Joint genetic analysis using variant sets reveals polygenic gene-context interactions.
Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-04-01
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| Series: | PLoS Genetics |
| Online Access: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1006693&type=printable |
| _version_ | 1826579471411445760 |
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| author | Francesco Paolo Casale Danilo Horta Barbara Rakitsch Oliver Stegle |
| author_facet | Francesco Paolo Casale Danilo Horta Barbara Rakitsch Oliver Stegle |
| author_sort | Francesco Paolo Casale |
| collection | DOAJ |
| description | Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods. |
| first_indexed | 2024-12-14T09:38:33Z |
| format | Article |
| id | doaj.art-0ab3dd7d328a46a2b2b3b1d23f4a799f |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2025-03-14T14:18:48Z |
| publishDate | 2017-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-0ab3dd7d328a46a2b2b3b1d23f4a799f2025-02-27T05:32:17ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042017-04-01134e100669310.1371/journal.pgen.1006693Joint genetic analysis using variant sets reveals polygenic gene-context interactions.Francesco Paolo CasaleDanilo HortaBarbara RakitschOliver StegleJoint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1006693&type=printable |
| spellingShingle | Francesco Paolo Casale Danilo Horta Barbara Rakitsch Oliver Stegle Joint genetic analysis using variant sets reveals polygenic gene-context interactions. PLoS Genetics |
| title | Joint genetic analysis using variant sets reveals polygenic gene-context interactions. |
| title_full | Joint genetic analysis using variant sets reveals polygenic gene-context interactions. |
| title_fullStr | Joint genetic analysis using variant sets reveals polygenic gene-context interactions. |
| title_full_unstemmed | Joint genetic analysis using variant sets reveals polygenic gene-context interactions. |
| title_short | Joint genetic analysis using variant sets reveals polygenic gene-context interactions. |
| title_sort | joint genetic analysis using variant sets reveals polygenic gene context interactions |
| url | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1006693&type=printable |
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