ABSTRACT
The rapid emergence of new SARS-CoV-2 variants raises a number of public health questions including the capability of diagnostic tests to detect new strains, the efficacy of vaccines, and how to map the geographical distribution of variants to better understand patterns of transmission and possible load on healthcare resources. Next-Generation Sequencing (NGS) is the primary method for detecting and tracing the emergence of new variants, but it is expensive, and it can take weeks before sequence data is available in public repositories. Here, we describe a Polymerase Chain Reaction (PCR)-based genotyping approach that is significantly less expensive, accelerates reporting on SARS-CoV-2 variants, and can be implemented in any testing lab performing PCR. Specific Single Nucleotide Polymorphisms (SNPs) and indels are identified that have high positive percent agreement (PPA) and negative percent agreement (NPA) compared to NGS for the major genotypes that circulated in 2021. Using a 48-marker panel, testing on 1,128 retrospective samples yielded a PPA and NPA in the 96.3 to 100% and 99.2 to 100% range, respectively, for the top 10 most prevalent lineages. The effect on PPA and NPA of reducing the number of panel markers was also explored. In addition, with the emergence of Omicron, we also developed an Omicron genotyping panel that distinguishes the Delta and Omicron variants using four (4) highly specific SNPs. Data from testing demonstrates the capability to use the panel to rapidly track the growing prevalence of the Omicron variant in the United States in December 2021.
ABSTRACT
This study reports on the displacement of Alpha (B.1.1.7) by Delta (B.1.617.2 and its substrains AY.1, AY.2, and AY.3) in the United States. By analyzing RT-qPCR testing results and viral sequencing results of samples collected across the United States, we show that the percentage of SARS-CoV-2 positive cases caused by Alpha dropped from 67% in May 2021 to less than 3.0% in just 10 weeks. We also show that the Delta variant has outcompeted the Iota (B.1.526) variant of interest and Gamma (P.1) variant of concern. An analysis of the mean quantification cycles (Cq) values in positive tests over time also reveal that Delta infections lead to a higher viral load on average compared to Alpha infections, but this increase is only 2 to 3x on average for our study design. Our results are consistent with the hypothesis that the Delta variant is more transmissible than the Alpha variant, and that this could be due to the Delta variants ability to establish a higher viral load earlier in the infection compared to the Alpha variant.
ABSTRACT
As of January of 2021, the highly transmissible B.1.1.7 variant of SARS-CoV-2, which was first identified in the United Kingdom (U.K.), has gained a strong foothold across the world. Because of the sudden and rapid rise of B.1.1.7, we investigated the prevalence and growth dynamics of this variant in the United States (U.S.), tracking it back to its early emergence and onward local transmission. We found that the RT-qPCR testing anomaly of S gene target failure (SGTF), first observed in the U.K., was a reliable proxy for B.1.1.7 detection. We sequenced 212 B.1.1.7 SARS-CoV-2 genomes collected from testing facilities in the U.S. from December 2020 to January 2021. We found that while the fraction of B.1.1.7 among SGTF samples varied by state, detection of the variant increased at a logistic rate similar to those observed elsewhere, with a doubling rate of a little over a week and an increased transmission rate of 35-45%. By performing time-aware Bayesian phylodynamic analyses, we revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with onward community transmission enabling the variant to spread to at least 30 states as of January 2021. Our study shows that the U.S. is on a similar trajectory as other countries where B.1.1.7 rapidly became the dominant SARS-CoV-2 variant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.
ABSTRACT
Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict potential COVID-19 cases using cross-sectional self-reported disease-related symptoms. Using a previously reported COVID-19 prediction model, we show that it is possible to conduct a GWAS on predicted COVID-19, and this GWAS benefits from the larger sample size to provide new insights into the genetic susceptibility of the disease. Furthermore, we find suggestive evidence that genetic variants for other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. Our findings demonstrate the added value of using self-reported symptom assessments to quickly monitor novel endemic viral outbreaks in a scenario of limited testing. Should there be another outbreak of a novel infectious disease, we recommend repeatedly collecting data of disease-related symptoms.
