Browsing by Department "Agronomy"

The population structure and evolution of basidiomycetes like rust fungi are influenced by complex reproductive cycles and dikaryotic life stages where two independent nuclear haplotypes are present in the cell. The ability to alternate between asexual (clonal) and sexual reproduction increases the evolutionary capacity in these species. Furthermore, exchange of intact nuclei (somatic hybridization) in rust fungi can allow for rapid generation of genetic variability outside of the sexual cycle. Puccinia coronata f. sp. avenae (Pca), the causal agent of oat crown rust, is a pathogen of global economic importance that is difficult to control due to rapid breakdown of host genetic resistance. The contribution of sexuality, clonality, and migration to virulence evolution varies across Pca populations. As such, the Pca pathosystem is ideal to address the role of mating type, recombination, mutation, and somatic hybridization in host adaptation. We expanded the existing resources for USA and South African populations by generating whole genome sequencing data of Taiwanese and Australian isolates. An atlas of 30 chromosome-level, fully-phased nuclear haplotypes from six USA isolates and nine Australian isolates was created to capture the genomic composition of key Pca lineages. At the haplotype level, we confirmed previous reports of genetic recombination in the USA population and additionally detected either sexual or cryptic recombination between Australian isolates, contrasting previous evaluations that suggested Pca populations in Australia to be purely clonal. We also identified somatic hybridization events in Pca that are not only associated with significant changes in fitness but also imply intercontinental migration of haplotypes, which provides further impetus for molecular monitoring of rust pathogen populations on a global scale. Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Non-inferiority is a reasonable approach to assessing the diagnostic accuracy of a new diagnostic test if it provides an easier administration or reduces the cost. The area under the receiver operating characteristic (ROC) curve is one of the common measures for the overall diagnostic accuracy. However, it may not differentiate the various shapes of the ROC curves with different diagnostic significances. The partial area under the ROC curve (PAUROC) may present an alternative that can provide additional and complimentary information for some diagnostic tests which require false-positive rate that does not exceed a certain level. Non-parametric and maximum likelihood methods can be used for the non-inferiority tests based on the difference in paired PAUROCs. However, their performance has not been investigated in finite samples. We propose to use the concept of generalized p-value to construct a non-inferiority test for diagnostic accuracy based on the difference in paired PAUROCs. Simulation results show that the proposed non-inferiority test not only adequately controls the size at the nominal level but also is uniformly more powerful than the non-parametric methods. The proposed method is illustrated with a numerical example using published data. Copyright ? 2007 John Wiley & Sons, Ltd.

A β-expectation tolerance interval procedure is derived from the concept of generalized pivotal quantity, which has been frequently used to obtain confidence intervals in situations where standard procedures do not lead to useful solutions. The proposed procedure can be applied to general balanced mixed linear models. Some practical examples are given to illustrate the proposed procedure. In addition, detailed simulation studies are conducted to evaluate its performance, showing that it can be recommended for use in practical applications. © 2004 Elsevier B.V. All rights reserved.v

Mutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Here we describe accuMUlate, a probabilistic mutation caller that reflects the design of a typical MA experiment while being flexible enough to accommodate properties unique to any particular experiment. Availability and implementation accuMUlate is available from https://github.com/dwinter/accuMUlate.