David G. Ashbrook

2.3k total citations
37 papers, 486 citations indexed

About

David G. Ashbrook is a scholar working on Molecular Biology, Genetics and Psychiatry and Mental health. According to data from OpenAlex, David G. Ashbrook has authored 37 papers receiving a total of 486 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 15 papers in Genetics and 6 papers in Psychiatry and Mental health. Recurrent topics in David G. Ashbrook's work include Genetic Mapping and Diversity in Plants and Animals (8 papers), Fibromyalgia and Chronic Fatigue Syndrome Research (5 papers) and Genetic Associations and Epidemiology (4 papers). David G. Ashbrook is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (8 papers), Fibromyalgia and Chronic Fatigue Syndrome Research (5 papers) and Genetic Associations and Epidemiology (4 papers). David G. Ashbrook collaborates with scholars based in United States, United Kingdom and China. David G. Ashbrook's co-authors include Reinmar Hager, Robert W. Williams, Lu Lu, Patrick O. McGowan, Wilfred C. de Vega, Megan K. Mulligan, Evan G. Williams, Beatrice Gini, James P. O’Callaghan and Suheeta Roy and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and SHILAP Revista de lepidopterología.

In The Last Decade

David G. Ashbrook

33 papers receiving 484 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David G. Ashbrook United States 14 165 125 84 70 47 37 486
Rebecca Birnbaum United States 8 243 1.5× 191 1.5× 132 1.6× 32 0.5× 27 0.6× 16 607
Oliver Pain United Kingdom 17 234 1.4× 353 2.8× 134 1.6× 56 0.8× 42 0.9× 40 756
Aslihan Dincer United States 9 270 1.6× 143 1.1× 38 0.5× 38 0.5× 55 1.2× 11 479
Stefan Busse Germany 16 129 0.8× 53 0.4× 110 1.3× 140 2.0× 41 0.9× 37 755
Douglas F. Levinson United States 12 237 1.4× 135 1.1× 195 2.3× 33 0.5× 40 0.9× 13 604
Liliana Laskaris Australia 9 96 0.6× 83 0.7× 91 1.1× 28 0.4× 36 0.8× 12 523
Akane Yoshikawa Japan 11 183 1.1× 110 0.9× 75 0.9× 36 0.5× 13 0.3× 30 399
Catherine Brégère Switzerland 13 199 1.2× 84 0.7× 36 0.4× 119 1.7× 65 1.4× 18 593
Luciana Le Sueur‐Maluf Brazil 12 102 0.6× 50 0.4× 25 0.3× 60 0.9× 33 0.7× 34 389
Yosuke Yamawaki Japan 12 314 1.9× 53 0.4× 41 0.5× 154 2.2× 47 1.0× 31 681

Countries citing papers authored by David G. Ashbrook

Since Specialization
Citations

This map shows the geographic impact of David G. Ashbrook's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David G. Ashbrook with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David G. Ashbrook more than expected).

Fields of papers citing papers by David G. Ashbrook

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David G. Ashbrook. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David G. Ashbrook. The network helps show where David G. Ashbrook may publish in the future.

