临床基因组测序解读与报告专家共识

doi:10.16288/j.yczz.24-296

[an error occurred while processing this directive]

Hereditas(Beijing) ›› 2025, Vol. 47 ›› Issue (3): 314-328.doi: 10.16288/j.yczz.24-296

• Guideline • Previous Articles Next Articles

Expert consensus on clinical genome sequencing interpretation and reporting

Yulan Lu¹(), Guozhuang Li²(), Yaqiong Wang³, Kexin Xu², Xinran Dong³, Jihao Cai², Bingbing Wu³, Huijun Wang³, Ping Fang⁹, Jian Wang¹⁰, Hua Wang²², Luming Sun¹¹, Yongyu Ye¹², Qing Li², Yaping Liu⁷, Li Liu¹, Ning Liu¹⁴, Jiaqi Liu¹⁵, Fang Song¹⁶, Lin Yang³, Zhengqing Qiu⁵, Zefu Chen¹⁷, Huaxia Luo¹⁸, Dan Guo⁶, Chanjuan Hao¹⁹, Sen Zhao²⁰, Shangzhi Huang⁸, Jing Peng²¹, Xiaoqiang Cai¹, Ruifang Sui⁴, Linkang Li¹³, Nan Wu²(), Wenhao Zhou¹(), Shuyang Zhang⁷()

1. Guangzhou Women and Children’s Medical Center, Guangzhou 511400, China
2. Department of Orthopedics, Peking Union Medical College Hospital, Beijing 100730, China
3. Center for Molecular Medicine, Children’s Hospital of Fudan University, Shanghai 201102, China
4. Department of Ophthalmology, Peking Union Medical College Hospital, Beijing 100730, China
5. Department of Pediatrics, Peking Union Medical College Hospital, Beijing 100730, China
6. Clinical Biosample Center, Peking Union Medical College Hospital, Beijing 100730, China
7. Center for Rare Diseases, State Key Laboratory for Complex, Severe, and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China
8. Department of Medical Genetics, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100005, China
9. Expert Committee, American College of Medical Genetics, Bethesda 20811, USA
10. International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
11. Prenatal Diagnosis Center, First Maternity and Infant Health Hospital, Tongji University, Shanghai 200040, China
12. Department of Spinal Surgery, Guangdong Provincial People’s Hospital, Southern Medical University, Guangzhou 510315, China
13. China Alliance for Rare Diseases, Beijing 100005, China
14. Center for Genetics and Prenatal Diagnosis, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052
15. Department of Breast Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing 100021, China
16. Department of Medical Genetics, Capital Institute of Pediatrics, Beijing 100020, China
17. Department of Spinal Orthopedics, Nanfang Hospital, Southern Medical University, Guangzhou 510450, China
18. Department of Pediatrics, Peking University First Hospital, Beijing 100034, China
19. Children’s Medical Center, Beijing Children’s Hospital, Capital Medical University, Beijing 100034
20. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston 77030, USA
21. Department of Pediatrics, Xiangya Hospital, Central South University, Changsha 410008, China
22. Department of Medical Genetics, Hunan Children’s Hospital, Changsha 410007, China

Received:2024-10-17 Revised:2025-01-21 Online:2025-03-20 Published:2025-01-22
Contact: Nan Wu, Wenhao Zhou, Shuyang Zhang E-mail:yulanlu@fudan.edu.cn;Guozhuang_Li@outlook.com;dr.wunan@pumch.cn;zwhchfu@126.com;shuyangzhang103@163.com
Supported by:
National Key Research and Development Program of China(2022YFC2703100);National Key Research and Development Program of China(2023YFC2507700);National Key Research and Development Program of China(2022YFC2703102);Shanghai Scientific and Technological Innovation Action Plan(20Z11900600);Shanghai Scientific and Technological Innovation Action Plan(22DZ2204800);CAMS Innovation Fund for Medical Sciences (CIFMS)(2021-I2M-1-051);CAMS Innovation Fund for Medical Sciences (CIFMS)(2021-I2M-1-052);CAMS Innovation Fund for Medical Sciences (CIFMS)(2023-I2M-C&T-A-003);National High Level Hospital Clinical Research Funding(2022-PUMCH-D-004);National High Level Hospital Clinical Research Funding(2022-PUMCH-C-033);Peking Union Medical College Hospital Public Welfare Project for Rare Disease Service Improvement(UPWARDS);Fundamental Research Funds for the Central Public Welfare Research Institutes of the Chinese Academy of Medical Sciences(2019PT320025)

