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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 Unpublished case data may also be obtained through public resources (e.g., ClinVar or locus specific databases) and from a laboratory’s own database.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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 ===== 图1 ===== ===== 图1 =====
 {{:​figure1.png|}} {{:​figure1.png|}}
-{{:图1.png|}}+{{:图1-1.jpg|}}
 ===== 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 ​ | 
行 495: 行 495:
 ^ :::    | (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 |
行 501: 行 501:
 ^ :::    | (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|}}