@article{lamontagne_christenson_rogers_jacob_stewart_2023, title={Relating Antimicrobial Resistance and Virulence in Surface-Water <i>E. coli</i>}, volume={11}, ISSN={["2076-2607"]}, DOI={10.3390/microorganisms11112647}, abstractNote={The role of the environment in the emergence and spread of antimicrobial resistance (AMR) is being increasingly recognized, raising questions about the public health risks associated with environmental AMR. Yet, little is known about pathogenicity among resistant bacteria in environmental systems. Existing studies on the association between AMR and virulence are contradictory, as fitness costs and genetic co-occurrence can be opposing influences. Using Escherichia coli isolated from surface waters in eastern North Carolina, we compared virulence gene prevalence between isolates resistant and susceptible to antibiotics. We also compared the prevalence of isolates from sub-watersheds with or without commercial hog operations (CHOs). Isolates that had previously been evaluated for phenotypic AMR were paired by matching isolates resistant to any tested antibiotic with fully susceptible isolates from the same sample date and site, forming 87 pairs. These 174 isolates were evaluated by conventional PCR for seven virulence genes (bfp, fimH, cnf-1, STa (estA), EAST-1 (astA), eae, and hlyA). One gene, fimH, was found in 93.1% of isolates. Excluding fimH, at least one virulence gene was detected in 24.7% of isolates. Significant negative associations were found between resistance to at least one antibiotic and presence of at least one virulence gene, tetracycline resistance and presence of a virulence gene, resistance and STa presence, and tetracycline resistance and STa presence. No significant associations were found between CHO presence and virulence, though some sub-significant associations merit further study. This work builds our understanding of factors controlling AMR dissemination through the environment and potential health risks.}, number={11}, journal={MICROORGANISMS}, author={LaMontagne, Connor D. and Christenson, Elizabeth C. and Rogers, Anna T. and Jacob, Megan E. and Stewart, Jill R.}, year={2023}, month={Nov} }
 @article{rogers_bian_krakowsky_peters_turnbull_nelson_holland_2022, title={Genomic prediction for the Germplasm Enhancement of Maize project}, volume={10}, ISSN={["1940-3372"]}, url={https://doi.org/10.1002/tpg2.20267}, DOI={10.1002/tpg2.20267}, abstractNote={AbstractThe Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public‐ and private‐sector maize (Zea mays L.) breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multienvironment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on 6 yr of topcross evaluations from the two GEM programs in Raleigh, NC, and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross‐validated within program and heterotic group. Predicted genetic gain from GS ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm.}, journal={PLANT GENOME}, author={Rogers, Anna R. and Bian, Yang and Krakowsky, Matthew and Peters, David and Turnbull, Clint and Nelson, Paul and Holland, James B.}, year={2022}, month={Oct} }
 @article{rogers_holland_2022, title={Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data}, volume={12}, ISSN={["2160-1836"]}, url={https://doi.org/10.1093/g3journal/jkab440}, DOI={10.1093/g3journal/jkab440}, abstractNote={Abstract
               Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.}, number={2}, journal={G3-GENES GENOMES GENETICS}, publisher={Oxford University Press (OUP)}, author={Rogers, Anna R. and Holland, James B.}, editor={Lipka, AEditor}, year={2022}, month={Feb} }
 @article{rogers_dunne_romay_bohn_buckler_ciampitti_edwards_ertl_flint-garcia_gore_et al._2021, title={The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment}, volume={11}, ISSN={["2160-1836"]}, DOI={10.1093/g3journal/jkaa050}, abstractNote={AbstractHigh-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.}, number={2}, journal={G3-GENES GENOMES GENETICS}, author={Rogers, Anna R. and Dunne, Jeffrey C. and Romay, Cinta and Bohn, Martin and Buckler, Edward S. and Ciampitti, Ignacio A. and Edwards, Jode and Ertl, David and Flint-Garcia, Sherry and Gore, Michael A. and et al.}, year={2021}, month={Feb} }