ArticleBMC genomics2014
Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- The maize mitogen-activated protein kinase kinase kinase gene ZmMAPKKK45 is associated with multiple disease resistance.The New phytologist · 2026Article
- Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize.Nature genetics · 2025Article
- Extreme-phenotype genome-wide association study (XP-GWAS) of powdery mildew race 2 W tolerance in the USDA Citrullus germplasm collection.Scientific reports · 2025Article
- Insights into maydis leaf blight resistance in maize: a comprehensive genome-wide association study in sub-tropics of India.BMC genomics · 2024Article
- Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.G3 (Bethesda, Md.) · 2024Article
- Hybrid allele-specific ChIP-seq analysis identifies variation in brassinosteroid-responsive transcription factor binding linked to traits in maize.Genome biology · 2023Article
- Brown midrib mutant and genome-wide association analysis uncover lignin genes for disease resistance in maize.The plant genome · 2023Article
- Elucidating the patterns of pleiotropy and its biological relevance in maize.PLoS genetics · 2023Article
- On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience.Molecular breeding : new strategies in plant improvement · 2021Article
- Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize.The plant genome · 2021Article
- The potential roles of different metacaspases in maize defense response.Plant signaling & behavior · 2021Article
- The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.G3 (Bethesda, Md.) · 2021Article
- Genomic basis of white pine blister rust quantitative disease resistance and its relationship with qualitative resistance.The Plant journal : for cell and molecular biology · 2020Article
- Using Maize Chromosome Segment Substitution Line Populations for the Identification of Loci Associated with Multiple Disease Resistance.G3 (Bethesda, Md.) · 2019Article
- Identification of a locus in maize controlling response to a host-selective toxin derived from Cochliobolus heterostrophus, causal agent of southern leaf blight.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Increased experimental conditions and marker densities identified more genetic loci associated with southern and northern leaf blight resistance in maize.Scientific reports · 2018Article
- Space Radiation Alters Genotype-Phenotype Correlations in Fear Learning and Memory Tests.Frontiers in genetics · 2018Article
- Article
- A gene encoding maize caffeoyl-CoA O-methyltransferase confers quantitative resistance to multiple pathogens.Nature genetics · 2017Article
- Enhancing genomic prediction with genome-wide association studies in multiparental maize populations.Heredity · 2017Article
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5 authors.
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Abstract
backgroundA previous study reported a comprehensive quantitative trait locus (QTL) and genome wide association study (GWAS) of southern leaf blight (SLB) resistance in the maize Nested Association Mapping (NAM) panel. Since that time, the genomic resources available for such analyses have improved substantially. An updated NAM genetic linkage map has a nearly six-fold greater marker density than the previous map and the combined SNPs and read-depth variants (RDVs) from maize HapMaps 1 and 2 provided 28.5 M genomic variants for association analysis, 17 fold more than HapMap 1. In addition, phenotypic values of the NAM RILs were re-estimated to account for environment-specific flowering time covariates and a small proportion of lines were dropped due to genotypic data quality problems. Comparisons of original and updated QTL and GWAS results confound the effects of linkage map density, GWAS marker density, population sample size, and phenotype estimates. Therefore, we evaluated the effects of changing each of these parameters individually and in combination to determine their relative impact on marker-trait associations in original and updated analyses.
resultsOf the four parameters varied, map density caused the largest changes in QTL and GWAS results. The updated QTL model had better cross-validation prediction accuracy than the previous model. Whereas joint linkage QTL positions were relatively stable to input changes, the residual values derived from those QTL models (used as inputs to GWAS) were more sensitive, resulting in substantial differences between GWAS results. The updated NAM GWAS identified several candidate genes consistent with previous QTL fine-mapping results.
conclusionsThe highly polygenic nature of resistance to SLB complicates the identification of causal genes. Joint linkage QTL are relatively stable to perturbations of data inputs, but their resolution is generally on the order of tens or more Mbp. GWAS associations have higher resolution, but lower power due to stringent thresholds designed to minimize false positive associations, resulting in variability of detection across studies. The updated higher density linkage map improves QTL estimation and, along with a much denser SNP HapMap, greatly increases the likelihood of detecting SNPs in linkage with causal variants. We recommend use of the updated genetic resources and results but emphasize the limited repeatability of small-effect associations.
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