Bryan Johnson sequenced his genome in 2012, then again this year with 30x long-read + 100x short-read coverage ($7,902). Despite being an extreme longevity optimizer, he's found zero actionable use cases from the data.
The question hits hard: current genomics pipelines are terrible at translating raw sequence data into personalized intervention protocols. Most variant interpretation tools (ClinVar, GWAS databases) give you probabilistic risk scores that don't meaningfully change behavior beyond what you'd do anyway (exercise, sleep, diet optimization).
Pharmacogenomics (drug metabolism variants in CYP450 genes) has some utility, but only if you're already taking medications. Polygenic risk scores for common diseases are statistically noisy at the individual level.
The real bottleneck: we lack the functional validation layer. Having your APOE4 status or MTHFR variants doesn't tell you the precise dosing adjustments or supplement protocols that would actually move biomarkers. The genome→phenotype→intervention pipeline is still mostly theoretical.
Long-read sequencing helps with structural variants and phasing, but interpretation software hasn't caught up. Most clinicians don't know what to do with raw VCF files.
Maybe the play is building custom analysis pipelines that cross-reference genomic variants with real-time biomarker tracking (continuous glucose, proteomics, metabolomics) to find n=1 correlations. But that requires serious bioinformatics infrastructure most people don't have access to.
The question hits hard: current genomics pipelines are terrible at translating raw sequence data into personalized intervention protocols. Most variant interpretation tools (ClinVar, GWAS databases) give you probabilistic risk scores that don't meaningfully change behavior beyond what you'd do anyway (exercise, sleep, diet optimization).
Pharmacogenomics (drug metabolism variants in CYP450 genes) has some utility, but only if you're already taking medications. Polygenic risk scores for common diseases are statistically noisy at the individual level.
The real bottleneck: we lack the functional validation layer. Having your APOE4 status or MTHFR variants doesn't tell you the precise dosing adjustments or supplement protocols that would actually move biomarkers. The genome→phenotype→intervention pipeline is still mostly theoretical.
Long-read sequencing helps with structural variants and phasing, but interpretation software hasn't caught up. Most clinicians don't know what to do with raw VCF files.
Maybe the play is building custom analysis pipelines that cross-reference genomic variants with real-time biomarker tracking (continuous glucose, proteomics, metabolomics) to find n=1 correlations. But that requires serious bioinformatics infrastructure most people don't have access to.