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Richard A Neher

Publications and source records attributed to Richard A Neher.

2 recordsLinked to original sources

Epistasis and the changing fitness landscapes of SARS-CoV-2.

Since its emergence in late 2019, millions of SARS-CoV-2 genomes have been generated as part of global efforts to monitor the evolution and spread of the virus. This unprecedented volume of data provides a unique opportunity to study viral evolution at unparalleled resolution. In particular, individual genomic sites can be observed to have mutated independently thousands of times. These mutation counts have been used to estimate site-specific mutation rates and fitness effects for most mutations across the viral genome. Here, we use these data to investigate how the landscape of mutational fitness costs has changed over the course of the pandemic. SARS-CoV-2 evolution over the past 6 years has been characterized by the emergence of distinct variants separated by long branches corresponding to evolutionary saltations involving up to 50 mutations. We compare inferred fitness landscapes of the Spike protein across these variants and find that shifts in the estimated effects of non-synonymous mutations are linked to genetic differences between them. Sites with altered fitness costs are enriched near positions where the genetic backgrounds differ. To explain the observed changes, we introduce a model with pairwise epistatic interactions between mutations and residues that differ between variants. This model is able to explain about half of the variance in the shifts of fitness effects and suggests that each mismatch between variants substantially alters mutation effects at typically 1 to 3 additional positions.

SARS-CoV-2

Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2.

The wealth of genomic data that was generated during the COVID-19 pandemic provides an exceptional opportunity to obtain information on the transmission of SARS-CoV-2. Specifically, there is great interest to better understand how the effective reproduction number [Formula: see text] and the overdispersion of secondary cases, which can be quantified by the negative binomial dispersion parameter k, changed over time and across regions and viral variants. The aim of our study was to develop a Bayesian framework to infer [Formula: see text] and k from viral sequence data. First, we developed a mathematical model for the distribution of the size of identical sequence clusters, in which we integrated viral transmission, the mutation rate of the virus, and incomplete case-detection. Second, we implemented this model within a Bayesian inference framework, allowing the estimation of [Formula: see text] and k from genomic data only. We validated this model in a simulation study. Third, we identified clusters of identical sequences in all SARS-CoV-2 sequences in 2021 from Switzerland, Denmark, and Germany that were available on GISAID. We obtained monthly estimates of the posterior distribution of [Formula: see text] and k, with the resulting [Formula: see text] estimates slightly lower than estimates obtained by other methods, and k comparable with previous results. We found comparatively higher estimates of k in Denmark which suggests less opportunities for superspreading and more controlled transmission compared to the other countries in 2021. Our model included an estimation of the case detection and sampling probability, but the estimates obtained had large uncertainty, reflecting the difficulty of estimating these parameters simultaneously. Our study presents a novel method to infer information on the transmission of infectious diseases and its heterogeneity using genomic data. With increasing availability of sequences of pathogens in the future, we expect that our method has the potential to provide new insights into the transmission and the overdispersion in secondary cases of other pathogens.

COVID-19