Philip Haycock

29.8k total citations · 6 hit papers
38 papers, 13.1k citations indexed

About

Philip Haycock is a scholar working on Genetics, Molecular Biology and Cancer Research. According to data from OpenAlex, Philip Haycock has authored 38 papers receiving a total of 13.1k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Genetics, 14 papers in Molecular Biology and 6 papers in Cancer Research. Recurrent topics in Philip Haycock's work include Genetic Associations and Epidemiology (23 papers), Epigenetics and DNA Methylation (10 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). Philip Haycock is often cited by papers focused on Genetic Associations and Epidemiology (23 papers), Epigenetics and DNA Methylation (10 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). Philip Haycock collaborates with scholars based in United Kingdom, United States and France. Philip Haycock's co-authors include George Davey Smith, Jack Bowden, Stephen Burgess, Gibran Hemani, Jie Zheng, Caroline L. Relton, David M. Evans, Denis Baird, Kaitlin H. Wade and Richard M. Martin and has published in prestigious journals such as The Lancet, Nature Communications and Bioinformatics.

In The Last Decade

Philip Haycock

37 papers receiving 13.1k citations

Hit Papers

Consistent Estimation in ... 2014 2026 2018 2022 2016 2018 2017 2014 2016 1000 2.0k 3.0k 4.0k 5.0k
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.

  1. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator
    2016 5.2k citations Jack Bowden, George Davey Smith et al. Genetic Epidemiology profile →
  2. The MR-Base platform supports systematic causal inference across the human phenome
    2018 4.4k citations Gibran Hemani, Jie Zheng et al. eLife profile →
  3. Recent Developments in Mendelian Randomization Studies
    2017 662 citations Jie Zheng, Denis Baird et al. Current Epidemiology Reports profile →
  4. Leucocyte telomere length and risk of cardiovascular disease: systematic review and meta-analysis
    2014 645 citations Philip Haycock, Stephen Kaptoge et al. BMJ profile →
  5. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis
    2016 406 citations Jie Zheng, A. Mesut Erzurumluoglu et al. Bioinformatics profile →
  6. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies
    2016 356 citations Philip Haycock, Stephen Burgess et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Philip Haycock United Kingdom 22 6.2k 3.4k 2.1k 1.9k 1.4k 38 13.1k
Gibran Hemani United Kingdom 40 8.3k 1.3× 4.7k 1.4× 1.8k 0.9× 2.1k 1.1× 1.3k 1.0× 110 16.4k
Adam S. Butterworth United Kingdom 35 4.2k 0.7× 2.7k 0.8× 2.1k 1.0× 1.6k 0.8× 958 0.7× 78 12.0k
Michael V. Holmes United Kingdom 41 3.5k 0.6× 2.5k 0.7× 1.6k 0.8× 1.8k 1.0× 804 0.6× 137 10.9k
Caroline L. Relton United Kingdom 61 4.6k 0.7× 6.8k 2.0× 1.7k 0.8× 1.5k 0.8× 1.3k 0.9× 252 15.5k
Sekar Kathiresan United States 53 4.7k 0.7× 3.2k 0.9× 1.4k 0.7× 2.0k 1.1× 658 0.5× 106 13.8k
J. Brent Richards Canada 44 3.3k 0.5× 2.6k 0.8× 1.3k 0.6× 1.2k 0.6× 816 0.6× 156 9.7k
Donald W. Bowden United States 59 3.7k 0.6× 4.1k 1.2× 1.9k 0.9× 1.9k 1.0× 508 0.4× 359 14.7k
Chia‐Yen Chen United States 34 3.6k 0.6× 2.5k 0.7× 1.1k 0.5× 1.3k 0.7× 973 0.7× 95 9.3k
Cosetta Minelli United Kingdom 41 3.1k 0.5× 1.8k 0.5× 1.2k 0.6× 1.4k 0.7× 764 0.6× 130 9.3k
Lisa J. Martin United States 59 2.0k 0.3× 2.0k 0.6× 1.8k 0.9× 2.1k 1.1× 1.4k 1.0× 354 13.9k