Co-authorship network of co-authors of David G. Ashbrook

This figure shows the co-authorship network connecting the top 25 collaborators of David G. Ashbrook. A scholar is included among the top collaborators of David G. Ashbrook based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David G. Ashbrook. David G. Ashbrook is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zuo, Yanning, Alexander S. Hatoum, Price E. Dickson, et al.. (2025). Acute opioid responses are modulated by dynamic interactions of Oprm1 and Fgf12. eLife.
3.
Zuo, Yanning, Alexander S. Hatoum, Price E. Dickson, et al.. (2025). Acute opioid responses are modulated by dynamic interactions of Oprm1 and Fgf12. eLife.
4.
O’Callaghan, James P., et al.. (2025). Epigenetic study of the long-term effects of gulf War illness. Frontiers in Genetics. 16. 1553410–1553410.
5.
Johnson, G. Allan, David G. Ashbrook, Gary P. Cofer, et al.. (2023). Merged magnetic resonance and light sheet microscopy of the whole mouse brain. Proceedings of the National Academy of Sciences. 120(17). e2218617120–e2218617120. 25 indexed citations
6.
Maksimov, Mikhail O., David G. Ashbrook, Flavia Villani, et al.. (2023). A novel quantitative trait locus implicates Msh3 in the propensity for genome-wide short tandem repeat expansions in mice. Genome Research. 33(5). 689–702. 4 indexed citations
7.
Bajpai, Akhilesh Kumar, Yufeng Chen, David G. Ashbrook, et al.. (2023). Expression Levels of the Tnni3k Gene in the Heart Are Highly Associated with Cardiac and Glucose Metabolism-Related Phenotypes and Functional Pathways. International Journal of Molecular Sciences. 24(16). 12759–12759. 5 indexed citations
8.
Long, Jarukit E., Adrián Jinich, Kyu Y. Rhee, et al.. (2023). Genome-wide screen identifies host loci that modulate Mycobacterium tuberculosis fitness in immunodivergent mice. G3 Genes Genomes Genetics. 13(9). 4 indexed citations
9.
Sullivan, Kathryn, Lu Lu, David G. Ashbrook, et al.. (2023). Increased development of T-bet+CD11c+ B cells predisposes to lupus in females: Analysis in BXD2 mouse and genetic crosses. Clinical Immunology. 257. 109842–109842. 3 indexed citations
10.
Ashbrook, David G., et al.. (2022). New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork. Genes. 13(4). 614–614. 3 indexed citations
11.
Sasani, Thomas A., David G. Ashbrook, Annabel C. Beichman, et al.. (2022). A natural mutator allele shapes mutation spectrum variation in mice. Nature. 605(7910). 497–502. 27 indexed citations
12.
Dietrich, Paula, Shanta Alli, Megan K. Mulligan, et al.. (2021). Identification of cyclin D1 as a major modulator of 3-nitropropionic acid-induced striatal neurodegeneration. Neurobiology of Disease. 162. 105581–105581. 6 indexed citations
13.
Xu, Fuyi, Jun Gao, Silke Bergmann, et al.. (2021). Genetic Dissection of the Regulatory Mechanisms of Ace2 in the Infected Mouse Lung. Frontiers in Immunology. 11. 607314–607314. 14 indexed citations
14.
Sandoval-Sierra, José Vladimir, Evan G. Williams, David G. Ashbrook, et al.. (2020). Body weight and high‐fat diet are associated with epigenetic aging in female members of the BXD murine family. Aging Cell. 19(9). e13207–e13207. 20 indexed citations
15.
Ashbrook, David G., et al.. (2019). A Cross-Species Systems Genetics Analysis Links APBB1IP as a Candidate for Schizophrenia and Prepulse Inhibition. Frontiers in Behavioral Neuroscience. 13. 266–266. 8 indexed citations
16.
Ashbrook, David G., et al.. (2018). Offspring genetic effects on maternal care. Frontiers in Neuroendocrinology. 52. 195–205. 5 indexed citations
17.
Ashbrook, David G., Snigdha Roy, Tobias Riede, et al.. (2018). Born to Cry: A Genetic Dissection of Infant Vocalization. Frontiers in Behavioral Neuroscience. 12. 250–250. 17 indexed citations
18.
Ashbrook, David G., Benjamin Hing, Lindsay T. Michalovicz, et al.. (2018). Epigenetic impacts of stress priming of the neuroinflammatory response to sarin surrogate in mice: a model of Gulf War illness. Journal of Neuroinflammation. 15(1). 86–86. 43 indexed citations
19.
Ashbrook, David G., Megan K. Mulligan, & Robert W. Williams. (2017). Post‐genomic behavioral genetics: From revolution to routine. Genes Brain & Behavior. 17(3). e12441–e12441. 5 indexed citations
20.
Ashbrook, David G. & Reinmar Hager. (2013). Empirical testing of hypotheses about the evolution of genomic imprinting in mammals. Frontiers in Neuroanatomy. 7. 6–6. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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