Evidence map›Paper›PMID 39824191›Full record

ArticleAmerican journal of human genetics2025

Characterizing substructure via mixture modeling in large-scale genetic summary statistics.

Hayley R Stoneman, Adelle M Price, Nikole Scribner Trout, Riley Lamont, Souha Tifour, Nikita Pozdeyev, Colorado Center for Personalized Medicine, Kristy Crooks, Meng Lin, Nicholas Rafaels and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Hayley R StonemanDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Adelle M PriceDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA.
Nikole Scribner TroutDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA.
Riley LamontMathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA.
Souha TifourDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA.
Nikita PozdeyevDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Colorado Center for Personalized Medicine
Kristy CrooksColorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pathology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Meng LinDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Nicholas RafaelsColorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Christopher R GignouxDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Katie M MarkerDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Audrey E HendricksDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO 80204, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Electronic address: audrey.hendricks@cuanschutz.edu.

Funding

PRS Center for Admixed Populations (CAPE)U01HG011715 · NHGRI · UNIVERSITY OF PENNSYLVANIA · PI Eimear Elizabeth Kenny, Leslie A Lange · 2021 to 2026
$5.2M
Genomic Approaches to Population Health in Multi-Ethnic Hospital SystemsR01HG011345 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI ARBOLEDA, VALERIE A, GIGNOUX, CHRISTOPHER R · 2020 to 2023
$3.1M
Methods to enable robust and efficient use of genetic summary dataR35HG011293 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI HENDRICKS, AUDREY E · 2020 to 2024
$2.1M
Research experience pathways in genomic data science (PATH-GDS) for underrepresented groupsR25HG012919 · NHGRI · UNIVERSITY OF COLORADO DENVER · PI Joshua P French, Audrey E Hendricks · 2023 to 2026
$1.1M
NHGRI NIH HHS R01 HG011345NHGRI NIH HHS R25 HG012919NHGRI NIH HHS R35 HG011293NHGRI NIH HHS U01 HG011715
6 · The paper itself

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 adjusting for substructure. In extensive simulations and application to public data, we show that Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and scans for potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse, publicly available summary data, resulting in improved and more equitable research.

Indexed as

Models, GeneticSoftwareComputer SimulationGene FrequencyGenetics, PopulationGenome-Wide Association StudyHumansModels, StatisticalPolymorphism, Single Nucleotideadmixedconfoundingequitable researchfederated learninggenetic similaritygenetic summary dataharmonizationlocal ancestrypopulation stratificationselectionsubstructuresummary data

Identifiers

PMID39824191
PMCPMC11866976

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.