Evidence map›Paper›PMID 40734899›Full record

ArticleBioinformatics advances2025

EpipwR: efficient power analysis for EWAS with continuous outcomes.

Jackson Barth, Austin W Reynolds

Abstract read
In one paragraph

Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

2 authors.

Jackson BarthDepartment of Statistical Science, Baylor University, Waco, TX 76798, United States.ORCID https://orcid.org/0009-0009-6307-9928
Austin W ReynoldsDepartment of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Epigenome-wide association studies (EWAS) have emerged as a popular way to investigate the pathophysiology of complex diseases and to assist in bridging the gap between genotypes and phenotypes. Despite the increasing popularity of EWAS, very few tools exist to aid researchers in power estimation and those are limited to case-control studies. The existence of user-friendly tools, expanding power calculation functionality to additional study designs, would be a significant aid to researchers planning EWAS. Results: We introduce EpipwR, an open-source R-package that can efficiently estimate power for EWAS with continuous or binary outcomes. EpipwR uses a quasi-simulated approach, meaning that data is generated only for CpG sites with methylation associated with the outcome, while Availability and implementation: The EpipwR R-package is currently available on Bioconductor or at github.com/jbarth216/EpipwR.

Identifiers

PMID40734899
PMCPMC12303865

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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.