A Bayesian Model for Paired Data in Genome-Wide Association Studies with Application to Breast Cancer.

Yashi Bu, Min Chen, Zhenyu Xuan, Xinlei Wang

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 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

4 authors.

Yashi BuDepartment of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.ORCID 0000-0001-7024-405X

Min ChenDepartment of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.ORCID 0000-0002-0676-3143

Zhenyu XuanDepartment of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080, USA.ORCID 0000-0001-9344-8493

Xinlei WangDepartment of Mathematics, University of Texas at Arlington, Arlington, TX 76019, USA.ORCID 0000-0002-8561-6511

Funding

Statistical and Deep Generative Modeling for Enhanced CyTOF Data Interpretation and DiscoveryR01GM160515 · NIGMS · UNIVERSITY OF TEXAS ARLINGTON · PI WANG, XINLEI · 2025 to 2025

$1.3M

DMS/NIGMS 2: The mathematics of epigenetic regulation in human cellsR01GM157608 · NIGMS · UNIVERSITY OF TEXAS DALLAS · PI Leonidas Bleris · 2024 to 2026

$824k

NIGMS NIH HHS R01 GM157608NIGMS NIH HHS R01 GM160515

6 · The paper itself

Abstract

Complex human diseases, including cancer, are linked to genetic factors. Genome-wide association studies (GWASs) are powerful for identifying genetic variants associated with cancer but are limited by their reliance on case-control data. We propose approaches to expanding GWAS by using tumor and paired normal tissues to investigate somatic mutations. We apply penalized maximum likelihood estimation for single-marker analysis and develop a Bayesian hierarchical model to integrate multiple markers, identifying SNP sets grouped by genes or pathways, improving detection of moderate-effect SNPs. Applied to breast cancer data from The Cancer Genome Atlas (TCGA), both single- and multiple-marker analyses identify associated genes, with multiple-marker analysis providing more consistent results with external resources. The Bayesian model significantly increases the chance of new discoveries.

Indexed as

single nucleotide polymorphismsomatic mutationTCGA

Identifiers

PMID41149035

DOI10.3390/e27101077

PMCPMC12562651

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.