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Large impact of genetic data processing steps on stability and reproducibility of set-based analyses in genome-wide association studies.

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Title: Large impact of genetic data processing steps on stability and reproducibility of set-based analyses in genome-wide association studies.
Authors: Kui N; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.; Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States., Yu Y; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States., Choi J; Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, WI 53233, United States., McCaw ZR; The Voleon Group, Berkeley, CA 94704, United States., Li X; Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States., Scheet P; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States., Huff C; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States., Sun R; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
Source: Genetics [Genetics] 2026 Sep 02; Vol. 234 (1).
Publication Type: Journal Article; Research Support, N.I.H., Extramural
Language: English
Journal Info: Publisher: Oxford University Press Country of Publication: United States NLM ID: 0374636 Publication Model: Print Cited Medium: Internet ISSN: 1943-2631 (Electronic) Linking ISSN: 00166731 NLM ISO Abbreviation: Genetics Subsets: MEDLINE
Imprint Name(s): Publication: 2021- : [Oxford] : Oxford University Press
Original Publication: Austin, Tex. [etc.]
MeSH Terms: Genome-Wide Association Study*/methods , Genome-Wide Association Study*/standards, Humans ; Reproducibility of Results ; Models, Genetic ; Genetic Risk Score ; Models, Statistical ; Polymorphism, Single Nucleotide
Abstract: Genome-wide association studies (GWAS) are a foundational tool in human genetics research, however, challenges in stability and reproducibility of GWAS results are often noted. The main goals of this work are to describe, analyze, and provide tools for solving such reproducibility challenges in a popular component of GWAS literature: set-based (a) hypothesis testing and (b) effect size estimation studies. Common forms of (a) include rare variant or gene-based association studies, while (b) frequently occurs in polygenic score construction and fine-mapping studies. Specifically, we focus on how the set-based natures of (a) and (b) often fuel non-reproducible results due to seemingly innocuous differences in data processing pipelines that are rarely discussed. Such obstacles present enormous challenges for the robustness and reliability of GWAS findings. First, we describe the processing challenges both qualitatively and quantitively, casting the statistical models in a model misspecification framework. Second, we analytically calculate the differences in power and amounts of bias that can arise in (a) and (b), respectively, due to small, relatively under-appreciated choices in data cleaning. Third, we provide tools for quantifying and avoiding the data quality obstacles in GWAS. We validate our analytical calculations through a simulation study, and we demonstrate the aforementioned challenges empirically through analysis of a pancreatic cancer dataset. In our analysis, we demonstrate that top associations, such as between pancreatic cancer and ATM, can be entirely lost due to small differences in data preparation, underscoring the need to make data processing choices clear and explicit.
(© The Author(s) 2026. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Competing Interests: Conflicts of interest: The authors declare no conflicts of interests.
Grant Information: R35 GM154843 United States GM NIGMS NIH HHS
Contributed Indexing: Keywords: genome-wide association study; model misspecification; reproducibility; set-based inference
Entry Date(s): Date Created: 20260323 Date Completed: 20260902 Latest Revision: 20260918
Update Code: 20260919
PubMed Central ID: PMC13535253
DOI: 10.1093/genetics/iyag079
PMID: 41870342
Database: MEDLINE
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  Data: Large impact of genetic data processing steps on stability and reproducibility of set-based analyses in genome-wide association studies.
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  Data: <searchLink fieldCode="AU" term="%22Kui+N%22">Kui N</searchLink>; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.; Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.<br /><searchLink fieldCode="AU" term="%22Yu+Y%22">Yu Y</searchLink>; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.<br /><searchLink fieldCode="AU" term="%22Choi+J%22">Choi J</searchLink>; Department of Mathematical and Statistical Sciences, Marquette University, Milwaukee, WI 53233, United States.<br /><searchLink fieldCode="AU" term="%22McCaw+ZR%22">McCaw ZR</searchLink>; The Voleon Group, Berkeley, CA 94704, United States.<br /><searchLink fieldCode="AU" term="%22Li+X%22">Li X</searchLink>; Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.; Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.<br /><searchLink fieldCode="AU" term="%22Scheet+P%22">Scheet P</searchLink>; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.<br /><searchLink fieldCode="AU" term="%22Huff+C%22">Huff C</searchLink>; Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.<br /><searchLink fieldCode="AU" term="%22Sun+R%22">Sun R</searchLink>; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
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  Data: Genome-wide association studies (GWAS) are a foundational tool in human genetics research, however, challenges in stability and reproducibility of GWAS results are often noted. The main goals of this work are to describe, analyze, and provide tools for solving such reproducibility challenges in a popular component of GWAS literature: set-based (a) hypothesis testing and (b) effect size estimation studies. Common forms of (a) include rare variant or gene-based association studies, while (b) frequently occurs in polygenic score construction and fine-mapping studies. Specifically, we focus on how the set-based natures of (a) and (b) often fuel non-reproducible results due to seemingly innocuous differences in data processing pipelines that are rarely discussed. Such obstacles present enormous challenges for the robustness and reliability of GWAS findings. First, we describe the processing challenges both qualitatively and quantitively, casting the statistical models in a model misspecification framework. Second, we analytically calculate the differences in power and amounts of bias that can arise in (a) and (b), respectively, due to small, relatively under-appreciated choices in data cleaning. Third, we provide tools for quantifying and avoiding the data quality obstacles in GWAS. We validate our analytical calculations through a simulation study, and we demonstrate the aforementioned challenges empirically through analysis of a pancreatic cancer dataset. In our analysis, we demonstrate that top associations, such as between pancreatic cancer and ATM, can be entirely lost due to small differences in data preparation, underscoring the need to make data processing choices clear and explicit.<br /> (© The Author(s) 2026. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
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  Data: Conflicts of interest: The authors declare no conflicts of interests.
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