000			03519nab a22004577a 4500
999			_c59033 _d59025
001			59033
003			MX-TxCIM
005			20240919020949.0
008			180111s2017 sz |||p|sp||| 00| 0 eng d
024	8		_ahttps://doi.org/10.3389/fpls.2017.01916
040			_aMX-TxCIM
041			_aeng
100	0		_95943 _aAo Zhang
245	1		_aEffect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations _h[Electronic Resource]
260			_aSwitzerland : _bFrontiers, _c2017.
500			_aPeer review
500			_aOpen Access
520			_aGenomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy (rMG) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability (h2), TPS and MD on rMG estimation. Our results showed that: (1) moderate rMG values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) rMG increased with an increase in h2, TPS and MD, both correlation and variance analyses showed that h2 is the most important factor and MD is the least important factor on rMG estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the rMG values for all the six trait-environment combinations were centered around zero, 49% predictions had rMG values above zero; (4) the trend observed in rMG differed with the trend observed in rMG/h, and h is the square root of heritability of the predicted trait, it indicated that both rMG and rMG/h values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.
526			_aMCRP _bFP2 _bFP3
546			_aText in English
650		7	_91132 _aGenomics _2AGROVOC
650		7	_aMaize _gAGROVOC _2 _91173
650		7	_aBreeding methods _gAGROVOC _2 _91030
700			_94567 _aHongwu Wang
700	1		_9870 _aBeyene, Y. _gGlobal Maize Program _8INT2891
700	1		_9869 _aFentaye Kassa Semagn _8INT2869 _gGlobal Maize Program
700	0		_95999 _aYubo Liu
700	0		_95938 _aShiliang Cao
700	0		_96000 _aZhenhai Cui
700	0		_96001 _aYanye Ruan
700	1		_9907 _aBurgueño, J. _gGenetic Resources Program _8INT3239
700	1		_9884 _aSan Vicente, F.M. _8INT3035 _gGlobal Maize Program
700	1		_9923 _aOlsen, M. _gGlobal Maize Program _8INT3333
700	1		_aPrasanna, B.M. _gGlobal Maize Program _8INT3057 _9887
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	0		_96002 _aHaiqiu Yu
700	0		_aXuecai Zhang _gGlobal Maize Program _8INT3400 _9951
773	0		_gv. 8:1916 _tFrontiers in Plant Science _wu56875
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19131
942			_2ddc _cJA _n0