Abstract

Abstract:

Genome sequencing (GS) refers to a technology that comprehensively and systematically detects the DNA sequences of an individual’s nuclear and mitochondrial genomes. It aims to identify genetic variants and investigate their roles in human health and disease progression. As an emerging diagnostic tool, GS offers significant support for clinical diagnosis due to its high throughput, accuracy, and comprehensiveness. However, the complexity of data analysis and interpretation requires substantial professional expertise and experience, posing considerable challenges. When applying GS technology for molecular diagnosis of genetic diseases, ethical and technical issues related to clinical application arise, including informed consent, diagnostic data interpretation, and defining the scope and content of clinical reports. This expert consensus outlines the core workflow of clinical genome sequencing (cGS), clarifies its testing scope and technical limitations, and provides key steps for data quality control, analysis, annotation, and variant interpretation. It also addresses controversial issues related to report content and informed consent. This consensus aims to assist professionals in accurately understanding and appropriately utilizing clinical genome sequencing, thereby improving diagnostic accuracy for genetic diseases, enhancing the clinical utility of the technology, and advancing medical scientific research.

Key words: genetic rare diseases, genome sequencing, high-throughput sequencing data analysis, variant interpretation, genetic testing report

Yulan Lu, Guozhuang Li, Yaqiong Wang, Kexin Xu, Xinran Dong, Jihao Cai, Bingbing Wu, Huijun Wang, Ping Fang, Jian Wang, Hua Wang, Luming Sun, Yongyu Ye, Qing Li, Yaping Liu, Li Liu, Ning Liu, Jiaqi Liu, Fang Song, Lin Yang, Zhengqing Qiu, Zefu Chen, Huaxia Luo, Dan Guo, Chanjuan Hao, Sen Zhao, Shangzhi Huang, Jing Peng, Xiaoqiang Cai, Ruifang Sui, Linkang Li, Nan Wu, Wenhao Zhou, Shuyang Zhang. Expert consensus on clinical genome sequencing interpretation and reporting[J]. Hereditas(Beijing), 2025, 47(3): 314-328.

Figures/Tables 5

Fig. 1

Table 1

Table 2

The annotation content and precautions of GS data"

