Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p=5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights. Author SummaryCOVID-19 clinical outcomes vary immensely, but a patients genetic make-up is an important determinant of how they will fare against the virus. While many genetic variants commonly found in the populations were previously found to be contributing to more severe disease by the COVID-19 Host Genetics Initiative, it isnt clear if more rare variants found in less individuals could also play a role. This is important because genetic variants with the largest impact on COVID-19 severity are expected to be rarely found in the population, and these rare variants require different technologies to be studies (usually whole-exome or whole-genome sequencing). Here, we combined sequencing results from 21 cohorts across 12 countries to perform a rare variant association study. In an analysis comprising 5,085 participants with severe COVID-19 and 571,737 controls, we found that the gene for toll-like receptor 7 (TLR7) on chromosome X was an important determinant of severe COVID-19. Importantly, despite being found on a sex chromosome, this observation was consistent across both sexes.

Evidence for SARS-CoV-2 Delta and Omicron co-infections and recombination

Between November 2021 and February 2022, SARS-CoV-2 Delta and Omicron variants co-circulated in the United States, allowing for co-infections and possible recombination events. We sequenced 29,719 positive samples during this period and analyzed the presence and fraction of reads supporting mutations specific to either the Delta or Omicron variant. We identified 18 co-infections, one of which displayed evidence of a low Delta-Omicron recombinant viral population. We also identified two independent cases of infection by a Delta-Omicron recombinant virus, where 100% of the viral RNA came from one clonal recombinant. In the three cases, the 5-end of the viral genome was from the Delta genome, and the 3-end from Omicron including the majority of the spike protein gene, though the breakpoints were different. Delta-Omicron recombinant viruses were rare, and there is currently no evidence that Delta-Omicron recombinant viruses are more transmissible between hosts compared to the circulating Omicron lineages.

Wastewater sequencing uncovers early, cryptic SARS-CoV-2 variant transmission

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

HLA-A*03:01 is associated with increased risk of fever, chills, and more severe reaction to Pfizer-BioNTech COVID-19 vaccination

COVID-19 vaccines are safe and highly effective, but some individuals experience unpleasant reactions to vaccination. As the majority of adults in the US have received a COVID-19 vaccine this year, there is an unprecedented opportunity to study the genetics of reactions to vaccination via surveys of individuals who are already part of genetic research studies. Here, we have queried 17,440 participants in the Helix DNA Discovery Project and Healthy Nevada Project about their reactions to COVID-19 vaccination. Our GWAS identifies an association between severe difficulties with daily routine after vaccination and HLA-A*03:01. This association was statistically significant only for those who received the Pfizer-BioNTech vaccine (BNT162b2; p=4.70E-11), but showed a trending association in those who received the Moderna vaccine (mRNA-1273; p=0.005) despite similar sample sizes for study. In Pfizer-BioNTech recipients, HLA-A*03:01 was associated with a two-fold increase in risk of severe vaccine reactions. The effect was consistent across ages, sexes, and whether the person had previously had a COVID-19 infection. The reactions experienced by HLA-A*03:01 carriers were driven by associations with chills, fever, fatigue, and in general feeling unwell.

Rapid displacement of SARS-CoV-2 variant B.1.1.7 by B.1.617.2 and P.1 in the United States

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.

Genomic epidemiology identifies emergence and rapid transmission of SARS-CoV-2 B.1.1.7 in the United States

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.

S gene dropout patterns in SARS-CoV-2 tests suggest spread of the H69del/V70del mutation in the US.

Recently, multiple novel strains of SARS-CoV-2 have been found to share the same deletion of amino acids H69 and V70 in the virus S gene. This includes strain B.1.1.7 / SARS-CoV-2 VUI 202012/01, which has been found to be more infectious than other strains of SARS-CoV-2, and its increasing presence has resulted in new lockdowns in and travel restrictions leaving the UK. Here, we analyze 2 million RT-PCR SARS-CoV-2 tests performed at Helix to identify the rate of S gene dropout, which has been recently shown to occur in tests from individuals infected with strains of SARS-CoV-2 that carry the H69del/V70del mutation. We observe a rise in S gene dropout in the US starting in early October, with 0.25% of our daily SARS-CoV-2-positive tests exhibiting this pattern during the first week. The rate of positive samples with S gene dropout has grown slowly over time, with last week exhibiting the highest level yet, at 0.5%. Focusing on the 14 states for which we have sufficient sample size to assess the frequency of this rare event (n>1000 SARS-CoV-2-positive samples), we see a recent expansion in the Eastern part of the US, concentrated in MA, OH, and FL. However, we cannot say from these data whether the S gene dropout samples we observe here represent the B.1.1.7. strain. Only with an expansion of genomic surveillance sequencing in the US will we know for certain the prevalence of the B.1.1.7 strain in the US.

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

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.