- A participant is building an open-source “covcheck” tool that turns 23andMe SNP data into a personal COVID-19 risk score by mining GWAS literature; the prototype lives at https://github.com/Geromics/covcheck/tree/feature/score/pmid/33888907 and a literature-tracking spreadsheet is at https://docs.google.com/spreadsheets/d/1ieO4jzxlyCtCzRKHywEWboD1s4jMdKJDtepmXVj6CC0/edit#gid=1926235615.

- Discussion highlights the methodological limits of GWAS: most significant SNPs may only “tag” causal variants, effect directions flip with allele labeling, and associations often vanish across ancestry backgrounds, raising doubts about biological interpretability.

- The group debated whether current statistical genetics practices can ever yield causal insight without a rigorous information-theoretic or coding-theory foundation, given the enormous emergent complexity and error-correction layers in biological systems.

- Gut-microbiome confounding and population stratification were cited as major obstacles; one proposed remedy is a large-scale study that couples whole-genome sequencing with ~$100 blood metabolomics panels and controlled “hyperdose” nutrient interventions to drive detectable metabolic shifts.

- Key references shared include the Nature review on host genetics of infectious disease (https://www.nature.com/articles/nrg3114), the large COVID-19 Host Genetics Initiative paper (https://www.nature.com/articles/s41586-021-03767-x), and its updated dataset on medRxiv (https://www.medrxiv.org/content/10.1101/2021.11.08.21265944v2).

- A YouTube talk on microbiome interactions (https://www.youtube.com/watch?v=58IhKxBQQpw) was recommended as background on how microbial populations further complicate genotype–phenotype mapping.