Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

Abstract

Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.

Identification of Drought Tolerant Rice (Oryza Sativa L.) Genotypes with Asian and African Backgrounds

“…To display the genetic dissimilarity of tested genotypes, a weighted neighbor-joining tree was constructed in DARwin 6.0.21 [ 44 ]. To generate a dendrogram, the genotypic data was subjected to hierarchical clustering with 1000 bootstrap p -values in KDCompute 1.5.2.beta [ 45 ].…”

Global warming and increasing maize cultivation demand comprehensive efforts in disease and insect resistance breeding in north‐western Europe

“…This indicates that the genetic contribution is rather small and highly affected by non‐genetic effects. For example, relatively small to moderate heritabilities ranging from 0.3 to 0.6 were reported by Schön et al (1993), Bohn et al (2000), and Badji et al (2020, 2021), thus restricting gain from selection.…”

“…The detected QTLs displayed large confidence intervals and their effects were usually not consistent across maize populations, thus the stability of the resistance is lacking. Nevertheless, presumably in the future, GS will provide more success in increasing insect resistance in plants including maize (Badji et al, 2020, 2021), suggesting that entomologists and breeders should not be discouraged by less promising results at this early stage of research.…”

Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

“…From the individual marker perspective, genomic prediction leverages "hidden replication" of alleles, and therefore the sum of predicted marker effects could be a more accurate predictor of genetic value than an individual phenotype. Examples of potential traits for this use are resistance to insect pests (Badji et al 2021), nematodes (Ravelombola et al 2020) and diseases (Adeyemo et al 2020), root traits (Wolfe et al 2017), postharvest storage (Roth et al 2020), and end-product quality traits (Dreisigacker et al 2021). This approach still requires the upfront investment in design and laborious phenotyping of the training population (TP), but the investment may decrease future costs by performing GS in several populations.…”