Evidence map›Paper›PMID 36266475›Full record

ReviewCommunications biology2022

Contribution of CRISPRable DNA to human complex traits.

Ranran Zhai, Chenqing Zheng, Zhijian Yang, Ting Li, Jiantao Chen, Xia Shen

Open access · goldAbstract readReview
In one paragraph

Review in Communications biology, 2022. 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
0.3field-weighted citation impact, top 47% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 1 institution in 2 countries.

Ranran ZhaiBiostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-5834-9120
Chenqing ZhengBiostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-4566-7947
Zhijian YangBiostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0003-4803-8633
Ting LiBiostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-9614-1566
Jiantao ChenBiostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0002-7214-2257
Xia ShenBiostatistics Group, School of Life Sciences, Sun Yat-sen University, Guangzhou, China. shenx@fudan.edu.cn.ORCID 0000-0003-4390-1979
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CRISPR-Cas is a powerful genome editing tool for various species and human cell lines, widely used in many research areas including studying the mechanisms, targets, and gene therapies of human diseases. Recent developments have even allowed high-throughput genetic screening using the CRISPR system. However, due to the practical and ethical limitations in human gene editing research, little is known about whether CRISPR-editable DNA segments could influence human complex traits or diseases. Here, we investigated the human genomic regions condensed with different CRISPR Cas enzymes' protospacer-adjacent motifs (PAMs). We found that Cas enzymes with GC-rich PAMs could interfere more with the genomic regions that harbor enriched heritability for human complex traits and diseases. The results linked GC content across the genome to the functional genomic elements in the heritability enrichment of human complex traits. We provide a genetic overview of the effects of high-throughput genome editing on human complex traits.

Indexed as

CRISPR-Cas SystemsMultifactorial InheritanceDNAGene EditingGenomicsHumansDNA

Identifiers

PMID36266475
PMCPMC9585070
OpenAlexW4306873956

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.