| 000 | 00595nab|a22002177a|4500 | ||
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| 999 |
_c63505 _d63497 |
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| 001 | 63505 | ||
| 003 | MX-TxCIM | ||
| 005 | 20210312231225.0 | ||
| 008 | 200805s2019||||xxk|||p|op||||00||0|eng|d | ||
| 022 | _a0931-2668 | ||
| 022 | _a1439-0388 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.1111/jbg.12404 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aTolhurst, D.J. _919215 |
|
| 245 | 1 | 0 | _aGenomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model |
| 260 |
_aUnited Kingdom : _bWiley, _c2019. |
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| 500 | _aPeer review | ||
| 520 | _aGenomic selection (GS) is a statistical and breeding methodology designed to improve genetic gain. It has proven to be successful in animal breeding; however, key points of difference have not been fully considered in the transfer of GS from animal to plant breeding. In plant breeding, individuals (varieties) are typically evaluated across a number of locations in multiple years (environments) in formally designed comparative experiments, called multi‐environment trials (METs). The design structure of individual trials can be complex and needs to be modelled appropriately. Another key feature of MET data sets is the presence of variety by environment interaction (VEI), that is the differential response of varieties to a change in environment. In this paper, a single‐step factor analytic linear mixed model is developed for plant breeding MET data sets that incorporates molecular marker data, appropriately accommodates non‐genetic sources of variation within trials and models VEI. A recently developed set of selection tools, which are natural derivatives of factor analytic models, are used to facilitate GS for a motivating data set from an Australian plant breeding company. The power and versatility of these tools is demonstrated for the variety by environment and marker by environment effects. | ||
| 546 | _aText in English | ||
| 650 | 7 |
_aMarker-assisted selection _2AGROVOC _910737 |
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| 650 | 7 |
_aFactor analysis _2AGROVOC _92156 |
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| 650 | 7 |
_aLinear models _2AGROVOC _96025 |
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| 650 | 7 |
_aEnvironment _2AGROVOC _91098 |
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| 700 | 1 |
_aMathews, K. _93392 |
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| 700 | 1 |
_aSmith, A.B. _919216 |
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| 700 | 1 |
_aCullis, B.R. _919217 |
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| 773 | 0 |
_gv. 136, no. 4, p. 279-300 _dUnited Kingdom : Wiley, 2019. _x1439-0388 _tJournal of Animal Breeding and Genetics |
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| 942 |
_cJA _n0 _2ddc |
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