Discovery of Pathogenic Variants Associated with Idiopathic Recurrent Pregnancy Loss Using Whole-Exome Sequencing

Abstract

Idiopathic recurrent pregnancy loss (RPL) is defined as at least two pregnancy losses before 20 weeks of gestation. Approximately 5% of pregnant couples experience idiopathic RPL, which is a heterogeneous disease with various causes including hormonal, chromosomal, and intrauterine abnormalities. Although how pregnancy loss occurs is still unknown, numerous biological factors are associated with the incidence of pregnancy loss, including genetic variants. Whole-exome sequencing (WES) was conducted on blood samples from 56 Korean patients with RPL and 40 healthy controls. The WES data were aligned by means of bioinformatic analysis, and the detected variants were annotated using machine learning tools to predict the pathogenicity of protein alterations. Each indicated variant was confirmed using Sanger sequencing. A replication study was also conducted in 112 patients and 114 controls. The Variant Effect Scoring Tool, Combined Annotation Dependent Depletion tool, Sorting Intolerant from Tolerant annotation tool, and various databases detected 10 potential variants previously associated with spontaneous abortion genes in patients by means of a bioinformatic analysis of WES data. Several variants were detected in more than one patient. Interestingly, several of the detected genes were functionally clustered, including some with a secretory function (mucin 4; MUC4; rs200737893 G>A and hyaluronan-binding protein 2; HABP2; rs542838125 G>T), in which growth arrest-specific 2 Like 2 (GAS2L2; rs140842796 C>T) and dynamin 2 (DNM2; rs763894364 G>A) are functionally associated with cell protrusion and the cytoskeleton. ATP Binding Cassette Subfamily C Member 6 (ABCC6) was the only gene with two variants. HABP2 (rs542838125 G>T), MUC4 (rs200737893 G>A), and GAS2L2 (rs140842796 C>T) were detected in only the patient group in the replication study. The combination of WES and machine learning tools is a useful method to detect potential variants associated with RPL. Using bioinformatic tools, we found 10 potential variants in 9 genes. WES data from patients are needed to better understand the causes of RPL.