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Modeling-based prediction tools for preimplantation genetic testing of mitochondrial DNA diseases: estimating symptomatic thresholds, risk, and chance of success

  • Genetics
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Journal of Assisted Reproduction and Genetics Aims and scope Submit manuscript

Abstract

Purpose

Preimplantation genetic testing (PGT) has become a reliable tool for preventing the germline transmission of mitochondrial DNA (mtDNA) variants. However, procedures are not standardized across mtDNA variants. In this study, we aim to estimate symptomatic thresholds, risk, and chance of success for PGT for mtDNA pathogenic variant carriers.

Methods

We performed a systematic analysis of heteroplasmy data including 455 individuals from 187 familial pedigrees with the common m.3243A>G, m.8344A>G, or m.8993T>G pathogenic variants. We applied binary logistic regression for estimating symptomatic thresholds of heteroplasmy, simplified Sewell-Wright formula and Kimura equations for predicting the risk of disease transmission, and binomial distribution for predicting minimum oocyte numbers.

Results

We estimated the symptomatic thresholds of m.8993T>G and m.8344A>G as 29.86% and 16.15%, respectively. We could not determine a threshold for m.3243A>G. We established models for mothers harboring common and rare mtDNA pathogenic variants to predict the risk of disease transmission and the number of oocytes required to produce an embryo with sufficiently low variant load. In addition, we provide a table allowing the prediction of transmission risk and the minimum required oocytes for PGT patients with different variant levels.

Conclusion

We have established models that can determine the symptomatic thresholds of common mtDNA pathogenic variants. We also constructed universal models applicable to nearly all mtDNA pathogenic variants which can predict risk and minimum numbers for PGT patients. These models have advanced our understanding of mtDNA disease pathogenesis and will enable more effective prevention of disease transmission using PGT.

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Data availability

All relevant data are within the manuscript and the supplementary materials. All raw data is publicly available at Mendeley Data: Ji, Dongmei; Zhang, Ning; Zou, Weiwei; Zhang, Zhikang; Marley, Jordan; Liu, Zhuoli; Liang, Chunmei; Shen, Lingchao; Liu, Yajing; Liang, Dan; Su, Tianhong; Du, Yinan; Cao, Yunxia (2022), “Data of Modeling-based prediction tools for preimplantation genetic testing of mitochondrial DNA diseases,” Mendeley Data, V1, doi: 10.17632/ynx4mmhbxr.1 (https://data.mendeley.com/datasets/ynx4mmhbxr).

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Acknowledgements

The authors thank Yuzhou Gao, Haoyu He, Yang Yang, Yuechen Wang, Pei Hong, Chongwu Xu, and Xuefeng Tian for collecting the data and helpful discussion.

Funding

The National Natural Science Foundation of China (Grant No: U20A20350 to Y.C., Grant No: 81971455 to D.J., Grant No: 82202043 to T.S., Grant No: 81871216 to Y.C., and Grant No: 31701162 to Y.D.), the National Key Research and Development Program of China (Grant No: 2021YFC2700901 to Y.D., D.J., W.Z., and D.L.), Shanghai Science and Technology Innovation Action Plan-Shanghai Sailing Program funded by Science and Technology Commission of Shanghai Municipality (Grant No: 22YF1431600 to T.S.), and the National College Students’ Innovation and Entrepreneurship Training Program (Grant No: 202210366004 to D.J. and N.Z.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Dongmei Ji, Ning Zhang and Weiwei Zou contributed equally to this work.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China

    Dongmei Ji, Ning Zhang, Weiwei Zou, Zhikang Zhang, Chunmei Liang, Lingchao Shen, Yajing Liu, Dan Liang & Yunxia Cao

  2. NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract (Anhui Medical University), Hefei, Anhui, China

    Dongmei Ji, Weiwei Zou, Zhikang Zhang, Chunmei Liang, Lingchao Shen, Yajing Liu, Dan Liang & Yunxia Cao

  3. Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People’s Republic of China, Hefei, Anhui, China

    Dongmei Ji, Weiwei Zou, Chunmei Liang, Yajing Liu, Dan Liang & Yunxia Cao

  4. School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, China

    Ning Zhang & Yinan Du

  5. First School of Clinical Medicine, Anhui Medical University, Hefei, Anhui, China

    Ning Zhang, Zhikang Zhang & Lingchao Shen

  6. Wellcome Centre for Mitochondrial Research, Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK

    Jordan Lee Marley

  7. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, Anhui, China

    Zhuoli Liu

  8. Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Tianhong Su

  9. Baoshan Branch, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Tianhong Su

Authors
  1. Dongmei Ji
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Contributions

Conceptualization, D.J., N.Z., and W.Z.; data curation, D.J., N.Z., W.Z., Z.Z, J.L.M., Z.L., and C.L.; formal analysis, D.J., N.Z., W.Z., Z.Z, J.L.M., Z.L., C.L., L.S., Y.L., and D.L.; funding acquisition, D.J., N.Z., T.S., Y.D., and Y.C.; investigation, D.J., N.Z., W.Z., Z.Z, J.L.M., Z.L., C.L., L.S., Y.L., and D.L.; methodology, D.J., N.Z., W.Z., T.S., Y.D., and Y.C.; project administration, T.S., Y.D., and Y.C.; resources, T.S., Y.D., and Y.C.; software, D.J., N.Z., and W.Z.; supervision, T.S., Y.D., and Y.C.; validation, D.J., N.Z., W.Z., T.S., Y.D., and Y.C.; visualization, D.J., N.Z., W.Z., L.S., Y.L., and D.L., writing—original draft, D.J., N.Z., and W.Z.; writing—review and editing, J.L.M., T.S., Y.D., and Y.C. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Tianhong Su, Yinan Du or Yunxia Cao.

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The study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui Medical University (PJ2020-08-15).

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Informed consent was obtained from all individual participants included in the study.

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The authors declare no competing interests.

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Ji, D., Zhang, N., Zou, W. et al. Modeling-based prediction tools for preimplantation genetic testing of mitochondrial DNA diseases: estimating symptomatic thresholds, risk, and chance of success. J Assist Reprod Genet 40, 2185–2196 (2023). https://doi.org/10.1007/s10815-023-02880-2

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