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How imputation errors bias genomic predictions.

E C G Pimentel1, C Edel1, R Emmerling1

  • 1Institute of Animal Breeding, Bavarian State Research Center for Agriculture, Grub 85586, Germany.

Journal of Dairy Science
|April 6, 2015
PubMed
Summary
This summary is machine-generated.

Genomic predictions can be biased by imputation errors. Using imputed genotypes systematically underestimated the genetic merit of top animals and overestimated that of bottom animals in Brown Swiss cattle.

Keywords:
allele frequencybiashaplotypesingle nucleotide polymorphism (SNP) effect

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Area of Science:

  • Animal Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genomic predictions are crucial for genetic selection in livestock.
  • Genotype imputation is often necessary due to the cost of high-density SNP chips.
  • Imputation errors can potentially introduce bias into genomic predictions.

Purpose of the Study:

  • To investigate the biasing effects of genotype imputation errors on genomic predictions.
  • To quantify the impact of imputation on direct genomic values (DGV) for multiple traits.

Main Methods:

  • Predicted direct genomic values (DGV) for 3,494 Brown Swiss candidates across 37 traits.
  • Compared DGV derived from observed 50K genotypes versus 50K genotypes imputed from a 6K chip.
  • Analyzed systematic changes in DGV attributed to imputation errors.

Main Results:

  • Imputation errors caused systematic biases in DGV.
  • Top-ranking animals' DGV were underestimated on average.
  • Bottom-ranking animals' DGV were overestimated on average when using imputed genotypes.

Conclusions:

  • Genotype imputation errors introduce a predictable bias in genomic predictions.
  • This bias favors lower-ranked animals and disadvantages top-ranked animals.
  • Careful consideration of imputation accuracy is essential for reliable genomic selection.