Short communication: Accounting for new mutations in genomic prediction models

Joaquim Casellas, Cecilia Esquivelzeta, Andrés Legarra

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)


Genomic evaluation models so far do not allow for accounting of newly generated genetic variation due to mutation. The main target of this research was to extend current genomic BLUP models with mutational relationships (model AM), and compare them against standard genomic BLUP models (model A) by analyzing simulated data. Model performance and precision of the predicted breeding values were evaluated under different population structures and heritabilities. The deviance information criterion (DIC) clearly favored the mutational relationship model under large heritabilities or populations with moderate-to-deep pedigrees contributing phenotypic data (i.e., differences equal or larger than 10 DIC units); this model provided slightly higher correlation coefficients between simulated and predicted genomic breeding values. On the other hand, null DIC differences, or even relevant advantages for the standard genomic BLUP model, were reported under small heritabilities and shallow pedigrees, although precision of the genomic breeding values did not differ across models at a significant level. This method allows for slightly more accurate genomic predictions and handling of newly created variation; moreover, this approach does not require additional genotyping or phenotyping efforts, but a more accurate handing of available data. © 2013 American Dairy Science Association.
Original languageEnglish
Pages (from-to)5398-5402
JournalJournal of Dairy Science
Issue number8
Publication statusPublished - 1 Aug 2013


  • Accuracy
  • Genomic selection
  • Mutation
  • Relationship matrix


Dive into the research topics of 'Short communication: Accounting for new mutations in genomic prediction models'. Together they form a unique fingerprint.

Cite this