SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing

被引:0
作者
Qiliang Ding
Cherith Somerville
Roozbeh Manshaei
Brett Trost
Miriam S. Reuter
Kelsey Kalbfleisch
Kaitlin Stanley
John B. A. Okello
S. Mohsen Hosseini
Eriskay Liston
Meredith Curtis
Mehdi Zarrei
Edward J. Higginbotham
Ada J. S. Chan
Worrawat Engchuan
Bhooma Thiruvahindrapuram
Stephen W. Scherer
Raymond H. Kim
Rebekah K. Jobling
机构
[1] Cardiac Genome Clinic,Ted Rogers Centre for Heart Research
[2] The Hospital for Sick Children,Division of Clinical and Metabolic Genetics
[3] The Hospital for Sick Children,The Centre for Applied Genomics
[4] The Hospital for Sick Children,Program in Genetics and Genome Biology
[5] The Hospital for Sick Children,CGEn
[6] The Hospital for Sick Children,MIT Sloan School of Management
[7] Massachusetts Institute of Technology,Department of Pathology
[8] The University of Texas MD Anderson Cancer Center,Genome Diagnostics, Department of Paediatric Laboratory Medicine
[9] The Hospital for Sick Children,Department of Molecular Genetics and the McLaughlin Centre
[10] University of Toronto,Fred A. Litwin Family Centre in Genetic Medicine
[11] University Health Network,Department of Medicine
[12] University of Toronto,undefined
来源
Human Genetics | 2023年 / 142卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page—supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP, a video tutorial series is available at https://bit.ly/SCIPVideos).
引用
收藏
页码:201 / 216
页数:15
相关论文
共 1083 条
[1]  
Abou Tayoun AN(2018)Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion Hum Mutat 39 1517-1524
[2]  
Pesaran T(2011)CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing Genome Res 21 974-984
[3]  
DiStefano MT(2019)OMIM.org: leveraging knowledge across phenotype-gene relationships Nucleic Acids Res 47 D1038-D1043
[4]  
Oza A(2022)Best practices for the interpretation and reporting of clinical whole genome sequencing NPJ Genom Med 7 27-161
[5]  
Rehm HL(2021)Samplot: a platform for structural variant visual validation and automated filtering Genome Biol 22 161-1222
[6]  
Biesecker LG(2006)Cri du Chat syndrome Orphanet J Rare Dis 1 33-451
[7]  
Harrison SM(2016)Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications Bioinformatics 32 1220-141
[8]  
Working CSVI(2020)A structural variation reference for medical and population genetics Nature 581 444-458
[9]  
G, A(2022)Copy number variations in a Brazilian cohort with autism spectrum disorders highlight the contribution of cell adhesion genes Clin Genet 101 134-97
[10]  
Abyzov AE(2020)Transcript expression-aware annotation improves rare variant interpretation Nature 581 452-533