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start [2017/06/22 17:12]
bioadmin [6.5 常见复杂疾病]
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bioadmin [6.4 药物基因组学]
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 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 Unpublished case data may also be obtained through public resources (e.g., ClinVar or locus specific databases) and from a laboratory’s own database.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. 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.
  
-本指南提供了两套标准:​ 一是用于对致病或可能致病的变异进行分类(表3),另一是用于对良性或可能良性的变异进行分类(表4)。致病变异标准可分为非常强(very strong,PVS1),强(strong,PS1~4);​ 中等(moderate,PM1~6),或辅助证据(supporting,PP1~5)。良性变异证据可分为独立(stand-alone,BA1),强(strong,BS1~4),或辅助证据(BP1~6)。其中,数字只是作为有助于参考的分类标注,不具有任何意义。每个类别中的数字不表示分类的任何差异,仅用来标记以帮助指代不同的规则。对于一个给定的变异,用户基于观察到的证据来选择标准。根据表5的评分规则把标准组合起来进而从5级系统中选择一个分类。这些规则适用于变异上的所有可用数据,无论是基于调查现有案例获得的数据,还是来源于先前公布的数据。未发表的数据也可以通过公共数据库(如ClinVar或位点特异数据库)和实验室自有数据库获得。为了对变异分类具有较好灵活性,基于收集的证据和专业判断,可以把某些依据用到不同的证据水平上去。例如,如果一个变异多次和已知致病性变异处于反式位置(位于另一染色体上),PM3可以上调到强(进一步指导见PM3 BP2顺/​反式检测)。相反,在数据并不像描述的那么强的情况下,可以改判变异到一个较低的水平(见表3注2 PS4)。如果一个变异不符合分类标准(致病的或良性的),或良性和致病的证据是相互矛盾的,则默认该变异为“意义不确定的”。程度判断评价标准如表6所示。请注意,当考虑所有依据以解读变异证据强度的差异时,须专家介入进行判断。+本指南提供了两套标准:​ 一是用于对致病或可能致病的变异进行分类(表3),另一是用于对良性或可能良性的变异进行分类(表4)。致病变异标准可分为非常强(very strong,PVS1),强(strong,PS1~4);​ 中等(moderate,PM1~6),或辅助证据(supporting,PP1~5)。良性变异证据可分为独立(stand-alone,BA1),强(strong,BS1~4),或辅助证据(BP1~6)。其中,数字只是作为有助于参考的分类标注,不具有任何意义。每个类别中的数字不表示分类的任何差异,仅用来标记以帮助指代不同的规则。对于一个给定的变异,用户基于观察到的证据来选择标准。根据表5的评分规则把标准组合起来进而从5级系统中选择一个分类。这些规则适用于变异上的所有可用数据,无论是基于调查现有案例获得的数据,还是来源于先前公布的数据。未发表的数据也可以通过公共数据库(如ClinVar或位点特异数据库)和实验室自有数据库获得。为了对变异分类具有较好灵活性,基于收集的证据和专业判断,可以把某些依据用到不同的证据水平上去。例如,如果一个变异多次和已知致病性变异处于反式位置(位于另一染色体上),PM3可以上调到强(进一步指导见PM3 BP2顺/​反式检测)。相反,在数据并不像描述的那么强的情况下,可以改判变异到一个较低的水平(见表3注2 PS4)。如果一个变异不符合分类标准(致病的或良性的),或良性和致病的证据是相互矛盾的,则默认该变异为“意义不确定的”。程度判断评价标准如图1所示。请注意,当考虑所有依据以解读变异证据强度的差异时,须专家介入进行判断。
  
 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.  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. 
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 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. 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.
  
-通过搜索公共人群数据库(如千人基因组数据库,NHLBI外显子测序数据库,EXAC数据库;​ 表1),并利用已发表文献中相同种族的对照数据进行基因变异频率分析(译者注:​ 此条款在指南更新时会有修改),通过分析变异基因在对照人群或普通人群中的携带频率,有助于评估该变异的潜在致病性。NHLBI外显子测序数据库来源于白种人和非裔美国人群,根据其数据覆盖量能够识别是否存在基因变异。尽管千人基因组数据库缺乏评估基因变异能力,但它囊括了更多的种族人群,因此其数据具有更广泛代表性的。EXAC数据库近期发布了一组来源于不同人群的6万多个外显子组的等位基因频率数据,包括了大约三分之二的NHLBI外显子测序数据。一般情况下,某一等位基因在对照人群的频率大于疾病预期人群(表7)时,可认为是罕见孟德尔疾病良性变异的强证据(BS1),如果频率超过5%时,则可认为是良性变异的独立证据(BA1)。此外,如果疾病发生在早期,且变异在健康成人中以隐性(纯合子)、显性(杂合子)或X-连锁(半合子)的状态存在,那么这就是良性变异的强证据(BS2)。如果数据库中未能检出变异的存在,应该确认建立该数据库采用的测序读长深度是否足以检测出该位点上的变异。如果在一个大样本的普通人群或队列数据的对照人群(>​1000人)中变异不存在(或隐性遗传的突变频率是低频),并且携带此变异的患者与对照人群为同一种族,那么可以认为该变异是致病性的中等证据(PM2)。许多良性变异是“个体化的”(即个人或家系独有的),因此即使在相同种族的人群中缺乏也不能作为致病性的充足甚至强的证据。+通过搜索公共人群数据库(如千人基因组数据库,NHLBI外显子测序数据库,EXAC数据库;​ 表1),并利用已发表文献中相同种族的对照数据进行基因变异频率分析(译者注:​ 此条款在指南更新时会有修改),通过分析变异基因在对照人群或普通人群中的携带频率,有助于评估该变异的潜在致病性。NHLBI外显子测序数据库来源于白种人和非裔美国人群,根据其数据覆盖量能够识别是否存在基因变异。尽管千人基因组数据库缺乏评估基因变异能力,但它囊括了更多的种族人群,因此其数据具有更广泛代表性的。EXAC数据库近期发布了一组来源于不同人群的6万多个外显子组的等位基因频率数据,包括了大约三分之二的NHLBI外显子测序数据。一般情况下,某一等位基因在对照人群的频率大于疾病预期人群(表6)时,可认为是罕见孟德尔疾病良性变异的强证据(BS1),如果频率超过5%时,则可认为是良性变异的独立证据(BA1)。此外,如果疾病发生在早期,且变异在健康成人中以隐性(纯合子)、显性(杂合子)或X-连锁(半合子)的状态存在,那么这就是良性变异的强证据(BS2)。如果数据库中未能检出变异的存在,应该确认建立该数据库采用的测序读长深度是否足以检测出该位点上的变异。如果在一个大样本的普通人群或队列数据的对照人群(>​1000人)中变异不存在(或隐性遗传的突变频率是低频),并且携带此变异的患者与对照人群为同一种族,那么可以认为该变异是致病性的中等证据(PM2)。许多良性变异是“个体化的”(即个人或家系独有的),因此即使在相同种族的人群中缺乏也不能作为致病性的充足甚至强的证据。
  
