Study: Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England. Image Credit: creativeneko/Shutterstock

Strength of SARS-CoV-2 genetic drift across time and spatial scales in England

In a recent study published on bioRxiv* preprint server, researchers developed and validated an approach for the joint inference of measurement noise and genetic drift by analyzing lineage frequency time series data.

Study: Lineage frequency time series reveal high levels of genetic drift in SARS-CoV-2 transmission in England. Image Credit: creativeneko/Shutterstock

Background

Random genetic drift in the dynamics of infectious disease outbreaks at the population level results from the randomness of transmission between hosts and host death or recovery. Studies have reported strong genetic drift in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences resulting from superspreading events, which are expected to significantly affect viral evolution and disease epidemiology. to coronavirus 2019 (COVID-19). Noise resulting from the measurement process, including bias in obtaining data across location and time, could confound estimates of genetic drift.

About the study

In the present study, researchers developed an approach to jointly infer the power of measurement noise and genetic drift from time-varying lineage frequency data, which overdispersed measurement noise (at instead of maintaining uniformity) and varying the potency of the overdispersion over time (instead of being constant). They also validated the accuracy of the approach through simulations.

HMM (Hidden Markov Modeling) was used with continuous observed states and hidden states representing observed and true frequencies respectively. The possibility of transition between hidden states was defined by genomic drift, in which the average true frequency was based on the true frequencies determined during the previous period. For rare frequencies, the variance is correlated to mean values ​​based on the effective population size [Ne

The possibility of emission between the observed and hidden states was based on the measurement noise such that the average value of the observed frequencies was equal to the real frequencies. In the case of rare frequencies, the value of the variance of the observed frequencies was correlated with the mean value indicating the time-dependent deviations from the uniform type sampling. The modeling was done assuming that the number of people and the lineage frequencies were high enough to apply the central limit theorem.

The model generated “superlineages” by grouping lineages based on phylogenetic distances such that the total value of lineage abundance and frequency exceeds the threshold value, yielding 486, 4083, 6225 and 24867 strains of pre-B of SARS-CoV-2. 1.177, B.1.177, Alpha and Delta variants, respectively. The team assumed that the Ne

Subsequently, the parameters most likely to represent the data set were determined. The model was validated by running simulations using time-varying Ne

The inferred Ne

Results

The power of genetic drift was consistently greater than that estimated from the observed number of SARS-CoV-2 positive people in England by one to three orders of magnitude, over time, even after correcting for measurement noise. The high genetic drift could not be explained on the basis of superdiffusion, but can be partially explained by deme community structures in host contact networks. The discrepancy could not be explained by corrections taking into account the epidemiological dynamics (SIR or SEIR modelling).

The sampling of people infected with SARS-CoV-2 from the English population was largely uniform for the dataset. The team found evidence of a spatial arrangement in the transmission dynamics of the B.1.177 variant, the Alpha variant and the Delta variant. The estimated Ne

The N inferred by HMMe

Conclusion

Overall, the study results showed that the strength of genetic drift in SARS-CoV-2 transmission in England was greater than estimated and indicated that further methods of modeling studies are needed. to better understand the mechanisms behind the high levels of SARS-CoV-2 genetic drift in England.

*Important Notice

bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be considered conclusive, guide clinical practice/health-related behaviors, or treated as established information.

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