John Garcia

3.1k total citations · 1 hit paper
16 papers, 1.3k citations indexed

About

John Garcia is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology and Genetics. According to data from OpenAlex, John Garcia has authored 16 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Cardiology and Cardiovascular Medicine, 5 papers in Molecular Biology and 4 papers in Genetics. Recurrent topics in John Garcia's work include Cardiomyopathy and Myosin Studies (6 papers), Cardiac electrophysiology and arrhythmias (5 papers) and Genomics and Rare Diseases (3 papers). John Garcia is often cited by papers focused on Cardiomyopathy and Myosin Studies (6 papers), Cardiac electrophysiology and arrhythmias (5 papers) and Genomics and Rare Diseases (3 papers). John Garcia collaborates with scholars based in United States, United Kingdom and Netherlands. John Garcia's co-authors include Scott Topper, Keith Nykamp, Yuya Kobayashi, Blanca Herrera, Nila Patil, Michael J. Anderson, Janita Thusberg, Martin P. Powers, Yuan-Yuan Ho and M. Jody Westbrook and has published in prestigious journals such as Circulation, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

John Garcia

16 papers receiving 1.2k 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.

Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria

2017 465 citations Keith Nykamp, Michael J. Anderson et al. Genetics in Medicine profile →

Peers

John Garcia

CCM

GENET

ONCOL

Kristiina Avela Finland

Hermine A. van Duyvenvoorde Netherlands

Ozge Ceyhan‐Birsoy United States

Mine Arslan‐Kirchner Germany

Karin Y. van Spaendonck‐Zwarts Netherlands

Jasmine Healy Canada

Marjolein Kriek Netherlands

Irene Bottillo Italy

Dennis J. Chia United States

Martin P. Powers United States

Kristiina Avela Finland

John Garcia

633 ×1.1

103 ×0.2

CCM

402 ×0.9

GENET

132 ×1.0

118 ×1.1

ONCOL

Citations per year, relative to John Garcia John Garcia (= 1×) peers Kristiina Avela

Countries citing papers authored by John Garcia

Since Specialization

Citations

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

Fields of papers citing papers by John Garcia

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Garcia

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

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

Vatta, Matteo, Ellen S. Regalado, Michael Parfenov, et al.. (2025). Analysis of TTN Truncating Variants in >74 000 Cases Reveals New Clinically Relevant Gene Regions. Circulation Genomic and Precision Medicine. 18(2). e004982–e004982. 3 indexed citations

Regalado, Ellen S., Dan Swartzlander, Rachel Lewis, et al.. (2024). P222: TTN truncating variants are enriched in cardiomyopathy/arrhythmia and neuromuscular cases and M-band exon 358 contributes to primary cardiomyopathy/arrhythmia. SHILAP Revista de lepidopterología. 2. 101119–101119. 1 indexed citations

Perez, Kimberly, Heather A. Jacene, Jason L. Hornick, et al.. (2022). SDHx mutations and temozolomide in malignant pheochromocytoma and paraganglioma. Endocrine Related Cancer. 29(9). 533–544. 9 indexed citations

Dellefave‐Castillo, Lisa, Allison L. Cirino, Thomas E. Callis, et al.. (2022). Assessment of the Diagnostic Yield of Combined Cardiomyopathy and Arrhythmia Genetic Testing. JAMA Cardiology. 7(9). 966–966. 36 indexed citations

Walsh, Roddy, Arnon Adler, Ahmad S. Amin, et al.. (2021). Evaluation of gene validity for CPVT and short QT syndrome in sudden arrhythmic death. European Heart Journal. 43(15). 1500–1510. 71 indexed citations

Morales, Ana, Susan Rojahn, John Garcia, et al.. (2021). Comprehensive Genetic Testing for Pediatric Hypertrophic Cardiomyopathy Reveals Clinical Management Opportunities and Syndromic Conditions. Pediatric Cardiology. 43(3). 616–623. 6 indexed citations

Perez, Kimberly, Matthew H. Kulke, Anu Chittenden, et al.. (2021). Clinical Implications of Pathogenic Germline Variants in Small Intestine Neuroendocrine Tumors (SI-NETs). JCO Precision Oncology. 5(5). 808–816. 10 indexed citations

Adler, Arnon, Valeria Novelli, Ahmad S. Amin, et al.. (2020). An International, Multicentered, Evidence-Based Reappraisal of Genes Reported to Cause Congenital Long QT Syndrome. Circulation. 141(6). 418–428. 204 indexed citations

Hosseini, S. Mohsen, Raymond H. Kim, Sharmila Udupa, et al.. (2018). Reappraisal of Reported Genes for Sudden Arrhythmic Death. Circulation. 138(12). 1195–1205. 210 indexed citations

10.

Giri, Veda N., Sarah Hegarty, Colette Hyatt, et al.. (2018). Germline genetic testing for inherited prostate cancer in practice: Implications for genetic testing, precision therapy, and cascade testing. The Prostate. 79(4). 333–339. 68 indexed citations

11.

Kobayashi, Yuya, Shan Yang, Keith Nykamp, et al.. (2017). Pathogenic variant burden in the ExAC database: an empirical approach to evaluating population data for clinical variant interpretation. Genome Medicine. 9(1). 13–13. 129 indexed citations

12.

Nykamp, Keith, Michael J. Anderson, Martin P. Powers, et al.. (2017). Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria. Genetics in Medicine. 19(10). 1105–1117. 465 indexed citations breakdown →

13.

Hom, Marianne S., et al.. (2017). Intrauterine Manipulator Use During Minimally Invasive Hysterectomy and Risk of Lymphovascular Space Invasion in Endometrial Cancer. Gynecologic Oncology. 147(1). 227–227. 1 indexed citations

14.

Garcia, John, Jackie Tahiliani, Amy Daly, et al.. (2016). Clinical Genetic Testing for the Cardiomyopathies and Arrhythmias: A Systematic Framework for Establishing Clinical Validity and Addressing Genotypic and Phenotypic Heterogeneity. Frontiers in Cardiovascular Medicine. 3. 20–20. 19 indexed citations

15.

Hart, Reece K., et al.. (2014). A Python package for parsing, validating, mapping and formatting sequence variants using HGVS nomenclature. Bioinformatics. 31(2). 268–270. 24 indexed citations

16.

Garcia, John, et al.. (1997). [Pheochromocytoma].. PubMed. 21(7). 715–8. 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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