 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. ​ 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. ​
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 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.hutchon.net/​ConfidOR.htm/​]] and [[http://​easycalculation.com/​statistics/​odds-ratio.php/​]]). 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.hutchon.net/​ConfidOR.htm/​]] and [[http://​easycalculation.com/​statistics/​odds-ratio.php/​]]).
  
-比值比(OR)或相对风险用于衡量基因型(即存在于基因组中的变异)和表型(即所患疾病/​结果)之间的关联,适用于任何孟德尔疾病或复杂疾病。本指南只涉及其在孟德尔疾病中的使用。相对风险与OR不同,但概率较小时相对风险近似等于OR。OR值为1.0意味着该变异与疾病风险不相关,大于1.0意味着变异与疾病风险正相关,小于1.0意味着变异与疾病风险负相关。一般情况下,具有孟德尔中等效应的变异,其OR值为3或者更大,高度外显的变异具有非常高的OR值,例如,APOE基因E4/​E4纯合子与E3/​E3纯合子相比,OR值为13(https://​www.tgen.org/​ home/​education-outreach/​past-summer-interns/​2012-summer-interns/​erika-kollitz.aspx#​.VOSi3C7G_vY)。OR值的置信区间(confidence interval,CI)也是一个重要的衡量工具。如果CI中包括1.0(如OR=2.5,CI=0.9~7.4),则关联的可信度很小。在上面APOE的例子中,CI为10~16。在线可获得简单的OR值计算器(http://​www.hutchon.net/​ConfidOR.htm/​and http://​easycalculation.com/​statistics/​odds-ratio.php/​)。+比值比(OR)或相对风险用于衡量基因型(即存在于基因组中的变异)和表型(即所患疾病/​结果)之间的关联,适用于任何孟德尔疾病或复杂疾病。本指南只涉及其在孟德尔疾病中的使用。相对风险与OR不同,但概率较小时相对风险近似等于OR。OR值为1.0意味着该变异与疾病风险不相关,大于1.0意味着变异与疾病风险正相关,小于1.0意味着变异与疾病风险负相关。一般情况下,具有孟德尔中等效应的变异,其OR值为3或者更大,高度外显的变异具有非常高的OR值,例如,APOE基因E4/​E4纯合子与E3/​E3纯合子相比,OR值为13(https://​www.tgen.org/​home/​education-outreach/​past-summer-interns/​2012-summer-interns/​erika-kollitz.aspx#​.VOSi3C7G_vY)。OR值的置信区间(confidence interval,CI)也是一个重要的衡量工具。如果CI中包括1.0(如OR=2.5,CI=0.9~7.4),则关联的可信度很小。在上面APOE的例子中,CI为10~16。在线可获得简单的OR值计算器(http://​www.hutchon.net/​ConfidOR.htm/​and http://​easycalculation.com/​statistics/​odds-ratio.php/​)。
 ==== 4.6 PM1 热点突变和/​或关键的、得到确认的功能域 ==== ==== 4.6 PM1 热点突变和/​或关键的、得到确认的功能域 ====
  
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 线粒体变异的命名法与核基因的标准命名法不同,使用基因名和m.编号(如m.8993T>​C)和p.编号,而不是标准的c.编号(见命名法)。目前公认的参考序列是人类线粒体DNA修订版剑桥参考序列:​ 基因库序列NC_012920 gi: 251831106(http://​www.mitomap.org/​MITOMAP/​HumanMitoSeq)。 线粒体变异的命名法与核基因的标准命名法不同,使用基因名和m.编号(如m.8993T>​C)和p.编号,而不是标准的c.编号(见命名法)。目前公认的参考序列是人类线粒体DNA修订版剑桥参考序列:​ 基因库序列NC_012920 gi: 251831106(http://​www.mitomap.org/​MITOMAP/​HumanMitoSeq)。
  
-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://​mitomap.org/​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.uu.se/​),​ secondary structures, sequences, and alignment of mitochondrial transfer RNAs (http://​mamittrna. u-strasbg.fr/​),​ mitochondrial haplogroups (http://​www. phylotree.org/​)and other information (http://​www.mtdnacommunity. org/​default.aspx),​ may prove useful in interpreting mitochondrial variants.+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://​mitomap.org/​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.uu.se/​),​ secondary structures, sequences, and alignment of mitochondrial transfer RNAs (http://​mamittrna.u-strasbg.fr/​),​ mitochondrial haplogroups (http://​www.phylotree.org/​)and other information (http://​www.mtdnacommunity.org/​default.aspx),​ may prove useful in interpreting mitochondrial variants.
  
