Genome-Wide Association Studies Accurately Flag More Deadly COVID-19 Variants

By Harvard TH Chan School of Public Health June 22, 2021

Compares mutations to mortality to identify variants that need to be controlled and contained.

Using genome-wide association studies (GWAS) methodology to analyze whole-genome sequencing data of SARS-CoV-2 mutations and COVID-19 mortality data, highly pathogenic variants of the virus to be flagged can be identified, according to Harvard TH Chan. for containment. School of Public Health and MIT researchers.

Using this biostatistical methodology, the researchers pinpointed a mutation in the variant known as P.1 or Gamma, as being linked to increased mortality and, possibly, greater transmissibility, higher infection rates and increased pathogenicity before the P.1 variant. had been identified.

The team’s methodology is detailed online June 23, 2021 in the journal Genetic Epidemiology.

“Based on our experience, the GWAS methodology may provide appropriate tools that can be used to analyze potential links between mutations at specific sites in viral genomes and disease outcomes,” said Christoph Lange, professor of biostatistics at Harvard Chan School and senior author of the article. . “This would allow for better real-time detection of new, harmful variants/new viral strains in pandemics.”

The first patients in Brazil with the P.1 variant were documented in January 2021 and within a few weeks, the variant caused a spike in cases in Manaus, Brazil. The city had already been hit hard by the pandemic in May 2020, and researchers believed the city’s residents had achieved population immunity because so many people in the area developed antibodies to the virus during that first wave. Instead, P.1, which has several mutations in the spike protein that the virus uses to attach to and invade a host cell, caused a second wave of infections and appeared to have higher transmissibility and more more likely to cause death than previously observed variants in the environment.

In September 2020, months before the first P.1 patient was documented, the Harvard Chan School and MIT team reused the methodology used in GWAS, which is widely used to link certain genetic variations to specific diseases, to the relative pathogenicity of different SARS-CoV-2 mutations. The team looked for links between each SARS-CoV-2 virus single-stranded RNA mutation and mortality in 7,548 COVID-19 patients. The data for the study came from the Global Avian Influenza Data Sharing Initiative (GISAID), which includes genetic sequence and related clinical and epidemiological data associated with SARS-CoV-2 and influenza viruses.

The researchers found one mutation — at locus 25,088bp in the virus’s genome — that alters the spike protein and was linked to a significant increase in mortality in COVID-19 patients. The team tagged the variant with this mutation, which was later identified as part of P.1.

The team’s biostatistical methodology should have broader applications than the P.1 variant and SARS-CoV-2, according to the researchers.

“We expect this approach would work in similar scenarios to other diseases, provided the quality of the data collected in public databases is sufficiently high,” said Georg Hahn, research associate and lecturer in biostatistics at Harvard Chan School and co-author of the study. first author of the newspaper.

Reference: June 23, 2021, Genetic Epidemiology.
DOI: 10.1002 / gepi.22421

Others from Harvard Chan School who contributed to the study were Sanghun Lee, Sharon Lutz, Sebastien Haneuse and Nan Laird.

This study was funded by the National Institutes of Health (1R01AI154470-01, 2U01HG008685, R01HG008976, U01HL089856, U01HL089897, P01HL120839, P01HL132825, 2U01HG008685), and the National Science Foundation (NSF PHY 2033046 and NSF GRFP 1745301092), and -ES0021092).

“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 Brazilian strain P.1, Georg Hahn, Chloe M. Wu, Sanghun Lee, Julian Hecker, Sharon M. Lutz, Surender Khurana, Lindsey R. Baden, Sebastien Haneuse, Dandi Qiao, Dawn L. DeMeo, Rudolph E. Tanzi, Manish C. Choudhary, Behzad Etemad, Abbas Mohammadi, Elmira Esmaeilzadeh, Michael H. Cho, Jonathan Z. Li, Adrienne G. Randolph, Nan M. Laird, Scott T. Weiss, Edwin K. Silverman, Katharina Ribbeck, and Christoph Lange, Genetic Epidemiology, online June 23, 2021.