保守性 PhD-SNPhttp://snps./phd-snp/phd-snp.html 進化保守性 SIFThttp://sift.進化保守性 SNP&GOhttp://snps-and-go.biocomp./snps-and-go蛋白結構/功能 Align GVGDhttp://agvgd./agvgd_input.php蛋白結構/功能和進化保守性 MAPPhttp://mendel./SidowLab/downloads/MAPP/index.html蛋白結構/功能和進化保守性 MutationTasterhttp://www.蛋白結構/功能和進化保守性 MutPredhttp://mutpred.蛋白結構/功能和進化保守性 PolyPhen-2http://genetics.bwh./pph2蛋白結構/功能和進化保守性 PROVEANhttp://provean./index.php變異序列和蛋白序列同源性之間的相似性比對和測量 nsSNPAnalyzerhttp://snpanalyzer.多序列比對和蛋白結構分析 Condelhttp://bg./fannsdb/綜合SIFT、PolyPhen-2和MutationAssessor進行綜合預測 CADDhttp://cadd.gs.對于來自模擬變異的等位基因進行不同的注釋 剪切位點預測GeneSplicerhttp://www.cbcb./software/GeneSplicer/gene_spl.shtml 馬爾可夫模型 Human Splicing Finderhttp://www./HSF/位置依賴的邏輯 MaxEntScanhttp://genes./burgelab/maxent/Xmaxentscan_scoreseq.html最大熵原則 NetGene2http://www.cbs./services/NetGene2神經網絡 NNSplicehttp://www./seq_tools/splice.html神經網絡 FSPLICEhttp://www./berry.phtml?topic=fsplice&group=programs&subgroup=gfind基于權重矩陣模型進行種特異性預測 核酸保守性預測GERPhttp://mendel./sidowlab/downloads/gerp/index.html基因組進化速率分析 PhastConshttp://compgen.bscb./phast/保守打分及鑒定保守元件 PhyloPhttp://compgen.bscb./phast/ http://compgen.bscb./phast/help-pages/phyloP.txt比對和分子進化樹：在家系特異或者所有分支中，計算保守或者加速的P值 表3 致病變異分級標準  致病性證據 分類 非常強 PVS1：當一個疾病的致病機制為功能喪失（LOF）時，無功能變異（無義突變、移碼突變、經典±1或2的剪接突變、起始密碼子變異、單個或多個外顯子缺失）注意事項：1. 該基因的LOF是否是導致該疾病的明確致病機制（如GFAP、MYH7）2. 3'端末端的功能缺失變異需謹慎解讀3.需注意外顯子選擇性缺失是否影響到蛋白質的完整性4.考慮一個基因存在多種轉錄本的情況 強 PS1：與先前已確定為致病性的變異有相同的氨基酸改變。例如：同一密碼子，G>C或G> T改變均可導致纈氨酸→亮氨酸的改變。注意剪切影響的改變。 PS2：患者的新發(fā)變異，且無家族史。（經雙親驗證） 注：僅僅確認父母還是不足夠的，還需注意捐卵、代孕、胚胎移植的差錯等情況。 PS3：體內、體外功能實驗已明確會導致基因功能受損的變異。 注：功能實驗需要驗證是有效的，且具有重復性與穩(wěn)定性。 PS4：變異出現在患病群體中的頻率顯著高于對照群體。注1：可選擇使用相對風險值或者OR值來評估，建議位點OR大于5.0且置信區(qū)間不包括1.0的可列入此項。（詳細見指南正文）。注2：極罕見的變異在病例對照研究可能無統計學意義，在多個具有相同表型的患者中優(yōu)先觀察到該變異且在對照中未觀察到可作為中等水平證據。 中等 PM1：位于熱點突變區(qū)域，和/或位于已知無良性變異的關鍵功能域（如酶的活性位點）。 