-如果已通过检测对异质性水平进行确定,应该对异质性或同质性,以及变异异质性的评估进行报道。不同组织类型的异质性百分比因检测样本的不同而有所改变,​ 因此,低异质性水平也必须结合所检测组织进行解读,且它们可能仅在受累及的组织中才是有意义的,如肌肉组织。超过275个与疾病相关的线粒体DNA变异已被记录(http://​mitomap.org/​bin/​view.pl/​ MITOMAP/​WebHome)。MitoMap是线粒体变异及单倍型相关信息的主要来源。其他资源,如频率信息(http://​www.mtdb.igp.uu.se/​)、二级结构、序列和线粒体转运RNA的比对(http://​mamittrna.u-strasbg.fr/​)、线粒体单倍群(http://​www.phylotree.org/​)[35]和其他信息(http://​www.mtdnacommunity.org/​default.aspx),可能在解读线粒体变异时是有用的。+如果已通过检测对异质性水平进行确定,应该对异质性或同质性,以及变异异质性的评估进行报道。不同组织类型的异质性百分比因检测样本的不同而有所改变,​ 因此,低异质性水平也必须结合所检测组织进行解读,且它们可能仅在受累及的组织中才是有意义的,如肌肉组织。超过275个与疾病相关的线粒体DNA变异已被记录(http://​mitomap.org/​bin/​view.pl/​MITOMAP/​WebHome)。MitoMap是线粒体变异及单倍型相关信息的主要来源。其他资源,如频率信息(http://​www.mtdb.igp.uu.se/​)、二级结构、序列和线粒体转运RNA的比对(http://​mamittrna.u-strasbg.fr/​)、线粒体单倍群(http://​www.phylotree.org/​)和其他信息(http://​www.mtdnacommunity.org/​default.aspx),可能在解读线粒体变异时是有用的。
  
 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. 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.
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 ==== 6.4 药物基因组学 ==== ==== 6.4 药物基因组学 ====
  
-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.ki.se/​.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.+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.ki.se/​. 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.
  
 确认基因变异在药物代谢中的作用具有挑战性,部分原因在于其表型只有在接触药物后才得以显现。不过,临床上现已报告了各种与药物疗效和副作用风险相关的基因变异,且其数量仍然在不断增加。相关基因的汇总及其有临床意义的变异可查询药物基因组学知识库网站(http://​www.pharmgkb.org/​)。有关细胞色素P450基因家族等位基因及其命名可查询网站http://​www.cypalleles.ki.se/​。尽管解读药物基因组变异已超出了本文的范围,还是对与解读及报告药物基因组结果相关的挑战和鉴别进行了讨论。 确认基因变异在药物代谢中的作用具有挑战性,部分原因在于其表型只有在接触药物后才得以显现。不过,临床上现已报告了各种与药物疗效和副作用风险相关的基因变异,且其数量仍然在不断增加。相关基因的汇总及其有临床意义的变异可查询药物基因组学知识库网站(http://​www.pharmgkb.org/​)。有关细胞色素P450基因家族等位基因及其命名可查询网站http://​www.cypalleles.ki.se/​。尽管解读药物基因组变异已超出了本文的范围,还是对与解读及报告药物基因组结果相关的挑战和鉴别进行了讨论。
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 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. 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.
  
-体细胞变异主要见于癌细胞,因为其等位基因比值高度可变,且肿瘤异质性也可导致样差异。在描述其变异时,具有原发性变异所没有的复杂性。变异的解读有助于选择治疗方案和预测治疗效果、也应用于评估整体生存率或肿瘤无进展生存期,因而体细胞变异的分类更加复杂。在对阴性结果解读时,了解测序分析的检测方法局限性变异可在何种等位基因频率时被检测到至关重要,此外也需要了解样本中肿瘤含量的特定信息。与胚系变异相比,体细胞变异的分类类别也不同,通常使用“敏感”、“拮抗”、“驱动”和“伴随”等术语。一个变异是否是体细胞变异需要通过患者胚系DNA的序列分析来证实。体细胞变异还需要另外的解读指南,除了参考原发性突变的数据库以外,还需要肿瘤特异性数据库作为参考。为了解决这个问题,最近AMP已经成立了一个工作组。+体细胞变异主要见于癌细胞,因为其等位基因比值高度可变,且肿瘤异质性也可导致样差异。在描述其变异时,具有原发性变异所没有的复杂性。变异的解读有助于选择治疗方案和预测治疗效果、也应用于评估整体生存率或肿瘤无进展生存期,因而体细胞变异的分类更加复杂。在对阴性结果解读时,了解测序分析的检测方法局限性(变异可在何种等位基因频率时被检测到)至关重要,此外也需要了解样本中肿瘤含量的特定信息。与胚系变异相比,体细胞变异的分类类别也不同,通常使用“敏感”、“拮抗”、“驱动”和“伴随”等术语。一个变异是否是体细胞变异需要通过患者胚系DNA的序列分析来证实。体细胞变异还需要另外的解读指南,除了参考原发性突变的数据库以外,还需要肿瘤特异性数据库作为参考。为了解决这个问题,最近AMP已经成立了一个工作组。
 ===== 7. 医疗工作者如何使用这些指南和建议 ===== ===== 7. 医疗工作者如何使用这些指南和建议 =====
  
 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. 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.
  
-临床实验室检测的主要目的是为医疗决策提供依据。在临床上,基因检测一般用于识别或确认疾病的原因,并帮助医工作者做出个性化的治疗决策,包括用药的选择。鉴于基因检测的复杂性,检测过程中需相关医工作者和临床实验室协作才能得到最佳结果。+临床实验室检测的主要目的是为医疗决策提供依据。在临床上,基因检测一般用于识别或确认疾病的原因,并帮助医工作者做出个性化的治疗决策,包括用药的选择。鉴于基因检测的复杂性,检测过程中需相关医工作者和临床实验室协作才能得到最佳结果。
  
 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. 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.
  
