Using pooled data for genomic prediction in a bivariate framework with missing data

Johnna L. Baller, Stephen D. Kachman, Larry A. Kuehn, Matthew L. Spangler

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Pooling samples to derive group genotypes can enable the economically efficient use of commercial animals within genetic evaluations. To test a multivariate framework for genetic evaluations using pooled data, simulation was used to mimic a beef cattle population including two moderately heritable traits with varying genetic correlations, genotypes and pedigree data. There were 15 generations (n = 32,000; random selection and mating), and the last generation was subjected to genotyping through pooling. Missing records were induced in two ways: (a) sequential culling and (b) random missing records. Gaps in genotyping were also explored whereby genotyping occurred through generation 13 or 14. Pools of 1, 20, 50 and 100 animals were constructed randomly or by minimizing phenotypic variation. The EBV was estimated using a bivariate single-step genomic best linear unbiased prediction model. Pools of 20 animals constructed by minimizing phenotypic variation generally led to accuracies that were not different than using individual progeny data. Gaps in genotyping led to significantly different EBV accuracies (p <.05) for sires and dams born in the generation nearest the pools. Pooling of any size generally led to larger accuracies than no information from generation 15 regardless of the way missing records arose, the percentage of records available or the genetic correlation. Pooling to aid in the use of commercial data in genetic evaluations can be utilized in multivariate cases with varying relationships between the traits and in the presence of systematic and randomly missing phenotypes.

Original languageEnglish (US)
Pages (from-to)489-501
Number of pages13
JournalJournal of Animal Breeding and Genetics
Issue number5
StatePublished - Sep 2022


  • DNA pooling
  • beef cattle
  • bivariate models
  • genomic prediction

ASJC Scopus subject areas

  • Food Animals
  • Animal Science and Zoology


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