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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1270/jsbbs.18141
|2 doi
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|a eng
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| 100 |
1 |
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|a Sun, Qi
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Genomic selection on shelling percentage and other traits for maize
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| 264 |
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|c 2019
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 01.10.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Genomic selection (GS) is the one of the new method for molecular marker-assisted selection (MAS) that can improve selection efficiency and thereby accelerate selective breeding progress. In the present study, we used the exotic germplasm LK1 to improve the shelling percentage of Qi319 by GS. Genome-wide marker effects for each trait were estimated based on the performance of the testcross and SNP data for F2 progenies in the training population. The accuracy of genomic predictions was estimated as the correlation between marker-predicted genotypic values and phenotypic values of the testcrosses for each trait in the validation population. Our study result indicated that selection response for shell percentage was 33.7%, which is greater than those for grain yield, kernel number per ear, or grain moisture at harvest. Selection response for tassel branch number and weight per 100 kernels was greater than 60%. The Higher trait heritability resulted in better prediction efficiency; Prediction accuracy increased with the training population size; Prediction efficiency did not differ significantly between SNP densities of 1000 bp and 55,000 bp. The results of the present research project will provide a basis for genome-wide selection technology in maize breeding, and lay the groundwork for the application of GS to germplasms that are useful in China
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| 650 |
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|a Journal Article
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4 |
|a genomic selection (GS)
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| 650 |
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4 |
|a maize
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| 650 |
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4 |
|a ridge regression-best linear unbiased prediction (RR-BLUP)
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| 650 |
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4 |
|a shelling percentage
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| 700 |
1 |
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|a Wang, Ping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Wenlan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Wencai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Shouping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Yanli
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Meng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Meng, Zhaodong
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Breeding science
|d 1998
|g 69(2019), 2 vom: 26. Juni, Seite 266-271
|w (DE-627)NLM098238299
|x 1344-7610
|7 nnns
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| 773 |
1 |
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|g volume:69
|g year:2019
|g number:2
|g day:26
|g month:06
|g pages:266-271
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|u http://dx.doi.org/10.1270/jsbbs.18141
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