How do imputation errors affect genomic breeding values?

Authors

  • Eduardo da Cruz Gouveia Pimentel Bavarian State Research Center for Agriculture (LfL) Institute of Animal Breeding
  • Christian Edel Bavarian State Research Center for Agriculture (LfL) Institute of Animal Breeding
  • Reiner Emmerling Bavarian State Research Center for Agriculture (LfL) Institute of Animal Breeding
  • Kay-Uwe Goetz Bavarian State Research Center for Agriculture (LfL) Institute of Animal Breeding

Keywords:

allele frequency, bias, haplotype, SNP effect

Abstract

The objective of this study was to investigate in more detail the biasing effects of imputation errors on genomic predictions. Genomic breeding values (GEBV) of 3494 Brown Swiss selection candidates for 37 production and conformation traits were predicted using either their observed 50k genotypes or their 50k genotypes imputed from a mimicked 6k chip. Changes in GEBV caused by imputation errors were shown to be systematic. The GEBV of top animals were on average underestimated and GEBV of bottom animals were on average overestimated when imputed genotypes were used instead of observed genotypes. This pattern might be explained by the fact that imputation algorithms will usually suggest the most frequent haplotype from the sample whenever a haplotype cannot be determined unambiguously. That was empirically shown to cause an advantage for the bottom animals and a disadvantage for the top animals.

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Published

2014-06-22

Issue

No. 48 (2014): Proceedings of the 2014 Interbull Meeting

Section

Articles

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