Countries citing papers authored by Philip Haycock

Since Specialization
Citations

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

Fields of papers citing papers by Philip Haycock

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philip Haycock

This figure shows the co-authorship network connecting the top 25 collaborators of Philip Haycock. A scholar is included among the top collaborators of Philip Haycock 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 Philip Haycock. Philip Haycock 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.
2.
Carnegie, Rebecca, Maria Carolina Borges, Hannah Jones, et al.. (2024). Omega-3 fatty acids and major depression: a Mendelian randomization study. Translational Psychiatry. 14(1). 222–222. 5 indexed citations
3.
Penha, Ricardo Cortez Cardoso, Karl Smith‐Byrne, Joshua Atkins, et al.. (2023). Common genetic variations in telomere length genes and lung cancer: a Mendelian randomisation study and its novel application in lung tumour transcriptome. eLife. 12. 11 indexed citations
4.
Haycock, Philip, Hermann Brenner, Jane C. Figueiredo, et al.. (2023). Causal relationships between risk of venous thromboembolism and 18 cancers: a bidirectional Mendelian randomization analysis. International Journal of Epidemiology. 53(1). 1 indexed citations
5.
Cho, Yoonsu, Philip Haycock, Eleanor Sanderson, et al.. (2020). Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework. Nature Communications. 11(1). 1010–1010. 76 indexed citations
6.
Arathimos, Ryan, Raquel Granell, Philip Haycock, et al.. (2019). Genetic and observational evidence supports a causal role of sex hormones on the development of asthma. Thorax. 74(7). 633–642. 26 indexed citations
7.
8.
Langdon, Ryan, Rebecca C. Richmond, Gibran Hemani, et al.. (2019). A Phenome-Wide Mendelian Randomization Study of Pancreatic Cancer Using Summary Genetic Data. Cancer Epidemiology Biomarkers & Prevention. 28(12). 2070–2078. 16 indexed citations
9.
Zheng, Jie, Tom G. Richardson, Louise A C Millard, et al.. (2018). PhenoSpD: an integrated toolkit for phenotypic correlation estimation and multiple testing correction using GWAS summary statistics. GigaScience. 7(8). 40 indexed citations
10.
Howell, Amy, Jie Zheng, Philip Haycock, et al.. (2018). Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors. Frontiers in Genetics. 9. 525–525. 20 indexed citations
11.
Richardson, Tom G., Philip Haycock, Jie Zheng, et al.. (2018). Systematic Mendelian randomization framework elucidates hundreds of CpG sites which may mediate the influence of genetic variants on disease. Human Molecular Genetics. 27(18). 3293–3304. 37 indexed citations
12.
Noyce, Alastair J., Demis A. Kia, Gibran Hemani, et al.. (2017). Estimating the causal influence of body mass index on risk of Parkinson disease: A Mendelian randomisation study. PLoS Medicine. 14(6). e1002314–e1002314. 114 indexed citations
13.
Zheng, Jie, Denis Baird, Maria Carolina Borges, et al.. (2017). Recent Developments in Mendelian Randomization Studies. Current Epidemiology Reports. 4(4). 330–345. 662 indexed citations breakdown →
14.
Zheng, Jie, Philip Haycock, Gibran Hemani, et al.. (2016). LD hub and MR-base: online platforms for preforming LD score regression and Mendelian randomization analysis using GWAS summary data. Behavior Genetics. 46(6). 815–815. 5 indexed citations
15.
Bowden, Jack, George Davey Smith, Philip Haycock, & Stephen Burgess. (2016). Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic Epidemiology. 40(4). 304–314. 5167 indexed citations breakdown →
16.
Zheng, Jie, A. Mesut Erzurumluoglu, Benjamin Elsworth, et al.. (2016). LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 33(2). 272–279. 406 indexed citations breakdown →
17.
Haycock, Philip, et al.. (2014). Leucocyte telomere length and risk of cardiovascular disease: systematic review and meta-analysis. BMJ. 349(jul08 3). g4227–g4227. 645 indexed citations breakdown →
18.
Ye, Zheng, Philip Haycock, Deepti Gurdasani, et al.. (2013). The Association Between Circulating Lipoprotein(a) and Type 2 Diabetes: Is It Causal?. Diabetes. 63(1). 332–342. 72 indexed citations
19.
Haycock, Philip. (2009). Fetal Alcohol Spectrum Disorders: The Epigenetic Perspective1. Biology of Reproduction. 81(4). 607–617. 119 indexed citations
20.
Soodyall, Himla, Heeran Makkan, Philip Haycock, & Thijessen Naidoo. (2008). The genetic prehistory of the Khoe and San. Southern African humanities. 20(1). 37–48. 13 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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