James M. Reecy

15.5k total citations · 1 hit paper
186 papers, 6.7k citations indexed

About

James M. Reecy is a scholar working on Genetics, Molecular Biology and Animal Science and Zoology. According to data from OpenAlex, James M. Reecy has authored 186 papers receiving a total of 6.7k indexed citations (citations by other indexed papers that have themselves been cited), including 107 papers in Genetics, 81 papers in Molecular Biology and 49 papers in Animal Science and Zoology. Recurrent topics in James M. Reecy's work include Genetic and phenotypic traits in livestock (76 papers), Genetic Mapping and Diversity in Plants and Animals (54 papers) and Meat and Animal Product Quality (21 papers). James M. Reecy is often cited by papers focused on Genetic and phenotypic traits in livestock (76 papers), Genetic Mapping and Diversity in Plants and Animals (54 papers) and Meat and Animal Product Quality (21 papers). James M. Reecy collaborates with scholars based in United States, Brazil and New Zealand. James M. Reecy's co-authors include Zhi‐Liang Hu, Carissa A. Park, Dorian J. Garrick, Richard G. Tait, Xiaolin Wu, Eric Fritz, James E. Koltes, Robert J. Schwartz, Francesco J. DeMayo and Donald C. Beitz and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and SHILAP Revista de lepidopterología.

In The Last Decade

James M. Reecy

179 papers receiving 6.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Bringing the Animal QTLdb and CorrDB into the future: meeting new challenges and providing updated services

2021 221 citations Zhi‐Liang Hu, Carissa A. Park et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James M. Reecy United States	44	3.7k	2.2k	1.7k	1.1k	1.0k	186	6.7k
Graham Plastow Canada	47	4.4k 1.2×	1.8k 0.8×	3.4k 1.9×	782 0.7×	1.6k 1.6×	313	8.3k
Luiz Lehmann Coutinho Brazil	43	2.9k 0.8×	1.9k 0.9×	1.5k 0.9×	1.0k 0.9×	860 0.8×	308	5.9k
Klaus Wimmers Germany	39	3.2k 0.9×	2.0k 0.9×	2.1k 1.2×	892 0.8×	611 0.6×	397	6.8k
Alan Archibald United Kingdom	48	4.9k 1.3×	3.1k 1.4×	1.5k 0.9×	914 0.8×	671 0.6×	214	7.9k
Antônio Reverter Australia	51	5.0k 1.4×	2.3k 1.0×	1.7k 1.0×	1.2k 1.1×	1.7k 1.6×	261	8.0k
M. F. Rothschild United States	42	3.5k 0.9×	1.0k 0.5×	2.3k 1.3×	534 0.5×	614 0.6×	258	5.7k
Juan F. Medrano United States	44	3.7k 1.0×	3.1k 1.4×	488 0.3×	895 0.8×	1.3k 1.2×	217	7.6k
Shuhong Zhao China	40	2.2k 0.6×	2.9k 1.3×	686 0.4×	1.6k 1.4×	401 0.4×	279	5.9k
Zhihua Jiang United States	35	1.8k 0.5×	1.9k 0.8×	943 0.5×	795 0.7×	393 0.4×	203	4.9k
Ruedi Fries Germany	49	6.4k 1.7×	3.1k 1.4×	948 0.6×	886 0.8×	894 0.9×	232	9.2k

Countries citing papers authored by James M. Reecy

Since Specialization

Citations

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

Fields of papers citing papers by James M. Reecy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James M. Reecy. 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 James M. Reecy. The network helps show where James M. Reecy may publish in the future.

Co-authorship network of co-authors of James M. Reecy

This figure shows the co-authorship network connecting the top 25 collaborators of James M. Reecy. A scholar is included among the top collaborators of James M. Reecy 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 James M. Reecy. James M. Reecy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Almeida, Vivian Vezzoni de, Heidge Fukumasu, Gabriel Costa Monteiro Moreira, et al.. (2024). Different oil sources impacting brain lipid and transcriptome profiles of pigs. Livestock Science. 284. 105490–105490. 1 indexed citations

Hu, Zhi‐Liang, Carissa A. Park, & James M. Reecy. (2023). A combinatorial approach implementing new database structures to facilitate practical data curation management of QTL, association, correlation and heritability data on trait variants. Database. 2023. 5 indexed citations

Santana, Miguel Henrique de Almeida, Vivian Vezzoni de Almeida, Gabriel Costa Monteiro Moreira, et al.. (2022). Dietary fatty acids applied to pig production and their relation to the biological processes: A review. Livestock Science. 265. 105092–105092. 13 indexed citations