注释大类	数据注释	注释内容与注意事项说明
基于变异检测的推断	变异的检测结果	根据变异检测推断合子类型(杂合变异、纯合变异、半合变异或嵌合变异等)、标注变异的类型(参见表3)和标注变异的检测质量
基于位点的致病性分析注释，参考美国医学遗传学与基因组学学会(The American college of medical genetics and genomics, ACMG)指南等变异致病性评级规则，为致病性评级提供所需的注释内容	变异的人群频率	包括变异在不同数据集中的人群频率，建议细化为等位基因频率及各合子类型个体数量，区分不同族群来源及表型标签(患病或正常)。在评估来自不同测序方案的数据集时(例如来自外显子捕获测序或GS)，应灵活计算合理的等位基因总数
	变异的生物学影响	包括变异对转录本中碱基的改变或对氨基酸的改变、对蛋白质的影响、对转录本剪接的影响、对基因调控的影响等。应注意转录本的选择(推荐采用MANE Select，MANE Plus Clinical或在疾病相关组织表达量较高的转录本)、变异描述符合人类基因组变异协会(Human Genome Variation Society, HGVS)规则、使用合适的变异危害预测评分或分级等
	变异与临床症状或疾病的关联	包括同一变异或相关变异(包括发生在同一染色体坐标的不同变异，或是造成了同样氨基酸变化的不同变异)在既往研究中的病例报道、既往的致病性评级结论及证据。此部分应额外关注信息更新，尤其是在现有的致病性结论相互冲突时
	其他自定义注释	包括标记出自行整理和维护的需要特殊关注的位点黑/白名单^注等额外参考信息
基于基因功能与关联疾病的注释	基因与疾病的关联	包括既往研究中对于基因与疾病关联的报道、结论和相关的衍生概念，例如由于基因异常而导致的不同临床表型及对应的遗传模式、发病年龄、外显率等
	基因的生物学功能	包括野生型基因在功能学实验中展示的生物学功能，例如结构性蛋白的定位、调控因子的通路或表达谱特征、酶蛋白的具体功能和代谢通路等
	其他自定义注释	包括需要特殊关注的基因黑/白名单、基因组学分析中基因对功能失活变异的耐受度(如预测的功能丧失(Predictive loss of function, pLOF)值)、标记存在假基因、同源序列或串联重复序列的特殊基因(如CYP21A2、SMN1、PHOX2B)等
变异注释过程中的注意事项	参考基因组版本	变异位点的注释高度依赖变异的染色体定位，因此需重点关注各注释数据库所依托的参考基因组版本，尤其是线粒体基因组的具体版本。目前常用的人类基因组参考序列有hg19, hg38, hs37d5等
	文件的标准格式	在进行数据注释时，往往会用到现成的工具软件，应注意变异记录文件在不同格式间的转化问题，包括VCF、BED及部分软件自定义的数据格式等
	变异坐标或区段描述	描述变异坐标或区段时，应明确所使用0-base/1-base的开闭区间定位方式。在每一步调用知识库进行信息注释或是使用工具软件时，应确认所用的定位方式和知识库/工具软件默认的输入格式是否一致
	CNV/SV变异注释	在对CNV或SV等区段型变异进行注释时，应注意覆盖比例的阈值，避免将仅局部相关或是长度差异过大的两个变异相互关联
	Indel变异注释	注释Indel变异时应考虑不同对齐方式导致的变异错配问题，尤其是在变异附近带有顺式单核苷酸变异时
	其他影响	对于GS数据，尤其应关注变异(尤其是结构变异)对基因调控可能产生的影响，建议注释已知调控元件、染色质结构信息(如Hi-C)、组蛋白修饰标记区等

Table 2

Table 3

Considerations for the interpretation and reporting of relevant variant types detected by cGS"

