Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-16T18:57:38.063Z Has data issue: false hasContentIssue false

Imputation of ungenotyped parental genotypes in dairy and beef cattle from progeny genotypes

Published online by Cambridge University Press:  09 April 2014

D. P. Berry*
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
S. McParland
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Co. Cork, Ireland
J. F. Kearney
Affiliation:
Irish Cattle Breeding Federation, Highfield House, Bandon, Co. Cork, Ireland
M. Sargolzaei
Affiliation:
Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada
M. P. Mullen
Affiliation:
Animal & Grassland Research and Innovation Centre, Teagasc, Athenry, Co. Galway, Ireland
Get access

Abstract

The objective of this study was to quantify the accuracy of imputing the genotype of parents using information on the genotype of their progeny and a family-based and population-based imputation algorithm. Two separate data sets were used, one containing both dairy and beef animals (n=3122) with high-density genotypes (735 151 single nucleotide polymorphisms (SNPs)) and the other containing just dairy animals (n=5489) with medium-density genotypes (51 602 SNPs). Imputation accuracy of three different genotype density panels were evaluated representing low (i.e. 6501 SNPs), medium and high density. The full genotypes of sires with genotyped half-sib progeny were masked and subsequently imputed. Genotyped half-sib progeny group sizes were altered from 4 up to 12 and the impact on imputation accuracy was quantified. Up to 157 and 258 sires were used to test the accuracy of imputation in the dairy plus beef data set and the dairy-only data set, respectively. The efficiency and accuracy of imputation was quantified as the proportion of genotypes that could not be imputed, and as both the genotype concordance rate and allele concordance rate. The median proportion of genotypes per animal that could not be imputed in the imputation process decreased as the number of genotyped half-sib progeny increased; values for the medium-density panel ranged from a median of 0.015 with a half-sib progeny group size of 4 to a median of 0.0014 to 0.0015 with a half-sib progeny group size of 8. The accuracy of imputation across different paternal half-sib progeny group sizes was similar in both data sets. Concordance rates increased considerably as the number of genotyped half-sib progeny increased from four (mean animal allele concordance rate of 0.94 in both data sets for the medium-density genotype panel) to five (mean animal allele concordance rate of 0.96 in both data sets for the medium-density genotype panel) after which it was relatively stable up to a half-sib progeny group size of eight. In the data set with dairy-only animals, sufficient sires with paternal half-sib progeny groups up to 12 were available and the within-animal mean genotype concordance rates continued to increase up to this group size. The accuracy of imputation was worst for the low-density genotypes, especially with smaller half-sib progeny group sizes but the difference in imputation accuracy between density panels diminished as progeny group size increased; the difference between high and medium-density genotype panels was relatively small across all half-sib progeny group sizes. Where biological material or genotypes are not available on individual animals, at least five progeny can be genotyped (on either a medium or high-density genotyping platform) and the parental alleles imputed with, on average, ⩾96% accuracy.

