Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix.
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| Title: | Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix. |
|---|---|
| Authors: | Patel, Roshni A.1,2,3 roshnip@uoregon.edu, Schraiber, Joshua G.3, Pennell, Matt4 mpennell@cornell.edu, Edge, Michael D.3 edgem@usc.edu |
| Source: | Proceedings of the National Academy of Sciences of the United States of America. 5/12/2026, Vol. 123 Issue 19, p1-11. 11p. |
| Subjects: | Epidemiological research, Genetic risk score, Heritability, Disease risk factors, Biobanks, Genome-wide association studies, Genetic databases |
| Abstract: | Observational studies are commonly used in psychology and epidemiology to identify risk factors correlated with health outcomes. However, these studies are vulnerable to confounding when shared genetic variation influences both the putative risk factor and outcome. Researchers have often controlled for this type of genetic confounding using polygenic scores, but these scores are noisy and biased estimators of a trait’s genetic component. While some newer methods offer significant improvements over polygenic scores, they still rely on genome-wide association studies (GWAS) summary statistics, which may be untenable for certain datasets. Here, we develop an analogous method that leverages a genetic relatedness matrix to control genetic confounding when testing for nongenetic risk factors. In simulations, we find that our method outperforms existing approaches, particularly at sample sizes that are large by the standards of much human research but smaller than datasets often used in human genetics. We also demonstrate that existing methods are susceptible to poor GWAS portability, whereas our method is inherently robust to such concerns, conditional on the availability of individual genotype data. Finally, we apply our method to the UK Biobank to reanalyze social risk factors for health outcomes in previously understudied cohorts. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193988620 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Patel%2C+Roshni+A%2E%22">Patel, Roshni A.</searchLink><relatesTo>1,2,3</relatesTo><i> roshnip@uoregon.edu</i><br /><searchLink fieldCode="AR" term="%22Schraiber%2C+Joshua+G%2E%22">Schraiber, Joshua G.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Pennell%2C+Matt%22">Pennell, Matt</searchLink><relatesTo>4</relatesTo><i> mpennell@cornell.edu</i><br /><searchLink fieldCode="AR" term="%22Edge%2C+Michael+D%2E%22">Edge, Michael D.</searchLink><relatesTo>3</relatesTo><i> edgem@usc.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink>. 5/12/2026, Vol. 123 Issue 19, p1-11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Epidemiological+research%22">Epidemiological research</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+risk+score%22">Genetic risk score</searchLink><br /><searchLink fieldCode="DE" term="%22Heritability%22">Heritability</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+risk+factors%22">Disease risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Biobanks%22">Biobanks</searchLink><br /><searchLink fieldCode="DE" term="%22Genome-wide+association+studies%22">Genome-wide association studies</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+databases%22">Genetic databases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Observational studies are commonly used in psychology and epidemiology to identify risk factors correlated with health outcomes. However, these studies are vulnerable to confounding when shared genetic variation influences both the putative risk factor and outcome. Researchers have often controlled for this type of genetic confounding using polygenic scores, but these scores are noisy and biased estimators of a trait’s genetic component. While some newer methods offer significant improvements over polygenic scores, they still rely on genome-wide association studies (GWAS) summary statistics, which may be untenable for certain datasets. Here, we develop an analogous method that leverages a genetic relatedness matrix to control genetic confounding when testing for nongenetic risk factors. In simulations, we find that our method outperforms existing approaches, particularly at sample sizes that are large by the standards of much human research but smaller than datasets often used in human genetics. We also demonstrate that existing methods are susceptible to poor GWAS portability, whereas our method is inherently robust to such concerns, conditional on the availability of individual genotype data. Finally, we apply our method to the UK Biobank to reanalyze social risk factors for health outcomes in previously understudied cohorts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1073/pnas.2533909123 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Epidemiological research Type: general – SubjectFull: Genetic risk score Type: general – SubjectFull: Heritability Type: general – SubjectFull: Disease risk factors Type: general – SubjectFull: Biobanks Type: general – SubjectFull: Genome-wide association studies Type: general – SubjectFull: Genetic databases Type: general Titles: – TitleFull: Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Patel, Roshni A. – PersonEntity: Name: NameFull: Schraiber, Joshua G. – PersonEntity: Name: NameFull: Pennell, Matt – PersonEntity: Name: NameFull: Edge, Michael D. IsPartOfRelationships: – BibEntity: Dates: – D: 12 M: 05 Text: 5/12/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00278424 Numbering: – Type: volume Value: 123 – Type: issue Value: 19 Titles: – TitleFull: Proceedings of the National Academy of Sciences of the United States of America Type: main |
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