SARS-CoV-2 Intra-host Variation Shows Evidence of Transmission and Convergent Evolution in a University Surveillance Cohort
Monitoring and understanding the transmission and evolution of SARS-CoV-2 remains a significant pub-lic health priority. Within-host genetic variation provides insight into viral evolution during infection and may help infer transmission events. In this study, we analyzed intrahost variation in SARS-CoV-2 genome sequences from Boston Universitys testing mandate. Focusing on intrahost single nucleotide variants (iSNVs), we inferred transmission events and assessed the selective forces shaping within-host viral evolution. To minimize false-positive iSNVs resulting from systematic biases, we implemented stringent data filtering and developed a heuristic to exclude contamination-derived artifacts arising from batched sequencing. We find that intrahost variation is limited and infrequently transmitted during acute infections, suggesting that shared iSNVs serve as highly specific but insensitive markers of transmission. We also observed incomplete purifying selection shaping within-host diversity, with the loci most affected changing among variants of concern. Finally, we identified a highly recurrent iSNV (G11083T) which may represent a site of positive selection. Our results highlight that within host variation provides insight towards within host pathogen evolution, in spite of a limited use towards genomic epidemiology. IMPACT STATEMENTSARS-CoV-2 is the most extensively sequenced pathogen to date, yet much of its genomic data remains underutilized. Intrahost variation, in particular, is less studied than consensus-level variation, partly because most datasets lack technical sequencing replicates to control false-positive signals. Using genomic data from a university testing mandate and applying rigorous filtering to systematically minimize false-positive iSNVs in a data-driven manner, we obtain insights into SARS-CoV-2 evolution and transmission from intrahost variation. Our work underscores the potential to use existing, large-scale datasets to better understand pathogen evolution in situ. DATA SUMMARYAll sequence data have been deposited in the Sequence Read Archive (SRA) of the national center for biotechnology information (NCBI), under the project accession number PRJNA892225. All code is open-access and available in GitHub at: https://github.com/Leacavalli/Sars-cov-2-Intrahost-Variation. Any additional supporting data has been provided within the article.