Evidence map›Paper›PMID 31321690›Full record

ArticleTransgenic research2019

Application of genome editing in farm animals: cattle.

Alison L Van Eenennaam

Abstract read
PubMed Publisher
In one paragraph

Article in Transgenic research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–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

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Article
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  13. Recent advances in the genomic resources for sheep.Mammalian genome : official journal of the International Mammalian Genome Society · 2023
    Review
  14. Perspectives in Genome-Editing Techniques for Livestock.Animals : an open access journal from MDPI · 2023
    Review
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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

1 author.

Alison L Van EenennaamUniversity of California, Davis, USA. alvaneenennaam@ucdavis.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Milk and meat from cattle and buffaloes contribute 45% of the global animal protein supply, followed by chickens (31%), and pigs (20%). In 2016, the global cattle population of 1.0 billion head produced 6.5 billion tons of cows' milk, and 66 million tons of beef. In the past century, cattle breeding programs have greatly increased the yield per animal with a resultant decrease in the emissions intensity per unit of milk or beef, but this has not been true in all regions. Genome editing research in cattle to date has focused on disease resistance (e.g. tuberculosis), production (e.g. myostatin knockout; production of all-male offspring), elimination of allergens (e.g. beta-lactoglobulin knockout) and welfare (e.g. polled or hornlessness) traits. Modeling has revealed how the use of genome editing to introduce beneficial alleles into cattle breeds could maintain or even accelerate the rate of genetic gain accomplished by conventional breeding programs, and is a superior approach to the lengthy process of introgressing those same alleles from distant breeds. Genome editing could be used to precisely introduce useful alleles (e.g. heat tolerance, disease resistance) and haplotypes into native locally-adapted cattle breeds, thereby helping to improve their productivity. As with earlier genetic engineering approaches, whether breeders will be able to employ genome editing in cattle genetic improvement programs will very much depend upon global decisions around the regulatory framework and governance of genome editing for food animals.

Indexed as

BreedingGenetic EngineeringAllelesAnimalsCattleChickensDisease ResistanceFemaleGene EditingHaplotypesHumansMalePhenotypeSwine

Identifiers

What OpenQuestion holds

Textmetadata
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.