-当医工作者提出基因检测需求时,需将患者的临床信息提供给实验室。由于医工作者越来越多地使用基因组全外显子组或全基因组测序,而详细的临床信息有助于对检测结果的解读,因此向实验室提供临床信息就变得越来越重要。例如,当一个实验室在基因组测序样品中发现一个罕见或新发的变异时,实验室负责人不能仅因为该变异是罕见的、新发现的或者新发的来确定它的致病性。该实验室必须通过患者的病史、家族史、体格检查和前期实验室检查对变异和基因进行评估,进而区分致病变异和其他偶然次要发现或良性变异。事实上,准确和完整的临床信息对于基因组水平DNA序列检测结果的解读是不可或缺的,若待测样品不能提供此类信息,实验室可以合理拒绝继续进行检测。+当医工作者提出基因检测需求时,需将患者的临床信息提供给实验室。由于医工作者越来越多地使用基因组(全外显子组或全基因组)测序,而详细的临床信息有助于对检测结果的解读,因此向实验室提供临床信息就变得越来越重要。例如,当一个实验室在基因组测序样品中发现一个罕见或新发的变异时,实验室负责人不能仅因为该变异是罕见的、新发现的或者新发的来确定它的致病性。该实验室必须通过患者的病史、家族史、体格检查和前期实验室检查对变异和基因进行评估,进而区分致病变异和其他偶然(次要)发现或良性变异。事实上,准确和完整的临床信息对于基因组水平DNA序列检测结果的解读是不可或缺的,若待测样品不能提供此类信息,实验室可以合理拒绝继续进行检测。
  
 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. 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.
  
-利用如高通量的靶向测序、全外显子组和全基因组测序等覆盖广泛表型的方法进行检测,实验室可能会发现候选的致病变异。对医工作者和患者后续的随访可能会发现更多的证据来支持某一变异的致病性。这些补充的表型信息可能是亚临床症状,需要进一步完善相关的临床检测例如,一个在SLC26A4基因与Pendred综合征相关的基因上有不确定变异的听力受损患者,需要进行CT检查判断有无颞骨异常。此外,当发现一个变异可能是新发变异,或者当一个变异在家系中与表型共分离,或者在隐性遗传致病基因中一个变异与另一个致病变异处于反式位置时,必须在其他家系成员中进行验证。显性遗传性疾病的变异在健康亲属中观察到时,可对其绝大部分变异进行过滤或删减,这样可使解读更加有效和准确。为此,我们强烈建议在开展外显子组或基因组测序时,尽力做到“核心家系”检测即母亲、父亲、患病儿童,尤其是对怀疑有隐性遗传或新发变异的患者。与成人患者相比,这显然在儿科患者中更易实现。在没有父母一方或双方时,纳入患病和正常的兄弟姐妹也是有意义的。+利用如高通量的靶向测序、全外显子组和全基因组测序等覆盖广泛表型的方法进行检测,实验室可能会发现候选的致病变异。对医工作者和患者后续的随访可能会发现更多的证据来支持某一变异的致病性。这些补充的表型信息可能是亚临床症状,需要进一步完善相关的临床检测(例如,一个在SLC26A4基因(与Pendred综合征相关的基因)上有不确定变异的听力受损患者,需要进行CT检查判断有无颞骨异常)。此外,当发现一个变异可能是新发变异,或者当一个变异在家系中与表型共分离,或者在隐性遗传致病基因中一个变异与另一个致病变异处于反式位置时,必须在其他家系成员中进行验证。显性遗传性疾病的情况下,在健康亲属中观察到绝大部分变异可以被过滤或删减,这样可使解读更加有效和准确。为此,强烈建议在开展外显子组或基因组测序时,尽力做到“核心家系”检测(即母亲、父亲、患病儿童),尤其是对怀疑有隐性遗传或新发变异的患者。与成人患者相比,这显然在儿科患者中更易实现。在没有父母一方或双方时,纳入患病和正常的兄弟姐妹也是有意义的。
  
 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). 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).
  
-许多遗传变异会导致一系列表型 (表达多样性),疾病发生的机率也可能不是100% (外显率降低),这些均进一步强调了向临床实验室提供全面的临床数据来帮助解读变异的重要性。在理想的情况下,建议应依据医疗保险可携性和责任法案HIPAA和机构审查委员会条例,将临床数据存入并通过集中存储库共享。重要的是,当家庭成员的信息对于解读结果是必需的时候,相关医工作者可以进一步帮助临床实验室收集家庭成员的DNA例如,当评估家系患者与疾病共分离时,父母的基因型分析可用来评估新发变异的发生,一级亲属可用来确定隐性遗传疾病变异阶段的同线或异线性)。+许多遗传变异会导致一系列表型(不同程度的现度),疾病发生的机率也可能不是100%(外显率降低),这些均进一步强调了向临床实验室提供全面的临床数据来帮助解读变异的重要性。在理想的情况下,建议应依据医疗保险可携性和责任法案(HIPAA)和机构审查委员会条例,将临床数据存入并通过集中存储库共享。重要的是,当家庭成员的信息对于解读结果是必需的时候,相关医工作者可以进一步帮助临床实验室收集家庭成员的DNA(例如,当评估家系患者与疾病共分离时,父母的基因型分析可用来评估新发变异的发生,一级亲属可用来确定隐性遗传疾病变异的同线或异线性)。
  
 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. ​ 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. ​
  
