Genome‐wide association analysis of COVID‐19 mortality risk in SARS‐CoV‐2 genomes identifies mutation in the SARS‐CoV‐2 spike protein that colocalizes with P.1 of the Brazilian strain

Abstract

SARS‐CoV‐2 mortality has been extensively studied in relation to host susceptibility. How sequence variations in the SARS‐CoV‐2 genome affect pathogenicity is poorly understood. Starting in October 2020, using the methodology of genome‐wide association studies (GWAS), we looked at the association between whole‐genome sequencing (WGS) data of the virus and COVID‐19 mortality as a potential method of early identification of highly pathogenic strains to target for containment. Although continuously updating our analysis, in December 2020, we analyzed 7548 single‐stranded SARS‐CoV‐2 genomes of COVID‐19 patients in the GISAID database and associated variants with mortality using a logistic regression. In total, evaluating 29,891 sequenced loci of the viral genome for association with patient/host mortality, two loci, at 12,053 and 25,088 bp, achieved genome‐wide significance (p values of 4.09e−09 and 4.41e−23, respectively), though only 25,088 bp remained significant in follow‐up analyses. Our association findings were exclusively driven by the samples that were submitted from Brazil (p value of 4.90e−13 for 25,088 bp). The mutation frequency of 25,088 bp in the Brazilian samples on GISAID has rapidly increased from about 0.4 in October/December 2020 to 0.77 in March 2021. Although GWAS methodology is suitable for samples in which mutation frequencies varies between geographical regions, it cannot account for mutation frequencies that change rapidly overtime, rendering a GWAS follow‐up analysis of the GISAID samples that have been submitted after December 2020 as invalid. The locus at 25,088 bp is located in the P.1 strain, which later (April 2021) became one of the distinguishing loci (precisely, substitution V1176F) of the Brazilian strain as defined by the Centers for Disease Control. Specifically, the mutations at 25,088 bp occur in the S2 subunit of the SARS‐CoV‐2 spike protein, which plays a key role in viral entry of target host cells. Since the mutations alter amino acid coding sequences, they potentially imposing structural changes that could enhance viral infectivity and symptom severity. Our analysis suggests that GWAS methodology can provide suitable analysis tools for the real‐time detection of new more transmissible and pathogenic viral strains in databases such as GISAID, though new approaches are needed to accommodate rapidly changing mutation frequencies over time, in the presence of simultaneously changing case/control ratios. Improvements of the associated metadata/patient information in terms of quality and availability will also be important to fully utilize the potential of GWAS methodology in this field.

Effects of Epitranscriptomic RNA Modifications on the Catalytic Activity of SARS-CoV-2 Replication Complex

“…RNAs 1b-g are complementary to 1a (Figure 2). All the nucleotides in RNA 1b are canonical, while those in 1c-g include the modified nucleotides Ψ, m 5 C, m 6 A, m 1 A and m 3 C, respectively. Between the RNA extension starting site (nucleotide 21) and the modified nucleotides, four or more canonical nucleotides were placed.…”

“…[3][4][5] In addition, answers in contexts such as affinity of ACE2 with spike protein and doses of virus exposed have also been suggested. 6 Further, genome wide association studies have been carried out to answer these questions on the ground of genetics. [7][8][9][10] In this paper, we report our studies on the effects of several RNA modifications (Figure 1)pseudouridine (Ψ), 5-methylcytosine (m 5 C), N 6 -methyladenosine (m 6 A), N 1 -methyladenosine (m 1 A), and N 3 -methylcytosine (m 3 C) that can potentially be installed on the SARS-CoV-2 RNA genome by the host epitranscriptomic machineryon the catalytic activity of SARS-CoV-2 replication complex (SC2RC), which includes RNA dependent RNA polymerase (RdRp).…”

“…6 Further, genome wide association studies have been carried out to answer these questions on the ground of genetics. [7][8][9][10] In this paper, we report our studies on the effects of several RNA modifications (Figure 1)pseudouridine (Ψ), 5-methylcytosine (m 5 C), N 6 -methyladenosine (m 6 A), N 1 -methyladenosine (m 1 A), and N 3 -methylcytosine (m 3 C) that can potentially be installed on the SARS-CoV-2 RNA genome by the host epitranscriptomic machineryon the catalytic activity of SARS-CoV-2 replication complex (SC2RC), which includes RNA dependent RNA polymerase (RdRp). Because viral genome replication and transcription are among the critical steps in the life cycle of viruses, we reasoned that the effect of epitranscriptomics on the activity of SC2RC would provide additional insights on the causes of different severity of symptoms inflicted by SARS-CoV-2 on different individuals.…”

Predictive model for severe COVID-19 using SARS-CoV-2 whole-genome sequencing and electronic health record data, March 2020-May 2021

“…A small number of genomic epidemiologic studies have explored the association of host and pathogen genetic variation and COVID-19 severity [8][9][10][11]. A study by Dite G et al, using UK biobank data, performed a GWAS in the human genome to identify single nucleotide polymorphisms (SNPs) associated with disease severity using a SNP score, as well as the impact of demographic and comorbidity risk factors on severe COVID-19 severity [12].…”

Demographic and Viral-Genetic Analyses of COVID-19 Severity in Bahrain Identify Local Risk Factors and a Protective Effect of Polymerase Mutations

“…For instance, the Alpha (B.1.1.7) variant has been associated with an increased mortality risk compared to previously circulating variants (Challen et al 2021), while the Omicron (B.1.1.529) variant has been associated with a decreased mortality risk compared to the Delta (B.1.617.2) variant (Nyberg et al 2022). Genome-wide association studies can be used to assess the severity-risk of every observed mutation in the SARS-CoV-2 genome (Hahn et al 2021), but such an approach can lack power when the majority of mutations are at low frequencies and higher frequency variants are grouped into emerging variants that spread through the population.…”