Bryan J. Barratt

6.6k total citations · 2 hit papers
35 papers, 3.0k citations indexed

About

Bryan J. Barratt is a scholar working on Genetics, Surgery and Molecular Biology. According to data from OpenAlex, Bryan J. Barratt has authored 35 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Genetics, 15 papers in Surgery and 9 papers in Molecular Biology. Recurrent topics in Bryan J. Barratt's work include Genetic Associations and Epidemiology (11 papers), Lipoproteins and Cardiovascular Health (9 papers) and Diabetes and associated disorders (5 papers). Bryan J. Barratt is often cited by papers focused on Genetic Associations and Epidemiology (11 papers), Lipoproteins and Cardiovascular Health (9 papers) and Diabetes and associated disorders (5 papers). Bryan J. Barratt collaborates with scholars based in United Kingdom, Sweden and United States. Bryan J. Barratt's co-authors include David Clayton, John A. Todd, William Y.S. Wang, Heather J. Cordell, Richard C. Becker, Lars Wallentin, Robert F. Storey, Stefan James, Philippe Gabríel Steg and Hugo A. Katus and has published in prestigious journals such as The Lancet, Circulation and Journal of the American College of Cardiology.

In The Last Decade

Bryan J. Barratt

35 papers receiving 2.9k 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.

Genome-wide association studies: theoretical and practical concerns

2005 797 citations William Y.S. Wang, Bryan J. Barratt et al. Nature Reviews Genetics profile →
Effect of CYP2C19 and ABCB1 single nucleotide polymorphisms on outcomes of treatment with ticagrelor versus clopidogrel for acute coronary syndromes: a genetic substudy of the PLATO trial

2010 582 citations Lars Wallentin, Stefan James et al. The Lancet profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bryan J. Barratt United Kingdom	23	1.3k	826	754	743	423	35	3.0k
Chia‐Ti Tsai Taiwan	34	507 0.4×	1.3k 1.6×	593 0.8×	2.4k 3.2×	632 1.5×	177	4.2k
Andreas Gardemann Germany	32	167 0.1×	656 0.8×	504 0.7×	737 1.0×	488 1.2×	72	2.6k
Hiroshi Kajio Japan	25	557 0.4×	575 0.7×	753 1.0×	491 0.7×	1.0k 2.4×	140	2.5k
Norbert Katz Germany	35	168 0.1×	1.2k 1.5×	684 0.9×	541 0.7×	484 1.1×	72	3.3k
C. Hübert France	12	937 0.7×	1.0k 1.2×	339 0.4×	2.8k 3.7×	1.7k 4.1×	24	4.4k
Carl J. Vaughan Ireland	25	219 0.2×	622 0.8×	1.5k 2.0×	1.1k 1.4×	467 1.1×	49	3.5k
Aldi T. Kraja United States	28	592 0.5×	619 0.7×	166 0.2×	363 0.5×	380 0.9×	69	1.8k
James K. Burmester United States	23	374 0.3×	572 0.7×	190 0.3×	161 0.2×	260 0.6×	44	1.8k
Timo Koivula Finland	29	372 0.3×	657 0.8×	470 0.6×	348 0.5×	604 1.4×	88	3.4k
Ewa Ehrenborg Sweden	34	334 0.3×	1.5k 1.9×	450 0.6×	724 1.0×	932 2.2×	84	3.4k

Countries citing papers authored by Bryan J. Barratt

Since Specialization

Citations

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

Fields of papers citing papers by Bryan J. Barratt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bryan J. Barratt

This figure shows the co-authorship network connecting the top 25 collaborators of Bryan J. Barratt. A scholar is included among the top collaborators of Bryan J. Barratt 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 Bryan J. Barratt. Bryan J. Barratt 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

Johansson, Åsa, Niclas Eriksson, Daniel Lindholm, et al.. (2016). Genome-wide association and Mendelian randomization study of NT-proBNP in patients with acute coronary syndrome. Human Molecular Genetics. 25(7). 1447–1456. 40 indexed citations

Chu, Audrey Y., Franco Giulianini, Bryan J. Barratt, et al.. (2015). Differential Genetic Effects on Statin-Induced Changes Across Low-Density Lipoprotein–Related Measures. Circulation Cardiovascular Genetics. 8(5). 688–695. 4 indexed citations

Varenhorst, Christoph, Niclas Eriksson, Åsa Johansson, et al.. (2014). TICAGRELOR PLASMA LEVELS BUT NOT CLINICAL OUTCOMES ARE ASSOCIATED WITH TRANSPORTER AND METABOLISM ENZYME GENETIC POLYMORPHISMS. Journal of the American College of Cardiology. 63(12). A25–A25. 7 indexed citations

