Evidence map›Paper›PMID 40411668›Full record

ReviewCurrent osteoporosis reports2025

Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations.

Qing Wu, Jingyuan Dai, Jianing Liu, Lang Wu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current osteoporosis reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. A genomic structural equation modelling analysis of the shared genetic architecture of the aging spine.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Qing WuDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA. Qing.Wu@osumc.edu.ORCID http://orcid.org/0000-0003-4679-8903
Jingyuan DaiDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.ORCID http://orcid.org/0000-0001-5096-0112
Jianing LiuDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
Lang WuPacific Center for Genome Research, University of Hawai'i at Mānoa, Honolulu, HI, USA.

Funding

Does Creating Person-specific Precision Thresholds Enhance the Ability of a Single Bone Density Measure to Predict Fractures in Minority Women?R01AG080017 · NIA · OHIO STATE UNIVERSITY · PI Qing Wu · 2024 to 2026
$1.9M
Precise Bone Density Reference Ranges to Reduce Systematic Disparities in Osteoporosis Healthcare for Hispanic WomenR21MD013681 · NIMHD · UNIVERSITY OF NEVADA LAS VEGAS · PI WU, QING · 2022 to 2023
$405k
National Institute of Aging R01AG080017NIA NIH HHS R01 AG080017NIMHD NIH HHS R21 MD013681NIMHD NIH HHS R21MD013681
6 · The paper itself

Abstract

purpose of reviewGenome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporosis genetics, highlights existing disparities, and explores strategies for more inclusive research. RECENT

findingsEuropean-focused GWAS have identified key loci for osteoporosis, including WNT signaling (SOST, LRP5) and RUNX2 transcriptional regulation. However, fewer than 40% of these variants can be replicated in Asian and African populations. Emerging studies in non-European groups reveal population-specific loci, sex-specific associations, and gene-environment interactions. Advances in machine learning (ML)-assisted GWAS and multi-omics integration are improving genetic discovery. Expanding GWAS in diverse populations, integrating multi-omics data, refining ML-based risk models, and standardizing biobank data are essential for equitable osteoporosis research. Future efforts must prioritize clinical translation to enhance personalized osteoporosis prevention and treatment.

Indexed as

Genome-Wide Association StudyOsteoporosisAfrican PeopleAsian PeopleBone DensityCore Binding Factor Alpha 1 SubunitGene-Environment InteractionGenetic Predisposition to DiseaseGenomicsHumansLow Density Lipoprotein Receptor-Related Protein-5Machine LearningOsteoporotic FracturesWhite PeopleCore Binding Factor Alpha 1 SubunitLow Density Lipoprotein Receptor-Related Protein-5Bone mineral densityGenetic disparitiesGenome-wide association studiesMulti-omicsOsteoporosisPolygenic risk scores

Identifiers

PMID40411668
PMCPMC12103327

What OpenQuestion holds

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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.