Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population

Kump, Kristen L.; Bradbury, Peter J.; Wisser, Randall J.; Buckler, Edward S.; Belcher, Araby R.; Oropeza-Rosas, Marco A.; Zwonitzer, John C.; Kresovich, Stephen; McMullen, Michael D.; Ware, Doreen; Balint‐Kurti, Peter; Holland, James B.

doi:10.1038/ng.747

Cited by 531 publications

(485 citation statements)

References 51 publications

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“…In each trial, the 866 BC 2 S 3 RILs were grown in a single replicate employing an augmented incomplete randomized blocks design widely used to phenotype large populations (Buckler et al ., 2009; Kump et al ., 2011; Tian et al ., 2011; Huang et al ., 2015). Two maize inbred lines W22 and Mo17 were randomly inserted in each incomplete block as checks.…”

Section: Methodsmentioning

confidence: 99%

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

Wang

Zhang

et al. 2015

New Phytologist

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Summary The number of leaves and their distributions on plants are critical factors determining plant architecture in maize (Zea mays), and leaf number is frequently used as a measure of flowering time, a trait that is key to local environmental adaptation.Here, using a large set of 866 maize‐teosinte BC 2S3 recombinant inbred lines genotyped by using 19 838 single nucleotide polymorphism markers, we conducted a comprehensive genetic dissection to assess the genetic architecture of leaf number and its genetic relationship to flowering time.We demonstrated that the two components of total leaf number, the number of leaves above (LA) and below (LB) the primary ear, were under relatively independent genetic control and might be subject to differential directional selection during maize domestication and improvement. Furthermore, we revealed that flowering time and leaf number are commonly regulated at a moderate level. The pleiotropy of the genes ZCN8, dlf1 and ZmCCT on leaf number and flowering time were validated by near‐isogenic line analysis. Through fine mapping, qLA1‐1, a major‐effect locus that specifically affects LA, was delimited to a region with severe recombination suppression derived from teosinte.This study provides important insights into the genetic basis of traits affecting plant architecture and adaptation. The genetic independence of LA from LB enables the optimization of leaf number for ideal plant architecture breeding in maize.

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Section: Methodsmentioning

confidence: 99%

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

Wang

Zhang

et al. 2015

New Phytologist

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“…Through deep RNA-seq, we obtained an average of 70 million reads for each inbred line, which resulted in the recovery of 1.03 million high-quality SNPs in the maize genome. The identified SNPs are of significance to the maize research community, especially in exploring the genetic architecture of quantitative traits in maize using GWAS, as genomic SNPs were often used in previous GWASs in maize, including leaf architecture 33 , leaf metabolites 34 and disease resistance 35,36 . Most of the newly identified SNPs were mapped to gene regions with an average of 40.3 SNPs per gene, which substantially complemented the maize SNP polymorphisms discovered by genome resequencing 13,14 .…”

Section: Discussionmentioning

confidence: 99%

RNA sequencing reveals the complex regulatory network in the maize kernel

et al. 2013

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RNA sequencing can simultaneously identify exonic polymorphisms and quantitate gene expression. Here we report RNA sequencing of developing maize kernels from 368 inbred lines producing 25.8 billion reads and 3.6 million single-nucleotide polymorphisms. Both the MaizeSNP50 BeadChip and the Sequenom MassArray iPLEX platforms confirm a subset of high-quality SNPs. Of these SNPs, we have mapped 931,484 to gene regions with a mean density of 40.3 SNPs per gene. The genome-wide association study identifies 16,408 expression quantitative trait loci. A two-step approach defines 95.1% of the eQTLs to a 10-kb region, and 67.7% of them include a single gene. The establishment of relationships between eQTLs and their targets reveals a large-scale gene regulatory network, which include the regulation of 31 zein and 16 key kernel genes. These results contribute to our understanding of kernel development and to the improvement of maize yield and nutritional quality.

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“…Importantly for genetic mapping applications, the line mean-basis heritability across the entire NAM population corresponds to the maximum amount of phenotypic variation among NAM lines that can be attributed to genetic effects and thus to the cumulative effects of QTL Kump et al, 2011). Correspondingly, heritability on a line-mean basis within a family indicates that the proportion of variation among line means that can be attributed to QTL within that family.…”

Section: Heritability Estimatesĥmentioning

confidence: 99%

“…The final model containing only significant terms was used to estimate the parameters reported in this study, which included unique genetic components of variance for each family. Best linear unbiased predictors for RILs were also obtained from these models for use in QTL mapping and genome-wide association studies Kump et al, 2011;Tian et al, 2011). Heritabilities on an individual plot basis (h p 2 ) (Holland et al, 2003) were estimated for the entire NAM population as: …”

Section: Statistical Analysesmentioning

confidence: 99%

“…Furthermore, the gene-rich regions of all NAM parental lines have been sequenced with next-generation sequencing methods to produce a maize HapMap composed of 1.6 million SNPs (Gore et al, 2009). The combination of large genetic diversity, dense genetic maps, founder sequence information and robust phenotypic data associated with the maize NAM has permitted high-resolution genetic mapping of QTL and genes affecting important quantitative traits Kump et al, 2011;Tian et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

et al. 2011

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Appropriate selection of parents for the development of mapping populations is pivotal to maximizing the power of quantitative trait loci detection. Trait genotypic variation within a family is indicative of the family's informativeness for genetic studies. Accurate prediction of the most useful parental combinations within a species would help guide quantitative genetics studies. We tested the reliability of genotypic and phenotypic distance estimators between pairs of maize inbred lines to predict genotypic variation for quantitative traits within families derived from biparental crosses. We developed 25 families composed of B200 random recombinant inbred lines each from crosses between a common reference parent inbred, B73, and 25 diverse maize inbreds. Parents and families were evaluated for 19 quantitative traits across up to 11 environments. Genetic distances (GDs) among parents were estimated with 44 simple sequence repeat and 2303 single-nucleotide polymorphism markers. GDs among parents had no predictive value for progeny variation, which is most likely due to the choice of neutral markers. In contrast, we observed for about half of the traits measured a positive correlation between phenotypic parental distances and within-family genetic variance estimates. Consequently, the choice of promising segregating populations can be based on selecting phenotypically diverse parents. These results are congruent with models of genetic architecture that posit numerous genes affecting quantitative traits, each segregating for allelic series, with dispersal of allelic effects across diverse genetic material. This architecture, common to many quantitative traits in maize, limits the predictive value of parental genotypic or phenotypic values on progeny variance.

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Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population

Cited by 531 publications

References 51 publications

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

The genetic architecture of leaf number and its genetic relationship to flowering time in maize

RNA sequencing reveals the complex regulatory network in the maize kernel

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

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