Behind the Research: Precision environmental health monitoring by longitudinal exposome and multi-omics profiling

Stanford Healthcare Innovation Team
Jun 23, 2022
Genome Research Publication Peng Snyder

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In Conversation with Peng Gao

Peng Gao is an assistant professor at The University of Pittsburgh’s School of Public Health and was formerly a postdoctoral fellow in The Snyder Lab at Stanford University School of Medicine.

Peng’s most recent paper published in the scientific journal Genome Research is an environmental health study that demonstrates the potential impact of the exposome on precision health and explores the associations between abiotic/biotic exposures and human biomolecules. This is the first study that integrated external exposures and internal biomolecular profiles together to see how the exposome shaped the human phenotypes leading to thousands of significant associations that are valuable resources for future exposome-phenome interactions studies.

Follow the link to read the full paper and listen to our conversation below:

https://genome.cshlp.org/content/early/2022/05/31/gr.276521.121.long

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On the cover: Natural killer (NK) cells (pale blue) attacking SARS-CoV-2 viruses (red) with machine learning methodology signified by patterned numerals. In this issue of Cell Systems, Zhang et al. (p. 598) propose a machine learning method that integrates single-cell mult-iomic data with GWAS summary statistics to discover cell-type-specific disease risk genes. Application to severe COVID-19 identifies over 1,000 risk genes in 19 human lung cell types. Genetic risk is found to be enriched within NK cells and CD56 bright cytokine-producing NK cells.

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