{"doi":"10.1111/age.12340","title":"Design of a low‐density <scp>SNP</scp> chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy","abstract":"<jats:title>Summary</jats:title><jats:p>Genotyping sheep for genome‐wide <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s 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) <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> chip and evaluate the accuracy of imputation from the 12k <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotypes to 50k <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> 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 <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotypes, imputed 50k <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotypes and real 50k <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotypes. The 12k chip design included 12 223 <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s with a high minor allele frequency that were selected with intermarker spacing of 50–475 kb. <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s for parentage and horned or polled tests also were represented. Chromosome ends were enriched with <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s to reduce edge effects on imputation. The imputation performance of the 12k <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> chip was evaluated using 50k <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> 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 (<jats:styled-content style=\"fixed-case\">GBLUP</jats:styled-content>) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (<jats:styled-content style=\"fixed-case\">GEBV</jats:styled-content>s) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean <jats:styled-content style=\"fixed-case\">GBLUP</jats:styled-content> 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 <jats:styled-content style=\"fixed-case\">GEBV</jats:styled-content>s as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was &gt;90%.</jats:p>","journal":"Animal Genetics","year":2015,"id":14495,"datarank":2.570756939364676,"base_score":3.8712010109078907,"endowment":3.8712010109078907,"self_citation_contribution":0.5806801516361837,"citation_network_contribution":1.9900767877284926,"self_endowment_contribution":0.5806801516361837,"citer_contribution":1.9900767877284926,"corpus_percentile":null,"corpus_rank":null,"citation_count":47,"citer_count":43,"citers_with_citation_signal":37,"citers_with_endowment":37,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":114252,"name":"K. Gore","orcid":null,"position":1,"is_corresponding":false},{"id":114253,"name":"J. H. J. van der Werf","orcid":null,"position":2,"is_corresponding":false},{"id":114254,"name":"B. J. Hayes","orcid":null,"position":3,"is_corresponding":false},{"id":114255,"name":"H. D. Daetwyler","orcid":null,"position":4,"is_corresponding":false},{"id":114251,"name":"S. Bolormaa","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":3.8712010109078907,"endowment":3.8712010109078907,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26360638","pmcid":null,"openalex_id":"https://openalex.org/W1946730245","authors":[],"funders":[{"funder_name":"Cooperative Research Centre for Sheep Industry Innovation, Meat and Livestock Australia","grant_id":"","title":null},{"funder_name":"Australian Wool Innovation Ltd","grant_id":"","title":null}],"total_grants":2,"fwci":2.9284,"citation_percentile":0.91150259,"influential_citations":2,"citation_trend":[{"year":2016,"count":3},{"year":2017,"count":7},{"year":2018,"count":2},{"year":2019,"count":2},{"year":2020,"count":6},{"year":2021,"count":6},{"year":2022,"count":8},{"year":2023,"count":5},{"year":2024,"count":2},{"year":2025,"count":2},{"year":2026,"count":4}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fage.12340","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/age.12340","host_type":"publisher"},{"url":"https://doi.org/10.1111/age.12340","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/26360638","host_type":"repository"}],"fields_of_study":["Genetic and phenotypic traits in livestock","Genetic Mapping and Diversity in Plants and Animals","Cancer-related molecular mechanisms research","Biology","Medicine","Agricultural and Food Sciences","Animals","Australia","Breeding","Gene Frequency","Genomics","Genotype","Meat","Oligonucleotide Array Sequence Analysis","Phenotype","Polymorphism, Single Nucleotide","Sheep, Domestic"],"mesh_terms":["Animals","Australia","Breeding","Gene Frequency","Genotype","Meat","Phenotype","Oligonucleotide Array Sequence Analysis","Polymorphism, Single Nucleotide","Genomics","Sheep, Domestic"],"keywords":["Imputation (statistics)","SNP","SNP genotyping","Biology","Genotyping","Genetics","Genotype","Genome-wide association study","Breed","Single-nucleotide polymorphism","Genomic selection","Minor allele frequency","Statistics","Gene","Mathematics","Missing data","Genomic Estimated Breeding Values"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-01T11:02:59.930772Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}