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Large Genomic Analysis Highlights COVID-19 Risk Factors

July 09, 2021

In March 2020, thousands of scientists around the world, including Yale's Renato Polimanti, PhD, MSc, Assistant Professor of Psychiatry; Gita Pathak, PhD, Postdoctoral Associate; and Frank Wendt, PhD, Postdoctoral Fellow, united to answer a pressing and complex question: What genetic factors influence why some COVID-19 patients develop severe, life-threatening disease requiring hospitalization, while others escape with mild symptoms or none at all?

A comprehensive summary of their findings to date, published in Nature, reveals 13 loci, or locations in the human genome, that are strongly associated with infection or severe COVID-19. The researchers also identified causal factors such as smoking and high body mass index.

These results come from one of the largest genome-wide association studies ever performed, which includes nearly 50,000 COVID-19 patients and two million uninfected controls.

The findings could help provide targets for future therapies and illustrate the power of genetic studies in learning more about infectious disease.

This global effort, called the COVID-19 Host Genomics Initiative, has grown to be one of the most extensive collaborations in human genetics and currently includes more than 3,500 authors and 61 studies from 25 countries.

To do their analysis, the consortium pooled clinical and genetic data from the nearly 50,000 patients in their study who tested positive for the virus, and two million controls across numerous biobanks, clinical studies, and direct-to-consumer genetic companies. Because of the large amount of data pouring in from around the world, the scientists were able to produce statistically robust analyses far more quickly, and from a greater diversity of populations, than any one group could have on its own.

The researchers will continue to study more data as it comes in and update their results through the “Matters Arising” format at Nature. They will begin to study what differentiates “long-haulers”, or patients whose COVID-19 symptoms persist for months, from others, and continue to identify additional loci associated with infection and severe disease.

Pathak, Wendt, and Polimanti were part of this large collaborative effort. Pathak is leading the analysis for the phenome-wide association study of the COVID-19 risk loci. She said that understanding different diseases and health outcomes that show potential evidence with genetic markers of COVID-19 will help in prioritizing medical conditions associated with COVID-19 infection.

Polimanti supervised and supported Pathak and Wendt’s contributions to the in-silico analysis team that performed the computational experiments to translate genetic associations into information regarding the pathogenesis of COVID-19. He pointed out that “genetic studies of massive cohorts are one of the most powerful approaches to generate hypotheses regarding the molecular basis of complex conditions such as COVID-19."

Pathak and Wendt continue their collaborative efforts with HGI for future computational experiments.

Submitted by Christopher Gardner on July 09, 2021