Crop Science · 2019 · 14 citations · 65 references
AgricultureCrop SciencesGeneticsBreeding For Disease ResistancePlant GeneticsPlant-microbe InteractionsDisease ResistanceDisease Resistance EvaluationDisease Resistance GeneticsPhenotyping For Disease ResistancePlant Disease ResistanceQuantitative Disease ResistancePartial Disease ResistancePathogen Resistance GenesPlant Disease Resistance GenesPlant Resistance GenesQuantitative Resistance LociResistance Locus CharacterizationResistance Trait EvaluationSoybean Breeding
Major quantitative disease resistance loci (QDRLs) are rare in the Phytophthora sojae (Kaufmann and Gerdemann)–soybean [ Glycine max (L). Merr.] pathosystem. A major QDRL on chromosome 18 (QDRL‐18) was identified in PI 427105B and PI 427106. QDRL‐18 represents a valuable resistance source for breeding programs. Thus, our objectives were to determine its isolate specificity and measure its effect on yield and resistance to both P. sojae and other soybean pathogens. We characterized near isogenic lines (NILs) developed from F 7 recombinant inbred lines heterozygous at QDRL‐18; NILs represent introgressions from PI 427105B, PI 427106, and susceptible ‘OX20‐8’. The introgressions from PI 427105B and PI 427106 increased resistance to P. sojae by 11 to 20% and 35 to 40%, respectively, based on laboratory and greenhouse assays, and increased yield by 13 to 29% under disease conditions. The resistant introgression from PI 427105B was also effective against seven P. sojae isolates with no isolate specificity detected. Based on quantitative polymerase chain reaction assays, NILs with the susceptible introgression had significantly higher relative levels of P. sojae colonization 48 h after inoculation. No pleiotropic effects for resistance to either soybean cyst nematode or Fusarium graminearum were detected. This information improves soybean breeders’ ability to make informed decisions regarding the deployment of QDRL‐18 in their respective breeding programs.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
SoyBase, the USDA-ARS soybean genetics and genomics database
David Grant, Rex T. Nelson, Steven B. Cannon et al. · Nucleic Acids Research · 2009 · 661 citations · Full text