The application provides a prediction method of pathogenic embryonic
mutation, comprising the following steps: obtaining a variation site set of an embryonic
mutation to be predicted, and performing
population frequency screening on the variation site; filtering variation sites which are not annotated or are only annotated as clinically uncertain by using a clinical variation
database; calculating a pathogenic
posterior probability score of a candidate pathogenic variation by using a variation
Bayesian inference (VBI) model with multiple types of function annotations; and cross- verifying the
posterior probability score, external pathogenic prediction tool scores and variation types to determine a final
pathogenicity discrimination result. Compared with an existing method based on
single site frequency or single function
score, the present application provides a
posterior probability evaluation model with more biological interpretation in the field of rare variation judgment, effectively improves the prediction accuracy in a complex scene, significantly improves the
risk classification accuracy based on multi-level evidence fusion discrimination, and effectively reduces the misdiagnosis and
missed diagnosis rate in clinical stratification.