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Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing

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Abstract

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother’s genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients’ aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies.

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Data availability

The gene scores used for the classification model construction are available at Rutgers University Community Repository (RUcore): https://doi.org/doi:10.7282/t3-fn30-rv12.

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Acknowledgements

We thank the patients who participated in and contributed to this study. We thank Leelabati Biswas and Mansour Aboelenain for helpful discussions. We gratefully acknowledge access to the HPC facilities and support of the computational STEM and bioinformatics scientists from the Office of Advanced Research Computing at Rutgers University.

Funding

This work is partly supported by a grant from the NIH/NICHD to KS, JX, and XT: R01-HD091331. YB was supported by the NIH/NIGMS grant R01-GM115486 and NIH/NIMH R01-MH115958.

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YB, KS, and JX contributed to the study conception and design. Patient samples were recruited by RTS and XT. Data analysis was performed by SS, KMT, XC, and JX. Pipeline development was performed by MM, YW, and YB. The first draft of the manuscript was written by SS and JX. All authors commented on the draft of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jinchuan Xing.

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Sun, S., Miller, M., Wang, Y. et al. Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing. Hum Genet 141, 1615–1627 (2022). https://doi.org/10.1007/s00439-022-02450-z

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