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Imputation of genotypes from low- to high-density genotyping platforms and implications for genomic selection

  • D. P. Berry (a1) and J. F. Kearney (a2)

Abstract

The objective of this study was to quantify the accuracy achievable from imputing genotypes from a commercially available low-density marker panel (2730 single nucleotide polymorphisms (SNPs) following edits) to a commercially available higher density marker panel (51 602 SNPs following edits) in Holstein–Friesian cattle using Beagle, a freely available software package. A population of 764 Holstein–Friesian animals born since 2006 were used as the test group to quantify the accuracy of imputation, all of which had genotypes for the high-density panel; only SNPs on the low-density panel were retained with the remaining SNPs to be imputed. The reference population for imputation consisted of 4732 animals born before 2006 also with genotypes on the higher density marker panel. The concordance between the actual and imputed genotypes in the test group of animals did not vary across chromosomes and was on average 95%; the concordance between actual and imputed alleles was, on average, 97% across all SNPs. Genomic predictions were undertaken across a range of production and functional traits for the 764 test group animals using either their real or imputed genotypes. Little or no mean difference in the genomic predictions was evident when comparing direct genomic values (DGVs) using real or imputed genotypes. The average correlation between the DGVs estimated using the real or imputed genotypes for the 15 traits included in the Irish total merit index was 0.97 (range of 0.92 to 0.99), indicating good concordance between proofs from real or imputed genotypes. Results show that a commercially available high-density marker panel can be imputed from a commercially available lower density marker panel, which will also have a lower cost, thereby facilitating a reduction in the cost of genomic selection. Increased available numbers of genotyped and phenotyped animals also has implications for increasing the accuracy of genomic prediction in the entire population and thus genetic gain using genomic selection.

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References

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