A novel test for gene-ancestry interactions in genome-wide association data.
Genome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or w...
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0048687&type=printable |
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| author | Joanna L Davies Jean-Baptiste Cazier Malcolm G Dunlop Richard S Houlston Ian P Tomlinson Chris C Holmes |
| author_facet | Joanna L Davies Jean-Baptiste Cazier Malcolm G Dunlop Richard S Houlston Ian P Tomlinson Chris C Holmes |
| author_sort | Joanna L Davies |
| collection | DOAJ |
| description | Genome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into "ancestry groups" and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions. |
| format | Article |
| id | doaj-art-4f95a85f6389448cbfa2cea43a75b6d9 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-4f95a85f6389448cbfa2cea43a75b6d92025-08-20T03:26:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-01712e4868710.1371/journal.pone.0048687A novel test for gene-ancestry interactions in genome-wide association data.Joanna L DaviesJean-Baptiste CazierMalcolm G DunlopRichard S HoulstonIan P TomlinsonChris C HolmesGenome-wide association study (GWAS) data on a disease are increasingly available from multiple related populations. In this scenario, meta-analyses can improve power to detect homogeneous genetic associations, but if there exist ancestry-specific effects, via interactions on genetic background or with a causal effect that co-varies with genetic background, then these will typically be obscured. To address this issue, we have developed a robust statistical method for detecting susceptibility gene-ancestry interactions in multi-cohort GWAS based on closely-related populations. We use the leading principal components of the empirical genotype matrix to cluster individuals into "ancestry groups" and then look for evidence of heterogeneous genetic associations with disease or other trait across these clusters. Robustness is improved when there are multiple cohorts, as the signal from true gene-ancestry interactions can then be distinguished from gene-collection artefacts by comparing the observed interaction effect sizes in collection groups relative to ancestry groups. When applied to colorectal cancer, we identified a missense polymorphism in iron-absorption gene CYBRD1 that associated with disease in individuals of English, but not Scottish, ancestry. The association replicated in two additional, independently-collected data sets. Our method can be used to detect associations between genetic variants and disease that have been obscured by population genetic heterogeneity. It can be readily extended to the identification of genetic interactions on other covariates such as measured environmental exposures. We envisage our methodology being of particular interest to researchers with existing GWAS data, as ancestry groups can be easily defined and thus tested for interactions.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0048687&type=printable |
| spellingShingle | Joanna L Davies Jean-Baptiste Cazier Malcolm G Dunlop Richard S Houlston Ian P Tomlinson Chris C Holmes A novel test for gene-ancestry interactions in genome-wide association data. PLoS ONE |
| title | A novel test for gene-ancestry interactions in genome-wide association data. |
| title_full | A novel test for gene-ancestry interactions in genome-wide association data. |
| title_fullStr | A novel test for gene-ancestry interactions in genome-wide association data. |
| title_full_unstemmed | A novel test for gene-ancestry interactions in genome-wide association data. |
| title_short | A novel test for gene-ancestry interactions in genome-wide association data. |
| title_sort | novel test for gene ancestry interactions in genome wide association data |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0048687&type=printable |
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