Abstract
KEY MESSAGE: Genome-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.
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Claims 11
Claim · model-written summary
Maize genotypes differed significantly in Striga resistance traits.
- Outcome
- measured traits associated with Striga resistancee0
Zea mays
StrigaOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance”
t0“tropical maize germplasm”
Claim · model-written summary
Genotype by environment variation was significant for Striga resistance traits.
- Material
- Genotypic and genotype x environmente0
- Outcome
- measured traits associated with Striga resistancee0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance”
t0“tropical maize germplasm”
Claim · model-written summary
Heritability estimates for measured traits ranged from 0.42 to 0.92.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Heritability estimates were moderate (0.42) to high (0.92) for measured traits”
t0“tropical maize germplasm”
Claim · model-written summary
GWAS identified 57 SNPs significantly associated with Striga resistance traits and grain yield, with low to moderate effects.
- Treatment
- artificial Striga infestatione0
- Gene
- SNPse0
- Outcome
- Striga resistance indicator traits and grain yielde0
- Value
- 57 SNPse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect”
t0“tropical maize germplasm”
Claim · model-written summary
Thirty-two candidate genes near significant SNPs had roles in defense against biotic stress.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified”
t0“tropical maize germplasm”
Claim · model-written summary
Genomic prediction was better for lines in new environments than for new lines.
- Outcome
- prediction of performance of lines in new environmentse0
- Direction
- bettere0
- Comparator
- prediction of performance of new linese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits”
t0“tropical maize germplasm”
Claim · model-written summary
Across-environment predictions had high accuracy for all traits.
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Predictions across environments revealed high accuracy for all the traits”
t0“tropical maize germplasm”
Claim · model-written summary
Including GWAS-detected SNPs slightly increased prediction accuracy.
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“inclusion of GWAS-detected SNPs led to slight increase in the accuracy”
t0“tropical maize germplasm”
Claim · model-written summary
Item-based collaborative filtering outperformed genomic prediction for Striga resistance indicators.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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”
t0“tropical maize germplasm”
Claim · model-written summary
Resistance to S. hermonthica was polygenic.
- Interactor
- S. hermonthica
Striga hermonthicae0parasite - Outcome
- polygenic nature of resistance to S. hermonthicae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results demonstrated the polygenic nature of resistance to S. hermonthica”
t0“tropical maize germplasm”
Claim · model-written summary
Using genomic prediction in Striga resistance breeding could increase genetic gain.
- Outcome
- increasing genetic gaine0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait”
t0“tropical maize germplasm”