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Application of multi-layer neural network and hyperspectral reflectance in genome-wide association study for grain yield in bread wheat

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Title Application of multi-layer neural network and hyperspectral reflectance in genome-wide association study for grain yield in bread wheat
 
Creator Fei, Shuaipeng
Hassan, Muhammad Adeel
Yonggui Xiao
Rasheed, Awais
Xianchun Xia
Ma, Yuntao
Luping Fu
Zhen Chen
He Zhonghu
 
Subject remote sensing
wheat
yields
genomes
quantitative trait loci
 
Description Grain 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.
 
Date 2022-12-01
2023-04-05T15:27:49Z
2023-04-05T15:27:49Z
 
Type Journal Article
 
Identifier Fei, S., Hassan, M.A., Xiao, Y., Rasheed, A., Xia, X., Ma, Y., Fu, L., Chen, Z. and He, Z. 2022. Ap-plication of multi-layer neural network and hyperspectral reflectance in genome-wide association study for grain yield in bread wheat. Field Crops Research 289:108730.
0378-4290
https://hdl.handle.net/10568/129909
https://doi.org/10.1016/j.fcr.2022.108730
 
Language en
 
Rights Copyrighted; all rights reserved
Limited Access
 
Publisher Elsevier
 
Source Field Crops Research