Webinar recap: Understanding complex disease biology – Insights into the genetics of long COVID

long covid webinar

This webinar, co-hosted by Sano Genetics and PrecisionLife, highlighted the complex nature of long Covid, which affects up to 30% of Covid-19 survivors and presents with more than 50 symptoms. The discussion brought to light the limitations of traditional genomic research, which has struggled to identify significant genetic risk factors, and underscored the necessity for innovative methodologies in understanding and treating this condition.

The link to the full webinar is here; a brief summary is below for easy reference.

About the speakers

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Charlotte Guzzo

As Co-founder and COO of Sano Genetics, Charlotte Guzzo is driving the company’s mission towards personalised medicine. Her work focuses on enhancing patient experience in medical research through digital solutions and at-home testing. Before joining Sano, she researched childhood cancer origins at the Wellcome Sanger Institute and worked in risk management at JPMorgan Chase.

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Heath O’Brien

Heath O’Brien is a genome scientist with extensive experience in genome analysis across diverse biological fields, ranging from bacterial pathogens to human genomics, in both academic and industrial settings. Originally a lab-based microbial population biologist, he shifted to data science to better manage the vast data outputs of modern genomic research, now primarily using R and Python and recently adopting NextFlow for its cloud computing capabilities. Heath’s work spans over 15 years, marking him as a seasoned expert in bioinformatics.

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Steve Gardner

Steve Gardner is an entrepreneur with 30 years of expertise in data science and informatics across healthcare and life sciences, currently serving as the CEO of PrecisionLife and Chair of the UK Bioindustry Association’s Genomic Advisory Committee. His career includes roles such as Global Director of Research Informatics at Astra A/B and consulting with over 20 biopharma companies in drug discovery and safety. Steve’s leadership at PrecisionLife underscores his long-standing commitment to innovating in genomic medicine.

Highlights from the webinar

Innovative genomic research and findings

Charlotte emphasised Sano’s role in advancing long COVID research, utilising our advanced digital platform to identify and test a diverse group of 3,761 patients. This extensive sampling allowed PrecisionLife to run a robust analysis of the genetic underpinnings of long COVID. Steve elaborated on how their unique analytics approach applied to this data led to the identification of 73 genes associated with long COVID, revealing new potential therapeutic targets that conventional methods might overlook.

Steve and Heath discussed the broader implications of these genetic analyses, not just for long COVID but for various complex chronic conditions. By employing advanced algorithms to parse through vast genomic data, Steve’s team at PrecisionLife is identifying potential drug targets and paving new treatment avenues.

Implications for precision medicine

The speakers also focused on how these genetic findings can be translated into clinical practice, discussing potential collaborations with pharmaceutical companies to accelerate the development of targeted therapies based on these genetic insights. Emphasising the importance of integrating patient data, the dialogue highlighted how genetic information could be used to craft personalised treatment plans, potentially enhancing the quality of life for millions affected by long COVID.


The webinar concluded with a hopeful outlook on the future of treating complex diseases like long COVID through precision medicine. It highlighted the critical role of genetic research in uncovering the disease's underpinnings, potentially leading to breakthroughs in how we treat not only long COVID but also other chronic diseases. As research progresses, we can anticipate more refined treatments that align closely with the genetic profiles of individual patients – which can lead to better outcomes for all.

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