Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population.
BACKGROUND
Genotype imputation is a key element of the implementation of genomic selection within the New Zealand sheep industry, but many factors can influence imputation accuracy. Our objective was to provide practical directions on the implementation of imputation strategies in a multi-breed sheep population genotyped with three single nucleotide polymorphism (SNP) panels: 5K, 50K and HD (600K SNPs).


RESULTS
Imputation from 5K to HD was slightly better (0.6 %) than imputation from 5K to 50K. Two-step imputation from 5K to 50K and then from 50K to HD outperformed direct imputation from 5K to HD. A slight loss in imputation accuracy was observed when a large fixed reference population was used compared to a smaller within-breed reference (including all 50K genotypes on animals from different breeds excluding those in the validation set i.e. to be imputed), but only for a few animals across all imputation scenarios from 5K to 50K. However, a major gain in imputation accuracy for a large proportion of animals (purebred and crossbred), justified the use of a fixed and large reference dataset for all situations. This study also investigated the loss in imputation accuracy specifically for SNPs located at the ends of each chromosome, and showed that only chromosome 26 had an overall imputation (5K to 50K) accuracy for 100 SNPs at each end higher than 60 % (r2). Most of the chromosomes displayed reduced imputation accuracy at least at one of their ends. Prediction of imputation accuracy based on the relatedness of low-density genotypes to those of the reference dataset, before imputation (without running an imputation software) was also investigated. FIMPUTE V2.2 outperformed BEAGLE 3.3.2 across all imputation scenarios.


CONCLUSIONS
Imputation accuracy in sheep breeds can be improved by following a set of recommendations on SNP panels, software, strategies of imputation (one- or two-step imputation), and choice of the animals to be genotyped using both high- and low-density SNP panels. We present a method that predicts imputation accuracy for individual animals at the low-density level, before running imputation, which can be used to restrict genomic prediction only to the animals that can be imputed with sufficient accuracy.
Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada.|Beef Improvement Opportunities, Guelph, ON, N1K1E5, Canada.
Ricardo V
RV
Ventura
Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada. miller@uoguelph.ca.|Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand. miller@uoguelph.ca.
Stephen P
SP
Miller
Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand.
Ken G
KG
Dodds
Department of Mathematics and Statistics, University of Otago, Dunedin, 9016, New Zealand.
Benoit
B
Auvray
Department of Mathematics and Statistics, University of Otago, Dunedin, 9016, New Zealand.
Michael
M
Lee
Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand.
Matthew
M
Bixley
Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand.
Shannon M
SM
Clarke
Invermay Agricultural Centre, AgResearch Limited, Mosgiel, 9053, New Zealand.
John C
JC
McEwan
Genet Sel Evol. 2014 Jul 15;46:41
25022768
BMC Genet. 2014 Sep 16;15:92
25223795
J Hum Genet. 2013 Oct;58(10):657-66
23863749
J Dairy Sci. 2008 Nov;91(11):4414-23
18946147
Anim Genet. 2015 Oct;46(5):544-56
26360638
PLoS One. 2009;4(4):e5350
19390634
BMC Genomics. 2014 Jun 17;15:478
24935670
Animal. 2014 Jan;8(1):1-10
24128704
Genet Sel Evol. 2013 Feb 13;45:3
23406470
J Anim Sci. 2013 Aug;91(8):3583-92
23736050
Genet Sel Evol. 2015 Dec 22;47:97
26694131
J Dairy Sci. 2014 May;97(5):3128-41
24582440
Animal. 2014 Nov;8(11):1743-53
25045914
J Anim Sci. 2014 Apr;92(4):1433-44
24663187
BMC Genet. 2014 Dec 29;15:157
25543517
Genet Sel Evol. 2014 Feb 04;46:9
24495673
Am J Hum Genet. 2006 Apr;78(4):629-44
16532393
J Dairy Sci. 2012 Jul;95(7):4114-29
22720968
Am J Hum Genet. 2009 Feb;84(2):210-23
19200528
Eur J Hum Genet. 2014 Nov;22(11):1321-6
24896149
Genet Sel Evol. 2011 Nov 28;43:40
22122853
Pac Symp Biocomput. 2014;:241-52
24297551
PLoS One. 2008;3(10):e3551
18958166
BMC Genomics. 2014 Aug 27;15:728
25164068
Anim Genet. 2012 Feb;43(1):72-80
22221027
Eur J Hum Genet. 2015 Jul;23(7):975-83
25293720
Eur J Hum Genet. 2015 Mar;23(3):395-400
24939589
Bioinformatics. 2007 Jul 1;23(13):i401-7
17646323
BMC Bioinformatics. 2008 Dec 01;9:513
19046436
Genetics. 2002 Mar;160(3):1113-22
11901127