Samuel E Aggrey

Summary

Affiliation: University of Georgia
Country: USA

Publications

  1. ncbi Identification of QTL controlling meat quality traits in an F2 cross between two chicken lines selected for either low or high growth rate
    Javad Nadaf
    Station de Recherches Avicoles, INRA, Centre de Recherches de Tours, Nouzilly, France
    BMC Genomics 8:155. 2007
  2. ncbi Dynamics of relative growth rate in Japanese quail lines divergently selected for growth and their control
    Samuel E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science, The University of Georgia, Athens, GA 30602 2772, USA
    Growth Dev Aging 67:47-54. 2003
  3. ncbi Accuracy of growth model parameters: effects of frequency and duration of data collection, and missing information
    Samuel E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
    Growth Dev Aging 71:45-54. 2008
  4. ncbi Logistic nonlinear mixed effects model for estimating growth parameters
    S E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science and Institute of Bioinformatics, University of Georgia, Athens 30602, USA
    Poult Sci 88:276-80. 2009
  5. ncbi Genetic properties of feed efficiency parameters in meat-type chickens
    Samuel E Aggrey
    Department of Poultry Science, University of Georgia, Athens, GA 30602, USA
    Genet Sel Evol 42:25. 2010
  6. ncbi Modification of animals versus modification of the production environment to meet welfare needs
    S E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science, University of Georgia, Athens, GA 30602, USA
    Poult Sci 89:852-4. 2010
  7. ncbi Identification of candidate genes at quantitative trait loci on chicken chromosome Z using orthologous comparison of chicken, mouse, and human genomes
    Georgina A Ankra-Badu
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science and Institute of Bioinformatics, University of Georgia, Athens, GA 30602-2772, USA
    In Silico Biol 5:593-604. 2005
  8. ncbi Proteomic analysis and differential expression in protein extracted from chicken with a varying growth rate and water-holding capacity
    Phodchanee Phongpa-Ngan
    Food Science and Technology, University of Georgia, Athens, Georgia 30602, United States
    J Agric Food Chem 59:13181-7. 2011
  9. ncbi miR-Explore: Predicting MicroRNA Precursors by Class Grouping and Secondary Structure Positional Alignment
    Bram Sebastian
    Institute of Bioinformatics, University of Georgia, Athens, GA
    Bioinform Biol Insights 7:133-42. 2013
  10. ncbi Mapping main, epistatic and sex-specific QTL for body composition in a chicken population divergently selected for low or high growth rate
    Georgina A Ankra-Badu
    Department of Poultry Science Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
    BMC Genomics 11:107. 2010

Collaborators

Detail Information

Publications11

  1. ncbi Identification of QTL controlling meat quality traits in an F2 cross between two chicken lines selected for either low or high growth rate
    Javad Nadaf
    Station de Recherches Avicoles, INRA, Centre de Recherches de Tours, Nouzilly, France
    BMC Genomics 8:155. 2007
    ..A total of 698 animals in 50 full-sib families were genotyped for 108 microsatellite markers covering 21 linkage groups...
  2. ncbi Dynamics of relative growth rate in Japanese quail lines divergently selected for growth and their control
    Samuel E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science, The University of Georgia, Athens, GA 30602 2772, USA
    Growth Dev Aging 67:47-54. 2003
    ..Furthermore, it is thought that different sets of genes may operate between the developmental period and maturation period...
  3. ncbi Accuracy of growth model parameters: effects of frequency and duration of data collection, and missing information
    Samuel E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
    Growth Dev Aging 71:45-54. 2008
    ....
  4. ncbi Logistic nonlinear mixed effects model for estimating growth parameters
    S E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science and Institute of Bioinformatics, University of Georgia, Athens 30602, USA
    Poult Sci 88:276-80. 2009
    ..The use of NLMM is recommended for modeling growth data in poultry because the predicted BW at different ages is more accurate than using the mean prediction function of the fixed effect model...
  5. ncbi Genetic properties of feed efficiency parameters in meat-type chickens
    Samuel E Aggrey
    Department of Poultry Science, University of Georgia, Athens, GA 30602, USA
    Genet Sel Evol 42:25. 2010
    ..We studied residual feed intake (RFI) and feed conversion ratio (FCR) over two age periods to delineate their genetic inter-relationships...
  6. ncbi Modification of animals versus modification of the production environment to meet welfare needs
    S E Aggrey
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science, University of Georgia, Athens, GA 30602, USA
    Poult Sci 89:852-4. 2010
    ..An integration of management, genetics, and genomic tools should be employed to genetically improve production and welfare traits with concurrent welfare risk assessments to address public and consumer concerns...
  7. ncbi Identification of candidate genes at quantitative trait loci on chicken chromosome Z using orthologous comparison of chicken, mouse, and human genomes
    Georgina A Ankra-Badu
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science and Institute of Bioinformatics, University of Georgia, Athens, GA 30602-2772, USA
    In Silico Biol 5:593-604. 2005
    ..Therefore, the nAchRs could be used as therapeutic targets for regulating feed intake and obesity. This study has identified 197 putative candidate genes in probable QTL regions of chicken chromosome Z...
  8. ncbi Proteomic analysis and differential expression in protein extracted from chicken with a varying growth rate and water-holding capacity
    Phodchanee Phongpa-Ngan
    Food Science and Technology, University of Georgia, Athens, Georgia 30602, United States
    J Agric Food Chem 59:13181-7. 2011
    ..This information identified protein markers associated with growth rate and water holding capacity. Some of those protein markers could be added to the chicken database...
  9. ncbi miR-Explore: Predicting MicroRNA Precursors by Class Grouping and Secondary Structure Positional Alignment
    Bram Sebastian
    Institute of Bioinformatics, University of Georgia, Athens, GA
    Bioinform Biol Insights 7:133-42. 2013
    ..Compared with global alignment, grouping miRNA by classes yields a better sensitivity with very high specificity for pre-miRNA prediction even when a simple positional based secondary and primary structure alignment are used...
  10. ncbi Mapping main, epistatic and sex-specific QTL for body composition in a chicken population divergently selected for low or high growth rate
    Georgina A Ankra-Badu
    Department of Poultry Science Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
    BMC Genomics 11:107. 2010
    ..We used Bayesian model selection to comprehensively map main, epistatic and sex-specific QTL in an F2 reciprocal intercross between two chicken lines divergently selected for high or low growth rate...
  11. ncbi Specificity and sensitivity of PROMIR, ERPIN and MIR-ABELA in predicting pre-microRNAs in the chicken genome
    Bram Sebastian
    Poultry Genetics and Biotechnology Laboratory, Department of Poultry Science, Institute of Bioinformatics, University of Georgia, Athens, GA 30602 2772, USA
    In Silico Biol 8:377-81. 2008
    ..94%, 99.46%, and 99.10% for ProMir-g, ERPIN and miR-abela, respectively. The sensitivities of the existing programs are low for chicken data and an efficient algorithm may be needed to predict novel chicken pre-miRNAs...