Numerous studies on Alzheimer’s polygenic risk scores (PRS) overlook sample overlap between IGAP and target datasets like ADNI. To address this, we developed Overlap-Adjusted Polygenic Risk Score (OA-PRS) and tested it on simulated data to assess biases from different scenarios by varying training, testing, and overlap proportions. OA-PRS was used to adjust for sample bias in simulations, then we applied OA-PRS to IGAP and ADNI datasets and validated through visual diagnosis. OA-PRS effectively adjusted for sample overlap in all simulation scenarios, as well as for IGAP and ADNI. The original IGAP PRS showed an inflated AUROC(0.915) on overlapping samples. OA-PRS reduced the AUROC to 0.726, closely aligning with the AUROC of non-overlapping samples(0.712). Further, visual diagnostics confirmed the effectiveness of our adjustments. With OA-PRS, we were able to adjust the IGAP summary-based PRS for the overlapped ADNI samples, allowing the dataset to be fully utilized without the risk of overfitting.

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