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Celebrating Rohan Fernando’s contributions to quantitative genetics

On the occasion of Rohan Fernando’s retirement from his position as Professor of Animal Science at Iowa State University, Genetics Selection Evolution is proud to publish a special series of papers by several of Rohan’s collaborators and colleagues (past and present) to honor his important contributions to quantitative genetics, especially to animal breeding. Details are described in the Editorial prepared for the series. Papers will be released as they have successfully completed the standard peer review process.

Organizing Editor: Jack Dekkers , Iowa State University, United States

  1. The single-step approach has become the most widely-used methodology for genomic evaluations when only a subset of phenotyped individuals in the pedigree are genotyped, where the genotypes for non-genotyped in...

    Authors: Tianjing Zhao and Hao Cheng
    Citation: Genetics Selection Evolution 2023 55:68
  2. Most genomic prediction applications in animal breeding use genotypes with tens of thousands of single nucleotide polymorphisms (SNPs). However, modern sequencing technologies and imputation algorithms can gen...

    Authors: Bruno D. Valente, Gustavo de los Campos, Alexander Grueneberg, Ching-Yi Chen, Roger Ros-Freixedes and William O. Herring
    Citation: Genetics Selection Evolution 2023 55:57
  3. Selection schemes distort inference when estimating differences between treatments or genetic associations between traits, and may degrade prediction of outcomes, e.g., the expected performance of the progeny ...

    Authors: Daniel Gianola, Rohan L. Fernando and Chris C. Schön
    Citation: Genetics Selection Evolution 2022 54:78
  4. Single-step genomic best linear unbiased prediction (GBLUP) involves a joint analysis of individuals with genotype information, and their ancestors, descendants, or contemporaries, without recorded genotypes. ...

    Authors: Dorian J. Garrick and Rohan L. Fernando
    Citation: Genetics Selection Evolution 2022 54:72
  5. The covariance matrix of breeding values is at the heart of prediction methods. Prediction of breeding values can be formulated using either an “observed” or a theoretical covariance matrix, and a major argume...

    Authors: Rodolfo J. C. Cantet, Belcy K. Angarita-Barajas, Natalia S. Forneris and Sebastián Munilla
    Citation: Genetics Selection Evolution 2022 54:64
  6. Single-step genomic predictions obtained from a breeding value model require calculating the inverse of the genomic relationship matrix

    Authors: Matias Bermann, Daniela Lourenco, Natalia S. Forneris, Andres Legarra and Ignacy Misztal
    Citation: Genetics Selection Evolution 2022 54:52
  7. Multiple breed evaluation using genomic prediction includes the use of data from multiple populations, or from parental breeds and crosses, and is expected to lead to better genomic predictions. Increased comp...

    Authors: Andrés Legarra, David González-Diéguez and Zulma G. Vitezica
    Citation: Genetics Selection Evolution 2022 54:10