Purpose: This study is aimed at increasing the accuracy of preimplantation genetic test for monogenic defects (PGT-M).
Methods: We applied Bayesian statistics to optimize data analyses of the mutated allele revealed by sequencing with aneuploidy and linkage analyses (MARSALA) method for PGT-M. In doing so, we developed a Bayesian algorithm for linkage analyses incorporating PCR SNV detection with genome sequencing around the known mutation sites in order to determine quantitatively the probabilities of having the disease-carrying alleles from parents with monogenic diseases. Both recombination events and sequencing errors were taken into account in calculating the probability.
Results: Data of 28 in vitro fertilized embryos from three couples were retrieved from two published research articles by Yan et al. (Proc Natl Acad Sci. 112:15964-9, 2015) and Wilton et al. (Hum Reprod. 24:1221-8, 2009). We found the embryos deemed "normal" and selected for transfer in the previous publications were actually different in error probability of 10-4-4%. Notably, our Bayesian model reduced the error probability to 10-6-10-4%. Furthermore, a proband sample is no longer required by our new method, given a minimum of four embryos or sperm cells.
Conclusion: The error probability of PGT-M can be significantly reduced by using the Bayesian statistics approach, increasing the accuracy of selecting healthy embryos for transfer with or without a proband sample.
Keywords: Bayesian statistics; Linkage analyses; MARSALA; PGT-M.