Genomic Predictions with Inclusion of Environmental Covariates to Improve Cassava for Disease Resistance and Yield

Download or read book Genomic Predictions with Inclusion of Environmental Covariates to Improve Cassava for Disease Resistance and Yield written by Alfred Adebo Ozimati and published by . This book was released on 2019 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cassava is the fourth largest source of calories in developing countries after, maize, rice and wheat. However, yield losses due to viral diseases such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD) continue to impact the production of cassava in Asia and Africa. In Sub-Saharan Africa, CBSD is considered more destructive particularly in the East, Central, and Southern parts of Africa. One of the major obstacles in breeding cassava for traits of preference such as fresh root yield and disease resistance is the long breeding cycle (8 to 10 years). Genomic selection (GS), which uses genome-wide DNA markers and phenotypic records from the training population (TP), could help shorten the cycle by enabling estimation of the breeding values (GEBVs) and total genetic value for selection candidates without phenotyping. The National Crops Resources Research Institute (NaCRRI), in Uganda is among the first cassava breeding programs to implement genomic selection. The present study covers three main areas. First, we assessed the impact of accelerated breeding on genetic variation, level of inbreeding, and trait correlations after one cycle of GS. Second, we tested genomic prediction accuracies for agronomic and disease traits in light of genotype-by-environment (G x E) interactions, providing opportunities when breeding for a wide adaptation. In the third objective, we tested genomic prediction accuracies for CBSD-related traits across breeding program (predictions of CBSD resistance in W. African clones, where the disease is non-existent) as a pre-emptive breeding strategy. The highlights of these three studies were that (i) there was genetic progress made for most traits from GS cycle zero (C0) to cycle one (C1). The results indicated that selection based on GEBVs did not erode the original genetic diversity of lines bred under a GS enabled breeding system. Based on these results, we do not expect GS to cause rapid inbreeding as clones are advanced from cycle to cycle (ii) Inclusion of G x E information in genomic prediction showed moderate to high prediction accuracies for CBSD-related traits plus other agronomic traits such as harvest index (HI), under the different cross-validation prediction schemes. However, the predictive ability for root and shoot weight per plot were generally lower across GS prediction models evaluated, except for a scenario of predicting unobserved environments. This result implies that selection can be made accurately for CBSD, dry matter content (DMC), and HI based on genomic prediction models that incorporated G x E estimates. However, additional phenotypic information may be needed for the clones, when also selecting for fresh root yield (iii) Moderate prediction accuracies were recorded for CBSD in West African clones for foliar disease symptom expression, but low prediction accuracies were observed for root necrosis. Based on these results, building a training set comprising West African clones is recommended to predict CBSD resistance in West Africa germplasm where the disease is yet non-existent. The collective output of these interrelated studies serve as vital information to breeders for enabling inter-regional genomic prediction and reducing multi-environment trial costs, without compromising genetic diversity levels across generations. The implementation of genomics-assisted breeding has the potential to help substantially improve cassava production in the developing world.