Agent-optimized GPU polygenic scoring at cohort scale

Abstract

A polygenic score (PGS) is a unitless weighted sum of allele dosages, interpretable only as a percentile within a reference distribution. Evaluating risk across models requires scoring each individual against a whole reference panel, but current CPU pipelines process targets or models sequentially, making full cross-product calculations prohibitively expensive. We reformulated PGS calculation as a cohort-wide sparse matrix product on a GPU and optimized it via an autonomous coding agent under read-only inputs, cold starts, and strict numerical validation against pgsc_calc. The scorer was "trained" by the agent accessing synthetic data and real 1000 Genomes data. On a synthetic benchmark, the scorer achieved a 10.6-fold speedup during the training course (184.9 s to 17.4 s) and ran 71-fold faster than pgsc_calc. However, on held-out real genomes, the synthetic-trained scorer was 15.3-fold slower than the real-data-trained version and diverged significantly from pgsc_calc (maximum absolute difference of 3.7x10-1 vs. 3.1x10-4). Three replications of the synthetic loop from an identical prompt showed that agent optimization trajectory variance explains 91 to 96% of total wall-clock variance, against 0 to 2.6% for the data sampled and 0.2 to 3.3% for repeated execution. A memory-bounded rebuild then scored all the 3,202 1000 Genome genotypes against all 5,385 models in 6.5 h within a 72 GB memory cap, released here as a frozen scoring manifest with ancestry-stratified reference distributions for the whole PGS Catalog