Abstract
Key messageGenome-wide association revealed that resistance to Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction.Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.
Highlights
The purple witchweed or giant witchweed, Striga hermonthica (Del.) Benth., is the most widespread parasitic weed posing a serious threat to maize production in sub-Saharan1 3 Vol.:(0123456789)Theoretical and Applied Genetics (2021) 134:941–958Africa (SSA) (Berner et al 1995; De Groote et al 2008; Spallek et al 2013)
For traits associated with Striga resistance, the ratio of genotypic variance to G × E interaction variance was highest for syndrome rating (SDR) with 8.1 and lowest for Striga count at 8 weeks after planting (WAP) with 0.7
The distribution of phenotypic values was unimodal for the number of emerged Striga plants at different intervals, area under the Striga number progress curve” (AUSNPC) and SDR indicating the quantitative nature of Striga resistance (Fig. 1)
Summary
The purple witchweed or giant witchweed, Striga hermonthica (Del.) Benth., is the most widespread parasitic weed posing a serious threat to maize production in sub-Saharan1 3 Vol.:(0123456789)Theoretical and Applied Genetics (2021) 134:941–958Africa (SSA) (Berner et al 1995; De Groote et al 2008; Spallek et al 2013). The purple witchweed or giant witchweed, Striga hermonthica (Del.) Benth., is the most widespread parasitic weed posing a serious threat to maize production in sub-Saharan. Maize plants infested with Striga become chlorotic, produce thin stalks with severe reduction in plant height, biomass and eventually grain yield (Menkir et al 2012, 2020). Many farmers experience total maize crop failure due to Striga infestation. Farmers who faced 100% yield loss usually move from one affected field to another, and abandoned fields become Striga seed banks. It is estimated that Striga infestation causes up to US$ 7 billion in crop losses (Berner et al 1995), affecting the livelihoods of over 100 million people (Badu-Apraku and Akinwale 2011)
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