变异类型	主要解释	特殊变异解读注意事项
单核苷酸变异 (single nucleotide variation, SNV)	•数量最多的待核查变异，尤其需要综合使用表型驱动和基因型驱动的过滤策略 •应考虑变异可能导致的所有可能后果(例如核查剪接注释、转录特异性影响等)	一些位点可能有多个等位基因(多等位基因变异)
小插入/缺失 (insertion and deletion, Indel)	•按照与SNV相同的过滤和分类步骤进行评估 •实验室通常会指定需要验证的插入/缺失变异大小范围，以供评估和报告	部分Indel可能由于长度、序列特征因素或变异检测算法偏好导致结果不准确，需要人工核查BAM文件，并通过一代测序进行确认
拷贝数变异 (copy number variation, CNV)	• PCR-free测序使得GS数据在编码和非编码区域覆盖均匀，可更敏感地检测大片段CNV，且对断点的检测较ES准确 •一般基于测序质量、参考人群频率以及所覆盖的外显子区域进行过滤分析。在解读过程中要核查遗传模式和拷贝数 •应用过滤策略筛选或与变异数据库中的既往变异比较时，应考虑CNV片段大小的多样性和所得断点精确性的问题	除与疾病相关的CNV外，涉及大片段、包含基因数量足够多的CNV也应纳入分析。在分析CNV时还应考虑由于片段大小和断点识别而导致的异质性问题
结构变异 (structure variation, SV)	GS可识别断点序列从而检测平衡易位和更复杂的结构变异。由于对正常人群中变异的认识不够充分，目前SV检测主要用于： •检测反复出现的致病性平衡SVs^注•对通过序列覆盖深度检测到的CNVs进行重新筛选/表征 •针对已知区域或感兴趣基因的SV(例如平衡重排)进行定向搜索	目前的SV检测仍主要用于辅助CNV检测。实验室应明确SV的分析范围和敏感性的局限。当难以判读cGS检出的SV结果时，可考虑采用其他方法进行验证。常规验证方法为(1)对于核型分析可能看得到的分辨率，检查核型分析；(2)易位或异常染色体结构，考虑FISH；(3)针对断裂点设计特异性PCR+Sanger测序；(4)其他基因组学方式，如长片段测序
纯合区域 (regions of homozygosity, ROH)	•单个染色体上广泛的同源单体型区(ROH)可能表明存在单亲二体性 (UPD)，应重点关注位于6、7、11、14、15、20号染色体上与UPD遗传病相关的ROH区域	跨越多个染色体的ROH可能是由近亲关系引起的。当检测到已知的父源或母源印迹异常导致疾病的ROH或UPD时，推荐通过甲基化检测或家系单倍型分型分析进一步明确
高同源性区域的变异	•特制算法可用于高同源性或具有已知假基因的区域中筛选变异	应在临床报告中描述局限性
短串联重复序列 (short tandem repeats, STR)	•相对于ES,GS可更好无偏移地检测STR •若检测结果中提及STR及重复度，在进一步遗传咨询或临床报告前需要用其他方法验证 •人工核查STR位点的原始数据是重要的QC步骤	当前GS检测STR的算法在灵敏度和特异性方面仍存在局限性。实验室应提供适合分析STR的基因列表与重复度范围的信息
线粒体变异 (mitochondrial DNA variation, mtDNA)	•GS可检测线粒体变异，但需要考虑不同样本类型(如血液、肌肉组织、皮肤组织等)的差异和局限性 •目前指南对评估线粒体新发变异的指导不足，通常仅报告已知致病性变异	在解读线粒体变异时应考虑检材选择而导致的检测局限性，以及组织特异性表现。分析线粒体变异结果时应考虑线粒体单倍群问题。此外，对于GS分析，线粒体来源的核序列(nuclear-mitochondrial segments, NUMT)可能会影响变异检测的敏感度
体细胞嵌合变异	•检出疑似体细胞嵌合变异时，需要其他方法验证 •GS在检测嵌合型CNV时，敏感性可能会比嵌合型SNV高	由于覆盖深度较低，GS检测体细胞嵌合变异的能力低于ES或靶向测序(target panel)

