Evidence map›Paper›PMID 32243438›Full record

ArticlePLoS genetics2020

Loss-of-function tolerance of enhancers in the human genome.

Duo Xu, Omer Gokcumen, Ekta Khurana

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 26% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
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

3 authors at 2 institutions in 1 country.

Duo XuInstitute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, United States of America.
Omer GokcumenDepartment of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, New York, United States of America.ORCID 0000-0003-4371-679X
Ekta KhuranaInstitute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, United States of America.
NewYork–Presbyterian Hospital · USUniversity at Buffalo, State University of New York · US

Funding

Predicting the functional impact of non-coding mutations using patient-specific gene regulatory networksR01CA218668 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI EKTA KHURANA, Neville Sanjana · 2018 to 2026
$4.7M
NCI NIH HHS R01 CA218668
6 · The paper itself

Abstract

Previous studies have surveyed the potential impact of loss-of-function (LoF) variants and identified LoF-tolerant protein-coding genes. However, the tolerance of human genomes to losing enhancers has not yet been evaluated. Here we present the catalog of LoF-tolerant enhancers using structural variants from whole-genome sequences. Using a conservative approach, we estimate that individual human genomes possess at least 28 LoF-tolerant enhancers on average. We assessed the properties of LoF-tolerant enhancers in a unified regulatory network constructed by integrating tissue-specific enhancers and gene-gene interactions. We find that LoF-tolerant enhancers tend to be more tissue-specific and regulate fewer and more dispensable genes relative to other enhancers. They are enriched in immune-related cells while enhancers with low LoF-tolerance are enriched in kidney and brain/neuronal stem cells. We developed a supervised learning approach to predict the LoF-tolerance of all enhancers, which achieved an area under the receiver operating characteristics curve (AUROC) of 98%. We predict 3,519 more enhancers would be likely tolerant to LoF and 129 enhancers that would have low LoF-tolerance. Our predictions are supported by a known set of disease enhancers and novel deletions from PacBio sequencing. The LoF-tolerance scores provided here will serve as an important reference for disease studies.

Indexed as

Loss of Function MutationConserved SequenceDiseaseEnhancer Elements, GeneticGene Expression RegulationGenetic Predisposition to DiseaseGenome, HumanHumansOrgan SpecificityReproducibility of ResultsROC CurveSupervised Machine Learning

Identifiers

PMID32243438
PMCPMC7159235
OpenAlexW3014306853

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.