Type
Full Paper
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berry, DP and Kearney, JF 2011. Imputation of genotypes from low- to high-density genotyping platforms and implications for genomic selection. Animal 5, 11621169.Google Scholar
Berry, DP, McClure, MC and Mullen, MP 2014. Within and across-breed imputation of high density genotypes in dairy and beef cattle from medium and low density genotypes. Journal of Animal Breeding and Genetics (in press), doi:10.1111/jbg.12067.CrossRefGoogle ScholarPubMed
Browning, BL and 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, 210223.Google Scholar
Browning, SR and 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, 10841097.CrossRefGoogle ScholarPubMed
Cromie, AR, Berry, DP, Wickham, B, Kearney, JF, Pena, J, van Kaam, JBCH, Gengler, N, Szyda, J, Schnyder, U, Coffey, M, Moster, B, Hagiya, K, Weller, JI, Abernethy, D and Spelman, R 2010. International genomic co-operation; who, what, when, where, why and how? InterBull Conference, No. 42, Riga, Latvia, 31 May, pp. 72–80.Google Scholar
Daetwyler, HD, Villanueva, B and Woolliams, JA 2008. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS One 3, e3395.CrossRefGoogle ScholarPubMed
Dassonneville, R, Fritz, S, Ducrocq, V and Boichard, D 2012. Imputation performance of 3 low-density marker panels in beef and dairy cattle. Journal of Dairy Science 95, 41364140.CrossRefGoogle ScholarPubMed
David, X, de Vries, A, Feddersen, E and Borchersen, S 2010. International genomic cooperation – EuroGenomics significantly improves reliability of genomic evaluations. Proceedings of the Interbull International Workshop, No. 41, Paris, France, 4–5 March, pp. 77–78.Google Scholar
Habier, D, Fernando, RL, Kizilkaya, K and Garrick, DJ 2011. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186.CrossRefGoogle ScholarPubMed
Hayes, BJ, Bowman, PJ, Chamberlain, AJ and Goddard, ME 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433443.Google Scholar
Huang, Y, Maltecca, C, Cassady, JP, Alexander, LJ, Snelling, WM and MacNeil, MD 2012. Effects of reduced panel, reference origin, and genetic relationship on imputation of genotypes in Hereford cattle. Journal of Animal Science 90, 42034208.CrossRefGoogle ScholarPubMed
Jorjani, H, Zumbach, B, Dürr, J and Santus, E 2010. Joint genomic evaluation of BSW populations. Proceedings of the Interbull International Workshop, No. 41, Paris, France, 4–5 March, pp. 8–16.Google Scholar
Lund, MS, Roos, APW, de Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, F and Su, G 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetics, Selection, Evolution 43, 43.Google Scholar
Meredith, BK, Kearney, JF, Finlay, EK, Bradley, DG, Fahey, AG, Berry, DP and Lynn, DJ 2012. Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genetics 13, 21.CrossRefGoogle ScholarPubMed
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.CrossRefGoogle ScholarPubMed
Muir, B, Van Doormaal, B and Kistemaker, G 2010. International genomic cooperation – North American perspective. Proceedings of the Interbull International Workshop, No. 41, Paris, France, 4–5 March, pp. 71–76.Google Scholar
Pimentel, ECG, Wensch-Dorendorf, M, König, S and Swalve, HH 2013. Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture. Genetics, Selection, Evolution 45, 12.CrossRefGoogle ScholarPubMed
Pryce, JE and Hayes, BJ 2012. A review of how dairy farmers can use and profit from genomic technologies. Animal Production Science 52, 180184.CrossRefGoogle Scholar
Pszczola, M, Mulder, HA and MPL, Calus 2011. Effect of enlarging the reference population with (un)genotyped animals on the accuracy of genomic selection in dairy cattle. Journal of Dairy Science 94, 431441.CrossRefGoogle ScholarPubMed
Saatchi, M, Schnabel, RD, Rolf, MM, Taylor, JF and Garrick, DJ 2012. Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle. Genetics Selection Evolution 44, 38.Google Scholar
Sargolzaei, M, Chesnais, JP and Schenkel, FS 2011. FImpute – an efficient imputation algorithm for dairy cattle populations. Journal of Dairy Science 94, 421.Google Scholar
VanRaden, PM, Null, DJ, Sargolzaei, M, Wiggans, GR, Tooker, ME, Cole, JB, Sonstegard, TS, Connor, EE, Winters, M, van Kaam, JBCHM, Valentini, A, Van Doormaal, BJ, Faust, MA and Doak, GA 2013. Genomic imputation and evaluation using high-density Holstein genotypes. Journal of Dairy Science 96, 668678.Google Scholar
Venot, E, Pabiou, T, Fouilloux, M-N, Coffey, M, Laloë, D, Guerrier, J, Cromie, A, Journaux, L, Flynn, J and Wickham, B 2007. Interbeef in practice: example of a joint genetic evaluation between France, Ireland and United Kingdom for pure bred Limousine weaning weights. Proceedings of the Interbull International Workshop, No. 36, Paris, France, 9–10 March, pp. 41–48.Google Scholar