GenesGeneticsPeer-reviewed journal article
Risk of recurrent pregnancy loss in the Ukrainian population using a combined effect of genetic variants
Eleni M. LoizidouiD, A. M. KucherenkoiD, Pavlo Tatarskyy, S. Yu. ChernushyniD, G. B. LivshytsiD, R. V. GulkovskyiiD, І. І. ВоробйоваiD, Yurii Antipkin, Olexandra GorodnaiD, Marika KaakineniD, Inga ProkopenkoiD, Л. А. ЛившицiD
Abstract
Recurrent pregnancy loss (RPL) affects nearly 5% of the women of reproductive age. Its heterogeneous and multifactorial nature complicate both diagnosis and treatment, as well as identification of the genetic contribution to RPL. Evidence about the aetiology of RPL is controversial; however, several biological mechanisms have been proposed. Given the current knowledge about the genetic susceptibility to idiopathic RPL, we aimed to evaluate the predictive ability of a combined variant panel to the risk of RPL in the Ukrainian sample of 114 cases and 106 healthy controls. We genotyped variants within the 12 genetic loci reflecting the main biological pathways involved in pregnancy maintenance: blood coagulation (F2, F5, F7, GP1A), hormonal regulation (ESR1, ADRB2), endometrium and placental function (ENOS, ACE), folate metabolism (MTHFR) and inflammatory response (IL6, IL8, IL10). We showed that a genetic risk score (GRS) calculated from the 12 variants was associated with an increased risk of RPL (odds ratio 1.56, 95% CI: 1.21,2.04, P=8.7x10-4). The receiver operator characteristic (ROC) analysis resulted in the area under the curve (AUC) of 0.64 (95% CI: 0.57, 0.72), indicating an improved ability of the GRS to classify women with and without RPL. In summary, implementation of the GRS approach can help defining women at higher risk to complex multifactorial conditions such as RPL. Future well-powered genome-wide association studies will help in the dissection of biological pathways not hypothesised previously for RPL and further improve the prediction and identification of those at risk for RPL.
- organism: human
- assay: GWAS summary statistics
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 14
- FWCI
- 0.74
- Field percentile
- top 37%
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