000			03229nab|a22004217a|4500
001			65758
003			MX-TxCIM
005			20231009164120.0
008			20221s2022||||mx |||p|op||||00||0|eng|d
022			_a0378-4290
022			_21872-6852 (Online)
024	8		_ahttps://doi.org/10.1016/j.fcr.2022.108730
040			_aMX-TxCIM
041			_aeng
100	1		_aFei, S. _928380
245	1	0	_aApplication of multi-layer neural network and hyperspectral reflectance in genome-wide association study for grain yield in bread wheat
260			_bElsevier, _c2022. _aAmsterdam (Netherlands) :
520			_aGrain yield (GY) is a primary trait for phenotype selection in crop breeding. Rapid and cost-effective prediction of GY before harvest from remote sensing platforms can be integrated with practical breeding activities. In this study, a natural population containing 166 wheat cultivars and elite lines was used for time-series prediction of GY using ground-based hyperspectral remote sensing. Canopy hyperspectral data (350–2500 nm) was collected at the flowering, early grain-filling (EGF), mid grain-filling (MGF), and late grain-filling (LGF) stages under four environments. GY was predicted by using full bands reflectance as input of multi-layer neural network. Genome-wide association study (GWAS) was performed using 373,106 markers from 660 K and 90 K single-nucleotide polymorphism (SNP) arrays in 166 wheat genotypes. Prediction accuracy for GY characterized by R2 values were 0.68, 0.69, 0.76, and 0.65 at flowering, EGF, MGF, and LGF, respectively. Among the 26 loci identified by predicted GY, 13 were located in similar positions to previously reported loci related to yield, and another 13 were potentially new loci. Linear regression (R2) ranged from 0.87 to 0.94 indicating that distinct cumulative effects of favorable alleles detected by predicted GY were increasing as compared to measured GY. This study highlights the feasibility of combining remote sensing with machine learning for wheat breeding decisions and to understand the underlying genetic basis of crop yield.
546			_aText in English
591			_aRasheed, A. : No CIMMYT Affiliation
650		7	_aRemote sensing _2AGROVOC _91986
650		7	_aWheat _2AGROVOC _91310
650		7	_aYields _2AGROVOC _91313
650		7	_aGenomes _2AGROVOC _91131
650		7	_aQuantitative Trait Loci _2AGROVOC _91853
700	1		_aHassan, M.A. _97723
700	0		_aYonggui Xiao _91687
700	1		_aAwais Rasheed _gGlobal Wheat Program _8I1706474 _91938
700	0		_aXianchun Xia _9377
700	1		_aMa, Y. _929186
700	0		_aLuping Fu _95903
700	0		_aZhen Chen _919674
700	1		_aHe Zhonghu _gGlobal Wheat Program _8INT2411 _9838
773	0		_tField Crops Research _dAmsterdam (Netherlands) : Elsevier, 2022. _gv. 289, art. 108730 _wG444314 _x0378-4290
942			_cJA _n0 _2ddc
999			_c65758 _d65750