Phenotype-Associated Gene Discovery with AlphaGenome

Identifying which genes are relevant to a phenotype is a prerequisite for editing the genome to treat disease, and the established tool, genome-wide association, has a known failure mode: where population structure correlates with the trait, a variant-level test cannot separate a causal locus from one that is merely ancestry-informative. We study this failure on a testbed built to make it unavoidable: a cohort from the 1000 Genomes Project relabelled by a phenotype, skin pigmentation, that coincides exactly with continental ancestry. For each candidate gene individually, we predict melanocyte RNA-seq with a frozen sequence-to-function model and train one classifier per gene on that signal alone; the balanced accuracy it reaches induces a ranking over genes. Over nine literature-curated pigmentation genes against thirty-three independently screened controls, the classifier's ranking recovers the panel at $\mathrm{AUC}=0.880$ ($p<0.001$, permutation null), where a variant-level association test on the same cohort and label reaches $0.724$. The gap is largest where a candidate list is drawn: four of the classifier's five highest-ranked genes are panel genes, against two for the association test, and by the threshold recovering its $5$th panel gene it admits $1$ control of $33$ where the two association estimators admit $6$ and $20$—an advantage significant against both estimators over the top 15% of the ranking, a cap fixed in advance ($p=0.007$, $p=0.006$). Over the whole ranking it persists but, at nine positives, is resolved only against the aggregating estimator. Since follow-up is spent only on the head of a candidate list, this means fewer ancestry-driven false leads per experiment, from a filter built into the representation rather than a model of the confound.