Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease
<p><strong>Motivation:</strong><br/> Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect meta-analysis methods. However, the power to detect genetic associations under fixed-effect mo...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2019
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| _version_ | 1797084728152555520 |
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| author | Magosi, LE Goel, A Hopewell, JC Farrall, M |
| author2 | The Cardiogramplusc4D Consortium |
| author_facet | The Cardiogramplusc4D Consortium Magosi, LE Goel, A Hopewell, JC Farrall, M |
| author_sort | Magosi, LE |
| collection | OXFORD |
| description | <p><strong>Motivation:</strong><br/> Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect meta-analysis methods. However, the power to detect genetic associations under fixed-effect models deteriorates with increasing hetero-geneity, so that some small-effect heterogeneous loci might go undetected. Han and Eskin devel-oped a modified random-effects meta-analysis approach (RE2) that is more powerful than tradi-tional fixed and random-effects methods at detecting small-effect heterogeneous genetic associa-tions, updating the method (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a fixed-effect meta-analysis.</p><br/> <p><strong>Results:</strong><br/> Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and SPRE (standardized predicted random-effects) statistics to reveal the distribution of genetic effect esti-mates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals.</p> |
| first_indexed | 2024-03-07T01:59:03Z |
| format | Journal article |
| id | oxford-uuid:9cb6dc1b-730f-44cc-8cdb-b91e84b6d93c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:59:03Z |
| publishDate | 2019 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:9cb6dc1b-730f-44cc-8cdb-b91e84b6d93c2022-03-27T00:38:11ZIdentifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery diseaseJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9cb6dc1b-730f-44cc-8cdb-b91e84b6d93cEnglishSymplectic Elements at OxfordOxford University Press2019Magosi, LEGoel, AHopewell, JCFarrall, MThe Cardiogramplusc4D Consortium<p><strong>Motivation:</strong><br/> Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect meta-analysis methods. However, the power to detect genetic associations under fixed-effect models deteriorates with increasing hetero-geneity, so that some small-effect heterogeneous loci might go undetected. Han and Eskin devel-oped a modified random-effects meta-analysis approach (RE2) that is more powerful than tradi-tional fixed and random-effects methods at detecting small-effect heterogeneous genetic associa-tions, updating the method (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a fixed-effect meta-analysis.</p><br/> <p><strong>Results:</strong><br/> Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and SPRE (standardized predicted random-effects) statistics to reveal the distribution of genetic effect esti-mates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals.</p> |
| spellingShingle | Magosi, LE Goel, A Hopewell, JC Farrall, M Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease |
| title | Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease |
| title_full | Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease |
| title_fullStr | Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease |
| title_full_unstemmed | Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease |
| title_short | Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease |
| title_sort | identifying small effect genetic associations overlooked by the conventional fixed effect model in a large scale meta analysis of coronary artery disease |
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