DrFARM: identification of pleiotropic genetic variants in genome-wide association studies
Abstract In a standard analysis, pleiotropic variants are identified by running separate genome-wide association studies (GWAS) and combining results across traits. But such statistical approach based on marginal summary statistics may lead to spurious results. We propose a new statistical approach,...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60439-4 |
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| author | Lap Sum Chan Gen Li Eric B. Fauman Xianyong Yin Markku Laakso Michael Boehnke Peter X. K. Song |
| author_facet | Lap Sum Chan Gen Li Eric B. Fauman Xianyong Yin Markku Laakso Michael Boehnke Peter X. K. Song |
| author_sort | Lap Sum Chan |
| collection | DOAJ |
| description | Abstract In a standard analysis, pleiotropic variants are identified by running separate genome-wide association studies (GWAS) and combining results across traits. But such statistical approach based on marginal summary statistics may lead to spurious results. We propose a new statistical approach, Debiased-regularized Factor Analysis Regression Model (DrFARM), through a joint regression model for simultaneous analysis of high-dimensional genetic variants and multilevel dependencies. This joint modeling strategy controls overall error to permit universal false discovery rate (FDR) control. DrFARM uses the strengths of the debiasing technique and the Cauchy combination test, both being theoretically justified, to establish a valid post selection inference on pleiotropic variants. Through extensive simulations, we show that DrFARM appropriately controls overall FDR. Applying DrFARM to data on 1031 metabolites measured on 6135 men from the Metabolic Syndrome in Men (METSIM) study, we identify five first-time reported putative causal genes, none of which had been implicated in any prior metabolite GWAS (including the prior METSIM analysis). |
| format | Article |
| id | doaj-art-ff91cfcf96c544e5bbcee24b905d877e |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-ff91cfcf96c544e5bbcee24b905d877e2025-08-20T03:45:34ZengNature PortfolioNature Communications2041-17232025-07-0116111410.1038/s41467-025-60439-4DrFARM: identification of pleiotropic genetic variants in genome-wide association studiesLap Sum Chan0Gen Li1Eric B. Fauman2Xianyong Yin3Markku Laakso4Michael Boehnke5Peter X. K. Song6Department of Biostatistics, University of MichiganDepartment of Biostatistics, University of MichiganInternal Medicine Research Unit, Pfizer Worldwide Research, Development and MedicalDepartment of Epidemiology, Nanjing Medical UniversityInstitute of Clinical Medicine, Internal Medicine, University of Eastern FinlandDepartment of Biostatistics, University of MichiganDepartment of Biostatistics, University of MichiganAbstract In a standard analysis, pleiotropic variants are identified by running separate genome-wide association studies (GWAS) and combining results across traits. But such statistical approach based on marginal summary statistics may lead to spurious results. We propose a new statistical approach, Debiased-regularized Factor Analysis Regression Model (DrFARM), through a joint regression model for simultaneous analysis of high-dimensional genetic variants and multilevel dependencies. This joint modeling strategy controls overall error to permit universal false discovery rate (FDR) control. DrFARM uses the strengths of the debiasing technique and the Cauchy combination test, both being theoretically justified, to establish a valid post selection inference on pleiotropic variants. Through extensive simulations, we show that DrFARM appropriately controls overall FDR. Applying DrFARM to data on 1031 metabolites measured on 6135 men from the Metabolic Syndrome in Men (METSIM) study, we identify five first-time reported putative causal genes, none of which had been implicated in any prior metabolite GWAS (including the prior METSIM analysis).https://doi.org/10.1038/s41467-025-60439-4 |
| spellingShingle | Lap Sum Chan Gen Li Eric B. Fauman Xianyong Yin Markku Laakso Michael Boehnke Peter X. K. Song DrFARM: identification of pleiotropic genetic variants in genome-wide association studies Nature Communications |
| title | DrFARM: identification of pleiotropic genetic variants in genome-wide association studies |
| title_full | DrFARM: identification of pleiotropic genetic variants in genome-wide association studies |
| title_fullStr | DrFARM: identification of pleiotropic genetic variants in genome-wide association studies |
| title_full_unstemmed | DrFARM: identification of pleiotropic genetic variants in genome-wide association studies |
| title_short | DrFARM: identification of pleiotropic genetic variants in genome-wide association studies |
| title_sort | drfarm identification of pleiotropic genetic variants in genome wide association studies |
| url | https://doi.org/10.1038/s41467-025-60439-4 |
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