Evidence map›Paper›PMID 32908596›Full record

ArticleEvolutionary applications2020

Population-level multiplexing: A promising strategy to manage the evolution of resistance against gene drives targeting a neutral locus.

Matthew P Edgington, Tim Harvey-Samuel, Luke Alphey

Open access · goldAbstract read
In one paragraph

Article in Evolutionary applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

12 citing papers in PubMed, 23 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Matthew P EdgingtonThe Pirbright Institute Woking UK.ORCID https://orcid.org/0000-0003-1922-9529
Tim Harvey-SamuelThe Pirbright Institute Woking UK.
Luke AlpheyThe Pirbright Institute Woking UK.
The Pirbright Institute · GB

Funding

Biotechnology and Biological Sciences Research Council BBS/E/I/00007038
6 · The paper itself

Abstract

CRISPR-based gene drives bias inheritance in their favour by inducing double-stranded breaks (DSBs) at wild-type homologous loci and using the drive transgene as a repair template-converting drive heterozygotes into homozygotes. Recent studies have shown that alternate end-joining repair mechanisms produce cut-resistant alleles that rapidly induce drive failure. Multiplexing-simultaneously targeting multiple sites at the wild-type locus-is commonly assumed to overcome this issue since resistance would need to develop at all target sites for the system to fail. This may work for some population suppression drives targeting essential (e.g. viability or fertility) genes if careful design can ensure cut-resistant alleles themselves have low fitness. However, here, models are used to demonstrate that this approach will be ineffective when targeting neutral loci. We then go on to compare the performance of four alternative population-level multiplexing approaches with standard individual-level multiplexing. Two of these approaches have mechanisms preventing them from becoming linked, thus avoiding multiple simultaneous DSBs and giving a large improvement. Releasing multiple unlinked drives gives a modest improvement, while releasing multiple drives that may become linked over time produces a decrease in performance under the conditions tested here. Based on performance and technical feasibility, we then take one approach forward for further investigation, demonstrating its robustness to different performance parameters and its potential for controlling very large target populations.

Indexed as

CRISPRgene drivegenetic engineeringmultiplexingpopulation alterationresistance

Identifiers

PMID32908596
PMCPMC7463328
OpenAlexW3010459581

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.