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The genomic and clinical landscape of fetal akinesia. | LitMetric

AI Article Synopsis

  • - The study investigates the genetic factors behind fetal akinesia (FA) by analyzing 51 patients from 47 families using next-generation sequencing (NGS) techniques, aiming to improve understanding of the condition.
  • - Researchers identified likely pathogenic gene variants in 37 cases, reporting a total of 41 novel variants and 14 new disease-gene associations specifically linked to fetal akinesia.
  • - The findings highlight that certain genetic defects, particularly in the genes associated with skeletal muscle diseases, may be underdiagnosed, leading to important recommendations for further research on novel genes linked to FA.

Article Abstract

Purpose: Fetal akinesia has multiple clinical subtypes with over 160 gene associations, but the genetic etiology is not yet completely understood.

Methods: In this study, 51 patients from 47 unrelated families were analyzed using next-generation sequencing (NGS) techniques aiming to decipher the genomic landscape of fetal akinesia (FA).

Results: We have identified likely pathogenic gene variants in 37 cases and report 41 novel variants. Additionally, we report putative pathogenic variants in eight cases including nine novel variants. Our work identified 14 novel disease-gene associations for fetal akinesia: ADSSL1, ASAH1, ASPM, ATP2B3, EARS2, FBLN1, PRG4, PRICKLE1, ROR2, SETBP1, SCN5A, SCN8A, and ZEB2. Furthermore, a sibling pair harbored a homozygous copy-number variant in TNNT1, an ultrarare congenital myopathy gene that has been linked to arthrogryposis via Gene Ontology analysis.

Conclusion: Our analysis indicates that genetic defects leading to primary skeletal muscle diseases might have been underdiagnosed, especially pathogenic variants in RYR1. We discuss three novel putative fetal akinesia genes: GCN1, IQSEC3 and RYR3. Of those, IQSEC3, and RYR3 had been proposed as neuromuscular disease-associated genes recently, and our findings endorse them as FA candidate genes. By combining NGS with deep clinical phenotyping, we achieved a 73% success rate of solved cases.

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Source
http://dx.doi.org/10.1038/s41436-019-0680-1DOI Listing

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