Challenges in Harnessing Shared Within-Host Severe Acute Respiratory Syndrome Coronavirus 2 Variation for Transmission Inference

BACKGROUND: The limited variation observed among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) consensus sequences makes it difficult to reconstruct transmission linkages in outbreak settings. Previous studies have recovered variation within individual SARS-CoV-2 infections but have not yet measured the informativeness of within-host variation for transmission inference. METHODS: We performed tiled amplicon sequencing on 307 SARS-CoV-2 samples, including 130 samples from 32 individuals in 14 households and 47 longitudinally sampled individuals, from 4 prospective studies with household membership data, a proxy for transmission linkage. RESULTS: Consensus sequences from households had limited diversity (mean pairwise distance, 3.06 single-nucleotide polymorphisms [SNPs]; range, 0–40). Most (83.1%, 255 of 307) samples harbored at least 1 intrahost single-nucleotide variant ([iSNV] median, 117; interquartile range [IQR], 17–208), above a minor allele frequency threshold of 0.2%. Pairs in the same household shared significantly more iSNVs (mean, 1.20 iSNVs; 95% confidence interval [CI], 1.02–1.39) than did pairs in different households infected with the same viral clade (mean, 0.31 iSNVs; 95% CI, .28–.34), a signal that decreases with increasingly stringent minor allele frequency thresholds. The number of shared iSNVs was significantly associated with an increased odds of household membership (adjusted odds ratio, 1.35; 95% CI, 1.23–1.49). However, the poor concordance of iSNVs detected across sequencing replicates (24.8% and 35.0% above a 0.2% and 1% threshold) confirms technical concerns that current sequencing and bioinformatic workflows do not consistently recover low-frequency within-host variants. CONCLUSIONS: Shared within-host variation may augment the information in consensus sequences for predicting transmission linkages. Improving sensitivity and specificity of within-host variant identification will improve the informativeness of within-host variation.