Episode

Mapping the Multi-Omic Era with Eric Green of Illumina

Podcast
Mendelspod Podcast
Published
May 28, 2026
Duration seconds
2662
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not_requested
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https://www.mendelspod.com/p/mapping-the-multi-omic-era-with-eric
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/v1/public/podcasts/mendelspod-podcast-742570/episodes/mapping-the-multi-omic-era-with-eric-green-of-illumina
Markdown
/podcast/mendelspod-podcast-742570/mapping-the-multi-omic-era-with-eric-green-of-illumina.md

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Summary

Dr. Eric Green returns to Mendelspod in a new role: Chief Medical Officer of Illumina. After more than three decades at the National Human Genome Research Institute, where he helped guide genomics from research initiatives to clinical reality, he now joins one of the industry’s most influential companies at a moment when the field is expanding beyond DNA alone. Green takes us on a tour around the world of multi-omics, which he says is not a branding exercise but a practical response to the limits of sequence data by itself. Genomics remains foundational, but many clinical questions require additional layers of biology, including RNA, epigenomics, proteomics, and single-cell analysis. As he puts it, “DNA sequence alone may not reveal it.” The discussion highlights rare disease as one of the clearest examples. Genome sequencing can solve roughly half of suspected cases, Green notes, but many patients remain undiagnosed because the relevant signal may lie in RNA splicing, epigenetic regulation, structural variation, or downstream protein effects. In those settings, multi-omic approaches can provide the missing evidence needed to move from uncertainty to diagnosis. In oncology, the challenge is different. Cancer genomes can be highly complex and heterogeneous, making it difficult to distinguish driver events from background noise. That is one reason why researchers and clinicians are increasingly incorporating methylation markers, transcriptomic data, and proteomic signals into early detection, disease sub typing, and monitoring strategies. Green also emphasizes that the next bottleneck may be less about generating data than interpreting it. “The human brain is not going to be the thing that’s going to crack this nut,” he says. “It’s going to be AI and computational biolog…