Dataset Information

Unraveling genomic complexity at a quantitative disease resistance locus in maize.

ABSTRACT: Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line "Tx303" conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart's wilt. To dissect the genetic basis of resistance in this region and to refine candidate gene hypotheses, we mapped resistance to the two diseases. Both resistance phenotypes were localized to overlapping regions, with the Stewart's wilt interval refined to a 95.9-kb segment containing three genes and the NLB interval to a 3.60-Mb segment containing 117 genes. Regions of the introgression showed little to no recombination, suggesting structural differences between the inbred lines Tx303 and "B73," the parents of the fine-mapping population. We examined copy number variation across the region using next-generation sequencing data, and found large variation in read depth in Tx303 across the region relative to the reference genome of B73. In the fine-mapping region, association mapping for NLB implicated candidate genes, including a putative zinc finger and pan1. We tested mutant alleles and found that pan1 is a susceptibility gene for NLB and Stewart's wilt. Our data strongly suggest that structural variation plays an important role in resistance conditioned by this region, and pan1, a gene conditioning susceptibility for NLB, may underlie the QTL.

SUBMITTER: Jamann TM

PROVIDER: S-EPMC4174945 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Unraveling genomic complexity at a quantitative disease resistance locus in maize.

Jamann Tiffany M TM Poland Jesse A JA Kolkman Judith M JM Smith Laurie G LG Nelson Rebecca J RJ

Genetics 20140709 1

Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line "Tx303" conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart's wilt. To dissect the genetic ba ...[more]

PMID: 25009146

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Project description:Disease resistance in plants is mostly quantitative, with both major and minor genes controlling resistance. This research aimed to optimize genomic selection (GS) models for use in breeding programs that are needed to select both major and minor genes for resistance. In this study, stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) of wheat (Triticum aestivum L.) was used as a model for quantitative disease resistance. The quantitative nature of stripe rust is usually phenotyped with two disease traits, infection type (IT) and disease severity (SEV). We compared two types of training populations composed of 2,630 breeding lines (BLs) phenotyped in single-plot trials from 4 years (2016-2020) and 475 diversity panel (DP) lines from 4 years (2013-2016), both across two locations. We also compared the accuracy of models using four different major gene markers and genome-wide association study (GWAS) markers as fixed effects. The prediction models used 31,975 markers that are replicated 50 times using a 5-fold cross-validation. We then compared GS models using a marker-assisted selection (MAS) to compare the prediction accuracy of the markers alone and in combination. GS models had higher accuracies than MAS and reached an accuracy of 0.72 for disease SEV. The major gene and GWAS markers had only a small to nil increase in the prediction accuracy more than the base GS model, with the highest accuracy increase of 0.03 for the major markers and 0.06 for the GWAS markers. There was a statistical increase in the accuracy using the disease SEV trait, BLs, population type, and combining years. There was also a statistical increase in the accuracy using the major markers in the validation sets as the mean accuracy decreased. The inclusion of fixed effects in low prediction scenarios increased the accuracy up to 0.06 for GS models using significant GWAS markers. Our results indicate that GS can accurately predict quantitative disease resistance in the presence of major and minor genes.

Dataset Information

Unraveling genomic complexity at a quantitative disease resistance locus in maize.

Publications

Unraveling genomic complexity at a quantitative disease resistance locus in maize.

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