000			03783nab|a22004577a|4500
001			62827
003			MX-TxCIM
005			20240919021229.0
008			201030s2021||||gw |||p|op||||00||0|eng|d
022			_a0040-5752
022			_a1432-2242 (Online)
024	8		_ahttps://doi.org/10.1007/s00122-020-03696-9
040			_aMX-TxCIM
041			_aeng
100	1		_aAtanda, A.S. _8001711295 _8001712571 _gGlobal Maize Program _gFormerly Global Wheat Program _98531
245	1	0	_aMaximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program
260			_aBerlin (Germany) : _bSpringer, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aKey message Historical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set. The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a "test-half-predict-half approach." Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT's maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or "test-half-predict-half" can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.
546			_aText in English
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aPlant breeding _gAGROVOC _2 _91203
650		7	_aMaize _2AGROVOC _91173
700	1		_aOlsen, M. _8INT3333 _9923 _gGlobal Maize Program
700	1		_aBurgueño, J. _8INT3239 _9907 _gGenetic Resources Program
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aDzidzienyo, D. _916831
700	1		_aBeyene, Y. _8INT2891 _9870 _gGlobal Maize Program
700	1		_aGowda, M. _8I1705963 _9795 _gGlobal Maize Program
700	1		_aDreher, K.A. _8I1706147 _9808 _gGenetic Resources Program
700	0		_aXuecai Zhang _gGlobal Maize Program _8INT3400 _9951
700	1		_aPrasanna, B.M. _gGlobal Maize Program _8INT3057 _9887
700	1		_aTongoona, P.B. _8001713456 _gFormerly Excellence in Breeding _9340
700	1		_aDanquah, E. _96758
700	1		_aOlaoye, G. _92299
700	1		_aRobbins, K. _95987
773	0		_dBerlin (Germany) : Springer, 2021. _x0040-5752 _gv. 134, no. 1, p. 279-294 _tTheoretical and Applied Genetics _wu444762
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21003
942			_cJA _n0 _2ddc
999			_c62827 _d62819