串联重复
三核苷酸重复扩增
遗传学
放大器
生物
微卫星
一致性
计算生物学
强直性营养不良
DNA测序
基因
基因组
等位基因
聚合酶链反应
作者
Kang Yang,Lei Zhu,Ji Zhang,Qian Yu,Feng Xu,Jiyuan Liu,Yuting Li,Xiaojie Zhang,Zhiqiang Wang,Ning Wang,Yuezhen Li,Yan Shi,Wan‐Jin Chen
标识
DOI:10.1093/clinchem/hvae164
摘要
Abstract Background Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility. Methods We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods. Results For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P < 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization. Conclusions dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI