Brendan J. Keating

15.5k total citations
102 papers, 2.8k citations indexed

About

Brendan J. Keating is a scholar working on Genetics, Molecular Biology and Transplantation. According to data from OpenAlex, Brendan J. Keating has authored 102 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Genetics, 29 papers in Molecular Biology and 25 papers in Transplantation. Recurrent topics in Brendan J. Keating's work include Renal Transplantation Outcomes and Treatments (25 papers), Genetic Associations and Epidemiology (22 papers) and Transplantation: Methods and Outcomes (11 papers). Brendan J. Keating is often cited by papers focused on Renal Transplantation Outcomes and Treatments (25 papers), Genetic Associations and Epidemiology (22 papers) and Transplantation: Methods and Outcomes (11 papers). Brendan J. Keating collaborates with scholars based in United States, United Kingdom and Netherlands. Brendan J. Keating's co-authors include Julian C. Knight, Dominic Kwiatkowski, Kirk A. Rockett, Håkon Håkonarson, Naveed Sattar, Yiran Guo, Aroon D. Hingorani, Folkert W. Asselbergs, Michael V. Holmes and Michael V. Holmes and has published in prestigious journals such as Circulation, Nature Medicine and Nature Genetics.

In The Last Decade

Brendan J. Keating

97 papers receiving 2.8k citations

Peers

Brendan J. Keating
Karin Boer Netherlands
Giulio Genovese United States
James A. McCormick United States
Victoria Álvarez Spain
Michael P. O’Donnell United States
Eric P. Smith United States
Leslie A. Lange United States
Sudha K. Iyengar United States
Adam Stevens United Kingdom
Sunil M. Kurian United States
Karin Boer Netherlands
Brendan J. Keating
Citations per year, relative to Brendan J. Keating Brendan J. Keating (= 1×) peers Karin Boer

Countries citing papers authored by Brendan J. Keating

Since Specialization
Citations

This map shows the geographic impact of Brendan J. Keating'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 Brendan J. Keating with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brendan J. Keating more than expected).

Fields of papers citing papers by Brendan J. Keating

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brendan J. Keating. 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 Brendan J. Keating. The network helps show where Brendan J. Keating may publish in the future.

Co-authorship network of co-authors of Brendan J. Keating

This figure shows the co-authorship network connecting the top 25 collaborators of Brendan J. Keating. A scholar is included among the top collaborators of Brendan J. Keating 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 Brendan J. Keating. Brendan J. Keating 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.
Vatte, Chittibabu, et al.. (2025). Circular RNAs in Cardiovascular Disease: Mechanisms, Biomarkers, and Therapeutic Frontiers. Biomolecules. 15(10). 1455–1455.
2.
Keating, Brendan J., et al.. (2025). Commentary: Molecular responses in pig heart to human xenotransplantation unveiled by longitudinal multi‐omic profiling. Clinical and Translational Medicine. 15(1). e70132–e70132. 1 indexed citations
3.
4.
Mattoo, Aprajita, Ian Jaffe, Brendan J. Keating, Robert A. Montgomery, & Massimo Mangiola. (2024). Improving long-term kidney allograft survival by rethinking HLA compatibility: from molecular matching to non-HLA genes. Frontiers in Genetics. 15. 1442018–1442018. 5 indexed citations
5.
Gao, Hui, Bao‐Li Loza, Sarah Gao, et al.. (2024). Assessing the Utility of a Genotype‐Guided Tacrolimus Equation in African American Kidney Transplant Recipients: A Single Institution Retrospective Study. The Journal of Clinical Pharmacology. 64(8). 944–952. 1 indexed citations
6.
Zanoni, Francesca, Y. Dana Neugut, Lili Liu, et al.. (2024). Genetic versus self-reported African ancestry of the recipient and neighborhood predictors of kidney transplantation outcomes in 2 multiethnic urban cohorts. American Journal of Transplantation. 24(6). 1003–1015. 1 indexed citations
7.
Shaked, Oren, Bao‐Li Loza, Kim M. Olthoff, et al.. (2024). Donor and recipient genetics: Implications for the development of posttransplant diabetes mellitus. American Journal of Transplantation. 24(10). 1794–1802. 2 indexed citations
8.
Gragert, Loren, Brendan J. Keating, Bonnie E. Lonze, et al.. (2024). Balancing equity and human leukocyte antigen matching in deceased-donor kidney allocation with eplet mismatch. American Journal of Transplantation. 25(6). 1226–1234. 3 indexed citations
9.
Zanoni, Francesca, et al.. (2024). Emerging role of genetics in kidney transplantation. Kidney International. 107(3). 424–433. 1 indexed citations
10.
Shaked, Abraham, Bao‐Li Loza, Elisabet Van Loon, et al.. (2022). Donor and recipient polygenic risk scores influence the risk of post-transplant diabetes. Nature Medicine. 28(5). 999–1005. 27 indexed citations
11.
Nguyen, Tam T. T. N., David P. Schladt, Danielle Berglund, et al.. (2020). Pharmacogenomics in kidney transplant recipients and potential for integration into practice. Journal of Clinical Pharmacy and Therapeutics. 45(6). 1457–1465. 6 indexed citations
12.
Zhang, Zhongyang, Madhav C. Menon, Weijia Zhang, et al.. (2020). Genome-wide non-HLA donor-recipient genetic differences influence renal allograft survival via early allograft fibrosis. Kidney International. 98(3). 758–768. 22 indexed citations
13.
Guo, Yuelong, Michael P. Busch, Mark Seielstad, et al.. (2018). Development and evaluation of a transfusion medicine genome wide genotyping array. Transfusion. 59(1). 101–111. 26 indexed citations
14.
Stapleton, Caragh P., Kelly A. Birdwell, Amy Jayne McKnight, et al.. (2018). Polygenic risk score as a determinant of risk of non-melanoma skin cancer in a European-descent renal transplant cohort. American Journal of Transplantation. 19(3). 801–810. 27 indexed citations
15.
Eichler, Florian, Jiankang Li, Yiran Guo, et al.. (2016). CSF1Rmosaicism in a family with hereditary diffuse leukoencephalopathy with spheroids. Brain. 139(6). 1666–1672. 53 indexed citations
16.
Menezes, Minal, Yiran Guo, Jianguo Zhang, et al.. (2015). Mutation in mitochondrial ribosomal protein S7 (MRPS7) causes congenital sensorineural deafness, progressive hepatic and renal failure and lactic acidemia. Human Molecular Genetics. 24(8). 2297–2307. 58 indexed citations
17.
Guo, Yiran, Jiankang Li, Hilda A. Pickett, et al.. (2014). Inherited bone marrow failure associated with germline mutation of ACD, the gene encoding telomere protein TPP1. Blood. 124(18). 2767–2774. 71 indexed citations
18.
Kullo, Iftikhar J., Raad A. Haddad, Cynthia A. Prows, et al.. (2014). Return of results in the genomic medicine projects of the eMERGE network. Frontiers in Genetics. 5. 50–50. 36 indexed citations
19.
Almoguera, Berta, Lyam Vazquez, John J. Connolly, et al.. (2014). Imputation of TPMT defective alleles for the identification of patients with high-risk phenotypes. Frontiers in Genetics. 5. 96–96. 12 indexed citations
20.
Zhao, Jianhua, Mingyao Li, Jonathan P. Bradfield, et al.. (2010). The role of height-associated loci identified in genome wide association studies in the determination of pediatric stature. BMC Medical Genetics. 11(1). 96–96. 46 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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