This project proposes a paired non-inferiority trial utilizing the retrospective testing of
research embryos to establish the optimized parameters for amplified trophectoderm biopsy and
whole genome sequencing compared to traditional preimplantation genetic testing for
aneuploidy (PGT-A).
The primary outcome is the de novo mutation rate, which will be compared between embryos
subjected to trophectoderm biopsy and whole genome sequencing versus reanalysis with Sanger
sequencing. Trio testing will be performed for each embryo using DNA from the genetic parents
in addition to embryo. The study will compare cases that include couples with at least one
embryo deemed unsuitable for transfer. A paired study design will be used, with embryos from
each couple split into two arms - one subjected to trophectoderm biopsy and whole genome
sequencing, the other to reanalysis with targeted sequencing.
Biopsied trophectoderm samples and the remaining embryo tissues from the whole genome
sequencing arm will undergo sequencing. Sequencing will also be performed on DNA samples from
each genetic parent.
Derivation of de novo mutation rates is a key goal, as these provide insights into effects of
paternal age and other factors on germline mutations in preimplantation embryos, increasing
the knowledge of the risks associated with advanced paternal age. Secondary metrics will be
investigated to supplement the analysis, including clean reads and clean bases indicating the
amount of high-quality data for the source templates. Mapping rate, unique rate and duplicate
rate, assessing data accuracy and quality. Comparison of multiple metrics will determine the
optimized parameters for performing amplified trophectoderm biopsy and whole genome
sequencing. This can then inform future research and clinical studies.
The de novo mutation rate will be derived by modelling the observed mutation rate as a
function of parental ages, specifically the paternal age. Whole genome sequencing of embryo
samples as well as both parents will identify raw numbers of de novo mutations. The paternal
age coefficient for the de novo mutation rate will be calculated using a regression model
with the number of de novo mutations as the dependent variable and paternal age as the key
independent variable. Covariates like maternal age and sequencing quality metrics will also
be included to account for potential confounding factors. The regression model will determine
the increase in de novo mutations per year of paternal age, providing the paternal age
coefficient. Comparing embryos from older and younger males will reveal differences in
mutation rates. The overall model will establish the quantitative relationship between
paternal age and de novo mutations in preimplantation embryos based on the study's whole
genome sequencing data.