Reference Population Optimization

Reference Population Optimization is a methodological framework in genomic prediction and selection that systematically enhances the design, composition, and structure of a reference population to maximize the accuracy and robustness of genetic predictions for stress tolerance traits.

17

Publications

811

Citations

89

Authors

33

Institutions

Publications per year

2017–2026

3

Authors

89

  1. BJ
    Ben J. Hayes

    The University of Queensland, Australia

    3 publications · 278 citations

    3

    h-index

  2. ME
    Michael E. Goddard

    The University of Melbourne, Australia

    2 publications · 253 citations

    2

    h-index

  3. JW
    Junping Wang

    Army Medical University, China

    2 publications · 179 citations

    2

    h-index

  4. GS
    Germán Spangenberg

    La Trobe University, Australia

    2 publications · 179 citations

    2

    h-index

  5. JW
    John W. Forster

    La Trobe University, Australia

    2 publications · 179 citations

    2

    h-index

1–5 of 89

Venues

  1. Journal of Dairy Science

    JDS · 4 publications · 246 citations

    4

    h-index

  2. Genetics Selection Evolution

    GSE · 2 publications · 102 citations

    2

    h-index

  3. Plant Breeding

    1 publications · 154 citations

    1

    h-index

  4. Pattern Recognition

    1 publications · 81 citations

    1

    h-index

  5. Frontiers in Genetics

    1 publications · 40 citations

    1

    h-index

1–5