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儿童基因组学解答:对 1000 多个儿科罕见病基因组的动态分析。

Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes.

机构信息

Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO.

Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO.

出版信息

Genet Med. 2022 Jun;24(6):1336-1348. doi: 10.1016/j.gim.2022.02.007. Epub 2022 Mar 16.

Abstract

PURPOSE

This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program.

METHODS

Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes.

RESULTS

Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases).

CONCLUSION

Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.

摘要

目的

本研究旨在通过“儿童基因组解答计划”(Genomic Answers for Kids program),为儿科罕见病队列提供全面的诊断和候选分析。

方法

对 960 个疑似遗传疾病的家庭进行了广泛的分析,包括短读长外显子组测序和短读长基因组测序(srGS);PacBio HiFi 长读长基因组测序(HiFi-GS);单核苷酸变异(SNV)、结构变异(SV)和重复变异的变异调用;以及机器学习变异优先级排序。结构化表型、优先变异和家系存储在 PhenoTips 数据库中,通过受控访问数据库的基因型和表型(database of Genotypes and Phenotypes)共享数据。

结果

诊断率从先前阴性基因检测患者的 11%到无经验患者的 34.5%不等。从基因组测序中纳入 SV 可使先前未解决病例的新诊断增加 13%。与 srGS 相比,HiFi-GS 产生的稀有编码 SV 增加了 4 倍以上,从而提高了发现率。具有未知意义的变异和基因仍然是最常见的发现(58%的非诊断病例)。

结论

计算优先级对诊断 SNV 有效。非 SNV 的彻底鉴定仍然具有挑战性,部分可以通过使用 HiFi-GS 测序来缓解。重要的是,通过实时数据共享来支持社区研究,以加速基因验证,并提供来自>1000 个人类等位基因的 HiFi 变异(SNV/SV)资源,以促进新的测序平台在罕见病诊断中的应用。

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