Ron Do
Impact in
- Genetics top 0.05%
- Genetic Associations and Epidemiology
- Genomics and Rare Diseases
- Forensic and Genetic Research
- Nephrology top 1%
Papers in
- Genetics 74
- Genetic Associations and Epidemiology 55
- Genomics and Rare Diseases 14
- Blood disorders and treatments 3
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- Glaucoma and retinal disorders 12
- Co-authors
- Benjamin M. Neale (4 shared papers)Marie Verbanck (9 shared papers)Chia‐Yen Chen (1 shared paper)Sekar Kathiresan (6 shared papers)Shamil R. Sunyaev (5 shared papers)Eric S. Lander (2 shared papers)Daniel M. Jordan (23 shared papers)Girish N. Nadkarni (22 shared papers)
- Journals
- Nature Genetics (17 papers)Nature Communications (10 papers)Journal of the American College of Cardiology (8 papers)Nature (7 papers)The American Journal of Human Genetics (5 papers)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Ron Do
158 papers receiving 34.8k citations
Ron Do's Hit Papers
Peers
Comparison fields: 5 of 148
- Genetics 11.3k
- Nephrology 677
- Cardiology and Cardiovascular Medicine 1.4k
- Surgery 2.0k
- Molecular Biology 2.6k
Countries citing papers authored by Ron Do
This map shows the geographic impact of Ron Do'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 Ron Do with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ron Do more than expected).
Fields of papers citing papers by Ron Do
This network shows the impact of papers produced by Ron Do. 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 Ron Do. The network helps show where Ron Do may publish in the future.
Co-authors
The 25 scholars most cited alongside Ron Do, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 164 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Analysis of protein-coding genetic variation in 60,706 humans Hit paper breakdown → | 2016 | 7891 |
| 2 | Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases Hit paper breakdown → | 2018 | 5722 |
| 3 | Discovery and refinement of loci associated with lipid levels Hit paper breakdown → | 2013 | 2415 |
| 4 | Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease Hit paper breakdown → | 2011 | 1563 |
| 5 | A High-Coverage Genome Sequence from an Archaic Denisovan Individual Hit paper breakdown → | 2012 | 1411 |
| 6 | Large-scale association analysis identifies new risk loci for coronary artery disease Hit paper breakdown → | 2012 | 1351 |
| 7 | Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes Hit paper breakdown → | 2012 | 1280 |
| 8 | Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores Hit paper breakdown → | 2015 | 968 |
| 9 | Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants Hit paper breakdown → | 2009 | 921 |
| 10 | Common variants associated with plasma triglycerides and risk for coronary artery disease Hit paper breakdown → | 2013 | 732 |
| 11 | Genetic analyses of diverse populations improves discovery for complex traits Hit paper breakdown → | 2019 | 701 |
| 12 | Mapping the human genetic architecture of COVID-19 Hit paper breakdown → | 2021 | 656 |
| 13 | Exome Sequencing, ANGPTL3 Mutations, and Familial Combined Hypolipidemia Hit paper breakdown → | 2010 | 627 |
| 14 | AKI in Hospitalized Patients with COVID-19 Hit paper breakdown → | 2020 | 573 |
| 15 | Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction Hit paper breakdown → | 2014 | 527 |
| 16 | Searching for missing heritability: Designing rare variant association studies Hit paper breakdown → | 2014 | 475 |
| 17 | Rare variant in scavenger receptor BI raises HDL cholesterol and increases risk of coronary heart disease Hit paper breakdown → | 2016 | 474 |
| 18 | 2012 | 323 | |
| 19 | 2012 | 306 | |
| 20 | 2012 | 281 |
About Ron Do
Ron Do is a scholar working on Genetics, Ophthalmology, Nephrology, Surgery and Cardiology and Cardiovascular Medicine, having authored 164 papers that have together received 35.6k indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (55 papers), Genomics and Rare Diseases (14 papers), Glaucoma and retinal disorders (12 papers), Lipoproteins and Cardiovascular Health (8 papers), Machine Learning in Healthcare (3 papers), Blood disorders and treatments (3 papers), Computational Drug Discovery Methods (3 papers) and Amyloidosis: Diagnosis, Treatment, Outcomes (3 papers). The work is most often cited by research in Genetics (11.3k citations), Nephrology (677 citations), Cardiology and Cardiovascular Medicine (1.4k citations), Surgery (2.0k citations) and Molecular Biology (2.6k citations). Ron Do has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Benjamin M. Neale, Marie Verbanck, Chia‐Yen Chen, Sekar Kathiresan, Shamil R. Sunyaev, Eric S. Lander, Daniel M. Jordan, Girish N. Nadkarni, James C. Engert and Hong‐Hee Won. Their work appears in journals such as Nature Genetics, Nature Communications, Journal of the American College of Cardiology, Nature and The American Journal of Human Genetics.
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.