Evidence map›Paper›PMID 33957961›Full record

ArticleGenome biology2021

Tejaas: reverse regression increases power for detecting trans-eQTLs.

Saikat Banerjee, Franco L Simonetti, Kira E Detrois, Anubhav Kaphle, Raktim Mitra, Rahul Nagial, Johannes Söding

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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

7 authors.

Saikat Banerjee *Quantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany. bnrj.saikat@gmail.com.
Franco L SimonettiQuantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany.
Kira E DetroisQuantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany.
Anubhav KaphleQuantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany.
Raktim MitraIndian Institute of Technology, Kanpur, India.
Rahul NagialIndian Institute of Technology, Kanpur, India.
Johannes SödingQuantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, Göttingen, 37077, Germany. soeding@mpibpc.mpg.de.ORCID http://orcid.org/0000-0001-9642-8244

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Trans-acting expression quantitative trait loci (trans-eQTLs) account for ≥70% expression heritability and could therefore facilitate uncovering mechanisms underlying the origination of complex diseases. Identifying trans-eQTLs is challenging because of small effect sizes, tissue specificity, and a severe multiple-testing burden. Tejaas predicts trans-eQTLs by performing L2-regularized "reverse" multiple regression of each SNP on all genes, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Combined with a novel unsupervised k-nearest neighbor method to remove confounders, Tejaas predicts 18851 unique trans-eQTLs across 49 tissues from GTEx. They are enriched in open chromatin, enhancers, and other regulatory regions. Many overlap with disease-associated SNPs, pointing to tissue-specific transcriptional regulation mechanisms.

Indexed as

SoftwareChromatinComputer SimulationGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypePolymorphism, Single NucleotideQuantitative Trait LociRegression AnalysisRegulatory Sequences, Nucleic AcidRisk FactorsChromatin

Identifiers

PMID33957961
PMCPMC8101255

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.