Preprint Open Access

A probabilistic approach to evaluate the likelihood of artificial genetic modification and its application to SARS-CoV-2 Omicron variant

Kakeya, Hideki; Matsumoto, Yoshihisa

A method to find a probability that a given bias of mutations occur naturally is proposed to test whether a newly detected virus is a product of natural evolution or artificial genetic modification. The probability is calculated based on the neutral theory of molecular evolution and binominal distribution of non-synonymous (N) and synonymous (S) mutations. Though most of the conventional analyses, including dN/dS analysis, assume that any kinds of point mutations from a nucleotide to another nucleotide occurs with the same probability, the proposed model takes into account the bias in mutations, where the equilibrium of mutations is considered to estimate the probability of each mutation. The proposed method is applied to evaluate whether the Omicron variant strain of SARS-CoV-2, whose spike protein includes 29 N mutations and only one S mutation, can emerge through natural evolution. The result of binomial test based on the proposed model shows that the bias of N/S mutations in the Omicron spike can occur with a probability of 1.6 x 10^(-3) or less. Even with the conventional model where the probabilities of any kinds of mutations are all equal, the strong N/S mutation bias in the Omicron spike can occur with a probability of 3.7 x 10^(-3), which means that the Omicron variant is highly likely a product of artificial genetic modification.

Files (494.1 kB)
Name Size
494.1 kB Download
All versions This version
Views 2,1651,955
Downloads 686576
Data volume 339.1 MB284.6 MB
Unique views 1,6961,548
Unique downloads 549489


Cite as