Detection of Mosaic Absence of Heterozygosity (AOH) Using Low-Pass Whole Genome Sequencing in Prenatal Diagnosis: A Preliminary Report.

Autor: Lü, Yan, Jiang, Yulin, Zhou, Xiya, Hao, Na, Xu, Chenlu, Guo, Ruidong, Chang, Jiazhen, Li, Mengmeng, Zhang, Hanzhe, Zhou, Jing, Zhang, Wei, Qi, Qingwei
Předmět:
Zdroj: Diagnostics (2075-4418); Sep2023, Vol. 13 Issue 18, p2895, 16p
Abstrakt: Objective: Mosaicism is a common biological phenomenon in organisms and has been reported in many types of chromosome abnormalities, including the absence of heterozygosity (AOH). Due to the detection limitations of the sequencing approach, mosaic AOH events are rarely assessed in clinical cases. Herein, we report the performance of mosaic AOH identification using a low-pass (5~8-fold) WGS method (termed 'CMA-seq', an abbreviation for 'Chromosome Analysis by Sequencing') in fetal genetic diagnosis. Methods: Thirty AOH-negative, eleven constitutional AOH, and three mosaic AOH samples were collected as training data sets to develop the algorithm and evaluate the suitable thresholds for distinguishing mosaic AOH. Twenty-four new chromosomal aberrant cases, along with sixteen constitutional AOH samples, which were previously ascertained via the SNP-array-based method, were used as a validation data set to measure the performance in terms of sensitivity and specificity of this algorithm. Results: A new statistic, 'D-value', was implemented to identify and distinguish constitutional and mosaic AOH events. The reporting thresholds for constitutional and mosaic AOH were also established. In the validation set consisting of 24 new cases, seven constitutional AOH cases and 1 mosaic AOH case were successfully identified, indicating that the results were consistent with those of the SNP-array-based method. The results of all sixteen constitutional AOH validation samples also met the threshold requirements. Conclusions: In this study, we developed a new bioinformatic algorithm to accurately distinguish mosaic AOH from constitutional AOH by low-pass WGS. However, due to the small sample size of the training data set, the algorithm proposed in this manuscript still needs further refinements. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
Nepřihlášeným uživatelům se plný text nezobrazuje