Table 3

Fig. 2

References 27

[1]	Patel RK, Jain M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PloS One, 2012, 7(2): e30619.
[2]	Pandey RV, Pabinger S, Kriegner A, Weinhäusel A. ClinQC: a tool for quality control and cleaning of Sanger and NGS data in clinical research. BMC Bioinformatics, 2016, 17: 1-9.
[3]	Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu BS, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu CL, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott DR. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res, 2018, 46(D1): D1062-D1067.
[4]	Gudmundsson S, Singer-Berk M, Watts NA, Phu W, Goodrich JK, Solomonson M, Genome Aggregation Database Consortium, Rehm HL, MacArthur DG, O'Donnell-Luria A. Variant interpretation using population databases: lessons from gnomAD. Hum Mutat, 2022 43(8): 1012-1030.
[5]	Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL, ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American college of medical genetics and genomics and the association for molecular pathology. Genet Med, 2015, 17(5): 405-423. doi: 10.1038/gim.2015.30 pmid: 25741868
[6]	Riggs ER, Andersen EF, Cherry AM, Kantarci S, Kearney H, Patel A, Raca G, Ritter DI, South ST, Thorland EC, Pineda-Alvarez D, Aradhya S, Martin CL. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American college of medical genetics and genomics (ACMG) and the clinical genome resource (ClinGen). Genet Med, 2021, 23(11): 2230.
[7]	Wong LJC, Chen T, Schmitt ES, Wang J, Tang S, Landsverk M, Li FY, Zhang SL, Wang Y, Zhang VW, Craigen WJ. Clinical and laboratory interpretation of mitochondrial mRNA variants. Hum Mutat, 2020, 41(10): 1783-1796.
[8]	Wong LJ, Chen T, Wang J, Tang S, Schmitt ES, Landsverk M, Li FY, Wang Y, Zhang SL, Zhang VW, Craigen WJ. Interpretation of mitochondrial tRNA variants. Genet Med, 2020, 22(5): 917-926.
[9]	Strande NT, Riggs ER, Buchanan AH, Ceyhan-Birsoy O, DiStefano M, Dwight SS, Goldstein J, Ghosh R, Seifert BA, Sneddon TP, Wright MW, Milko LV, Cherry JM, Giovanni MA, Murray MF, O'Daniel JM, Ramos EM, Santani AB, Scott AF, Plon SE, Rehm HL, Martin CL, Berg JS. Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by the clinical genome resource. Am J Hum Genet, 2017, 100(6): 895-906. doi: S0002-9297(17)30160-X pmid: 28552198
[10]	Austin-Tse CA, Jobanputra V, Perry DL, Bick D, Taft RJ, Venner E, Gibbs RA, Young T, Barnett S, Belmont JW, Boczek N, Chowdhury S, Ellsworth KA, Guha S, Kulkarni S, Marcou C, Meng L, Murdock DR, Rehman AU, Spiteri E, Thomas-Wilson A, Kearney HM, Rehm HL, Medical Genome Initiative. Best practices for the interpretation and reporting of clinical whole genome sequencing. NPJ Genom Med, 2022, 7(1): 27. doi: 10.1038/s41525-022-00295-z pmid: 35395838
[11]	Deignan JL, Chung WK, Kearney HM, Monaghan KG, Rehder CW, Chao EC, ACMG Laboratory Quality Assurance Committee. Points to consider in the reevaluation and reanalysis of genomic test results: a statement of the American college of medical genetics and genomics (ACMG). Genet Med, 2019, 21(6): 1267-1270. doi: 10.1038/s41436-019-0478-1 pmid: 31015575
[12]	David KL, Best RG, Brenman LM, Bush L, Deignan JL, Flannery D, Hoffman JD, Holm I, Miller DT, O’Leary J, Pyeritz RE, ACMG Social Ethical Legal Issues Committee. Patient re-contact after revision of genomic test results: points to consider—a statement of the American college of medical genetics and genomics (ACMG). Genet Med, 2019, 21(4): 769-771.
[13]	Costain G, Jobling R, Walker S, Reuter MS, Snell M, Bowdin S, Cohn RD, Dupuis L, Hewson S, Mercimek- Andrews S, Shuman C, Sondheimer N, Weksberg R, Yoon G, Meyn MS, Stavropoulos DJ, Scherer SW, Mendoza- Londono R, Marshall CR. Periodic reanalysis of whole- genome sequencing data enhances the diagnostic advantage over standard clinical genetic testing. Eur J Hum Genet, 2018, 26(5): 740-744.
[14]	Chong R, Insigne KD, Yao D, Burghard CP, Wang J, Hsiao YHE, Jones EM, Goodman DB, Xiao XS, Kosuri S. A multiplexed assay for exon recognition reveals that an unappreciated fraction of rare genetic variants cause large-effect splicing disruptions. Mol Cell, 2019, 73(1): 183-194.
[15]	Soemedi R, Cygan KJ, Rhine CL, Wang J, Bulacan C, Yang J, Bayrak-Toydemir P, McDonald J, Fairbrother WG. Pathogenic variants that alter protein code often disrupt splicing. Nat Genet, 2017, 49(6): 848-855. doi: 10.1038/ng.3837 pmid: 28416821
