Evidence map›Paper›PMID 41358319›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Cell-type aware transcriptome-wide association study of mammographic density phenotypes.

Adriana Sistig, Joseph H Rothstein, Sinan Zhu, S Taylor Head, Yung-Han Chang, Ninah Achacoso, Stacey E Alexeeff, Vignesh Arasu, Tejomay Gadgil, Lawrence D Gerstley and 13 more

Abstract readPreprint
In one paragraph

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

0numbers the graph read from it
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

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

23 authors.

Adriana SistigDepartment of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Joseph H RothsteinDepartment of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Sinan ZhuCentre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore.
S Taylor HeadDepartment of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Yung-Han ChangQuantitative Sciences Program, University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
Ninah AchacosoDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Stacey E AlexeeffDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Vignesh ArasuDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Tejomay GadgilDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Lawrence D GerstleyDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Laurie R MargoliesDepartment of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Lori C SakodaDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Li ShenDepartment of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Cara L Smith GueyeDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Marvella VillaseñorDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Mark WestleyDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
MODE/BCAC Consortia
Arjun BhattacharyaDepartment of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Robert J KleinDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0003-3539-5391
Laurel A HabelDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Xiaoyu SongDepartment of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID 0000-0003-1909-6244
Pei WangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Weiva SiehDepartment of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Funding

Epidemiologic StudiesU19CA148065 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI AHSAN, HABIBUL, BRUGGE, JOAN SIEFERT · 2010 to 2014
$10.6M
Proteogenomic translator for cancer biomarker discovery towards precision medicineU24CA271114 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Avi Ma'ayan, Pei Wang · 2022 to 2026
$5.0M
Systems Biology based Proteogenomic Translator for Cancer Marker Discovery towards Precision MedicineU24CA210993 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SCHADT, ERIC E, WANG, PEI · 2016 to 2020
$4.6M
Genetic Predictors of Prostate Cancer SurvivalR01CA244948 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ROBERT J. KLEIN · 2021 to 2026
$4.1M
Radiomic and genomic predictors of breast cancer riskR01CA264987 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Vignesh A Arasu, Li Shen · 2021 to 2026
$3.5M
Genomic and Transcriptomic Analysis of Mammographic DensityR01CA237541 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI HABEL, LAUREL A, SIEH, WEIVA · 2020 to 2023
$2.0M
Integration of genetic, gene expression and environmental data to inform biological basis of mammographic densityR01CA244670 · NCI · UNIVERSITY OF WASHINGTON · PI LINDSTROEM, SARA · 2021 to 2024
$1.8M
Alternative splicing and isoform expression as mediators for the genetic etiology of breast cancerR21CA293419 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BHATTACHARYA, ARJUN, LINDSTROEM, SARA · 2024 to 2024
$407k
NCI NIH HHS R01 CA237541NCI NIH HHS R01 CA244670NCI NIH HHS R01 CA244948NCI NIH HHS R01 CA264987NCI NIH HHS R21 CA293419NCI NIH HHS U19 CA148065NCI NIH HHS U24 CA210993NCI NIH HHS U24 CA271114
6 · The paper itself

Abstract

Background: Mammographic density (MD) phenotypes are highly heritable and strongly associated with breast cancer risk. Genetic variants identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability, and the responsible genes remain largely unknown. Transcriptome-wide association studies (TWAS) can improve power and identify genes associated with MD through their genetically regulated gene expression (GReX) levels. However, cell-type heterogeneity in bulk tissue samples can obscure disease associations. Here, we conduct TWAS of MD phenotypes using standard approaches and a new cell-type-aware framework. Methods: The study population included 24,158 women of European ancestry who underwent screening with Hologic (n=20,282) or GE (n=3,876) digital mammography and participated in Kaiser's Research Program on Genes Environment and Health. Dense area (DA), nondense area (NDA), and percent density (PD) were measured centrally using Cumulus6. Tissue-level gene expression was estimated using standard elastic-net models. Cell-type-specific expression in mammary epithelial, fibroblast, and adipocyte cells were estimated using MiXcan2. Linear regression was used to assess associations of GReX levels with MD phenotypes, adjusted for age at mammography, BMI, and other covariates. Statistical significance was determined by controlling the false-discovery rate at 0.05. Results: A total of 20 genes at 16 independent loci were significantly associated with MD phenotypes, including 6 novel genes at 6 independent loci not found by prior GWAS or TWAS of MD phenotypes. We discovered that one of the novel MD genes, Conclusion: This TWAS identified novel genes for MD phenotypes and breast cancer risk, and prioritized genes at known GWAS loci that are likely to be causally associated through their expression levels in mammary epithelial, fibroblast, or adipocyte cells. Disentangling the distinct effects of gene expression in different mammary cell types through cell-type-aware analysis can yield new gene discoveries and insights into the biological basis of dense vs. nondense breast tissue.

Identifiers

PMID41358319
PMCPMC12676395

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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.