Comprehensive strategy improves the genetic diagnosis of different polycystic kidney diseases

Autor: Jing Zhang, Tian-Ying Wei, Kai Yang, Zuguo Liu, Huaying Hu, Cunxi Li, Zhan-Ke Feng, Chao Liang, Wei Qiu, Qing Guo
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Cellular and Molecular Medicine
ISSN: 1582-4934
1582-1838
Popis: Polycystic kidney disease (PKD) is known to occur in three main forms, namely autosomal dominant PKD (ADPKD), autosomal recessive PKD (ARPKD) and syndromic PKD (SPKD), based on the clinical manifestations and genetic causes, which are diagnosable from the embryo stage to the later stages of life. Selection of the genetic test for the individuals with diagnostic imaging reports of cystic kidneys without a family history of the disease continues to be a challenge in clinical practice. With the objective of maintaining a limit on the time and medical cost of the procedure, a practical strategy for genotyping and targeted validation to resolve cystogene variations was developed in our clinical laboratory, which combined the techniques of whole‐exome sequencing (WES), Long‐range PCR (LR‐PCR), Sanger sequencing and multiplex ligation–dependent probe amplification (MLPA) to work in a stepwise approach. In this context, twenty‐six families with renal polycystic disorders were enrolled in the present study. Thirty‐two variants involving four ciliary genes (PKD1, PKHD1, TMEM67 and TMEM107) were identified and verified in 23 families (88.5%, 23/26), which expanded the variant spectrum by 16 novel variants. Pathogenic variations in five foetuses of six families diagnosed with PKD were identified using prenatal ultrasound imaging. Constitutional biallelic and digenic variations constituted the pathogenic patterns in these foetuses. The preliminary clinical data highlighted that the WES + LR PCR‐based workflow followed in the present study is efficient in detecting divergent variations in PKD. The biallelic and digenic mutations were revealed as the main pathogenic patterns in the foetuses with PKD.
Databáze: OpenAIRE
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