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Predicting embryonic aneuploidy rate and identifying candidate genes in IVF patients using synonymou

Updated: Sep 29, 2022

Jason Liu Siqi Sun Jinchuan Xing. Rutgers, The State University of New Jersey

Aneuploidy is the presence of an abnormal number of chromosomes in a cell. Aneuploidy in eggs is one of the most common contributors to female infertility and miscarriage. Besides female age, recent studies show that genetic variants in the maternal genome contribute to egg aneuploidy. Previous studies focused on nonsynonymous single nucleotide variants (nsSNVs) and loss-of-function variants to identify genetic causes of aneuploidy. However, so far synonymous single nucleotide variants (sSNVs) have been overlooked, even though they can also have functional impact and be involved in disease causation. In this study, we focused on sSNVs to identify new genetic factors contributing to aneuploidy. With two IVF patient whole-exome sequencing datasets, we first calculated functional scores of sSNVs using synVep and summed scores for each gene. On both of these datasets, we then used the AVA,Dx pipeline to create a machine-learning based classifier to predict the aneuploidy rate in IVF patients and identify candidate genes. The two prediction models achieved an area under the receiver operating curve of 0.69 and 0.71, respectively. Selecting stringent prediction score cutoffs can achieve high precisions in both datasets. For example, a prediction score cutoff of 0.57 identified 12% of the patients with 88% precision in one dataset. Some of the top genes selected by the models, including MEI1, CEP131, and TERB1, are potential aneuploidy risk genes. These candidate genes are involved in meiotic recombination, centromere duplication, and meiotic telomere reattachment, respectively. Variants in these genes and their partner molecules may cause errors in chromosome segregation, a primary cause of aneuploidy. By analyzing sSNVs, we revealed new candidate genes for future studies. Finally, we demonstrate that classifiers based on sSNVs can also be used to predict embryonic aneuploidy rate.

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