Abstract
Striga hermonthica is a widespread, destructive parasitic plant that causes substantial yield loss to maize productivity in sub-Saharan Africa. Under severe Striga infestation, yield losses can range from 60 to 100% resulting in abandonment of farmers’ lands. Diverse methods have been proposed for Striga management; however, host plant resistance is considered the most effective and affordable to small-scale famers. Thus, conducting a genome-wide association study to identify quantitative trait nucleotides controlling S. hermonthica resistance and mining of relevant candidate genes will expedite the improvement of Striga resistance breeding through marker-assisted breeding. For this study, 150 diverse maize inbred lines were evaluated under Striga infested and non-infested conditions for two years and genotyped using the genotyping-by-sequencing platform. Heritability estimates of Striga damage ratings, emerged Striga plants and grain yield, hereafter referred to as Striga resistance-related traits, were high under Striga infested condition. The mixed linear model (MLM) identified thirty SNPs associated with the three Striga resistance-related traits based on the multi-locus approaches (mrMLM, FASTmrMLM, FASTmrEMMA and pLARmEB). These SNPs explained up to 14% of the total phenotypic variation. Under non-infested condition, four SNPs were associated with grain yield, and these SNPs explained up to 17% of the total phenotypic variation. Gene annotation of significant SNPs identified candidate genes (Leucine-rich repeats, putative disease resistance protein and VQ proteins) with functions related to plant growth, development, and defense mechanisms. The marker-effect prediction was able to identify alleles responsible for predicting high yield and low Striga damage rating in the breeding panel. This study provides valuable insight for marker validation and deployment for Striga resistance breeding in maize.
Highlights
Striga hermonthica is a widespread, destructive parasitic plant that causes substantial yield loss to maize productivity in sub-Saharan Africa
The marked reduction in grain yield observed in the resistant and susceptible benchmark indicates the occurrence of severe parasite infestation across the test environments, eliciting significant differences in resistance or susceptibility reactions among the inbred lines
The diversity panel used in our study displayed considerable phenotypic variation for the three Striga resistance-related traits recorded under Striga infestation, and this is consistent with the findings in other studies[25]
Summary
Striga hermonthica is a widespread, destructive parasitic plant that causes substantial yield loss to maize productivity in sub-Saharan Africa. The mixed linear model (MLM) identified thirty SNPs associated with the three Striga resistance-related traits based on the multilocus approaches (mrMLM, FASTmrMLM, FASTmrEMMA and pLARmEB) These SNPs explained up to 14% of the total phenotypic variation. Host plant resistance is considered a cost effective, environmental feasible and affordable option for smallholder farmers It is an essential component of any successful integrated approach for controlling Striga parasitism. The methods include polygenic-background control-based least angle regression plus empirical Bayes (pLARmEB), fast multi-locus random-SNP-effect efficient mixed model association (FASTmrEMMA), iterative-sure independence screening expectation–maximization (EM)-Bayesian LASSO (ISIS EMBLASSO) and fast multi-locus random-SNP-effect mixed linear model (FASTmrMLM)[18,19,20,21,22] These methods can effectively detect small-effect QTNs and improve the efficiency and accuracy of GWAS. Few studies have implemented the above GWAS methods to detect important loci controlling different traits in m aize[23]
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