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Hereditas(Beijing) >

2025 , Vol. 47 >Issue 3: 314 - 328

DOI: https://doi.org/10.16288/j.yczz.24-296

Guideline

Expert consensus on clinical genome sequencing interpretation and reporting

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  • 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 date: 2024-10-17

  Revised date: 2025-01-21

  Online published: 2025-01-22

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)

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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

Cite this article

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 . DOI: 10.16288/j.yczz.24-296

References

[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, Weinh?usel 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.
[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.
[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.
[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.
[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.
[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, H?berle J, Hayflick SJ, Hempel M, Itkis YS, Kishita Y, Klopstock T, Krylova TD, Lamperti C, Lenz D, Makowski C, Mosegaard S, Müller MF, Mu?oz-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, R?tig 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.
[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.
[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.
[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.
[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.
[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, ?stergaard 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.
[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.
[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.
[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.
[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.
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