Adversarial validation of in silico perturbation profiles for intelligence-associated genes in human prefrontal cortex

Genome-wide association studies (GWAS) have identified numerous intelligence-associated loci, but the cell-type-specific mechanisms through which these genes influence cognition remain largely unknown. Standardized adversarial-validation frameworks for assessing in silico perturbation claims from foundation models are currently lacking.

We developed a reproducible adversarial-validation framework and applied it to 21 intelligence-linked genes using Geneformer-based in silico gene-deletion perturbations in human dorsolateral prefrontal cortex (DLPFC) single-cell RNA-seq data (500 cells, 3 donors). The framework incorporates expression-matched empirical null testing (61 control genes), control gene-pair combinatorial nulls (20 random pairs), cross-model comparison with scGPT, donor-aware pseudobulk analysis, and donor-aware combinatorial permutation testing.