Manje Gowda, Dan Makumbi, Biswanath Das, Christine Nyaga, Titus Kosgei, Jose Crossa, Yoseph Beyene, Osval A. Montesinos-López, Michael S. Olsen & Boddupalli M. Prasanna

Theoretical and Applied Genetics March 2021

Key message

Genome-wide association revealed that resistance to Striga hermonthica is infuenced by multiple genomic regions with moderate efects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction.

Abstract

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 artifcial 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 efectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artifcial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were signifcant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs signifcantly associated with Striga resistance indicator traits and grain yield (GY) under artifcial Striga infestation with low to moderate efect. A set of 32 candidate genes physically near the signifcant SNPs with roles in plant defense against biotic stresses were identifed. GP with diferent 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 fltering approach that incorporates related traits evaluated in diferent 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.

See file:///C:/Users/Admin/Downloads/Gowda2021_Article_GeneticDissectionOfStrigaHermo.pdf

Figure 3:

Manhattan plots of the GWAS for six diferent Striga-related traits in IMAS association mapping panel. The dashed horizontal line depicts the signifcance threshold (P=2×10–6 for Striga resistance traits and P=5.6×10–6 for GY). The X-axis indicates the SNP location along the 10 chromosomes, with chromosomes separated by different colors; Y-axis is the—log10(P observed) for each analysis