[16]	Yépez VA, Gusic M, Kopajtich R, Mertes C, Smith NH, Alston CL, Ban R, Beblo S, Berutti R, Blessing H, Ciara E, Distelmaier F, Freisinger P, Häberle J, Hayflick SJ, Hempel M, Itkis YS, Kishita Y, Klopstock T, Krylova TD, Lamperti C, Lenz D, Makowski C, Mosegaard S, Müller MF, Muñoz-Pujol G, Nadel A, Ohtake A, Okazaki Y, Procopio E, Schwarzmayr T, Smet J, Staufner C, Stenton SL, Strom TM, Terrile C, Tort F, Van Coster R, Vanlander A, Wagner M, Xu MT, Fang F, Ghezzi D, Mayr JA, Piekutowska-Abramczuk D, Ribes A, Rötig A, Taylor RW, Wortmann SB, Murayama K, Meitinger T, Gagneur J, Prokisch H. Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med, 2022, 14(1): 38. doi: 10.1186/s13073-022-01019-9 pmid: 35379322
[17]	Gonorazky HD, Naumenko S, Ramani AK, Nelakuditi V, Mashouri P, Wang PQ, Kao D, Ohri K, Viththiyapaskaran S, Tarnopolsky MA, Mathews KD, Moore SA, Osorio AN, Villanova D, Kemaladewi DU, Cohn RD, Brudno M, Dowling JJ. Expanding the boundaries of RNA sequencing as a diagnostic tool for rare Mendelian disease. Am J Hum Genet, 2019, 104(3): 466-483. doi: S0002-9297(19)30012-6 pmid: 30827497
[18]	Ketkar S, Burrage LC, Lee B. RNA sequencing as a diagnostic tool. JAMA, 2023, 329(1): 85-86.
[19]	Robertson KD. DNA methylation and human disease. Nat Rev Genet, 2005, 6(8): 597-610. doi: 10.1038/nrg1655 pmid: 16136652
[20]	Aref-Eshghi E, Bend EG, Colaiacovo S, Caudle M, Chakrabarti R, Napier M, Brick L, Brady L, Carere DA, Levy MA, Kerkhof J, Stuart A, Saleh M, Beaudet AL, Li CM, Kozenko M, Karp N, Prasad C, Siu VM, Tarnopolsky MA, Ainsworth PJ, Lin HX, Rodenhiser DI, Krantz ID, Deardorff MA, Schwartz CE, Sadikovic B., Diagnostic utility of genome-wide DNA methylation testing in genetically unsolved individuals with suspected hereditary conditions. Am J Hum Genet, 2019, 104(4): 685-700. doi: S0002-9297(19)30104-1 pmid: 30929737
[21]	Sadikovic B, Levy MA, Kerkhof J, Aref-Eshghi E, Schenkel L, Stuart A, McConkey H, Henneman P, Venema A, Schwartz CE, Stevenson RE, Skinner SA, DuPont BR, Fletcher RS, Balci TB, Siu VM, Granadillo JL, Masters J, Kadour M, Friez MJ, van Haelst MM, Mannens MMAM, Louie RJ, Lee JA, Tedder ML, Alders M. Clinical epigenomics: genome-wide DNA methylation analysis for the diagnosis of Mendelian disorders. Genet Med, 2021, 23(6): 1065-1074. doi: 10.1038/s41436-020-01096-4 pmid: 33547396
[22]	Levy MA, McConkey H, Kerkhof J, Barat-Houari M, Bargiacchi S, Biamino E, Bralo MP, Cappuccio G, Ciolfi A, Clarke A, DuPont BR, Elting MW, Faivre L, Fee T, Fletcher RS, Cherik F, Foroutan A, Friez MJ, Gervasini C, Haghshenas S, Hilton BA, Jenkins Z, Kaur S, Lewis S, Louie RJ, Maitz S, Milani D, Morgan AT, Oegema R, Østergaard E, Pallares NR, Piccione M, Pizzi S, Plomp AS, Poulton C, Reilly J, Relator R, Rius R, Robertson S, Rooney K, Rousseau J, Santen GWE, Santos-Simarro F, Schijns J, Squeo GM, St John M, Thauvin-Robinet C, Traficante G, van der Sluijs PJ, Vergano SA, Vos N, Walden KK, Azmanov D, Balci T, Banka S, Gecz J, Henneman P, Lee JA, Mannens MMAM, Roscioli T, Siu V, Amor DJ, Baynam G, Bend EG, Boycott K, Brunetti-Pierri N, Campeau PM, Christodoulou J, Dyment D, Esber N, Fahrner JA, Fleming MD, Genevieve D, Kerrnohan KD, McNeill A, Menke LA, Merla G, Prontera P, Rockman- Greenberg C, Schwartz C, Skinner SA, Stevenson RE, Vitobello A, Tartaglia M, Alders M, Tedder ML, Sadikovic B. Novel diagnostic DNA methylation episignatures expand and refine the epigenetic landscapes of Mendelian disorders. HGG Adv, 2022, 3(1): 100075.
[23]	Jagadeesh KA, Birgmeier J, Guturu H, Deisseroth CA, Wenger AM, Bernstein JA, Bejerano G. Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritization. Genet Med, 2019, 21(2): 464-470. doi: 10.1038/s41436-018-0072-y pmid: 29997393
[24]	Chen ZF, Zheng Y, Yang YX, Huang YZ, Zhao S, Zhao HQ, Yu CX, Dong XY, Zhang YQ, Wang LL, Zhao ZY, Wang SR, Yang Y, Ming Y, Su JZ, Qiu GX, Wu ZH, Zhang TJG, Wu N. PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning. Am J Hum Genet, 2022, 109(2): 270-281. doi: 10.1016/j.ajhg.2021.12.008 pmid: 35063063
[25]	Deisseroth CA, Birgmeier J, Bodle EE, Kohler JN, Matalon DR, Nazarenko Y, Genetti CA, Brownstein CA, Schmitz-Abe K, Schoch K, Cope H, Signer R, Undiagnosed Diseases Network, Martinez-Agosto JA, Shashi V, Beggs AH, Wheeler MT, Bernstein JA, Bejerano G. ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis. Genet Med, 2019, 21(7): 1585-1593. doi: 10.1038/s41436-018-0381-1 pmid: 30514889
[26]	Birgmeier J, Deisseroth CA, Hayward LE, Galhardo LMT, Tierno AP, Jagadeesh KA, Stenson PD, Cooper DN, Bernstein JA, Haeussler M, Bejerano G. AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature. Genet Med, 2020, 22(2): 362-370. doi: 10.1038/s41436-019-0643-6 pmid: 31467448
[27]	Birgmeier J, Haeussler M, Deisseroth CA, Steinberg EH, Jagadeesh KA, Ratner AJ, Guturu H, Wenger AM, Diekhans ME, Stenson PD, Cooper DN, Ré C, Beggs AH, Bernstein JA, Bejerano G. AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature. Sci Transl Med, 2020, 12(544): eaau9113.

