Joint mapping of quantitative trait loci for multiple binary characters
Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disea...
| Autores principales: | , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Oxford University Press
2005
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/166752 |
| _version_ | 1855529766236454912 |
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| author | Xu, Chenwu Li, Zhikang Xu, Shizhong |
| author_browse | Li, Zhikang Xu, Chenwu Xu, Shizhong |
| author_facet | Xu, Chenwu Li, Zhikang Xu, Shizhong |
| author_sort | Xu, Chenwu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. Joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice. |
| format | Journal Article |
| id | CGSpace166752 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2005 |
| publishDateRange | 2005 |
| publishDateSort | 2005 |
| publisher | Oxford University Press |
| publisherStr | Oxford University Press |
| record_format | dspace |
| spelling | CGSpace1667522024-12-22T05:44:55Z Joint mapping of quantitative trait loci for multiple binary characters Xu, Chenwu Li, Zhikang Xu, Shizhong quantitative trait loci genetic mapping disease resistance phenotypic variation phenotypes Joint mapping for multiple quantitative traits has shed new light on genetic mapping by pinpointing pleiotropic effects and close linkage. Joint mapping also can improve statistical power of QTL detection. However, such a joint mapping procedure has not been available for discrete traits. Most disease resistance traits are measured as one or more discrete characters. These discrete characters are often correlated. Joint mapping for multiple binary disease traits may provide an opportunity to explore pleiotropic effects and increase the statistical power of detecting disease loci. We develop a maximum-likelihood method for mapping multiple binary traits. We postulate a set of multivariate normal disease liabilities, each contributing to the phenotypic variance of one disease trait. The underlying liabilities are linked to the binary phenotypes through some underlying thresholds. The new method actually maps loci for the variation of multivariate normal liabilities. As a result, we are able to take advantage of existing methods of joint mapping for quantitative traits. We treat the multivariate liabilities as missing values so that an expectation-maximization (EM) algorithm can be applied here. We also extend the method to joint mapping for both discrete and continuous traits. Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice. 2005-02-01 2024-12-19T12:56:36Z 2024-12-19T12:56:36Z Journal Article https://hdl.handle.net/10568/166752 en Oxford University Press Xu, Chenwu; Li, Zhikang and Xu, Shizhong. 2005. Joint mapping of quantitative trait loci for multiple binary characters. |
| spellingShingle | quantitative trait loci genetic mapping disease resistance phenotypic variation phenotypes Xu, Chenwu Li, Zhikang Xu, Shizhong Joint mapping of quantitative trait loci for multiple binary characters |
| title | Joint mapping of quantitative trait loci for multiple binary characters |
| title_full | Joint mapping of quantitative trait loci for multiple binary characters |
| title_fullStr | Joint mapping of quantitative trait loci for multiple binary characters |
| title_full_unstemmed | Joint mapping of quantitative trait loci for multiple binary characters |
| title_short | Joint mapping of quantitative trait loci for multiple binary characters |
| title_sort | joint mapping of quantitative trait loci for multiple binary characters |
| topic | quantitative trait loci genetic mapping disease resistance phenotypic variation phenotypes |
| url | https://hdl.handle.net/10568/166752 |
| work_keys_str_mv | AT xuchenwu jointmappingofquantitativetraitlociformultiplebinarycharacters AT lizhikang jointmappingofquantitativetraitlociformultiplebinarycharacters AT xushizhong jointmappingofquantitativetraitlociformultiplebinarycharacters |