000 | 02948nab a22004217a 4500 | ||
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_c30431 _d30431 |
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001 | G98561 | ||
003 | MX-TxCIM | ||
005 | 20240919021149.0 | ||
008 | 121211s2013 xxu|||p op||| | e eng d | ||
022 | _a1945-1296 | ||
040 | _aMX-TxCIM | ||
041 | _aeng | ||
090 | _aCIS-7450 | ||
100 | 1 |
_aArief, V.N. _91426 |
|
245 | 1 | 0 | _aUsing molecular marker order to compare genetic structure in plant populations undergoing selection |
260 |
_aUSA : _bUCLA Department of Statistics, _c2013. |
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500 | _aPeer-review: No - Open Access: Yes|http://www.jenvstat.org/ | ||
500 | _aPeer review | ||
500 | _aOpen Access | ||
520 | _aMany ecological studies compare the genetic structure of populations undergoing natu- ral or artificial selection across different environments. High-throughput molecular markers are now commonly used for these comparisons and provide information on the adapta- tion of the populations to their environments. The genetic structure reflects the history of selection, mutation, migration, and the reproductive breeding system of the populations in their environments. This can be investigated by comparing the ordering of markers obtained from the population with that provided by a recombination or physical map. In populations undergoing selection many genes (markers) have low or zero frequency and commonly used disequilibrium coefficients become unstable under these conditions. A method is presented for ordering bi-allelic markers for populations of self-fertilizing plant species which consist of mixtures of related homozygous genotypes. This provides stable pairwise marker similarity measures even when marker frequencies are low, identification of marker combinations that reflect phenomena that cause differentiation (such as selection and migration), and genetic information on the adaptation of the populations to the environments. The method is illustrated using data from a plant breeding program and inferences are made about accumulation of desirable genes (such as for disease resistance). | ||
536 | _aGenetic Resources Program|Global Wheat Program | ||
546 | _aText in English | ||
594 | _aINT3049|INT2692|CCJL01 | ||
595 | _aCSC | ||
650 | 7 |
_aPlant breeding _gAGROVOC _2 _91203 |
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650 | 7 |
_aGenetic markers _2AGROVOC _91848 |
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650 | 7 |
_aPlant population _2AGROVOC _91211 |
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650 | 7 |
_aSelection _2AGROVOC _94749 |
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700 | 1 |
_aDeLacy, I.H. _91427 |
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700 | 1 |
_9885 _aWenzl, P. _gGenetic Resources Program _8INT3049 |
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700 | 1 |
_9851 _aDreisigacker, S. _gGlobal Wheat Program _8INT2692 |
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700 | 1 |
_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959 |
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700 | 1 |
_aDieters, M.J.J. _8001712805 _gExcellence in Breeding _91430 |
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700 | 1 |
_91429 _aBasford, K.E. |
|
773 | 0 |
_tJournal of Environmental Statistics _gv. 4, no. 4 _dUSA : UCLA Department of Statistics, 2013. _x1945-1296 |
|
856 | 4 |
_uhttp://hdl.handle.net/10883/3446 _yOpen Access through DSpace |
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942 |
_cJA _2ddc _n0 |