John M. Hickey

8.5k total citations · 1 hit paper
96 papers, 5.1k citations indexed

About

John M. Hickey is a scholar working on Genetics, Plant Science and Molecular Biology. According to data from OpenAlex, John M. Hickey has authored 96 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Genetics, 49 papers in Plant Science and 9 papers in Molecular Biology. Recurrent topics in John M. Hickey's work include Genetic and phenotypic traits in livestock (73 papers), Genetic Mapping and Diversity in Plants and Animals (69 papers) and Genetics and Plant Breeding (41 papers). John M. Hickey is often cited by papers focused on Genetic and phenotypic traits in livestock (73 papers), Genetic Mapping and Diversity in Plants and Animals (69 papers) and Genetics and Plant Breeding (41 papers). John M. Hickey collaborates with scholars based in United Kingdom, Australia and Sweden. John M. Hickey's co-authors include Gregor Gorjanc, Gustavo de los Campos, Hans D. Daetwyler, Ricardo Pong‐Wong, M.P.L. Calus, R. Chris Gaynor, Matthew A. Cleveland, José Crossa, Raman Babu and Ian Mackay and has published in prestigious journals such as Nature Genetics, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

John M. Hickey

94 papers receiving 5.0k citations

Hit Papers

Whole-Genome Regression and Prediction Methods Applied to... 2012 2026 2016 2021 2012 200 400 600

Peers

John M. Hickey
Gregor Gorjanc United Kingdom
M. Ron Israel
Daniela Lourenço United States
P.J. Bowman Australia
Just Jensen Denmark
Bruce Tier Australia
Gregor Gorjanc United Kingdom
John M. Hickey
Citations per year, relative to John M. Hickey John M. Hickey (= 1×) peers Gregor Gorjanc

Countries citing papers authored by John M. Hickey

Since Specialization
Citations

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

Fields of papers citing papers by John M. Hickey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John M. Hickey

This figure shows the co-authorship network connecting the top 25 collaborators of John M. Hickey. A scholar is included among the top collaborators of John M. Hickey 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 John M. Hickey. John M. Hickey 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.
Djikeng, Appolinaire, Samuel E. Aggrey, Okeyo Mwai, et al.. (2025). The African Animal Breeding Network as a pathway towards genetic improvement of livestock. Nature Genetics. 57(3). 498–504. 1 indexed citations
2.
Jong, G. de, Owen Powell, Gregor Gorjanc, John M. Hickey, & R. Chris Gaynor. (2023). Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs. Crop Science. 63(6). 3338–3355. 7 indexed citations
3.
Obšteter, Jana, Justin Holl, John M. Hickey, & Gregor Gorjanc. (2021). AlphaPart—R implementation of the method for partitioning genetic trends. Genetics Selection Evolution. 53(1). 6 indexed citations
4.
Powell, Owen, Raphael Mrode, R. Chris Gaynor, et al.. (2021). Genomic evaluations using data recorded on smallholder dairy farms in low- to middle-income countries. SHILAP Revista de lepidopterología. 2(6). 366–370. 8 indexed citations
5.
Gaynor, R. Chris, Gregor Gorjanc, & John M. Hickey. (2020). AlphaSimR: an R package for breeding program simulations. G3 Genes Genomes Genetics. 11(2). 127 indexed citations
6.
Whalen, Andrew, Gregor Gorjanc, & John M. Hickey. (2020). AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data. Bioinformatics. 36(15). 4369–4371. 12 indexed citations
7.
Fradgley, Nick, Keith A. Gardner, James Cockram, et al.. (2019). A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders. PLoS Biology. 17(2). e3000071–e3000071. 55 indexed citations
8.
Friedrich, Juliane, E. Strandberg, Enrique Sánchez-Molano, et al.. (2019). Genetic dissection of complex behaviour traits in German Shepherd dogs. Heredity. 123(6). 746–758. 19 indexed citations
9.
Johnsson, Martin, R. Chris Gaynor, Janez Jenko, et al.. (2019). Removal of alleles by genome editing (RAGE) against deleterious load. Genetics Selection Evolution. 51(1). 14–14. 39 indexed citations
10.
Gorjanc, Gregor & John M. Hickey. (2018). AlphaMate: a program for optimizing selection, maintenance of diversity and mate allocation in breeding programs. Bioinformatics. 34(19). 3408–3411. 33 indexed citations
11.
Whalen, Andrew, Gregor Gorjanc, & John M. Hickey. (2018). Parentage assignment with genotyping‐by‐sequencing data. Journal of Animal Breeding and Genetics. 136(2). 102–112. 23 indexed citations
12.
Gorjanc, Gregor, Mara Battagin, R. Chris Gaynor, et al.. (2018). A Strategy To Exploit Surrogate Sire Technology in Livestock Breeding Programs. G3 Genes Genomes Genetics. 9(1). 203–215. 23 indexed citations
13.
Jenko, Janez, Gregor Gorjanc, Matthew A. Cleveland, et al.. (2015). Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs. Genetics Selection Evolution. 47(1). 55–55. 103 indexed citations
14.
Pandey, Manish K., et al.. (2014). Selection of appropriate genomic selection model in an unstructured germplasm set of peanut (Arachis hypogaea L.). Murdoch Research Repository (Murdoch University). 1 indexed citations
15.
Jenko, Janez, Gregor Gorjanc, Gábor Mészáros, et al.. (2014). Use of Genome Editing in Animal Breeding Programs. Proceedings of the World Congress on Genetics Applied to Livestock Production. 17.
16.
Gorjanc, Gregor, John Woolliams, & John M. Hickey. (2014). Hierarchical Quantitative Genetic Model Using Genomic Information. Proceedings of the World Congress on Genetics Applied to Livestock Production. 68. 1 indexed citations
17.
Hickey, John M., Gregor Gorjanc, Matthew A. Cleveland, et al.. (2014). Sequencing Millions of Animals for Genomic Selection 2.0. Proceedings of the World Congress on Genetics Applied to Livestock Production. 377. 7 indexed citations
18.
Mészáros, Gábor, Gregor Gorjanc, Janez Jenko, John Woolliams, & John M. Hickey. (2014). Selection on Recombination Rate to Increase Genetic Gain. Proceedings of the World Congress on Genetics Applied to Livestock Production. 21. 2 indexed citations
19.
Clark, Sam, Brian Kinghorn, John M. Hickey, & J. H. J. van der Werf. (2013). The effect of genomic information on optimal contribution selection in livestock breeding programs. Genetics Selection Evolution. 45(1). 44–44. 62 indexed citations
20.
Gorjanc, Gregor, John M. Hickey, & Piter Bijma. (2012). Reliability of breeding values in selected populations.. Repository of the University of Ljubljana (University of Ljubljana). 143–146. 1 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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