Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Ames, Nicholas"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Mining Wild Barley for Powdery Mildew Resistance
    (2014-10) Ames, Nicholas
    Powdery mildew, caused by Blumeria graminis f. sp. hordei (Bgh) is a worldwide disease with severe impact on yield in barley (Hordeum vulgare). This study describes the characterization of a collection of 316 wild barley accessions, known as the Wild Barley Diversity Collection (WBDC), for resistance to powdery mildew. The WBDC was phenotyped for reaction to 40 different Bgh isolates at the seedling stage and then genotyped with three different marker sets: 3,072 Single Nucleotide Polymorphisms, 600 Diverse Array Technology (DaRT) markers, and 8,616 Diverse Array Technology-Sequencing (DArT-Seq) markers. Fifty-two accessions exhibited resistance to 90% (20 of 40) of the Bgh isolates. Significant marker-trait associations were found for all marker types at nineteen different loci across the barley genome encompassing all chromosomes except 1H in a GWAS analysis. These marker-trait associations will be useful for incorporating powdery mildew resistance into barley breeding programs.
  • Thumbnail Image
    Item
    Reducing And Exploiting Genotype By Environment Interaction In The Context Of Genomewide Prediction In 969 Maize Biparental Populations
    (2018-03) Ames, Nicholas
    Multi-environment testing remains crucial in genomewide selection, and environmental effects (Ej) complicate selection. We aimed to: 1) determine if past year’s data on previous populations can be used to eliminate environments for a current training population; 2) assess if genomewide predictions can reduce the number of environments used in subsequent phenotypic selection; 3) identify which statistical models and environmental factors are best for estimating Ej; and 4) determine the predictive ability in models that include and exclude genotype × environment interaction effects. A total of 969 Monsanto maize (Zea mays L.) populations were genotyped and phenotyped at multiple U.S. locations from 2000 to 2008. Environmental data from the National Oceanic and Atmospheric Administration were gathered and interpolated. The data included 154,000 lines, 448 million marker data points, 3.2 million phenotypic observations, 1395 unique environments, and 1.3 million environmental covariable data points. For 27 biparental crosses that we chose as test populations, environmental stability and an index that used genomewide predictions and phenotypic data could replace one out of four environments in phenotypic evaluation. Correlations between predicted and observed Ej were between 0.25 and 0.35 even when only two environmental factors (precipitation and heat units) were used. A nonfactorial model for line performance in a given environment effectively combined both the line genetic effect and Ej, doubling prediction ability for grain yield and test weight. We speculate that this model can be combined with crop modelling for additional prediction ability in predicting plant performance in a given environment.