Detection of hemophilia A genetic variants using third-generation long-read sequencing

IF 3.2 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2024-07-20 DOI:10.1016/j.cca.2024.119884
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Abstract

Background

Hemophilia A (HA) is an X-linked recessive genetic disorder caused by pathogenic variations of the factor VIII −encoding gene, F8 gene. Due to the large size and diverse types of variations in the F8 gene, causative mutations in F8 cannot be simultaneously detected in one step by traditional molecular analysis, and genetic molecular diagnosis and prenatal screening of HA still face significant difficulties and challenges in clinical practice. Therefore, we aimed to develop and validate an efficient, accurate, and time-saving method for the genetic detection of HA.

Methods

A comprehensive analysis of hemophilia A (CAHEA) method based on long-range PCR and long-read sequencing (LRS) was used to detect F8 gene mutations in 14 clinical HA samples. The LRS results were compared with those of the conventional methods to evaluate the accuracy and sensitivity of the proposed approach.

Results

The CAHEA method successfully identified 14 F8 variants in all probands, including 3 small insertion deletions, 4 single nucleotide variants, and 7 intron 22 inversions in a “one-step” manner, of which 2 small deletions have not been reported previously. Moreover, this method provided an opportunity to analyze the mechanism of rearrangement and the pathogenicity of F8 variants. The LRS results were validated and found to be in 100% agreement with those obtained using the conventional method.

Conclusion

Our proposed LRS-based F8 gene detection method is an accurate and reproducible genetic screening and diagnostic method with significant clinical value. It provides efficient, comprehensive, and accurate genetic screening and diagnostic services for individuals at high risk of HA as well as for premarital and prenatal populations.

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利用第三代长线程测序技术检测 A 型血友病基因变异
背景甲型血友病(HA)是一种X连锁隐性遗传疾病,由编码第八因子的基因F8基因的致病变异引起。由于 F8 基因体积庞大、变异类型多样,传统的分子分析方法无法一步同时检测出 F8 基因的致病变异,因此 HA 的遗传分子诊断和产前筛查在临床实践中仍面临着巨大的困难和挑战。方法采用基于长程 PCR 和长线程测序(LRS)的 A 型血友病综合分析(CAHEA)方法检测 14 例临床 HA 样本中的 F8 基因突变。结果 CAHEA方法以 "一步到位 "的方式成功鉴定了所有样本中的14个F8基因变异,包括3个小的插入缺失、4个单核苷酸变异和7个22号内含子倒位,其中2个小的插入缺失以前从未报道过。此外,这种方法还为分析 F8 变异的重排机制和致病性提供了机会。结论我们提出的基于 LRS 的 F8 基因检测方法是一种准确、可重复的基因筛查和诊断方法,具有重要的临床价值。它为 HA 高危人群以及婚前和产前人群提供了高效、全面、准确的基因筛查和诊断服务。
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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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