Anna Rogers

College of Agriculture and Life Sciences

Works (4)

Updated: December 19th, 2023 05:01

2023 journal article

Relating Antimicrobial Resistance and Virulence in Surface-Water <i>E. coli</i>

MICROORGANISMS, 11(11).

By: C. LaMontagne *, E. Christenson *, A. Rogers n, M. Jacob n & J. Stewart *

author keywords: virulence; antimicrobial resistance (AMR); surface water; commercial animal agriculture; E. coli
topics (OpenAlex): Pharmaceutical and Antibiotic Environmental Impacts; Antibiotic Resistance in Bacteria; Bacteriophages and microbial interactions
TL;DR: This work builds the understanding of factors controlling AMR dissemination through the environment and potential health risks by comparing virulence gene prevalence between isolates resistant and susceptible to antibiotics and comparing the prevalence of isolates from sub-watersheds with or without commercial hog operations. (via Semantic Scholar)
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Source: Web Of Science
Added: December 18, 2023

2022 journal article

Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data

G3-GENES GENOMES GENETICS, 12(2).

By: A. Rogers n & J. Holland n 

Ed(s): A. Lipka

author keywords: genotype-by-environment interactions; multienvironment; genomic prediction; environmental covariates; dominance genetic variance; shared data resource
MeSH headings : Gene-Environment Interaction; Genome, Plant; Genomics; Genotype; Models, Genetic; Phenotype; Plant Breeding; Zea mays / genetics
topics (OpenAlex): Genetic and phenotypic traits in livestock; Genetic Mapping and Diversity in Plants and Animals; Genetics and Plant Breeding
TL;DR: Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, it is 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. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 8, 2022

2022 article

Genomic prediction for the Germplasm Enhancement of Maize project

Rogers, A. R., Bian, Y., Krakowsky, M., Peters, D., Turnbull, C., Nelson, P., & Holland, J. B. (2022, October 24). PLANT GENOME.

By: A. Rogers n, Y. Bian *, M. Krakowsky n, D. Peters *, C. Turnbull*, P. Nelson*, J. Holland n 

MeSH headings : Zea mays / genetics; Plant Breeding; Genomics; Alleles; Edible Grain / genetics
topics (OpenAlex): Genetic and phenotypic traits in livestock; Genetic Mapping and Diversity in Plants and Animals; Genetics and Plant Breeding
TL;DR: 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, so using GS models trained within program should be able to more effectively deliver on its mission to broaden the genetic diversity of the U.S. maize crop. (via Semantic Scholar)
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1. No Poverty (OpenAlex)
13. Climate Action (Web of Science)
15. Life on Land (Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: October 25, 2022

2021 journal article

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment

G3-GENES GENOMES GENETICS, 11(2).

By: A. Rogers n, J. Dunne n , C. Romay *, M. Bohn *, E. Buckler *, I. Ciampitti *, J. Edwards *, D. Ertl ...

author keywords: Genotype-by-environment interaction; multienvironment; environmental covariates; dominance genetic variance
MeSH headings : Gene-Environment Interaction; Genotype; Models, Genetic; Phenotype; Plant Breeding; Zea mays
topics (OpenAlex): Genetic and phenotypic traits in livestock; Genetics and Plant Breeding; Genetic Mapping and Diversity in Plants and Animals
TL;DR: 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, curated and analyzed genotypic and phenotypic data on 1918 maize hybrids and environmental data from 65 testing environments. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: June 10, 2021

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