Evidence map›Paper›PMID 40671098›Full record

ArticleGenome biology2025

ClipperQTL: ultrafast and powerful eGene identification method.

Heather J Zhou, Xinzhou Ge, Jingyi Jessica Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 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

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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
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Heather J ZhouDepartment of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Xinzhou GeDepartment of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
Jingyi Jessica LiDepartment of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA. lijy03@g.ucla.edu.

Funding

Statistical Methods for Elucidating Regulatory Mechanisms and Functional Impacts of Transcriptome Variation at Population and Single-Cell ScalesR35GM140888 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, JINGYI JESSICA · 2021 to 2025
$2.0M
Robust Identification and accurate quantification of RNA transcripts on a system wide scaleR01GM120507 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, JINGYI JESSICA · 2016 to 2021
$1.7M
iDISCOVER: Integrated Data Science Training in CardioVascular MedicineT32HL139450 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX, PING, PEIPEI · 2018 to 2022
$1.3M
Alfred P. Sloan Foundation Sloan Research FellowshipJohnson and Johnson WiSTEM2D AwardNational Science Foundation DBI-1846216National Science Foundation DGE-1829071NHLBI NIH HHS T32 HL139450NHLBI NIH HHS T32HL139450NIGMS NIH HHS R01 GM120507NIGMS NIH HHS R01GM120507NIGMS NIH HHS R35 GM140888NIGMS NIH HHS R35GM140888W. M. Keck Foundation UCLA David Geffen School of Medicine W.M. Keck Foundation Junior Faculty Award
6 · The paper itself

Abstract

A central task in expression quantitative trait locus analysis is to identify cis-eGenes, i.e., genes whose expression levels are regulated by at least one local genetic variant. Existing cis-eGene identification methods are either computationally expensive, requiring thousands of permutations per gene (FastQTL), or statistically underpowered (eigenMT and TreeQTL). We propose ClipperQTL, which requires only one permutation for data sets with large sample sizes (>450; ClipperQTL works on smaller data sets too). We show that ClipperQTL performs as well as FastQTL and runs up to 500 times faster. The R package ClipperQTL is available at https://github.com/heatherjzhou/ClipperQTL .

Indexed as

Quantitative Trait LociSoftwareHumans

Identifiers

PMID40671098
PMCPMC12265108

What OpenQuestion holds

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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 OpenQuestion graph.