{"doi":"10.1111/age.12800","title":"Evaluation of genotyping concordance for commercial bovine <scp>SNP</scp> arrays using quality‐assurance samples","abstract":"<jats:title>Summary</jats:title><jats:p><jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> arrays are widely used in genetic research and agricultural genomics applications, and the quality of <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotyping data is of paramount importance. In the present study, <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotyping concordance and discordance were evaluated for commercial bovine <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> arrays based on two types of quality assurance (<jats:styled-content style=\"fixed-case\">QA</jats:styled-content>) samples provided by Neogen GeneSeek. The genotyping discordance rates (<jats:styled-content style=\"fixed-case\">GDR</jats:styled-content>s) between chips were on average between 0.06% and 0.37% based on the <jats:styled-content style=\"fixed-case\">QA</jats:styled-content> type I data and between 0.05% and 0.15% based on the <jats:styled-content style=\"fixed-case\">QA</jats:styled-content> type <jats:styled-content style=\"fixed-case\">II</jats:styled-content> data. The average genotyping error rate (<jats:styled-content style=\"fixed-case\">GER</jats:styled-content>) pertaining to single <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> chips, based on the <jats:styled-content style=\"fixed-case\">QA</jats:styled-content> type <jats:styled-content style=\"fixed-case\">II</jats:styled-content> data, varied between 0.02% and 0.08% per <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s had considerably elevated <jats:styled-content style=\"fixed-case\">GDR</jats:styled-content>s and <jats:styled-content style=\"fixed-case\">GER</jats:styled-content>s compared to the <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele ‘dropout’ (i.e. from <jats:styled-content style=\"fixed-case\">AB</jats:styled-content> to <jats:styled-content style=\"fixed-case\">AA</jats:styled-content> or <jats:styled-content style=\"fixed-case\">BB</jats:styled-content>). Simultaneous allotyping errors on both alleles (e.g. mistaking <jats:styled-content style=\"fixed-case\">AA</jats:styled-content> for <jats:styled-content style=\"fixed-case\">BB</jats:styled-content> or vice versa) were relatively rare. Finally, a list of <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s with a <jats:styled-content style=\"fixed-case\">GER</jats:styled-content> greater than 1% is provided. Interpretation of association effects of these <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s, for example in genome‐wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> arrays.</jats:p>","journal":"Animal Genetics","year":2019,"id":647454,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":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":1221787,"name":"J. Xu","orcid":"0009-0000-3180-3672","position":1,"is_corresponding":false},{"id":1296911,"name":"H. Li","orcid":"0009-0000-9424-9576","position":2,"is_corresponding":false},{"id":1686899,"name":"R. Ferretti","orcid":null,"position":3,"is_corresponding":false},{"id":1422085,"name":"J. He","orcid":"0009-0007-2145-8503","position":4,"is_corresponding":false},{"id":1686900,"name":"J. Qiu","orcid":null,"position":5,"is_corresponding":false},{"id":1686901,"name":"Q. Xiao","orcid":null,"position":6,"is_corresponding":false},{"id":1686902,"name":"B. Simpson","orcid":null,"position":7,"is_corresponding":false},{"id":1686903,"name":"T. Michell","orcid":null,"position":8,"is_corresponding":false},{"id":1627240,"name":"S. D. Kachman","orcid":null,"position":9,"is_corresponding":false},{"id":1686904,"name":"R. G. Tait","orcid":null,"position":10,"is_corresponding":false},{"id":1686905,"name":"S. Bauck","orcid":null,"position":11,"is_corresponding":false},{"id":1686898,"name":"X.‐L. Wu","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Evaluation of genotyping concordance for commercial bovine <scp>SNP</scp> arrays using quality‐assurance samples","abstract":"<jats:title>Summary</jats:title><jats:p><jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> arrays are widely used in genetic research and agricultural genomics applications, and the quality of <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotyping data is of paramount importance. In the present study, <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> genotyping concordance and discordance were evaluated for commercial bovine <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> arrays based