Metrics

Comments

www.chinagene.cn
备案号：京ICP备09063187号

质控大类	质控指标	描述	阈值/单位
溯源质控	样本溯源	使用分子标签技术，家系亲缘分析等保障样本溯源的准确性	通过/失败^注1
	性别溯源	使用性染色体覆盖度或SRY基因计算并推测测序样本性别	通过/失败^注2
	数据污染溯源	通过杂合度比例的偏移值评估个体间的污染风险	通过/失败^注3(≤2%)
数据质控	原始数据量(Gb)	测序下机原始数据量	≥100 Gb
	数据质量值Q30(%)	测序下机原始数据中测序质量评分大于30的百分比	≥85%
	N碱基比例(%)	测序序列中无法准确比对及分析的百分比	≤5%
	接头序列比例(%)	测序序列两端接头二聚体比例	≤1%
	GC分布(%)	碱基GC偏好性	39%~44%
	插入片段长度中位数(bp)	DNA插入片段大小的中位数	≥300 bp
	比对率(%)	用于提示可能存在的非人源污染，需结合样本溯源综合评估	≥99%
	重复比例(%)	测序数据中重复序列的占比	≤10%
	平均测序深度(×)	有效数据回贴后全基因组的平均覆盖深度	≥30×
	全基因组20×以上覆盖区域(%)	全基因组范围内20×深度以上的区域占比	≥90%
	转换颠换率Ti/Tv	根据物种及测序覆盖区域，转换颠换率具有很强的保守性，可评估数据稳定性	≥2

