{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ma Y"],"funding":["National Natural Science Foundation of China","National Research Foundation of Korea","Clinical Medicine Plus X - Young Scholars Project","Innovation Fund for Outstanding Doctoral Candidates of Peking University Health Science Center"],"pagination":["356"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12533355"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(1)"],"pubmed_abstract":["<h4>Background</h4>Inclusion of individuals with diverse or admixed genetic ancestries is crucial to discover novel findings that may be missed by genomics analyses rooted solely in European population.<h4>Results</h4>Here, we present an analysis framework, SPAmix, which is scalable to a large-scale biobank data analysis including hundreds of thousands of admixed individuals and is universally applicable to various types of complex traits including quantitative traits, time-to-event traits, ordinal traits, and longitudinal traits. Since no alternative model is fitted, SPAmix primarily focuses on association p values. For each genetic variant, SPAmix uses genotype data and genetic principal components to estimate individual-specific allele frequency, which is subsequently used to calibrate "],"journal":["Genome biology"],"pubmed_title":["SPAmix: a scalable, accurate, and universal analysis framework for large-scale genetic association studies in admixed populations."],"pmcid":["PMC12533355"],"funding_grant_id":["62411540239","82441004","62273010","PKU2023LCXQ043","BMU2025BSS001","32471202","2024K2A9A2A0601368311"],"pubmed_authors":["Lee S","Bi W","Xu P","Zhou XJ","Kim H","Zhang JF","Ma Y","Zhou W","Mao F","Xu H","Miao L","Xu LL","Zhang P","Li Y"],"additional_accession":[]},"is_claimable":false,"name":"SPAmix: a scalable, accurate, and universal analysis framework for large-scale genetic association studies in admixed populations.","description":"<h4>Background</h4>Inclusion of individuals with diverse or admixed genetic ancestries is crucial to discover novel findings that may be missed by genomics analyses rooted solely in European population.<h4>Results</h4>Here, we present an analysis framework, SPAmix, which is scalable to a large-scale biobank data analysis including hundreds of thousands of admixed individuals and is universally applicable to various types of complex traits including quantitative traits, time-to-event traits, ordinal traits, and longitudinal traits. Since no alternative model is fitted, SPAmix primarily focuses on association p values. For each genetic variant, SPAmix uses genotype data and genetic principal components to estimate individual-specific allele frequency, which is subsequently used to calibrate ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-07-15T11:56:06.441Z","creation":"2026-07-04T03:12:17.056Z"},"accession":"S-EPMC12533355","cross_references":{"pubmed":["41102819"],"doi":["10.1186/s13059-025-03827-9"]}}