{"doi":"10.1159/000334084","title":"Performance of Genotype Imputations Using Data from the 1000 Genomes Project","abstract":"<jats:p>Genotype imputations based on 1000 Genomes (1KG) Project data have the advantage of imputing many more SNPs than imputations based on HapMap data. It also provides an opportunity to discover associations with relatively rare variants. Recent investigations are increasingly using 1KG data for genotype imputations, but only limited evaluations of the performance of this approach are available. In this paper, we empirically evaluated imputation performance using 1KG data by comparing imputation results to those using the HapMap Phase II data that have been widely used. We used three reference panels: the CEU panel consisting of 120 haplotypes from HapMap II and 1KG data (June 2010 release) and the EUR panel consisting of 566 haplotypes also from 1KG data (August 2010 release). We used Illumina 324,607 autosomal SNPs genotyped in 501 individuals of European ancestry. Our most important finding was that both 1KG reference panels provided much higher imputation yield than the HapMap II panel. There were more than twice as many successfully imputed SNPs as there were using the HapMap II panel (6.7 million vs. 2.5 million). Our second most important finding was that accuracy using both 1KG panels was high and almost identical to accuracy using the HapMap II panel. Furthermore, after removing SNPs with MACH Rsq &lt;0.3, accuracy for both rare and low frequency SNPs was very high and almost identical to accuracy for common SNPs. We found that imputation using the 1KG-EUR panel had advantages in successfully imputing rare, low frequency and common variants. Our findings suggest that 1KG-based imputation can increase the opportunity to discover significant associations for SNPs across the allele frequency spectrum. Because the 1KG Project is still underway, we expect that later versions will provide even better imputation performance.</jats:p>","journal":"Human Heredity","year":2012,"id":15157,"datarank":2.535656137408882,"base_score":3.6888794541139363,"endowment":3.6888794541139363,"self_citation_contribution":0.5533319181170905,"citation_network_contribution":1.9823242192917916,"self_endowment_contribution":0.5533319181170905,"citer_contribution":1.9823242192917916,"corpus_percentile":null,"corpus_rank":null,"citation_count":39,"citer_count":35,"citers_with_citation_signal":31,"citers_with_endowment":31,"datacite_reuse_total":4,"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":116762,"name":"Lihua Wang","orcid":null,"position":1,"is_corresponding":false},{"id":21928,"name":"Tuomo Rankinen","orcid":null,"position":2,"is_corresponding":false},{"id":116763,"name":"Claude Bouchard","orcid":null,"position":3,"is_corresponding":false},{"id":116764,"name":"D.C. Rao","orcid":null,"position":4,"is_corresponding":false},{"id":21719,"name":"Yun Ju Sung","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":3.6888794541139363,"endowment":3.6888794541139363,"datacite_reuse_total":4,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"22212296","pmcid":"PMC3322630","openalex_id":"https://openalex.org/W1989743336","authors":[],"funders":[{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM028719","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"R01 HL045670","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"GM 28719","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HL 45670","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"U10 HL054473","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HL 54473","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"U01 HL054473","title":null}],"total_grants":7,"fwci":3.1692,"citation_percentile":0.91534011,"influential_citations":4,"citation_trend":[{"year":2012,"count":6},{"year":2013,"count":5},{"year":2014,"count":5},{"year":2015,"count":7},{"year":2016,"count":2},{"year":2017,"count":5},{"year":2018,"count":2},{"year":2020,"count":1},{"year":2021,"count":1},{"year":2022,"count":2},{"year":2025,"count":2},{"year":2026,"count":1}],"oa_status":"bronze","license":"https://www.karger.com/Services/SiteLicenses","oa_locations":[{"url":"https://www.karger.com/Article/Pdf/334084","host_type":"journal"},{"url":"https://www.karger.com/Article/Pdf/334084","host_type":"BRONZE"},{"url":"https://www.karger.com/Article/Pdf/334084","host_type":"publisher"},{"url":"https://doi.org/10.1159/000334084","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/22212296","host_type":"repository"},{"url":"http://europepmc.org/articles/PMC3322630","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3322630","host_type":"repository"}],"fields_of_study":["Genetic Associations and Epidemiology","Genetic Mapping and Diversity in Plants and Animals","Genetic and phenotypic traits in livestock","Medicine","Biology","Computational Biology","Genome-Wide Association Study","Genotype","HapMap Project","Humans","Polymorphism, Single Nucleotide","Reproducibility of Results","White People"],"mesh_terms":["Genotype","Humans","Reproducibility of Results","Computational Biology","Polymorphism, Single Nucleotide","White People","Genome-Wide Association Study","HapMap Project"],"keywords":["International HapMap Project","Imputation (statistics)","1000 Genomes Project","Single-nucleotide polymorphism","Haplotype","Genome-wide association study","Haplotype estimation","Genotype","Genetics","Minor allele frequency","Biology","Statistics","Computer science","Computational biology","Missing data","Gene","Mathematics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Partnerships for the goals"}],"linked_datasets":[{"doi":"10.6084/m9.figshare.21331194.v1","title":"Additional file 1 of A joint use of pooling and imputation for genotyping SNPs","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.21331194","title":"Additional file 1 of A joint use of pooling and imputation for genotyping SNPs","publisher":"figshare","resource_type":"JournalArticle"},{"doi":"10.6084/m9.figshare.5122996.v1","title":"Supplementary Material for: Performance of Genotype Imputations Using Data from the 1000 Genomes Project","publisher":"Karger Publishers","resource_type":"Dataset"},{"doi":"10.6084/m9.figshare.5122996","title":"Supplementary Material for: Performance of Genotype Imputations Using Data from the 1000 Genomes Project","publisher":"Karger Publishers","resource_type":"Dataset"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-01T16:36:38.421167Z","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":[]}