biostudies-literatureStoneman HRNHGRI NIH HHS235-253https://www.ebi.ac.uk/biostudies/studies/S-EPMC11866976biostudies-literatureUnknown112(2)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 aAmerican journal of human geneticsCharacterizing substructure via mixture modeling in large-scale genetic summary statistics.PMC11866976R01 HG011345R25 HG012919U01 HG011715Brunetti TMCrawford JPattee JJulian CGCoughlin CRArehart CHLamont RTeague CLacy RTrout NSPozdeyev NRadwan ARaghavan SSwartz CTCulver ERafaels NColorado Center for Personalized MedicineStewart AMKu LTifour SMaldonado BLCasteel EMShortt JAShalowitz ELEdelmann MNLee YMNadrash ATodd EBJohnson RKAnderson HDTanaka AStoneman HRGignoux CRMarker KMPrice AMTaylor MRGTrinkley KEMartin JLSherif HLin MVargas LBFisher MJGreene CSMcDaniel NLFrye TCLesny JABurke EEHawkes BMayer DBrooks IMKudron ELGeorge HMonte AAquilante CLKao DSutton KJHendricks AEKonigsberg IGilliland EKLowery JTMarceau DRasouli NGates BLBrutus-Lestin JCrooks KArbogast KLange EMWiley LKCole JBHearst EFranklin AWMoore EfalseCharacterizing substructure via mixture modeling in large-scale genetic summary statistics.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 a2025-01-01T00:00:00Z2025 Feb2026-05-26T11:09:05.719Z2025-04-04T13:11:07.417ZS-EPMC118669763982419110.1016/j.ajhg.2024.12.007