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Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy.
Anim Genet. 2015 Oct; 46(5):544-56.AG

Abstract

Genotyping sheep for genome-wide SNPs at lower density and imputing to a higher density would enable cost-effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low-density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50-475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single-breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%.

Authors+Show Affiliations

AgriBio, Centre for AgriBioscience, DEDJTR, Bundoora, VIC, 3083, Australia. Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW, 2351, Australia.School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW, 2351, Australia. School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351, Australia.AgriBio, Centre for AgriBioscience, DEDJTR, Bundoora, VIC, 3083, Australia. Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW, 2351, Australia. School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia.AgriBio, Centre for AgriBioscience, DEDJTR, Bundoora, VIC, 3083, Australia. Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW, 2351, Australia. School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26360638

Citation

Bolormaa, S, et al. "Design of a Low-density SNP Chip for the Main Australian Sheep Breeds and Its Effect On Imputation and Genomic Prediction Accuracy." Animal Genetics, vol. 46, no. 5, 2015, pp. 544-56.
Bolormaa S, Gore K, van der Werf JH, et al. Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy. Anim Genet. 2015;46(5):544-56.
Bolormaa, S., Gore, K., van der Werf, J. H., Hayes, B. J., & Daetwyler, H. D. (2015). Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy. Animal Genetics, 46(5), 544-56. https://doi.org/10.1111/age.12340
Bolormaa S, et al. Design of a Low-density SNP Chip for the Main Australian Sheep Breeds and Its Effect On Imputation and Genomic Prediction Accuracy. Anim Genet. 2015;46(5):544-56. PubMed PMID: 26360638.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy. AU - Bolormaa,S, AU - Gore,K, AU - van der Werf,J H J, AU - Hayes,B J, AU - Daetwyler,H D, Y1 - 2015/09/11/ PY - 2015/07/03/accepted PY - 2015/9/12/entrez PY - 2015/9/12/pubmed PY - 2015/12/25/medline KW - genomic estimated breeding values KW - minor allele frequency SP - 544 EP - 56 JF - Animal genetics JO - Anim. Genet. VL - 46 IS - 5 N2 - Genotyping sheep for genome-wide SNPs at lower density and imputing to a higher density would enable cost-effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low-density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50-475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single-breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%. SN - 1365-2052 UR - https://www.unboundmedicine.com/medline/citation/26360638/Design_of_a_low_density_SNP_chip_for_the_main_Australian_sheep_breeds_and_its_effect_on_imputation_and_genomic_prediction_accuracy_ L2 - https://doi.org/10.1111/age.12340 DB - PRIME DP - Unbound Medicine ER -