# Addressing overlapping sample challenges in genome-wide association studies: Meta-reductive approach

## TL;DR

This paper introduces a new method to improve the accuracy of genetic risk scores by adjusting for overlapping datasets in genetic studies.

## Contribution

The novel Meta-Reductive Approach (MRA) recalibrates GWAS summary statistics to neutralize overlapping sample influences.

## Key findings

- MRA recalibrates summary statistics using algebraic derivations to match individual-level data results.
- Validation on Alzheimer's datasets showed MRA summary statistics matched individual-level data exactly.
- MRA enhances PRS accuracy when using meta-analyzed GWAS data.

## Abstract

Polygenic risk scores (PRS) are instrumental in genetics, offering insights into an individual level genetic risk to a range of diseases based on accumulated genetic variations. These scores rely on Genome-Wide Association Studies (GWAS). However, precision in PRS is often challenged by the requirement of extensive sample sizes and the potential for overlapping datasets that can inflate PRS calculations. In this study, we present a novel methodology, Meta-Reductive Approach (MRA), that was derived algebraically to adjust GWAS results, aiming to neutralize the influence of select cohorts. Our approach recalibrates summary statistics using algebraic derivations. Validating our technique with datasets from Alzheimer disease studies, we showed that the summary statistics of the MRA and those derived from individual-level data yielded the exact same values. This innovative method offers a promising avenue for enhancing the accuracy of PRS, especially when derived from meta-analyzed GWAS data.