-工作者如何使用基因检测提供的证据来进行医疗管理决策是一个关键问题。目前变异分析是不完善的,报道的变异分类也并不是100%确定的。一般来说,根据推荐的分类方法划分为致病性的变异符合经验数据形成的标准,所以医工作者可以在临床决策时采用分子检测信息。应尽力避免使用此类信息作为孟德尔疾病的唯一证据,在可能的情况下应与其他临床资料相结合。通常情况下,一个有足够的证据被划分为可能致病的变异,当与可疑疾病的其证据相结合时,医工作者可以使用分子检测信息进行临床决策的制定。例如,产前超声可能显示关键的证实证据结果,对于产后的病例,其他数据如酶检测、体格检查,或影像学研究可能最终支持临床决策。然而,推荐进行所有如上所述的可能的后续检测,追踪可能致病变异相关的附加证据的产生,因为这有可能将可能的致病性变异重新归类为致病变异。意义不明确的变异不宜应用于临床决策。应努力将变异分类为致病性或良性。当虽然变异的重新分类正在进行中时,对可疑致病的患者进行额外的监测应审慎。一个有足够证据被考虑为可能良性的变异,医工作者可以结合其信息,推断此变异不是该患者致病的原因例如,变异在患病的家族成员中不分离且可排除复杂遗传模式。一个有足够证据被考虑为良性的变异,医工作者可以得出此变异不是该患者致病原因的结论。+工作者如何使用基因检测提供的证据来进行医疗管理决策是一个关键问题。目前变异分析是不完善的,报道的变异分类也并不是100%确定的。一般来说,根据推荐的分类方法划分为致病性的变异符合经验数据形成的标准,所以医工作者可以在临床决策时采用分子检测信息。应尽力避免使用此类信息作为孟德尔疾病的唯一证据,在可能的情况下应与其他临床资料相结合。通常情况下,一个有足够的证据被划分为可能致病的变异,当与可疑疾病的其证据相结合时,医工作者可以使用分子检测信息进行临床决策的制定。例如,产前超声可能显示关键的证据,对于产后的病例,其他数据如酶检测、体格检查,或影像学研究可能最终支持临床决策。然而,推荐进行所有如上所述的可能的后续检测,追踪可能致病变异相关的附加证据的产生,因为这有可能将可能的致病性变异重新归类为致病变异。意义不明确的变异不宜应用于临床决策。应努力将变异分类为致病性或良性。当变异的重新分类正在进行中时,对可疑致病的患者进行额外的监测应审慎。一个有足够证据被考虑为可能良性的变异,医工作者可以结合其信息,推断此变异不是该患者致病的原因例如,变异并不与家族中的某位患病成员分离,而也不太能是复杂遗传模式。一个有足够证据被考虑为良性的变异,医工作者可以得出此变异不是该患者致病原因的结论。
  
 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. ​ 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. ​
  
