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

Published online by Cambridge University Press:  28 February 2011

D. P. Berry  and
J. F. Kearney
D. P. Berry*
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, County Cork, Ireland
J. F. Kearney
Affiliation:
Irish Cattle Breeding Federation, Highfield House, Bandon, County Cork, Ireland
*
†E-mail: donagh.berry@teagasc.ie
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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.

Keywords

genomic selectionimputegenotypecattle
Type
Full Paper
Information
animal , Volume 5 , Issue 8 , August 2011 , pp. 1162 - 1169
Copyright
Copyright © The Animal Consortium 2011

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References

Berry, DP, Kearney, JF, Harris, BL 2009. Genomic selection in Ireland. Proceedings of the Interbull International Workshop – Genomic Information in Genetic Evaluations, Uppsala, Sweden, 26–29 January 2009. Interbull Bulletin 39, 29–34.Google Scholar
Berry, DP, Shalloo, L, Cromie, AR, Olori, V, Veerkamp, RF, Dillon, P, Amer, PR, Evans, RD, Kearney, JF, Wickham, B 2007. The economic breeding index: a generation on. Technical report to the Irish Cattle Breeding Federation. Irish Cattle Breeding Federation Society Ltd, Bandon, Co Cork, Ireland.Google Scholar
Browning, SR, Browning, BL 2007. Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering. American Journal of Human Genetics 81, 1084–1097.CrossRefGoogle ScholarPubMed
Browning, BL, Browning, SR 2009. A unified approach to genotype imputation and haplotype phase inference for large data sets of trios and unrelated individuals. American Journal of Human Genetics 84, 210–223.CrossRefGoogle ScholarPubMed
Cole, JB, VanRaden, PM, O'Connell, JR, Van Tassell, CP, Sonstegard, TS, Schnabel, RD, Taylor, JF, Wiggans, GR 2009. Distribution and location of genetic effects for dairy traits. Journal of Dairy Science 92, 2931–2946.CrossRefGoogle ScholarPubMed
Daetwyler, HD, Villanueva, B, Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3, e3395.Google ScholarPubMed
Druet, T, Georges, M 2010. A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics 184, 789–798.CrossRefGoogle ScholarPubMed
Druet, T, Schrooten, C, de Roos, APW 2010. Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle. Journal of Dairy Science 93, 5443–5454.CrossRefGoogle ScholarPubMed
Habier, D, Fernando, RL, Dekkers, JCM 2009. Genomic selection using low-density marker panels. Genetics 182, 343–353.CrossRefGoogle ScholarPubMed
Hao, K, Chudin, E, McElwee, J, Schadt, EE 2009. Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies. BMC Genetics 10, 27.CrossRefGoogle ScholarPubMed
Harris, BL, Johnson, DL 2010. Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation. Journal of Dairy Science 93, 1243–1252.CrossRefGoogle ScholarPubMed
Howie, BN, Donnelly, P, Marchini, J 2009. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genetics 5, e1000529.CrossRefGoogle ScholarPubMed
Marchini, J, Howie, B, Myers, S, McVean, G, Donnelly, P 2007. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genetics 39, 906–913.CrossRefGoogle ScholarPubMed
Matukumalli, LK, Lawley, CT, Schnabel, RD, Taylor, JF, Allan, MF, Heaton, MP, O'Connell, J, Moore, SS, Smith, TPL, Sonstegard, TS, Van Tassell, CP 2009. Development and characterization of a high density SNP genotyping assay for cattle. PLoS ONE 4, e5350.CrossRefGoogle ScholarPubMed
Meuwissen, THE, Hayes, BJ, Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.CrossRefGoogle ScholarPubMed
Nothnagel, M, Ellinghaus, D, Schreiber, S, Krawczak, M, Franke, A 2009. A comprehensive evaluation of SNP genotype imputation. Human Genetics 125, 163–171.CrossRefGoogle ScholarPubMed
Pryce, JE, Bolormaa, S, Chamberlain, AJ, Bowman, PJ, Savin, K, Goddard, ME, Hayes, BJ 2010. A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. Journal of Dairy Science 93, 3331–3345.Google ScholarPubMed
Purcell, S, Cherny, SS, Sham, PC 2003. Genetic power calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19, 149–150.CrossRefGoogle ScholarPubMed
Servin, B, Stephens, M 2007. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genetics 3, e114.CrossRefGoogle ScholarPubMed
VanRaden, PM 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.CrossRefGoogle ScholarPubMed
Weigel, KA, van Tassell, CP, O'Connell, JR, VanRaden, PM, Wiggans, GR 2010a. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. Journal of Dairy Science 93, 2229–2238.CrossRefGoogle ScholarPubMed
Weigel, KA, de los Campos, G, Vazquez, AI, Rosa, GJM, Gianola, D, Van Tassell, CP 2010b. Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle. Journal of Dairy Science 93, 5423–5435.CrossRefGoogle ScholarPubMed
Weigel, KA, de los Campos, G, González-Recio, O, Naya, H, Wu, XL, Long, N, Rosa, GJM, Gianola, D 2009. Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. Journal of Dairy Science 92, 5248–5257.CrossRefGoogle ScholarPubMed
Zhang, Z, Druet, T 2010. Marker imputation with low-density marker panels in Dutch Holstein cattle. Journal of Dairy Science 93, 5487–5494.CrossRefGoogle ScholarPubMed