ArticleG3 (Bethesda, Md.)2020
Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize.
Article in G3 (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 16 citations in OpenAlex.
- Identification and fine-mapping ofMolecular breeding : new strategies in plant improvement · 2025Article
- Identification of loci conferring resistance to 4 foliar diseases of maize.G3 (Bethesda, Md.) · 2024Article
- Spatiotemporal Dynamics of Maize (Frontiers in microbiology · 2021Review
- Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize.International journal of molecular sciences · 2020Review
- How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crops are hosts to numerous plant pathogenic microorganisms. Maize has several major disease issues; thus, breeding multiple disease resistant (MDR) varieties is critical. While the genetic basis of resistance to multiple fungal pathogens has been studied in maize, less is known about the relationship between fungal and bacterial resistance. In this study, we evaluated a disease resistance introgression line (DRIL) population for the foliar disease Goss's bacterial wilt and blight (GW) and conducted quantitative trait locus (QTL) mapping. We identified a total of ten QTL across multiple environments. We then combined our GW data with data on four additional foliar diseases (northern corn leaf blight, southern corn leaf blight, gray leaf spot, and bacterial leaf streak) and conducted multivariate analysis to identify regions conferring resistance to multiple diseases. We identified 20 chromosomal bins with putative multiple disease effects. We examined the five chromosomal regions (bins 1.05, 3.04, 4.06, 8.03, and 9.02) with the strongest statistical support. By examining how each haplotype effected each disease, we identified several regions associated with increased resistance to multiple diseases and three regions associated with opposite effects for bacterial and fungal diseases. In summary, we identified several promising candidate regions for multiple disease resistance in maize and specific DRILs to expedite interrogation.
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