期刊
THEORETICAL AND APPLIED GENETICS
卷 134, 期 3, 页码 941-958出版社
SPRINGER
DOI: 10.1007/s00122-020-03744-4
关键词
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资金
- Bill and Melinda Gates Foundation (BMGF)
- United States Agency for International Development (USAID) through Stress Tolerant Maize for Africa (STMA, B MGF) Project [OPP1134248]
- AG2MW project (Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods, BMGF Investment) [INV-003439]
- IMAS Project
- CGIAR Research Program MAIZE
- Government of Australia
- Government of Belgium
- Government of Canada
- Government of China
- Government of France
- Government of India
- Government of Japan
- Government of Korea
- Government of Mexico
- Government of Netherlands
- Government of New Zealand
- Government of Norway
- Government of Sweden
- Government of Switzerland
- Government of UK
- Government of USA
- World Bank
This study evaluated a diverse set of tropical maize lines for Striga resistance, detected quantitative trait loci associated with resistance through GWAS, and assessed the effectiveness of genomic prediction in breeding for resistance. Results showed the polygenic nature of resistance to S. hermonthica and suggested that implementation of genomic prediction could potentially increase genetic gain for this important trait.
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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