-基因检测的证据如何使用也依赖于临床背景和检测指征。在产前诊断的病例中,如果该家庭正在考虑不可逆的宫内治疗或终止妊娠等决定时,需要在采取行动之前慎重考虑报告中证据的份量和胎儿超声等其信息。当基因检测结果是产前检查的唯一证据时,需要向受检家庭慎重解释可能致病的变异。关键是医工作者应与临床实验室深入沟通,以了解所检测到的变异致病性确认依据,以期为患者提供准确的遗传咨询和临床决策。+基因检测的证据如何使用也依赖于临床背景和检测指征。在产前诊断的病例中,如果该家庭正在考虑的决定将导致不可逆的后果时,如宫内治疗或终止妊娠等,需要在采取行动之前慎重考虑报告中证据的份量和胎儿超声等其信息。当基因检测结果是产前检查的唯一证据时,需要向受检家庭慎重解释可能致病的变异。关键是相关的工作者应与临床实验室深入沟通,以了解所检测到的变异是如何被分类的,以期为患者提供准确的遗传咨询和临床决策。
 ===== 8 参考文献(略) ===== ===== 8 参考文献(略) =====
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 ===== 表1 人群数据库,疾病特异性数据库和序列数据库 ===== ===== 表1 人群数据库,疾病特异性数据库和序列数据库 =====
 {{:​table1.png|}} {{:​table1.png|}}
 ^人群数据库|| ^人群数据库||
 |Exome Aggregation Consortium http://​exac.broadinstitute.org/​ |本数据库中的变异信息是通过对61486个独立个体进行全外显子测序获得。同时也是多种特殊疾病和群体遗传学研究中的一部分。库中不包括儿科疾病患者及其相关人群。| |Exome Aggregation Consortium http://​exac.broadinstitute.org/​ |本数据库中的变异信息是通过对61486个独立个体进行全外显子测序获得。同时也是多种特殊疾病和群体遗传学研究中的一部分。库中不包括儿科疾病患者及其相关人群。|
-|Exome Variant Server http://​evs.gs.washington.edu/​EVS|本数据库中的变异信息是通过对几个欧洲和非洲裔大规模人群的全外显子测序获得。当缺乏变异信息时,库中以覆盖数据替代默认该数据已覆盖。|+|Exome Variant Server http://​evs.gs.washington.edu/​EVS|本数据库中的变异信息是通过对几个欧洲和非洲裔大规模人群的全外显子测序获得。当缺乏变异信息时默认该数据已覆盖。|
 |1000 Genomes Project http://​browser.1000genomes.org|本数据库中的变异信息是通过对26个种群进行低覆盖度的全基因组测序和高覆盖度的靶序列测序获得。本库所提供的信息比Exome Variant Server更具多样性,但也包含有低质量的数据,有些群体中还包含有关联性个体在内。| |1000 Genomes Project http://​browser.1000genomes.org|本数据库中的变异信息是通过对26个种群进行低覆盖度的全基因组测序和高覆盖度的靶序列测序获得。本库所提供的信息比Exome Variant Server更具多样性,但也包含有低质量的数据,有些群体中还包含有关联性个体在内。|
-|dbSNP http://​www.ncbi.nlm.nih.gov/​snp|本数据库由多种来源获得的短片段遗传变异通常≤50bp)信息组成。库中可能缺乏溯源性研究的细节,也可能包含致病性突变在内。| +|dbSNP http://​www.ncbi.nlm.nih.gov/​snp|本数据库由多种来源获得的短片段遗传变异(通常≤50 bp)信息组成。库中可能缺乏溯源性研究的细节,也可能包含致病性突变在内。| 
-|dbVar http://​www.ncbi.nlm.nih.gov/​dbvar|本数据库由多种来源获得的基因结构变异通常>50bp)信息组成。|+|dbVar http://​www.ncbi.nlm.nih.gov/​dbvar|本数据库由多种来源获得的基因结构变异(通常>50 bp)信息组成。|
 ^疾病数据库|| ^疾病数据库||
 |ClinVar http://​www.ncbi.nlm.nih.gov/​clinvar|对变异与表型和临床表型之间的关联进行确定的数据库。| |ClinVar http://​www.ncbi.nlm.nih.gov/​clinvar|对变异与表型和临床表型之间的关联进行确定的数据库。|
-| OMIM http://​www.omim.org|本数据库所含人类基因和相关遗传背景,同时具有疾病相关基因遗传变异的代表性样本收录与遗传疾病典型相关的样本变异信息。|+| OMIM http://​www.omim.org|本数据库所含人类基因和相关遗传背景,同时具有疾病相关基因遗传变异的代表性样本收录与遗传疾病典型相关的样本变异信息。|
 |Human Gene Mutation Database http://​www.hgmd.org|本数据库中的变异注释有文献发表。库中大部分内容需付费订阅。| |Human Gene Mutation Database http://​www.hgmd.org|本数据库中的变异注释有文献发表。库中大部分内容需付费订阅。|
 ^其他特殊数据库|| ^其他特殊数据库||
-|Human Genome Variation Society http://​www.hgvs.org/​dblist/​dblist.html|本数据库由人类基因组变异协会HGVS开发,提供数千种专门针对人群中的特殊变异进行的注释。数据库很大一部分是基于Leiden Open Variation Database system建立。|+|Human Genome Variation Society http://​www.hgvs.org/​dblist/​dblist.html|本数据库由人类基因组变异协会(HGVS)开发,提供数千种专门针对人群中的特殊变异进行的注释。数据库很大一部分是基于Leiden Open Variation Database system建立。|
 | Leiden Open Variation Database http://​www.lovd.nl| | | Leiden Open Variation Database http://​www.lovd.nl| |
-| DECIPHER http://​decipher.sanger.ac.uk|使用Ensemble基因组浏览器,将基因芯片数据和临床表型进行关联,便于临床医生和研究人员使用的细胞分子遗传学数据库。|+| DECIPHER http://​decipher.sanger.ac.uk|使用Ensemble基因组浏览器,将基因芯片数据和临床表型进行关联,便于临床医生和研究人员使用的细胞分子遗传学数据库。||
 ^序列数据库|| ^序列数据库||
-| NCBI Genome ​ http://​www.ncbi.nlm.nih.gov/​genome |人类全基因组参考序列的来源| +| NCBI Genome ​ http://​www.ncbi.nlm.nih.gov/​genome |人类全基因组参考序列的来源
-| RefSeqGene ​ http://​www.ncbi.nlm.nih.gov/​refseq/​rsg|医学相关基因参考序列|+| RefSeqGene ​ http://​www.ncbi.nlm.nih.gov/​refseq/​rsg|医学相关基因参考序列|
 | Locus Reference Genomic (LRG)  http://​www.lrg-sequence.org| | | Locus Reference Genomic (LRG)  http://​www.lrg-sequence.org| |
-| MitoMap http://​www.mitomap.org/​MITOMAP/​HumanMitoSeq|对“剑桥版-人类线粒体DNA参考序列”进行修订后形成|+| MitoMap http://​www.mitomap.org/​MITOMAP/​HumanMitoSeq|对“剑桥版-人类线粒体DNA参考序列”进行修订后形成|
  
