Targeted sequencing analysis of ACVR2A gene identifies novel risk variants associated with preeclampsia

Abstract

Background: Preeclampsia (PE) is the most common complication of pregnancy that remains to be a major cause of maternal and fetal mortality. Prediction and early diagnosis of PE would allow for timely initiation of preventive therapy. According to recent studies of ACVR2A gene polymorphism is associated with PE, but it is still unclear whether these findings reflect specific pathogenetic mechanisms of this disease.

Methods: We performed targeted next-generation sequencing (NGS) sequencing of ACVR2A gene by means of Ion Torrent Personal Genome machine (PGM) Sequencer. A genetic analysis of patients with PE and control group was performed. Bioinformatics analysis using Polyphen2 (Boston, MA), SIFT (La Jolla, CA), and SnpSift software were used. To select genetic markers in PE patients two additive models and score analysis were applied.

Results: Based on the score analysis, we detected two substitutions (rs145399059 and rs17692648) and one insertion insAA at position 148642724 that were associated with PE in our cohorts. We also detected a variant rs17742573 that can be considered as protective against preeclampsia.

Conclusions: Our data suggest that some variants in ACVR2A gene are associated with PE. But more studies are required to reveal the role of ACVR2A gene in the pathogenesis of this disease during pregnancy.

Keywords:

• Polymorphism
• mutations
• SNP
• pre-eclampsia
• ACVR2A
• NGS
• pregnancy

Acknowledgements

In this research, we used the equipment from the Resource centers “biobank” and “Development of molecular and cellular technology” of Saint Petersburg State University. The biological samples and data used in the study were provided from collection “Collection of biosamples of patients with obstetric pathologies” (developed within project 0558-2017-0045) by FSBSI “The Research Institute of Obstetrics, Gynaecology and Reproductology named after D.O. Ott”.

Disclosure statement

The authors declare no conflicts of interest.

Additional information

Funding

This work was supported by Russian Scientific Foundation [No.14-50-00069] (research) and by the Government Offices of Russian Federation [074-U01] (bioinformatics).