Evidence map›Paper›PMID 39167757›Full record

ArticleJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research2024

Genomic structural variations link multiple genes to bone mineral density in a multi-ethnic cohort study: Louisiana osteoporosis study.

Kuan-Jui Su, Chuan Qiu, Jonathan Greenbaum, Xiao Zhang, Anqi Liu, Yong Liu, Zhe Luo, Shashank Sajjan Mungasavalli Gnanesh, Qing Tian, Lan-Juan Zhao and 2 more

Abstract read
In one paragraph

Article in Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

12 authors.

Kuan-Jui SuDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Chuan QiuDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Jonathan GreenbaumDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Xiao ZhangDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Anqi LiuDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Yong LiuDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Zhe LuoDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Shashank Sajjan Mungasavalli GnaneshDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Qing TianDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Lan-Juan ZhaoDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Hui ShenDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.
Hong-Wen DengDeming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA 70112, United States.

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Tanika Nicole Kelly · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI HUI SHEN · 2017 to 2026
$24.3M
Intensive Lifestyle Intervention, Metabolomics, and Risk of Frailty Fracture in Overweight or Obese Patients with Type 2 DiabetesR01AG068232 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI JOHNSON, KAREN C, ZHAO, QI · 2021 to 2025
$3.1M
Identification of Metabolomic Profiles for Sarcopenia Traits in Older Whites and BlacksR01AG061917 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI SHEN, HUI, ZHAO, QI · 2019 to 2023
$3.0M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
NIAMS NIH HHS R01 AR069055NIA NIH HHS R01 AG061917NIA NIH HHS R01 AG068232NIA NIH HHS U19 AG055373NIGMS NIH HHS P20 GM109036NIH HHS P20GM109036
6 · The paper itself

Abstract

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets.

Indexed as

Bone DensityOsteoporosisAdultAgedCohort StudiesEthnicityFemaleGenomic Structural VariationHumansLouisianaMaleMiddle Agedbone mineral densitygenomics structural variantsomicsosteoporosiswhole-genome sequencing

Identifiers

PMID39167757
PMCPMC11425707

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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.