Accuracy of whole genome prediction with single-step GBLUP in a Chinese yellow-feathered chicken population

2019 ◽  
Vol 230 ◽  
pp. 103817 ◽  
Author(s):  
Ning Gao ◽  
Jinyan Teng ◽  
Rongyang Pan ◽  
Xiujin Li ◽  
Shaopan Ye ◽  
...  
2019 ◽  
Vol 98 (5) ◽  
pp. 1968-1975 ◽  
Author(s):  
Jinyan Teng ◽  
Ning Gao ◽  
Haibin Zhang ◽  
Xiujin Li ◽  
Jiaqi Li ◽  
...  

2018 ◽  
Vol 9 ◽  
Author(s):  
Saad Haider ◽  
Michael B. Black ◽  
Bethany B. Parks ◽  
Briana Foley ◽  
Barbara A. Wetmore ◽  
...  

2016 ◽  
Vol 4 (4) ◽  
pp. 487-496
Author(s):  
Michael J. McGeachie ◽  
George L. Clemmer ◽  
Damien C. Croteau‐Chonka ◽  
Peter J. Castaldi ◽  
Michael H. Cho ◽  
...  

2020 ◽  
Author(s):  
Yixin An ◽  
Lin Chen ◽  
Yongxiang Li ◽  
Chunhui Li ◽  
Yunsu Shi ◽  
...  

Abstract Background: Kernel row number (KRN) is an important trait for the domestication and improvement of maize. To explore the genetic basis of KRN has great research significance and can provide the valuable information for molecular assisted selection.Results: In this study, one single-locus method (MLM) and six multi-locus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) for KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate the total KRN-associated tagSNPs identified by all GWAS models.Conclusions: These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.


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