Anna Preece

1.3k total citations
8 papers, 986 citations indexed

About

Anna Preece is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Anna Preece has authored 8 papers receiving a total of 986 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 4 papers in Genetics and 3 papers in Cellular and Molecular Neuroscience. Recurrent topics in Anna Preece's work include Genetic Associations and Epidemiology (4 papers), Peroxisome Proliferator-Activated Receptors (2 papers) and Receptor Mechanisms and Signaling (2 papers). Anna Preece is often cited by papers focused on Genetic Associations and Epidemiology (4 papers), Peroxisome Proliferator-Activated Receptors (2 papers) and Receptor Mechanisms and Signaling (2 papers). Anna Preece collaborates with scholars based in United Kingdom, Ireland and Australia. Anna Preece's co-authors include Michael O’Donovan, Nigel Williams, Michael J. Owen, Nadine Norton, Stanley Zammit, Hywel Williams, Valentina Moskvina, Gillian Spurlock, Nicholas J. Bray and Paul R. Buckland and has published in prestigious journals such as Biological Psychiatry, Human Molecular Genetics and Molecular Psychiatry.

In The Last Decade

Anna Preece

8 papers receiving 954 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anna Preece United Kingdom 8 501 445 280 172 121 8 986
Virginia K. Lasseter United States 18 827 1.7× 956 2.1× 305 1.1× 198 1.2× 82 0.7× 23 1.7k
Tatsuyuki Muratake Japan 15 343 0.7× 211 0.5× 294 1.1× 185 1.1× 50 0.4× 34 828
Marc P. Forrest United States 17 676 1.3× 465 1.0× 288 1.0× 186 1.1× 34 0.3× 28 1.1k
Lyudmila Georgieva United Kingdom 10 516 1.0× 670 1.5× 122 0.4× 131 0.8× 29 0.2× 15 1.0k
Ahmed El-Kordi Germany 11 343 0.7× 266 0.6× 299 1.1× 283 1.6× 64 0.5× 12 970
Itaru Kushima Japan 17 456 0.9× 314 0.7× 179 0.6× 229 1.3× 17 0.1× 81 899
Satomi Umeda‐Yano Japan 18 256 0.5× 251 0.6× 139 0.5× 157 0.9× 30 0.2× 23 682
Benjamin McClintock United States 4 247 0.5× 133 0.3× 316 1.1× 138 0.8× 32 0.3× 6 608
Viktoriya D. Nikolova United States 17 613 1.2× 242 0.5× 210 0.8× 170 1.0× 112 0.9× 29 1.1k
Nicole Lewandowski United States 8 345 0.7× 73 0.2× 340 1.2× 195 1.1× 53 0.4× 10 744

Countries citing papers authored by Anna Preece

Since Specialization
Citations

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

Fields of papers citing papers by Anna Preece

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anna Preece. 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 Anna Preece. The network helps show where Anna Preece may publish in the future.

Co-authorship network of co-authors of Anna Preece

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

All Works

8 of 8 papers shown
1.
Peirce, T, Nicholas J. Bray, Nigel Williams, et al.. (2006). Convergent Evidence for 2′,3′-Cyclic Nucleotide 3′-Phosphodiesterase as a Possible Susceptibility Gene for Schizophrenia. Archives of General Psychiatry. 63(1). 18–18. 95 indexed citations
2.
Norton, Nadine, Hywel Williams, Sarah Dwyer, et al.. (2005). No evidence for association between polymorphisms in GRM3and schizophrenia. BMC Psychiatry. 5(1). 23–23. 43 indexed citations
3.
Bray, Nicholas J., Anna Preece, Nigel Williams, et al.. (2005). Haplotypes at the dystrobrevin binding protein 1 (DTNBP1) gene locus mediate risk for schizophrenia through reduced DTNBP1 expression. Human Molecular Genetics. 14(14). 1947–1954. 146 indexed citations
4.
Norton, Nadine, Valentina Moskvina, Derek W. Morris, et al.. (2005). Evidence that interaction between neuregulin 1 and its receptor erbB4 increases susceptibility to schizophrenia. American Journal of Medical Genetics Part B Neuropsychiatric Genetics. 141B(1). 96–101. 140 indexed citations
5.
Kirov, George, Dobril Ivanov, Nigel Williams, et al.. (2004). Strong evidence for association between the dystrobrevin binding protein 1 gene (DTNBP1) and schizophrenia in 488 parent-offspring trios from Bulgaria. Biological Psychiatry. 55(10). 971–975. 122 indexed citations
6.
Williams, Nigel, Anna Preece, Gillian Spurlock, et al.. (2004). Support for RGS4 as a susceptibility gene for schizophrenia. Biological Psychiatry. 55(2). 192–195. 103 indexed citations
7.
Williams, Nigel, Anna Preece, Derek W. Morris, et al.. (2004). Identification in 2 Independent Samples of a Novel Schizophrenia RiskHaplotype of the Dystrobrevin Binding Protein Gene (DTNBP1). Archives of General Psychiatry. 61(4). 336–336. 156 indexed citations
8.
Williams, Nigel, Anna Preece, Gillian Spurlock, et al.. (2003). Support for genetic variation in neuregulin 1 and susceptibility to schizophrenia. Molecular Psychiatry. 8(5). 485–487. 181 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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