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

Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE

Original Publication: San Francisco, CA : Public Library of Science

Genome-Wide Association Study*/methods ; Alzheimer Disease*/genetics ; Multifactorial Inheritance*/genetics ; Genetic Predisposition to Disease*; Humans ; Polymorphism, Single Nucleotide ; Sample Size ; Risk Factors

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.
Competing Interests: The author declares no competing interests.
(Copyright: © 2024 Rajabli, Emekci. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Update of: bioRxiv. 2023 Dec 11:2023.12.08.570867. doi: 10.1101/2023.12.08.570867. (PMID: 38168201)

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R01 AG070864 United States AG NIA NIH HHS

Date Created: 20240801 Date Completed: 20240801 Latest Revision: 20240806

20240806

PMC11293628

10.1371/journal.pone.0296207

39088468