Controlling misdiagnosis errors in preimplantation genetic diagnosis: a comprehensive model encompassing extrinsic and intrinsic sources of error

• Published in: Human reproduction (Oxford) Vol. 16; no. 1; pp. 43 - 50
• Main Authors: Lewis, C.M., Pinêl, T., Whittaker, J.C., Handyside, A.H.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01-01-2001
• Subjects: Bayes Theorem; Biological and medical sciences; Biotechnology; Decision Support Techniques; Diagnostic Errors; Embryo Transfer; False Negative Reactions; Female; Fundamental and applied biological sciences. Psychology; Genes, Dominant; Genes, Recessive; Genetic Diseases, Inborn - diagnosis; Genetic Diseases, Inborn - genetics; Genetic engineering; Genetic technics; genetics; Genotype; Humans; In vitro gene amplification. Pcr technique; mathematical modelling; Methods. Procedures. Technologies; misdiagnosis; Models, Theoretical; Polymerase Chain Reaction; Pregnancy; Preimplantation Diagnosis - methods; Preimplantation Diagnosis - statistics & numerical data; preimplantation genetic diagnosis; Probability; Risk; genetics; mathematical modelling; preimplantation genetic diagnosis; misdiagnosis; Chromosomal aberration; Human; Polymerase chain reaction; Preimplantation diagnosis; Gene amplification; Embryo; Error; Female; Genotype; Genetics; Mutation; Mathematical model
• ISSN: 0268-1161; 1460-2350
• DOI: 10.1093/humrep/16.1.43

We have developed a mathematical model to explore accuracy of preimplantation genetic diagnosis (PGD) using single cell polymerase chain reaction (PCR). The model encompasses both extrinsic technical errors and intrinsic errors related to nuclear and chromosomal abnormalities. Using estimates for these errors, we have calculated the probability of a serious error (affected embryo diagnosed as unaffected) using a variety of strategies designed to increase the accuracy of PGD. Additional information from genotyping a linked marker or a second biopsied cell reduces the probability of replacing an affected embryo, while ensuring that sufficient unaffected embryos can be replaced. For a recessive disease, two genotypes are required to ensure a low probability of replacing an affected embryo (<1%) with a high proportion of unaffected embryos eligible for replacement (68%). These genotypes may be from a single cell with linked marker, or disease genotypes from two cells. PGD of a dominant disease is more difficult, as it relies on the amplification of a single copy of the mutation. Genotypes from two biopsied cells are required to ensure that a high proportion of unaffected embryos are eligible for replacement. This model can be used as a clinical tool to prioritize embryos for transfer in a PGD cycle.