针对印度 29 种常见溶酶体贮积症开发、验证和应用基于单分子分子反转探针的新型综合基因筛查方法。

IF 3.8 3区 医学 Q2 GENETICS & HEREDITY
Harsh Sheth, Aadhira Nair, Riddhi Bhavsar, Mahesh Kamate, Vykuntaraju K Gowda, Ashish Bavdekar, Sandeep Kadam, Sheela Nampoothiri, Inusha Panigrahi, Anupriya Kaur, Siddharth Shah, Sanjeev Mehta, Sujatha Jagadeesan, Indrani Suresh, Seema Kapoor, Shruti Bajaj, Radha Rama Devi, Ashka Prajapati, Koumudi Godbole, Harsh Patel, Zulfiqar Luhar, Raju C Shah, Anand Iyer, Sunita Bijarnia, Ratna Puri, Mamta Muranjan, Ami Shah, Suvarna Magar, Neerja Gupta, Naresh Tayade, Ajit Gandhi, Ajit Sowani, Shrutikaa Kale, Anil Jalan, Dhaval Solanki, Ashwin Dalal, Shrikant Mane, C Ratna Prabha, Frenny Sheth, Chaitanya G Joshi, Madhvi Joshi, Jayesh Sheth
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引用次数: 0

摘要

背景:溶酶体贮积症(LSDs)目前的临床诊断途径是先进行连续的生化酶学检测,然后再进行DNA测序,这种方法需要反复进行,诊断率低,而且由于临床表现重叠而成本高昂。在此,我们介绍了一种新型的低成本、高通量测序方法,该方法使用单分子分子反转探针(smMIPs)筛查印度 29 种常见 LSD 相关基因中的致病性单核苷酸变异(SNVs)和拷贝数变异(CNVs):针对目标基因(n = 23;人类基因组的 53.7 kb)的外显子和外显子内含子边界设计了 903 个 smMIPs,并将这些 smMIPs 进行等摩尔汇集以创建测序文库。在对 50 例患者进行广泛验证后,我们筛选了 300 例经生化诊断(187 例)或临床怀疑(113 例)患有 LSD 的患者。据观察,在已被生化诊断为 LSD 的患者中,诊断率为 83.4%。此外,临床高度怀疑 LSD 的患者的诊断率为 73.9%(n = 54/73),而临床高度怀疑 LSD 的患者的诊断率仅为 2.4%(n = 1/40)。除了检测 SNV 外,该检测方法还能检测单外显子和多外显子拷贝数变异,可信度很高。重要的是,尼曼-皮克病 C 型和神经细胞类脂质沉着病--6 种无法进行生化检测的疾病,都可以用我们的检测方法诊断出来。最后,我们观察到,与全血相比,从干血斑中提取的 DNA 的检测效果并不逊色:我们开发了一种灵活、可扩展的检测方法,可可靠地检测印度 29 种常见 LSD 的遗传原因。与目前的临床范例相比,该检测方法能在相同或更低的成本下,在多种样本类型中综合检测多种变异类型,同时提高诊断率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development, validation and application of single molecule molecular inversion probe based novel integrated genetic screening method for 29 common lysosomal storage disorders in India.

Background: Current clinical diagnosis pathway for lysosomal storage disorders (LSDs) involves sequential biochemical enzymatic tests followed by DNA sequencing, which is iterative, has low diagnostic yield and is costly due to overlapping clinical presentations. Here, we describe a novel low-cost and high-throughput sequencing assay using single-molecule molecular inversion probes (smMIPs) to screen for causative single nucleotide variants (SNVs) and copy number variants (CNVs) in genes associated with 29 common LSDs in India.

Results: 903 smMIPs were designed to target exon and exon-intron boundaries of targeted genes (n = 23; 53.7 kb of the human genome) and were equimolarly pooled to create a sequencing library. After extensive validation in a cohort of 50 patients, we screened 300 patients with either biochemical diagnosis (n = 187) or clinical suspicion (n = 113) of LSDs. A diagnostic yield of 83.4% was observed in patients with prior biochemical diagnosis of LSD. Furthermore, diagnostic yield of 73.9% (n = 54/73) was observed in patients with high clinical suspicion of LSD in contrast with 2.4% (n = 1/40) in patients with low clinical suspicion of LSD. In addition to detecting SNVs, the assay could detect single and multi-exon copy number variants with high confidence. Critically, Niemann-Pick disease type C and neuronal ceroid lipofuscinosis-6 diseases for which biochemical testing is unavailable, could be diagnosed using our assay. Lastly, we observed a non-inferior performance of the assay in DNA extracted from dried blood spots in comparison with whole blood.

Conclusion: We developed a flexible and scalable assay to reliably detect genetic causes of 29 common LSDs in India. The assay consolidates the detection of multiple variant types in multiple sample types while having improved diagnostic yield at same or lower cost compared to current clinical paradigm.

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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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