Andrade, Bruno Gabriel Nascimento, Rafael R. C. Cuadrat, Tainã Figueiredo Cardoso, et al.. (2022). Stool and Ruminal Microbiome Components Associated With Methane Emission and Feed Efficiency in Nelore Beef Cattle. Frontiers in Genetics. 13. 812828–812828. 21 indexed citations

Moreira, Gabriel Costa Monteiro, Clarissa Boschiero, Aline Silva Mello César, et al.. (2019). Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens. BMC Genomics. 20(1). 669–669. 23 indexed citations

Moreira, Gabriel Costa Monteiro, Clarissa Boschiero, Aline Silva Mello César, et al.. (2018). Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken. Scientific Reports. 8(1). 16222–16222. 42 indexed citations

Oliveira, Priscila Silva Neubern de, Luiz Lehmann Coutinho, P. C. Tizioto, et al.. (2018). An integrative transcriptome analysis indicates regulatory mRNA-miRNA networks for residual feed intake in Nelore cattle. Scientific Reports. 8(1). 17072–17072. 35 indexed citations

César, Aline Silva Mello, Luciana Correia de Almeida Regitano, James M. Reecy, et al.. (2018). Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits. BMC Genomics. 19(1). 499–499. 42 indexed citations

Dong, Qian, Jack C. M. Dekkers, Raymond R. R. Rowland, et al.. (2018). The Effects of Vaccination and WUR Genotype on Blood Gene Expression Response to Co-infection with PRRSV and PCV2 in Pigs. Proceedings of the World Congress on Genetics Applied to Livestock Production. 993. 1 indexed citations

10.

Hu, Zhi‐Liang, et al.. (2018). Development of Animal QTLdb and CorrDB: Resynthesizing Big Data to Improve Meta-analysis of Genetic and Genomic Information. Proceedings of the World Congress on Genetics Applied to Livestock Production. 954. 2 indexed citations

11.

Oliveira, Priscila Silva Neubern de, Aline Silva Mello César, Michele Lopes do Nascimento, et al.. (2014). Positional candidate genes for residual intake and gain in Nelore beef cattle. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 555. 2 indexed citations

12.

César, Aline Silva Mello, Luciana Correia de Almeida Regitano, James E. Koltes, et al.. (2014). RNA sequencing analysis identifies retinoic acid pathway genes as differentially expressed in animals with extreme intramuscular fat GEBVs in Nellore steers. Proceedings of the World Congress on Genetics Applied to Livestock Production. 596. 1 indexed citations

13.

Peters, Sunday O., Kadir Kızılkaya, Dorian J. Garrick, et al.. (2014). Genome-wide Association Study of First Service Conception Rate in Brangus Heifers using Probit, Robit and Logit models. Proceedings of the World Congress on Genetics Applied to Livestock Production. 643. 1 indexed citations

14.

Nafikov, Rafael A., J. P. Schoonmaker, Dorian J. Garrick, et al.. (2013). Association of polymorphisms in solute carrier family 27, isoform A6 (SLC27A6) and fatty acid-binding protein-3 and fatty acid-binding protein-4 (FABP3 and FABP4) with fatty acid composition of bovine milk. Journal of Dairy Science. 96(9). 6007–6021. 48 indexed citations

15.

Koltes, James E., Bishnu Prasad Mishra, Dinesh Kumar, et al.. (2009). A nonsense mutation in cGMP-dependent type II protein kinase ( PRKG2 ) causes dwarfism in American Angus cattle. Proceedings of the National Academy of Sciences. 106(46). 19250–19255. 39 indexed citations

16.

White, James P., James M. Reecy, Tyrone A. Washington, et al.. (2009). Overload‐induced skeletal muscle extracellular matrix remodelling and myofibre growth in mice lacking IL‐6. Acta Physiologica. 197(4). 321–332. 45 indexed citations

17.

Knight, Travis J., et al.. (2008). DNA polymorphisms in bovine fatty acid synthase are associated with beef fatty acid composition¹. Animal Genetics. 39(1). 62–70. 115 indexed citations

18.

Hu, Zhi‐Liang & James M. Reecy. (2007). Animal QTLdb: beyond a repository. Mammalian Genome. 18(1). 1–4. 70 indexed citations

19.

Weaber, Robert L, E. J. Pollak, Dorian J. Garrick, et al.. (2006). From research to application: a model for educating beef producers in animal breeding technologies.. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006. 1 indexed citations

20.

Reecy, James M., et al.. (2003). Recent Advances That Impact Skeletal Muscle Growth and Development Research. Journal of Animal Science. 81. 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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