A systematic analysis of gene–gene interaction in multiple sclerosis
Abstract Background For the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to assume independent contributions of individual variants to the phenotype. Many tools have therefore been developed to inves...
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| Format: | Article |
| Language: | English |
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BMC
2022-04-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12920-022-01247-3 |
| _version_ | 1811306306260172800 |
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| author | Lotfi Slim Clément Chatelain Hélène de Foucauld Chloé-Agathe Azencott |
| author_facet | Lotfi Slim Clément Chatelain Hélène de Foucauld Chloé-Agathe Azencott |
| author_sort | Lotfi Slim |
| collection | DOAJ |
| description | Abstract Background For the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to assume independent contributions of individual variants to the phenotype. Many tools have therefore been developed to investigate the interactions between distant loci, or epistasis. Among them, the recently proposed EpiGWAS models the interactions between a target variant and the rest of the genome. However, applying this approach to studying interactions along all genes of a disease map is not straightforward. Here, we propose a pipeline to that effect, which we illustrate by investigating a multiple sclerosis GWAS dataset from the Wellcome Trust Case Control Consortium 2 through 19 disease maps from the MetaCore pathway database. Results For each disease map, we build an epistatic network by connecting the genes that are deemed to interact. These networks tend to be connected, complementary to the disease maps and contain hubs. In addition, we report 4 epistatic gene pairs involving missense variants, and 25 gene pairs with a deleterious epistatic effect mediated by eQTLs. Among these, we highlight the interaction of GLI-1 and SUFU, and of IP10 and NF- $$\kappa$$ κ B, as they both match known biological interactions. The latter pair is particularly promising for therapeutic development, as both genes have known inhibitors. Conclusions Our study showcases the ability of EpiGWAS to uncover biologically interpretable epistatic interactions that are potentially actionable for the development of combination therapy. |
| first_indexed | 2024-04-13T08:42:03Z |
| format | Article |
| id | doaj.art-4ea6e8dbb9bf45e5a0407387c38998bb |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-04-13T08:42:03Z |
| publishDate | 2022-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-4ea6e8dbb9bf45e5a0407387c38998bb2022-12-22T02:53:51ZengBMCBMC Medical Genomics1755-87942022-04-0115111410.1186/s12920-022-01247-3A systematic analysis of gene–gene interaction in multiple sclerosisLotfi Slim0Clément Chatelain1Hélène de Foucauld2Chloé-Agathe Azencott3CBIO, MINES ParisTech, PSL Research UniversityTranslational Sciences, SANOFI R&DTranslational Sciences, SANOFI R&DCBIO, MINES ParisTech, PSL Research UniversityAbstract Background For the most part, genome-wide association studies (GWAS) have only partially explained the heritability of complex diseases. One of their limitations is to assume independent contributions of individual variants to the phenotype. Many tools have therefore been developed to investigate the interactions between distant loci, or epistasis. Among them, the recently proposed EpiGWAS models the interactions between a target variant and the rest of the genome. However, applying this approach to studying interactions along all genes of a disease map is not straightforward. Here, we propose a pipeline to that effect, which we illustrate by investigating a multiple sclerosis GWAS dataset from the Wellcome Trust Case Control Consortium 2 through 19 disease maps from the MetaCore pathway database. Results For each disease map, we build an epistatic network by connecting the genes that are deemed to interact. These networks tend to be connected, complementary to the disease maps and contain hubs. In addition, we report 4 epistatic gene pairs involving missense variants, and 25 gene pairs with a deleterious epistatic effect mediated by eQTLs. Among these, we highlight the interaction of GLI-1 and SUFU, and of IP10 and NF- $$\kappa$$ κ B, as they both match known biological interactions. The latter pair is particularly promising for therapeutic development, as both genes have known inhibitors. Conclusions Our study showcases the ability of EpiGWAS to uncover biologically interpretable epistatic interactions that are potentially actionable for the development of combination therapy.https://doi.org/10.1186/s12920-022-01247-3GWASEpistasisMultiple sclerosisGene–gene interactionCausal inference |
| spellingShingle | Lotfi Slim Clément Chatelain Hélène de Foucauld Chloé-Agathe Azencott A systematic analysis of gene–gene interaction in multiple sclerosis BMC Medical Genomics GWAS Epistasis Multiple sclerosis Gene–gene interaction Causal inference |
| title | A systematic analysis of gene–gene interaction in multiple sclerosis |
| title_full | A systematic analysis of gene–gene interaction in multiple sclerosis |
| title_fullStr | A systematic analysis of gene–gene interaction in multiple sclerosis |
| title_full_unstemmed | A systematic analysis of gene–gene interaction in multiple sclerosis |
| title_short | A systematic analysis of gene–gene interaction in multiple sclerosis |
| title_sort | systematic analysis of gene gene interaction in multiple sclerosis |
| topic | GWAS Epistasis Multiple sclerosis Gene–gene interaction Causal inference |
| url | https://doi.org/10.1186/s12920-022-01247-3 |
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