Evidence map›Paper›PMID 38826475›Full record

ArticlebioRxiv : the preprint server for biology2025

Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations.

Luke J Dillard, Gina M Calabrese, Larry D Mesner, Charles R Farber

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Luke J DillardCenter for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908.ORCID 0000-0001-8293-0479
Gina M CalabreseCenter for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908.
Larry D MesnerCenter for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908.
Charles R FarberCenter for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908.ORCID 0000-0002-6748-4711

Funding

Identification of Novel Genes Impacting Osteoblast ActivityR01AR079839 · NIAMS · UNIVERSITY OF COLORADO DENVER · PI ACKERT-BICKNELL, CHERYL LYNNE, FARBER, CHARLES R · 2021 to 2025
$3.3M
Genetic analysis of bone strengthR01AR068345 · NIAMS · UNIVERSITY OF VIRGINIA · PI FARBER, CHARLES R, HOROWITZ, MARK C · 2016 to 2020
$3.1M
Informing Osteoporosis GWAS Using NetworksR01AR077992 · NIAMS · UNIVERSITY OF VIRGINIA · PI FARBER, CHARLES R · 2020 to 2025
$2.8M
Large-Scale Genetic Analysis of Bone Strength in Diversity Outbred MiceR01AR082880 · NIAMS · UNIVERSITY OF VIRGINIA · PI Charles R Farber · 2024 to 2026
$2.2M
NIAMS NIH HHS R01 AR068345NIAMS NIH HHS R01 AR077992NIAMS NIH HHS R01 AR079839NIAMS NIH HHS R01 AR082880
6 · The paper itself

Abstract

Genome-wide association studies (GWASs) have identified many sources of genetic variation associated with bone mineral density (BMD), a clinical predictor of fracture risk and osteoporosis. Aside from the identification of causal genes, other difficult challenges to informing GWAS include characterizing the roles of predicted causal genes in disease and providing additional functional context, such as the cell type predictions or biological pathways in which causal genes operate. Leveraging single-cell transcriptomics (scRNA-seq) can assist in informing BMD GWAS by linking disease-associated variants to genes and providing a cell type context for which these causal genes drive disease. Here, we use large-scale scRNA-seq data from bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) from Diversity Outbred (DO) mice to generate cell type-specific networks and contextualize BMD GWAS-implicated genes. Using trajectories inferred from the scRNA-seq data that map cell state transitions, we identify networks enriched with genes that exhibit the most dynamic changes in expression across trajectories. We discover 21 network driver genes, which are likely to be causal for human BMD GWAS associations that colocalize with expression/splicing quantitative trait loci (eQTL/sQTL). These driver genes, including

Indexed as

Mesenchymal Stem Cells (MSC)OsteoblastsOsteocytesOsteoporosisStromal cellsSystems Biology

Identifiers

PMID38826475
PMCPMC11142079

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.