 ===== 表2 生物信息分析工具 ===== ===== 表2 生物信息分析工具 =====
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 {{:​table3.png|}} {{:​table3.png|}}
 ^   ​致病性证据 ​  ​^ ​  ​分类 ​  ^ ^   ​致病性证据 ​  ​^ ​  ​分类 ​  ^
-|非常强 |PVS1:当一个疾病的致病机制为功能丧失(LOF)时,无功能变异(无义突变、移码突变、经典±1或2的剪接突变、起始密码子变异、单个或多个外显子缺失)注意事项:1. 该基因的LOF是否是导致该疾病的明确致病机制(如GFAP、MYH7)2. 3'​端末端的功能缺失变异需谨慎解读3.需注意外显子选择性缺失是否影响到蛋白质的完整性4.考虑一个基因存在多种转录本的情况| +|非常强 |PVS1:当一个疾病的致病机制为功能丧失(LOF)时,无功能变异(无义突变、移码突变、经典±1或2的剪接突变、起始密码子变异、单个或多个外显子缺失)注:1. 该基因的LOF是否是导致该疾病的明确致病机制(如GFAP、MYH7)2. 3'​端末端的功能缺失变异需谨慎解读3.需注意外显子选择性缺失是否影响到蛋白质的完整性4.考虑一个基因存在多种转录本的情况
-|强   ​|PS1:与先前已确定为致病性的变异有相同的氨基酸改变。例如:同一密码子,G>​C或G>​ T改变均可导致缬氨酸→亮氨酸的改变。注意剪切影响的改变。| +|强   ​|PS1:与先前已确定为致病性的变异有相同的氨基酸改变。例如:同一密码子,G>​C或G>​T改变均可导致缬氨酸→亮氨酸的改变。注意剪切影响的改变。| 
-|:::​|PS2:患者的新发变异,且无家族史(经双亲验证) 注:仅仅确认父母还,还需注意捐卵、代孕、胚胎移植的差错等情况。|+|:::​|PS2:患者的新发变异,且无家族史(经双亲验证)。 注:仅仅确认父母还不够,还需注意捐卵、代孕、胚胎移植的差错等情况。|
 |:::​|PS3:体内、体外功能实验已明确会导致基因功能受损的变异。 注:功能实验需要验证是有效的,且具有重复性与稳定性。| |:::​|PS3:体内、体外功能实验已明确会导致基因功能受损的变异。 注:功能实验需要验证是有效的,且具有重复性与稳定性。|
-|:::​|PS4:变异出现在患病群体中的频率显著高于对照群体。注1:可选择使用相对风险值或者OR值来评估,建议位点OR大于5.0且置信区间不包括1.0的可列入此项。(详细见指南正文)。2:极罕见的变异在病例对照研究可能无统计学意义,原先在多个具有相同表型的患者中观察到该变异且在对照中未观察到可作为中等水平证据。|+|:::​|PS4:变异出现在患病群体中的频率显著高于对照群体。注 1:可选择使用相对风险值或者OR值来评估,建议位点OR大于5.0且置信区间不包括1.0的可列入此项。(详细见指南正文)。2:极罕见的变异在病例对照研究可能无统计学意义,原先在多个具有相同表型的患者中观察到该变异且在对照中未观察到可作为中等水平证据。|
 |  中等 ​ | PM1:位于热点突变区域,和/​或位于已知无良性变异的关键功能域(如酶的活性位点)。| |  中等 ​ | PM1:位于热点突变区域,和/​或位于已知无良性变异的关键功能域(如酶的活性位点)。|
-|:::​|PM2:ESP数据库、千人数据库、EXAC数据库中正常对照人群中未发现的变异(或隐性遗传病中极低频位点)(表6) 注意事项: 高通量测序得到的插入/​缺失人群数据质量较差| +|:::​|PM2:ESP数据库、千人数据库、EXAC数据库中正常对照人群中未发现的变异(或隐性遗传病中极低频位点)(表6) 注: 高通量测序得到的插入/​缺失人群数据质量较差| 
-|:::​|PM3:在隐性遗传病中,在反式位置上检测到致病变异。 注:这种情况必须通过患者父母或后代验证。|+|:::​|PM3:在隐性遗传病中,在反式位置上检测到致病变异。 注:这种情况必须通过患者父母或后代验证。|
 |:::​|PM4:非重复区框内插入/​缺失或终止密码子丧失导致的蛋白质长度变化。| |:::​|PM4:非重复区框内插入/​缺失或终止密码子丧失导致的蛋白质长度变化。|
 |:::​|PM5:新的错义突变导致氨基酸变化,此变异之前未曾报道,但是在同一位点,导致另外一种氨基酸的变异已经确认是致病性的,如:现在观察到的是Arg156Cys,而Arg156His是已知致病的。注意剪切影响的改变。| |:::​|PM5:新的错义突变导致氨基酸变化,此变异之前未曾报道,但是在同一位点,导致另外一种氨基酸的变异已经确认是致病性的,如:现在观察到的是Arg156Cys,而Arg156His是已知致病的。注意剪切影响的改变。|
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 | 支持证据 | PP1:突变与疾病在家系中共分离(在家系多个患者中检测到此变异) 注:如果有更多的证据,可作为更强的证据。| | 支持证据 | PP1:突变与疾病在家系中共分离(在家系多个患者中检测到此变异) 注:如果有更多的证据,可作为更强的证据。|
 |:::|PP2: 对某个基因来说,如果这个基因的错义变异是造成某种疾病的原因,并且这个基因中良性变异所占的比例很小,在这样的基因中所发现的新的错义变异。| |:::|PP2: 对某个基因来说,如果这个基因的错义变异是造成某种疾病的原因,并且这个基因中良性变异所占的比例很小,在这样的基因中所发现的新的错义变异。|
-|:::​|PP3:多种统计方法预测出该变异会对基因或基因产物造成有害的影响,包括保守性预测、进化预测、剪接位点影响等。注意事项:由于做预测时许多生物信息算法使用相同或非常相似的输入,每个算法不应该算作一个独立的标准。PP3在一个任何变异的评估中只能使用一次。|+|:::​|PP3:多种统计方法预测出该变异会对基因或基因产物造成有害的影响,包括保守性预测、进化预测、剪接位点影响等。注:由于做预测时许多生物信息算法使用相同或非常相似的输入,每个算法不应该算作一个独立的标准。PP3在一个任何变异的评估中只能使用一次。|
 |:::​|PP4:变异携带者的表型或家族史高度符合某种单基因遗传疾病。| |:::​|PP4:变异携带者的表型或家族史高度符合某种单基因遗传疾病。|
 |:::​|PP5:有可靠信誉来源的报告认为该变异为致病的,但证据尚不足以支持进行实验室独立评估。| |:::​|PP5:有可靠信誉来源的报告认为该变异为致病的,但证据尚不足以支持进行实验室独立评估。|
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 {{:​table4.png|}} {{:​table4.png|}}
 ^良性影响的证据^ ​ 分类 ​ ^ ^良性影响的证据^ ​ 分类 ​ ^
