000			02754nab|a22003737a|4500
999			_c61989 _d61981
001			61989
003			MX-TxCIM
005			20240919020951.0
008			200529s2020||||xxk|||p|op||||00||0|eng|d
022			_a2045-2322
024	8		_ahttps://doi.org/10.1038/s41598-020-65011-2
040			_aMX-TxCIM
041			_aeng
100	1		_aLopez-Cruz, M. _92348
245	1		_aRegularized selection indices for breeding value prediction using hyper-spectral image data
260			_aLondon (United Kingdom) : _bNature Publishing Group, _c2020.
500			_aPeer review
500			_aOpen Access
520			_aHigh-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT?s (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by standard SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.
546			_aText in English
650		7	_2AGROVOC _91853 _aQuantitative Trait Loci
650		7	_2AGROVOC _92624 _aStatistical methods
650		7	_2AGROVOC _93634 _aPhenotypes
700	1		_aOlson, E. _913649
700	1		_aRovere, G. _913650
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aDreisigacker, S. _8INT2692 _9851 _gGlobal Wheat Program
700	1		_aMondal, S. _gFormerly Global Wheat Program _8INT3211 _9904
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_aDe los Campos, G. _8CCAG01 _92349 _gGenetic Resources Program
773	0		_tNature Scientific Reports _gv. 10, no. 1, art. 8195 _dLondon (United Kingdom) : Nature Publishing Group, 2020. _x2045-2322 _wa58025
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20890
942			_cJA _n0 _2ddc