Evidence map›Paper›PMID 34256818›Full record

ArticleGenome biology2021

2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies.

Sangyoon Yi, Xianyang Zhang, Lu Yang, Jinyan Huang, Yuanhang Liu, Chen Wang, Daniel J Schaid, Jun Chen

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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

8 authors.

Sangyoon YiDepartment of Statistics, Texas A&M University, College Station, TX, 77843, USA.
Xianyang ZhangDepartment of Statistics, Texas A&M University, College Station, TX, 77843, USA. zhangxiany@stat.tamu.edu.
Lu YangDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
Jinyan HuangState Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Rui-Jin Hospital, Shanghai Jiao Tong University, Shanghai, 200025, China.
Yuanhang LiuDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
Chen WangDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
Daniel J SchaidDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
Jun ChenDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA. chen.jun2@mayo.edu.

Funding

Methods for Analysis of Genomic Data with Auxiliary InformationR21HG011662 · NHGRI · MAYO CLINIC ROCHESTER · PI CHEN, JUN, ZHANG, XIANYANG · 2021 to 2022
$434k
NHGRI NIH HHS R21 HG011662
6 · The paper itself

Abstract

One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

Indexed as

AlgorithmsGenome-Wide Association StudyAtlases as TopicCarcinoma, HepatocellularDatasets as TopicDNA MethylationFalse Positive ReactionsGastrointestinal MicrobiomeGenomicsHepatitis BHepatitis B virusHumansLinear ModelsLiver NeoplasmsAssociation testingConfoundingFalse discovery rate

Identifiers

PMID34256818
PMCPMC8276451

What OpenQuestion holds

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LicenceCC BY
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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.