PM2：ESP數據庫、千人數據庫、EAC數據庫中正常對照人群中未發(fā)現的變異（或隱性遺傳病中極低頻位點）（表6） 注意事項： 高通量測序得到的插入/缺失人群數據質量較差 PM3：在隱性遺傳病中，在反式位置上檢測到致病變異。 注意：這種情況必須通過患者父母或后代驗證。 PM4：非重復區(qū)框內插入/缺失或終止密碼子喪失導致的蛋白質長度變化。 PM5：新的錯義突變到氨基酸變化，此變異之前未曾報道，但是在同一位點，導致另外一種氨基酸的變異已經確認是致病性的，如：現在觀察到的是Arg156Cys，而Arg156His是已知致病的。注意剪切影響的改變。 PM6： 無父母樣本驗證的新發(fā)變異。 支持證據 PP1：突變與疾病成家系共分離（在家系多個患者中檢測到此變異） 注：如果有更多的證據，可作為更強的證據。 PP2： 對某個基因來說，如果這個基因的錯義變異是造成某種疾病的原因，并且這個基因中良性變異所占的比例很小，在這樣的基因中所發(fā)現的新的錯義變異。 PP3：多種統計方法預測出該變異會對基因或基因產物造成有害的影響，包括保守性預測、進化預測、剪接位點影響等。注意事項：由于做預測時許多生物信息算法使用相同或非常相似的輸入，每個算法不應該算作一個獨立的標準。PP3在一個任何變異的評估中只能使用一次。 PP4：變異攜帶者的表型或家族史高度符合某種單基因遺傳疾病。 PP5：有可靠信譽來源的報告認為該變異為致病的，但證據尚不足以支持進行實驗室獨立評估。 表4 良性變異分類標準  良性影響的證據 分類 獨立證據 BA1：ESP數據庫、千人數據庫、EAC數據庫中等位基因頻率>5%的變異 強BS1：等位基因頻率大于疾病發(fā)病率 BS2：對于早期完全外顯的疾病，在健康成年人中發(fā)現該變異（隱性遺傳病發(fā)現純合、顯性遺傳病發(fā)現雜合，或者X連鎖半合子）。 BS3： 在體內外實驗中確認對蛋白質功能和剪接沒有影響的變異。 BS4：在一個家系成員中缺乏共分離 注意事項：這部分需要考慮復雜疾病和外顯率問題 支持證據BP1：已知一個疾病的致病原因是由于某基因的截短變異，在此基因中所發(fā)現的錯義變異。 BP2：在顯性遺傳病中又發(fā)現了另一條染色體上同一基因的一個已知致病變異，或者是任意遺傳模式遺傳病中又發(fā)現了同一條染色體上同一基因的一個已知致病變異。 BP3：功能未知重復區(qū)域內的缺失/插入，同時沒有導致基因編碼框改變。 BP4：種統計方法預測出該變異會對基因或基因產物無影響，包括保守性預測、進化預測、剪接位點影響等。注意事項：由于做預測時許多生物信息算法使用相同或非常相似的輸入，每個算法不應該算作一個獨立的標準。BP4在一個任何變異的評估中只能使用一次。 BP5：發(fā)現的變異在疾病中具有可替代的分子基礎。 BP6：有可靠信譽來源的報告認為該變異為良性的，但證據尚不足以支持進行實驗室獨立評估。 BP7：同義變異且預測不影響剪接。 表5 遺傳變異分類聯合標準規(guī)則  致病 (i) 1個非常強（PVS1）和 (a) ≥1個強（PS1-PS4）或 (b) ≥2個中等（PM1-PM6）或 (c) 1個中等（PM1-PM6）和1個支持（PP1-PP5）或 (d) ≥2個支持（PP1-PP5） (ii) ≥2 個強（PS1-PS4）或 (iii) 1個強（PS1）和 (a) ≥3個中等（PM1-PM6）或 (b) 2個中等（PM1-PM6）和≥2個支持（PP1-PP5）或 (c) 1個中等（PM1-PM6）和≥4個支持（PP1-PP5） 可能致病 (i) 1個非常強（PVS1）和1個中等（PM1-PM6）或 (ii) 1個強（PS1-PS4）和1-2個中等（PM1-PM6）或 (iii) 1個強（PS1-PS4）和≥2個支持（PP1-PP5）或 (iv) ≥3個中等（PM1-PM6）或 (v) 2個中等（PM1-PM6）和≥2個支持（PP1-PP5）或 (vi) 1個中等（PM1-PM6）和≥4個支持（PP1-PP5） 良性 (i) 1個獨立（BA1）或 (ii) ≥2個強（BS1-BS4） 可能良性 (i) 1個強（BS1-BS4）和1個支持（BP1-BP7）或 (ii) ≥2個支持（BP1-BP7） 意義不明 (i) 不滿足上述標準或 (ii) 良性和致病標準相互矛盾 表6 評估人群中變異頻率來策劃變異分類 
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