A unified mixed model method for association mapping that accounts for multiple levels of relatedness
As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2006
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/100018 |
| _version_ | 1855524366069006336 |
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| author | Yu, J. Pressoir, G. Briggs, W.H. Vroh Bi, Irie Yamasaki, M. Doebley, J.F. Mcmullen, M.D. Gaut, B.S. Nielsen, D.M. Holland, J.B. Kresovich, Stephen |
| author_browse | Briggs, W.H. Doebley, J.F. Gaut, B.S. Holland, J.B. Kresovich, Stephen Mcmullen, M.D. Nielsen, D.M. Pressoir, G. Vroh Bi, Irie Yamasaki, M. Yu, J. |
| author_facet | Yu, J. Pressoir, G. Briggs, W.H. Vroh Bi, Irie Yamasaki, M. Doebley, J.F. Mcmullen, M.D. Gaut, B.S. Nielsen, D.M. Holland, J.B. Kresovich, Stephen |
| author_sort | Yu, J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure1,2. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping. |
| format | Journal Article |
| id | CGSpace100018 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2006 |
| publishDateRange | 2006 |
| publishDateSort | 2006 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1000182024-08-27T10:35:52Z A unified mixed model method for association mapping that accounts for multiple levels of relatedness Yu, J. Pressoir, G. Briggs, W.H. Vroh Bi, Irie Yamasaki, M. Doebley, J.F. Mcmullen, M.D. Gaut, B.S. Nielsen, D.M. Holland, J.B. Kresovich, Stephen population structure plant genetics phenotypes gene expression As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure1,2. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping. 2006-02 2019-03-03T05:54:45Z 2019-03-03T05:54:45Z Journal Article https://hdl.handle.net/10568/100018 en Limited Access Springer Yu, J., Pressoir, G., Briggs, W.H., Vroh Bi, I., Yamasaki, M., Doebley, J.F., … & Kresovich, S. (2006). A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics, 38, 203-208. |
| spellingShingle | population structure plant genetics phenotypes gene expression Yu, J. Pressoir, G. Briggs, W.H. Vroh Bi, Irie Yamasaki, M. Doebley, J.F. Mcmullen, M.D. Gaut, B.S. Nielsen, D.M. Holland, J.B. Kresovich, Stephen A unified mixed model method for association mapping that accounts for multiple levels of relatedness |
| title | A unified mixed model method for association mapping that accounts for multiple levels of relatedness |
| title_full | A unified mixed model method for association mapping that accounts for multiple levels of relatedness |
| title_fullStr | A unified mixed model method for association mapping that accounts for multiple levels of relatedness |
| title_full_unstemmed | A unified mixed model method for association mapping that accounts for multiple levels of relatedness |
| title_short | A unified mixed model method for association mapping that accounts for multiple levels of relatedness |
| title_sort | unified mixed model method for association mapping that accounts for multiple levels of relatedness |
| topic | population structure plant genetics phenotypes gene expression |
| url | https://hdl.handle.net/10568/100018 |
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