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Improving root characterisation for genomic prediction in cassava
Sunday, 2020/08/09 | 06:29:54

BO YonisDP Del CarpioM WolfeJL JanninkP KulakowI Rabbi

 

Science Reports  2020 May 14;10(1):8003.  doi: 10.1038/s41598-020-64963-9.

 

ABSTRACT

 

Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava.

 

See https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224197/

 

Figure 3&4: Genome-wide association results of size and shape-related traits using de-regressed BLUPs of mean values (not corrected for CMD). (A) Area. (B) Perimeter; (C). Feret; (D). Solidity; (E). Aspect ratio; (F). Circularity; (G). Roundness. Blue horizontal line indicates the Bonferroni statistical threshold.

 

Genome-wide association results of standard deviation-derived size and shape-related traits using de-regressed BLUPs of mean CMD-corrected values. (A). Area; (B). Perimeter; (C). Feret; (D). Solidity; (E). Aspect ratio; (F). Circularity; (G). Roundness. Blue horizontal line indicates the Bonferroni statistical threshold.

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