scholarly journals Genome-Wide Association Mapping and Genomic Prediction Elucidate the Genetic Architecture of Morphological Traits in Arabidopsis

2016 ◽  
Vol 170 (4) ◽  
pp. 2187-2203 ◽  
Author(s):  
Rik Kooke ◽  
Willem Kruijer ◽  
Ralph Bours ◽  
Frank Becker ◽  
André Kuhn ◽  
...  
Author(s):  
Chalermpol Phumichai ◽  
Pornsak Aiemnaka ◽  
Piyaporn Nathaisong ◽  
Sirikan Hunsawattanakul ◽  
Phasakorn Fungfoo ◽  
...  

2016 ◽  
Vol 213 (3) ◽  
pp. 1346-1362 ◽  
Author(s):  
Manus P. M. Thoen ◽  
Nelson H. Davila Olivas ◽  
Karen J. Kloth ◽  
Silvia Coolen ◽  
Ping-Ping Huang ◽  
...  

2017 ◽  
Author(s):  
Siraj Ismail Kayondo ◽  
Dunia Pino Del Carpio ◽  
Roberto Lozano ◽  
Alfred Ozimati ◽  
Marnin Wolfe ◽  
...  

AbstractCassava (Manihot esculenta Crantz), a key carbohydrate dietary source for millions of people in Africa, faces severe yield loses due to two viral diseases: cassava brown streak disease (CBSD) and cassava mosaic disease (CMD). The completion of the cassava genome sequence and the whole genome marker profiling of clones from African breeding programs (www.nextgencassava.org) provides cassava breeders the opportunity to deploy additional breeding strategies and develop superior varieties with both farmer and industry preferred traits. Here the identification of genomic segments associated with resistance to CBSD foliar symptoms and root necrosis as measured in two breeding panels at different growth stages and locations is reported. Using genome-wide association mapping and genomic prediction models we describe the genetic architecture for CBSD severity and identify loci strongly associated on chromosomes 4 and 11. Moreover, the significantly associated region on chromosome 4 colocalises with a Manihot glaziovii introgression segment and the significant SNP markers on chromosome 11 are situated within a cluster of nucleotide-binding site leucine-rich repeat (NBS-LRR) genes previously described in cassava. Overall, predictive accuracy values found in this study varied between CBSD severity traits and across GS models with Random Forest and RKHS showing the highest predictive accuracies for foliar and root CBSD severity scores.


2019 ◽  
Vol 69 (4) ◽  
pp. 611-620
Author(s):  
Yuanyuan Wang ◽  
Guirong Li ◽  
Xinlei Guo ◽  
Runrun Sun ◽  
Tao Dong ◽  
...  

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Siraj Ismail Kayondo ◽  
Dunia Pino Del Carpio ◽  
Roberto Lozano ◽  
Alfred Ozimati ◽  
Marnin Wolfe ◽  
...  

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