Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits
Abstract We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple “gene products” (e.g., encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT ac...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-025-03480-2 |
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| author | Jeffrey Okamoto Xianyong Yin Brady Ryan Joshua Chiou Francesca Luca Roger Pique-Regi Hae Kyung Im Jean Morrison Charles Burant Eric B. Fauman Markku Laakso Michael Boehnke Xiaoquan Wen |
| author_facet | Jeffrey Okamoto Xianyong Yin Brady Ryan Joshua Chiou Francesca Luca Roger Pique-Regi Hae Kyung Im Jean Morrison Charles Burant Eric B. Fauman Markku Laakso Michael Boehnke Xiaoquan Wen |
| author_sort | Jeffrey Okamoto |
| collection | DOAJ |
| description | Abstract We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple “gene products” (e.g., encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT achieves higher power than existing methods, maintains calibrated false discovery rates, and detects the true causal gene product(s). We apply Multi-INTACT to GWAS on 1408 metabolites, integrating the GTEx expression and UK Biobank protein QTL datasets. Multi-INTACT infers 52 to 109% more metabolite causal genes than protein-alone or expression-alone analyses and indicates both gene products are relevant for most gene nominations. |
| format | Article |
| id | doaj-art-f63f557f14d64118bffba9efd45eef1b |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-f63f557f14d64118bffba9efd45eef1b2025-02-09T12:39:21ZengBMCGenome Biology1474-760X2025-02-0126112310.1186/s13059-025-03480-2Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traitsJeffrey Okamoto0Xianyong Yin1Brady Ryan2Joshua Chiou3Francesca Luca4Roger Pique-Regi5Hae Kyung Im6Jean Morrison7Charles Burant8Eric B. Fauman9Markku Laakso10Michael Boehnke11Xiaoquan Wen12Department of Biostatistics and Center for Statistical Genetics, University of MichiganDepartment of Biostatistics and Center for Statistical Genetics, University of MichiganDepartment of Biostatistics and Center for Statistical Genetics, University of MichiganInternal Medicine Research Unit, Pfizer Worldwide Research, Development and MedicalCenter for Molecular Medicine and Genetics, Wayne State UniversityCenter for Molecular Medicine and Genetics, Wayne State UniversitySection of Genetic Medicine, Department of Medicine, University of ChicagoDepartment of Biostatistics and Center for Statistical Genetics, University of MichiganDepartment of Internal Medicine, University of MichiganInternal Medicine Research Unit, Pfizer Worldwide Research, Development and MedicalInstitute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University HospitalDepartment of Biostatistics and Center for Statistical Genetics, University of MichiganDepartment of Biostatistics and Center for Statistical Genetics, University of MichiganAbstract We present multi-integration of transcriptome-wide association studies and colocalization (Multi-INTACT), an algorithm that models multiple “gene products” (e.g., encoded RNA transcript and protein levels) to implicate causal genes and relevant gene products. In simulations, Multi-INTACT achieves higher power than existing methods, maintains calibrated false discovery rates, and detects the true causal gene product(s). We apply Multi-INTACT to GWAS on 1408 metabolites, integrating the GTEx expression and UK Biobank protein QTL datasets. Multi-INTACT infers 52 to 109% more metabolite causal genes than protein-alone or expression-alone analyses and indicates both gene products are relevant for most gene nominations.https://doi.org/10.1186/s13059-025-03480-2 |
| spellingShingle | Jeffrey Okamoto Xianyong Yin Brady Ryan Joshua Chiou Francesca Luca Roger Pique-Regi Hae Kyung Im Jean Morrison Charles Burant Eric B. Fauman Markku Laakso Michael Boehnke Xiaoquan Wen Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits Genome Biology |
| title | Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits |
| title_full | Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits |
| title_fullStr | Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits |
| title_full_unstemmed | Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits |
| title_short | Multi-INTACT: integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits |
| title_sort | multi intact integrative analysis of the genome transcriptome and proteome identifies causal mechanisms of complex traits |
| url | https://doi.org/10.1186/s13059-025-03480-2 |
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