Åkerblom, Axel, Niclas Eriksson, Lars Wallentin, et al.. (2014). Polymorphism of the cystatin C gene in patients with acute coronary syndromes: Results from the PLATelet inhibition and patient Outcomes study. American Heart Journal. 168(1). 96–102.e2. 14 indexed citations

Hagström, Emil, Niclas Eriksson, Åsa Johansson, et al.. (2013). Are There Any Causal Relations Between Growth Differentiation Factor 15 and Outcomes in Patients With Acute Coronary Syndrome? : - A Report From the Plato Gwas Study. Circulation. 128(22). 2 indexed citations

Danik, Jacqueline Suk, Daniel I. Chasman, Jean MacFadyen, et al.. (2013). Lack of association between SLCO1B1 polymorphisms and clinical myalgia following rosuvastatin therapy. American Heart Journal. 165(6). 1008–1014. 73 indexed citations

Wallentin, Lars, Stefan James, Robert F. Storey, et al.. (2012). GREATER EFFICACY OF TICAGRELOR COMPARED TO CLOPIDOGREL IN ACUTE CORONARY SYNDROME IS NOT DRIVEN BY OUTCOMES IN POOR METABOLIZERS OF CLOPIDOGREL. Journal of the American College of Cardiology. 59(13). E500–E500. 1 indexed citations

Chasman, Daniel I., Franco Giulianini, Jean MacFadyen, et al.. (2012). Genetic Determinants of Statin-Induced Low-Density Lipoprotein Cholesterol Reduction. Circulation Cardiovascular Genetics. 5(2). 257–264. 208 indexed citations

Wallentin, Lars, Stefan James, Robert F. Storey, et al.. (2010). Effect of CYP2C19 and ABCB1 single nucleotide polymorphisms on outcomes of treatment with ticagrelor versus clopidogrel for acute coronary syndromes: a genetic substudy of the PLATO trial. The Lancet. 376(9749). 1320–1328. 582 indexed citations breakdown →

10.

Zhuang, Joanna, Krina T. Zondervan, Fredrik Nyberg, et al.. (2010). Optimizing the power of genome‐wide association studies by using publicly available reference samples to expand the control group. Genetic Epidemiology. 34(4). 319–326. 12 indexed citations

11.

Alfirevic, Ana, Tracy Mills, Daniel F. Carr, et al.. (2007). Tacrine-induced liver damage: an analysis of 19 candidate genes. Pharmacogenetics and Genomics. 17(12). 1091–1100. 34 indexed citations

12.

Alfirevic, Ana, Tracy Mills, Pauline Harrington, et al.. (2006). Serious carbamazepine-induced hypersensitivity reactions associated with the HSP70 gene cluster. Pharmacogenetics and Genomics. 16(4). 287–296. 46 indexed citations

13.

Petry, Clive J., Ken K. Ong, Bryan J. Barratt, et al.. (2005). Common polymorphism in H19 associated with birthweight and cord blood IGF-II levels in humans. BMC Genetics. 6(1). 22–22. 64 indexed citations

14.

Howson, Joanna M. M., Bryan J. Barratt, John A. Todd, & Heather J. Cordell. (2005). Comparison of population‐ and family‐based methods for genetic association analysis in the presence of interacting loci. Genetic Epidemiology. 29(1). 51–67. 18 indexed citations

15.

Wang, William Y.S., Bryan J. Barratt, David Clayton, & John A. Todd. (2005). Genome-wide association studies: theoretical and practical concerns. Nature Reviews Genetics. 6(2). 109–118. 797 indexed citations breakdown →

16.

Pask, Rebecca, Helen Rance, Bryan J. Barratt, et al.. (2004). Investigating the utility of combining Φ29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray™ genotyping. BMC Biotechnology. 4(1). 15–15. 30 indexed citations

17.

Lowe, Christopher, Jonathan D. Cooper, Juliet Chapman, et al.. (2004). Cost-effective analysis of candidate genes using htSNPs: a staged approach. Genes and Immunity. 5(4). 301–305. 40 indexed citations

18.

Cordell, Heather J., Bryan J. Barratt, & David Clayton. (2004). Case/pseudocontrol analysis in genetic association studies: A unified framework for detection of genotype and haplotype associations, gene‐gene and gene‐environment interactions, and parent‐of‐origin effects. Genetic Epidemiology. 26(3). 167–185. 184 indexed citations

19.

Walter, Melanie, E. D. Albert, Matthias Conrad, et al.. (2003). IDDM2/insulin VNTR modifies risk conferred by IDDM1/HLA for development of Type 1 diabetes and associated autoimmunity. Diabetologia. 46(5). 712–720. 85 indexed citations

20.

Mein, Charles A., Bryan J. Barratt, Michael Dunn, et al.. (2000). Evaluation of Single Nucleotide Polymorphism Typing with Invader on PCR Amplicons and Its Automation. Genome Research. 10(3). 330–343. 154 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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