Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes
In self‐pollinated species, the variance–covariance matrix of breeding values of the genetic strains evaluated in multienvironment trials (MET) can be partitioned into additive effects, additive × additive effects, and their interaction with environments. The additive relationship matrix A can be us...
| Autores principales: | , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2007
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| Acceso en línea: | https://hdl.handle.net/10568/166515 |
| _version_ | 1855519409821450240 |
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| author | Burgueño, Juan Crossa, José Cornelius, Paul L. Trethowan, Richard McLaren, Graham Krishnamachari, Anitha |
| author_browse | Burgueño, Juan Cornelius, Paul L. Crossa, José Krishnamachari, Anitha McLaren, Graham Trethowan, Richard |
| author_facet | Burgueño, Juan Crossa, José Cornelius, Paul L. Trethowan, Richard McLaren, Graham Krishnamachari, Anitha |
| author_sort | Burgueño, Juan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In self‐pollinated species, the variance–covariance matrix of breeding values of the genetic strains evaluated in multienvironment trials (MET) can be partitioned into additive effects, additive × additive effects, and their interaction with environments. The additive relationship matrix A can be used to derive the additive × additive genetic variance–covariance relationships among strains, Ã. This study shows how to separate total genetic effects into additive and additive × additive and how to model the additive × environment interaction and additive × additive × environment interaction by incorporating variance–covariance structures constructed as the Kronecker product of a factor‐analytic model across sites and the additive (A) and additive × additive relationships (Ã), between strains. Two CIMMYT international trials were used for illustration. Results show that partitioning the total genotypic effects into additive and additive × additive and their interactions with environments is useful for identifying wheat (Triticum aestivum L.) lines with high additive effects (to be used in crossing programs) as well as high overall production. Some lines and environments had high positive additive × environment interaction patterns, whereas other lines and environments showed a different additive × additive × environment interaction pattern. |
| format | Journal Article |
| id | CGSpace166515 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2007 |
| publishDateRange | 2007 |
| publishDateSort | 2007 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1665152024-12-22T05:44:47Z Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes Burgueño, Juan Crossa, José Cornelius, Paul L. Trethowan, Richard McLaren, Graham Krishnamachari, Anitha In self‐pollinated species, the variance–covariance matrix of breeding values of the genetic strains evaluated in multienvironment trials (MET) can be partitioned into additive effects, additive × additive effects, and their interaction with environments. The additive relationship matrix A can be used to derive the additive × additive genetic variance–covariance relationships among strains, Ã. This study shows how to separate total genetic effects into additive and additive × additive and how to model the additive × environment interaction and additive × additive × environment interaction by incorporating variance–covariance structures constructed as the Kronecker product of a factor‐analytic model across sites and the additive (A) and additive × additive relationships (Ã), between strains. Two CIMMYT international trials were used for illustration. Results show that partitioning the total genotypic effects into additive and additive × additive and their interactions with environments is useful for identifying wheat (Triticum aestivum L.) lines with high additive effects (to be used in crossing programs) as well as high overall production. Some lines and environments had high positive additive × environment interaction patterns, whereas other lines and environments showed a different additive × additive × environment interaction pattern. 2007-01 2024-12-19T12:56:21Z 2024-12-19T12:56:21Z Journal Article https://hdl.handle.net/10568/166515 en Wiley Burgueño, Juan; Crossa, José; Cornelius, Paul L.; Trethowan, Richard; McLaren, Graham and Krishnamachari, Anitha. 2007. Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes. Crop Science, Volume 47 no. 1 p. 311-320 |
| spellingShingle | Burgueño, Juan Crossa, José Cornelius, Paul L. Trethowan, Richard McLaren, Graham Krishnamachari, Anitha Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes |
| title | Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes |
| title_full | Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes |
| title_fullStr | Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes |
| title_full_unstemmed | Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes |
| title_short | Modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes |
| title_sort | modeling additive x environment and additive x additive x environment using genetic covariances of relatives of wheat genotypes |
| url | https://hdl.handle.net/10568/166515 |
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