on two types of quality assurance (<jats:styled-content style=\"fixed-case\">QA</jats:styled-content>) samples provided by Neogen GeneSeek. The genotyping discordance rates (<jats:styled-content style=\"fixed-case\">GDR</jats:styled-content>s) between chips were on average between 0.06% and 0.37% based on the <jats:styled-content style=\"fixed-case\">QA</jats:styled-content> type I data and between 0.05% and 0.15% based on the <jats:styled-content style=\"fixed-case\">QA</jats:styled-content> type <jats:styled-content style=\"fixed-case\">II</jats:styled-content> data. The average genotyping error rate (<jats:styled-content style=\"fixed-case\">GER</jats:styled-content>) pertaining to single <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> chips, based on the <jats:styled-content style=\"fixed-case\">QA</jats:styled-content> type <jats:styled-content style=\"fixed-case\">II</jats:styled-content> data, varied between 0.02% and 0.08% per <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> and between 0.01% and 0.06% per sample. These results indicate that genotyping concordance rate was high (i.e. from 99.63% to 99.99%). Nevertheless, mitochondrial and Y chromosome <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s had considerably elevated <jats:styled-content style=\"fixed-case\">GDR</jats:styled-content>s and <jats:styled-content style=\"fixed-case\">GER</jats:styled-content>s compared to the <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s on the 29 autosomes and X chromosome. The majority of genotyping errors resulted from single allotyping errors, which also included the opposite instances for allele ‘dropout’ (i.e. from <jats:styled-content style=\"fixed-case\">AB</jats:styled-content> to <jats:styled-content style=\"fixed-case\">AA</jats:styled-content> or <jats:styled-content style=\"fixed-case\">BB</jats:styled-content>). Simultaneous allotyping errors on both alleles (e.g. mistaking <jats:styled-content style=\"fixed-case\">AA</jats:styled-content> for <jats:styled-content style=\"fixed-case\">BB</jats:styled-content> or vice versa) were relatively rare. Finally, a list of <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s with a <jats:styled-content style=\"fixed-case\">GER</jats:styled-content> greater than 1% is provided. Interpretation of association effects of these <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content>s, for example in genome‐wide association studies, needs to be taken with caution. The genotyping concordance information needs to be considered in the optimal design of future bovine <jats:styled-content style=\"fixed-case\">SNP</jats:styled-content> arrays.</jats:p>","is_dataset_classified":null,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"31172566","pmcid":null,"openalex_id":"https://openalex.org/W2970780691","authors":[],"funders":[{"funder_name":"Bairen Plan of Hunan Province, China","grant_id":"XZ2016-08-07","title":null},{"funder_name":"Neogen GeneSeek","grant_id":"","title":null},{"funder_name":"Hunan Co-Innovation center of Animal Production Safety, China","grant_id":"","title":null},{"funder_name":"Neogen GeneSeek","grant_id":"","title":null},{"funder_name":"Hunan Co-Innovation center of Animal Production Safety, China","grant_id":"","title":null}],"total_grants":5,"fwci":0.6442,"citation_percentile":0.73060811,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2021,"count":3},{"year":2023,"count":1},{"year":2025,"count":2},{"year":2026,"count":1}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/age.12800","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1111/age.12800","host_type":"publisher"},{"url":"https://doi.org/10.1111/age.12800","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/31172566","host_type":"repository"}],"fields_of_study":["Genetic and phenotypic traits in livestock","Genetic Mapping and Diversity in Plants and Animals","Molecular Biology Techniques and Applications","Animals","Cattle","Genotype","Polymorphism, Single Nucleotide"],"mesh_terms":["Animals","Cattle","Genotype","Polymorphism, Single Nucleotide"],"keywords":["Genotyping","SNP","Concordance","SNP genotyping","Biology","Genetics","SNP array","Molecular Inversion Probe","Autosome","Single-nucleotide polymorphism","Allele","Chromosome","Genome-wide association study","Genotype","Computational biology","Gene","Quality assurance","Genotyping Errors","Mendelian Errors","Bovine snp chips"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Zero hunger"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-09T22:34:07.485042Z","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":[]}