-|独立证据| BA1:ESP数据库、千人数据库、EAC数据库中等位基因频率>​5%的变异| +|独立证据| BA1:ESP数据库、千人数据库、ExAC数据库中等位基因频率>​5%的变异
-|强|BS1:等位基因频率大于疾病发病率|+|强|BS1:等位基因频率大于疾病发病率|
 |:::​|BS2:对于早期完全外显的疾病,在健康成年人中发现该变异(隐性遗传病发现纯合、显性遗传病发现杂合,或者X连锁半合子)。| |:::​|BS2:对于早期完全外显的疾病,在健康成年人中发现该变异(隐性遗传病发现纯合、显性遗传病发现杂合,或者X连锁半合子)。|
 |:::|BS3: 在体内外实验中确认对蛋白质功能和剪接没有影响的变异。| |:::|BS3: 在体内外实验中确认对蛋白质功能和剪接没有影响的变异。|
-|:::​|BS4:在一个家系成员中缺乏共分离| +|:::​|BS4:在一个家系成员中缺乏共分离
-|:::|注意事项:这部分需要考虑复杂疾病和外显率问题|+|:::​|注:这部分需要考虑复杂疾病和外显率问题|
 |支持证据|BP1:已知一个疾病的致病原因是由于某基因的截短变异,在此基因中所发现的错义变异。| |支持证据|BP1:已知一个疾病的致病原因是由于某基因的截短变异,在此基因中所发现的错义变异。|
 |:::​|BP2:在显性遗传病中又发现了另一条染色体上同一基因的一个已知致病变异,或者是任意遗传模式遗传病中又发现了同一条染色体上同一基因的一个已知致病变异。| |:::​|BP2:在显性遗传病中又发现了另一条染色体上同一基因的一个已知致病变异,或者是任意遗传模式遗传病中又发现了同一条染色体上同一基因的一个已知致病变异。|
 |:::​|BP3:功能未知重复区域内的缺失/​插入,同时没有导致基因编码框改变。| |:::​|BP3:功能未知重复区域内的缺失/​插入,同时没有导致基因编码框改变。|
-|:::​|BP4:种统计方法预测出该变异会对基因或基因产物无影响,包括保守性预测、进化预测、剪接位点影响等。注意事项:由于做预测时许多生物信息算法使用相同或非常相似的输入,每个算法不应该算作一个独立的标准。BP4在一个任何变异的评估中只能使用一次。|+|:::|BP4:种统计方法预测出该变异会对基因或基因产物无影响,包括保守性预测、进化预测、剪接位点影响等。注:由于做预测时许多生物信息算法使用相同或非常相似的输入,每个算法不应该算作一个独立的标准。BP4在一个任何变异的评估中只能使用一次。|
 |:::​|BP5:在已经有另一分子致病原因的病例中发现的变异。| |:::​|BP5:在已经有另一分子致病原因的病例中发现的变异。|
 |:::​|BP6:有可靠信誉来源的报告认为该变异为良性的,但证据尚不足以支持进行实验室独立评估。| |:::​|BP6:有可靠信誉来源的报告认为该变异为良性的,但证据尚不足以支持进行实验室独立评估。|
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 ===== 表5 遗传变异分类联合标准规则 ===== ===== 表5 遗传变异分类联合标准规则 =====
 {{:​table5.png?​400 |}} {{:​table5.png?​400 |}}
-^ 致病 ​   | (i) 1个非常强(PVS1)和|+^ 致病的    | (i) 1个非常强(PVS1)和|
 ^ :::    | (a) ≥1个强(PS1-PS4)或 |      ​ ^ :::    | (a) ≥1个强(PS1-PS4)或 |      ​
 ^ :::    | (b) ≥2个中等(PM1-PM6)或 ​ |  ^ :::    | (b) ≥2个中等(PM1-PM6)或 ​ | 
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 ^ :::    | (b) 2个中等(PM1-PM6)和≥2个支持(PP1-PP5)或 ​ | ^ :::    | (b) 2个中等(PM1-PM6)和≥2个支持(PP1-PP5)或 ​ |
 ^ :::    | %%(c)%% 1个中等(PM1-PM6)和≥4个支持(PP1-PP5) ​ | ^ :::    | %%(c)%% 1个中等(PM1-PM6)和≥4个支持(PP1-PP5) ​ |
-^ 可能致病 ​   | (i) 1个非常强(PVS1)和1个中等(PM1-PM6)或 ​ |+^ 可能致病的    | (i) 1个非常强(PVS1)和1个中等(PM1-PM6)或 ​ |
 ^ :::    | (ii) 1个强(PS1-PS4)和1-2个中等(PM1-PM6)或 | ^ :::    | (ii) 1个强(PS1-PS4)和1-2个中等(PM1-PM6)或 |
 ^ :::    | (iii) 1个强(PS1-PS4)和≥2个支持(PP1-PP5)或 | ^ :::    | (iii) 1个强(PS1-PS4)和≥2个支持(PP1-PP5)或 |
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 ^ :::    | (v) 2个中等(PM1-PM6)和≥2个支持(PP1-PP5)或 | ^ :::    | (v) 2个中等(PM1-PM6)和≥2个支持(PP1-PP5)或 |
 ^ :::    | (vi) 1个中等(PM1-PM6)和≥4个支持(PP1-PP5) | ^ :::    | (vi) 1个中等(PM1-PM6)和≥4个支持(PP1-PP5) |
-^ 良性 ​   | (i) 1个独立(BA1)或 |+^ 良性的    | (i) 1个独立(BA1)或 |
 ^ :::    | (ii) ≥2个强(BS1-BS4) | ^ :::    | (ii) ≥2个强(BS1-BS4) |
-^ 可能良性 ​   | (i) 1个强(BS1-BS4)和1个支持(BP1-BP7)或 |+^ 可能良性的    | (i) 1个强(BS1-BS4)和1个支持(BP1-BP7)或 |
 ^ :::    | (ii) ≥2个支持(BP1-BP7) | ^ :::    | (ii) ≥2个支持(BP1-BP7) |
-^ 意义不明 ​   | (i) 不满足上述标准或 |+^ 意义不明确的 ​   | (i) 不满足上述标准或 |
 ^ :::    | (ii) 良性和致病标准相互矛盾 | ^ :::    | (ii) 良性和致病标准相互矛盾 |
  
 ===== 表6 评估人群中变异频率来策划变异分类 ===== ===== 表6 评估人群中变异频率来策划变异分类 =====
 {{:​table6.png |}} {{:​table6.png |}}
 +{{:​表7-1.jpg|}}
 +{{:​表7-3.jpg|}}