Expert consensus on clinical genome sequencing interpretation and reporting

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 27

Related Articles 12

Recommended Articles

Metrics

Comments

[1]	Hongyuan Zheng, Lin Yan, Chao Yang, Yarong Wu, Jingliang Qin, Tongyu Hao, Dajin Yang, Yunchang Guo, Xiaoyan Pei, Tongyan Zhao, Yujun Cui. Population genomics study of Vibrio alginolyticus [J]. Hereditas(Beijing), 2021, 43(4): 350-361.
[2]	Qiang Wei, Yan Ao, Manman Yang, Tao Chen, Hu Han, Xingju Zhang, Ran Wang, Qiuju Xia, Fangfang Jiang, Yong Li. Identification of genomic insertion of dominant-negative GHR mutation transgenes in Wuzhishan pig using whole genome sequencing method [J]. Hereditas(Beijing), 2021, 43(12): 1149-1158.
[3]	Xiaoli Shi,Yilin He,Hongqing Ling. Progress on wheat A genome illustration and its evolutional analysis [J]. Hereditas(Beijing), 2019, 41(9): 836-844.
[4]	Bo Wang,Fang Liu,Erchun Zhang,Chenliang Wo,Jason Chen,Puyi Qian,Haorong Lu,Wenjun Zeng,Tai Chen,Jinpu Wei,Qian Wan,Ren Wang,Xun Xu. The China National GeneBank—owned by all, completed by all and shared by all [J]. Hereditas(Beijing), 2019, 41(8): 761-772.
[5]	Wenwen Deng, Caiwu Li, Siyue Zhao, Rengui Li, Yongguo He, Daifu Wu, Shengzhi Yang, Yan Huang, Hemin Zhang, Likou Zou. Whole genome sequencing reveals the distribution of resistance and virulence genes of pathogenic Escherichia coli CCHTP from giant panda [J]. Hereditas(Beijing), 2019, 41(12): 1138-1147.
[6]	Weimin Kuang, Li Yu. Mitogenome assembly strategies and software applications in the genome era [J]. Hereditas(Beijing), 2019, 41(11): 979-993.
[7]	Wenwen Deng,Mei Long,Shengzhi Yang,Likou Zou. Evolution and the flanking sequences of β-lactamase resistance gene bla_OKP [J]. Hereditas(Beijing), 2018, 40(7): 585-592.
[8]	Shenghan Gao,Haiying Yu,Shuangyang Wu,Sen Wang,Jianing Geng,Yingfeng Luo,Songnian Hu. Advances of sequencing and assembling technologies for complex genomes [J]. Hereditas(Beijing), 2018, 40(11): 944-963.
[9]	Liya Sun, Qinghe Xing, Lin He. Retrospect and prospect of the genetic research on birth defects in China [J]. Hereditas(Beijing), 2018, 40(10): 800-813.
[10]	Chao Yang, Ruifu Yang, Yujun Cui. Bacterial genome-wide association study: methodologies and applications [J]. Hereditas(Beijing), 2018, 40(1): 57-65.
[11]	Qianzhi Shao, Yi Jiang, Jinyu Wu. Whole-genome sequencing and its application in the research and diagnoses of genetic diseases [J]. Hereditas(Beijing), 2014, 36(11): 1087-1098.
[12]	YANG Gong, MA Ru-Hui, LI Bei, HUANG Lai. Progress on horse genome project [J]. HEREDITAS, 2010, 32(3): 211-218.