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

Abstract

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. 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. 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. 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.