scholarly journals Genome-wide association mapping and genomic prediction unravels CBSD resistance in a Manihot esculenta breeding population

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.




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


Author(s):  
Chalermpol Phumichai ◽  
Pornsak Aiemnaka ◽  
Piyaporn Nathaisong ◽  
Sirikan Hunsawattanakul ◽  
Phasakorn Fungfoo ◽  
...  


Genetics ◽  
2014 ◽  
Vol 198 (4) ◽  
pp. 1699-1716 ◽  
Author(s):  
Brenda F. Owens ◽  
Alexander E. Lipka ◽  
Maria Magallanes-Lundback ◽  
Tyler Tiede ◽  
Christine H. Diepenbrock ◽  
...  


2020 ◽  
Vol 139 (3) ◽  
pp. 508-520 ◽  
Author(s):  
David Sewordor Gaikpa ◽  
Silvia Koch ◽  
Franz Joachim Fromme ◽  
Dörthe Siekmann ◽  
Tobias Würschum ◽  
...  


Crop Science ◽  
2020 ◽  
Vol 60 (1) ◽  
pp. 211-225 ◽  
Author(s):  
Caiyun Liu ◽  
Sivakumar Sukumaran ◽  
Diego Jarquin ◽  
Jose Crossa ◽  
Susanne Dreisigacker ◽  
...  


2018 ◽  
Vol 9 (1) ◽  
pp. 125-133 ◽  
Author(s):  
Moses Nyine ◽  
Shichen Wang ◽  
Kian Kiani ◽  
Katherine Jordan ◽  
Shuyu Liu ◽  
...  


2015 ◽  
Vol 47 (1) ◽  
pp. 36-48 ◽  
Author(s):  
Y. L. Bernal Rubio ◽  
J. L. Gualdrón Duarte ◽  
R. O. Bates ◽  
C. W. Ernst ◽  
D. Nonneman ◽  
...  


2016 ◽  
Vol 170 (4) ◽  
pp. 2187-2203 ◽  
Author(s):  
Rik Kooke ◽  
Willem Kruijer ◽  
Ralph Bours ◽  
Frank Becker ◽  
André Kuhn ◽  
...  


2021 ◽  
Author(s):  
Chenggen Chu ◽  
Shichen Wang ◽  
Jackie C. Rudd ◽  
Amir M.H. Ibrahim ◽  
Qingwu Xue ◽  
...  

Abstract Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over ten years, through testing yield in 40–66 lines each year at 6–14 locations with 38–41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, were used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement.



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