Approximate Bayesian computation supports a high incidence of chromosomal mosaicism in blastocyst-stage human embryos.

Chromosome mis-segregation is common in human meiosis and mitosis, and the resulting aneuploidies are the leading cause of pregnancy loss. Preimplantation genetic testing for aneuploidy (PGT-A) seeks to prioritize chromosomally normal embryos for transfer based on genetic analysis of a biopsy of approximately five trophectoderm cells from blastocyst-stage fertilized (IVF) embryos. While modern PGT-A platforms classify these biopsies as aneuploid, euploid, or mosaic (possessing a mixture of normal and aneuploid cells), the underlying incidences of aneuploid, euploid, and mosaic embryos and the rates of meiotic and mitotic error that produced them remain largely unknown. To address this knowledge gap, we paired a recent method for embryo simulation with approximate Bayesian computation (ABC) to infer rates of meiotic and mitotic error that best explain published PGT-A data. By simulating from these posterior distributions, we also evaluated the chromosomal status of entire embryos. For a published clinical sample, we estimated a 39-43% probability of meiotic error per meiosis, as well as a 1.0-3.0% probability of mitotic error per mitosis, depending on assumptions about spatial clustering of aneuploid cells within mosaic embryos. In addition, our analyses suggest that less than 1% of blastocysts are fully euploid, and that many embryos possess low-level mosaic clones that are not captured during biopsy. These broad conclusions were relatively insensitive to potential misclassification of mosaic biopsies. Together, our work helps overcome the limitations of embryo biopsies to estimate the fundamental rates of cell division errors that are the main causes of human pregnancy loss.