ArticleJDS communications2024
Commercial perspectives: Genome editing as a breeding tool for health and well-being in dairy cattle.
Article in JDS communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed.
- Non-Coding Negative Regulatory Features in Livestock Genomes: Functional Annotation and Causal Validation.Animals : an open access journal from MDPI · 2026Review
- Precision phenotyping of reproductive events in dairy cattle under heat stress: epidemiological validity and implications for genetic improvement in tropical systems.Tropical animal health and production · 2026Review
- Nutrient Utilization, Requirements and Nutrigenomics in Sheep and Goats.Animals : an open access journal from MDPI · 2026Review
- Selection index for genetic improvement of beef performance in Huaxi cattle of Xinjiang, China.BMC genomics · 2026Article
- Impact of mutations inFrontiers in endocrinology · 2026Article
- Genomics and phenomics: Who will be the dairy cows of the future?JDS communications · 2025Review
- The Evolution and Role of Molecular Tools in Measuring Diversity and Genomic Selection in Livestock Populations (Traditional and Up-to-Date Insights): A Comprehensive Exploration.Veterinary sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome editing is the latest breeding tool capable of accelerating the rate of genetic improvement for health and well-being traits in food animals. It enables the introduction of beneficial alleles within a single generation, including those that are of low frequency or absent in the population, while effectively bypassing linkage drag. For the dairy industry, genome editing can be used to make rapid genetic improvements that are precise, efficient, and transgene-free for functional traits that are not practically addressed without disrupting conventional breeding goals for overall economic merit based on genomic selection. Herein, various case studies for dairy cattle breeding are presented that demonstrate applications of genome editing for enhancing heat stress tolerance, reduced disease susceptibility, and other qualitative traits absent in some breeds. One case highlights the success of simultaneous editing of multiple loci through recent advancements in embryonic stem cell biology. Multiplexed editing is crucial for addressing the polygenic nature inherent to many economically important traits in livestock. However, maximizing the benefits of genome editing depends on the continued discovery of targets for editing that are commercially important. Commercialization also depends on rapidly evolving regulatory statutes for risk assessment, where some countries already permit the commercialization of cattle with non-GMO genome alterations through existing regulations. New breeding technologies such as genome editing are now poised to have significant impact in equipping elite performance cattle to be more resilient to infectious disease and climate change without the loss of production gains obtained from decades of selection.
Identifiers
What OpenQuestion holds
Registered trials
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.