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bioRxiv · 10.64898/2026.08.21.746287

Evaluating Aggregated Gene Level eQTL Scores

Meyer, D.·Popko, N.·Laub, D.·Schofield, P.·Amariuta, T.·Alexandrov, L. B.·Carter, H.
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Abstract

Genetic feature engineering, used in methods such as transcriptome-wide association study, supports gene-trait association testing by aggregating single variants into gene-level features predictive of expression. To evaluate how different model architectures, LD filtering thresholds, and variant prioritization methods affect expression prediction quality, we trained over 3 million models and evaluated their performance in independent cohorts. Using the best performing models to impute expression and immunotherapy response as an example trait, we found a significant association with the reactive oxygen species pathway (p=0.032). Our model training workflow will support genetic feature engineering towards improved complex trait modeling.

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Meyer, D., Popko, N., Laub, D., Schofield, P., Amariuta, T., Alexandrov, L. B., Carter, H.. 2026-08-26. Evaluating Aggregated Gene Level eQTL Scores. https://doi.org/10.64898/2026.08.21.746287

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