{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Stoneman HR"],"funding":["NHGRI NIH HHS"],"pagination":["235-253"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11866976"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["112(2)"],"pubmed_abstract":["Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations. There is a need for methods to enable the harmonization of summary data where the underlying substructure is matched between datasets. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to enable the harmonization of genetic summary data by estimating and a"],"journal":["American journal of human genetics"],"pubmed_title":["Characterizing substructure via mixture modeling in large-scale genetic summary statistics."],"pmcid":["PMC11866976"],"funding_grant_id":["R01 HG011345","R25 HG012919","U01 HG011715"],"pubmed_authors":["Brunetti TM","Crawford J","Pattee J","Julian CG","Coughlin CR","Arehart CH","Lamont R","Teague C","Lacy R","Trout NS","Pozdeyev N","Radwan A","Raghavan S","Swartz CT","Culver E","Rafaels N","Colorado Center for Personalized Medicine","Stewart AM","Ku L","Tifour S","Maldonado BL","Casteel EM","Shortt JA","Shalowitz EL","Edelmann MN","Lee YM","Nadrash A","Todd EB","Johnson RK","Anderson HD","Tanaka A","Stoneman HR","Gignoux CR","Marker KM","Price AM","Taylor MRG","Trinkley KE","Martin JL","Sherif H","Lin M","Vargas LB","Fisher MJ","Greene CS","McDaniel NL","Frye TC","Lesny JA","Burke EE","Hawkes B","Mayer D","Brooks IM","Kudron EL","George H","Monte A","Aquilante CL","Kao D","Sutton KJ","Hendricks AE","Konigsberg I","Gilliland EK","Lowery JT","Marceau D","Rasouli N","Gates BL","Brutus-Lestin J","Crooks K","Arbogast K","Lange EM","Wiley LK","Cole JB","Hearst E","Franklin AW","Moore E"],"additional_accession":[]},"is_claimable":false,"name":"Characterizing substructure via mixture modeling in large-scale genetic summary statistics.","description":"Genetic summary data are broadly accessible and highly useful, including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into summary data, such as allele frequencies, masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted-for substructure limits summary data usability, especially for understudied or admixed populations. There is a need for methods to enable the harmonization of summary data where the underlying substructure is matched between datasets. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to enable the harmonization of genetic summary data by estimating and a","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2026-05-26T11:09:05.719Z","creation":"2025-04-04T13:11:07.417Z"},"accession":"S-EPMC11866976","cross_references":{"pubmed":["39824191"],"doi":["10.1016/j.ajhg.2024.12.007"]}}