Daniel Quiat

1.9k total citations · 1 hit paper
14 papers, 1.4k citations indexed

About

Daniel Quiat is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Daniel Quiat has authored 14 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Cardiology and Cardiovascular Medicine and 4 papers in Surgery. Recurrent topics in Daniel Quiat's work include Cardiomyopathy and Myosin Studies (4 papers), Muscle Physiology and Disorders (3 papers) and Congenital heart defects research (3 papers). Daniel Quiat is often cited by papers focused on Cardiomyopathy and Myosin Studies (4 papers), Muscle Physiology and Disorders (3 papers) and Congenital heart defects research (3 papers). Daniel Quiat collaborates with scholars based in United States, Brazil and Australia. Daniel Quiat's co-authors include Eric N. Olson, James A. Richardson, Xiaoxia Qi, Brett A. Johnson, Lillian B. Sutherland, Eva van Rooij, Robert J. Kelm, Jonathan G. Seidman, Christine E. Seidman and Sarah U. Morton and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Investigation and Developmental Cell.

In The Last Decade

Daniel Quiat

12 papers receiving 1.3k 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.

A Family of microRNAs Encoded by Myosin Genes Governs Myosin Expression and Muscle Performance

2009 784 citations Eva van Rooij, Daniel Quiat et al. Developmental Cell profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Quiat United States	8	1.1k	679	211	142	120	14	1.4k
Heeyoung Seok South Korea	15	2.0k 1.8×	1.5k 2.1×	420 2.0×	85 0.6×	101 0.8×	27	2.4k
Francesco Chemello Italy	18	1.2k 1.0×	227 0.3×	246 1.2×	208 1.5×	70 0.6×	34	1.4k
Priyatansh Gurha United States	23	1.1k 1.0×	353 0.5×	572 2.7×	82 0.6×	41 0.3×	44	1.7k
Joseph B. Moore United States	18	526 0.5×	156 0.2×	173 0.8×	81 0.6×	64 0.5×	52	958
Britta Vogel Germany	20	813 0.7×	521 0.8×	343 1.6×	27 0.2×	52 0.4×	33	1.2k
Mònica Suelves Spain	16	906 0.8×	157 0.2×	50 0.2×	140 1.0×	48 0.4×	26	1.1k
Jiuzhou Huo United States	9	508 0.5×	251 0.4×	150 0.7×	77 0.5×	64 0.5×	12	863
Sajedah M. Hindi United States	19	886 0.8×	123 0.2×	81 0.4×	285 2.0×	101 0.8×	24	1.1k
Yefei Wen United States	12	870 0.8×	121 0.2×	55 0.3×	257 1.8×	56 0.5×	16	1.0k
Sveva Romani Italy	7	795 0.7×	761 1.1×	55 0.3×	36 0.3×	30 0.3×	8	1.1k

Countries citing papers authored by Daniel Quiat

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Quiat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Quiat

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

All Works

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14 of 14 papers shown

Li, Kun, Daniel Quiat, Fei She, et al.. (2024). Genetic diagnosis of facioscapulohumeral muscular dystrophy type 1 using rare-variant linkage analysis and long-read genome sequencing. Genetics in Medicine Open. 2. 101817–101817.

Kim, Yuri, Anissa Viveiros, Daniel Reichart, et al.. (2023). Genetic Contribution to End-Stage Cardiomyopathy Requiring Heart Transplantation. Circulation Genomic and Precision Medicine. 16(5). 452–461. 2 indexed citations

Abrams, Dominic J., et al.. (2023). Characterization of the cardiac and non-cardiac phenotype associated with genetic variants in KCNJ2. European Heart Journal. 44(Supplement_2).

Timberlake, Andrew T., Daniel Quiat, Michael L. Cunningham, et al.. (2023). 1. Mutations in FOXI3 Cause Microtia and Craniofacial Microsomia. Plastic & Reconstructive Surgery Global Open. 11(5S). 1–1. 1 indexed citations

Hylind, Robyn J., Alexandre C. Pereira, Daniel Quiat, et al.. (2022). Population Prevalence of Premature Truncating Variants in Plakophilin-2 and Association With Arrhythmogenic Right Ventricular Cardiomyopathy: A UK Biobank Analysis. Circulation Genomic and Precision Medicine. 15(3). e003507–e003507. 9 indexed citations

Repetti, Giuliana G., Yuri Kim, Alexandre C. Pereira, et al.. (2021). Discordant clinical features of identical hypertrophic cardiomyopathy twins. Proceedings of the National Academy of Sciences. 118(10). 27 indexed citations

Morton, Sarah U., Daniel Quiat, Jonathan G. Seidman, & Christine E. Seidman. (2021). Genomic frontiers in congenital heart disease. Nature Reviews Cardiology. 19(1). 26–42. 124 indexed citations

Quiat, Daniel, et al.. (2021). Left Ventricular Outflow Tract Gradient Is Associated With Coronary Artery Obstruction in Children With Williams-Beuren Syndrome. Journal of Cardiothoracic and Vascular Anesthesia. 35(12). 3677–3680. 2 indexed citations

Quiat, Daniel, Leora Witkowski, Hana Zouk, Kevin P. Daly, & Amy E. Roberts. (2020). Retrospective Analysis of Clinical Genetic Testing in Pediatric Primary Dilated Cardiomyopathy: Testing Outcomes and the Effects of Variant Reclassification. Journal of the American Heart Association. 9(11). e016195–e016195. 27 indexed citations

10.

Quiat, Daniel, Tomasz Kula, Chisato Shimizu, et al.. (2020). High-Throughput Screening of Kawasaki Disease Sera for Antiviral Antibodies. The Journal of Infectious Diseases. 222(11). 1853–1857. 12 indexed citations

11.

Quiat, Daniel, Tomasz Kula, Chisato Shimizu, et al.. (2017). Unbiased Screening of Kawasaki Disease Sera for Viral Antigen Exposure. Open Forum Infectious Diseases. 4(suppl_1). S684–S685. 1 indexed citations

12.

Quiat, Daniel & Eric N. Olson. (2013). MicroRNAs in cardiovascular disease: from pathogenesis to prevention and treatment. Journal of Clinical Investigation. 123(1). 11–18. 242 indexed citations

13.

Quiat, Daniel, Kevin A. Voelker, Jimin Pei, et al.. (2011). Concerted regulation of myofiber-specific gene expression and muscle performance by the transcriptional repressor Sox6. Proceedings of the National Academy of Sciences. 108(25). 10196–10201. 124 indexed citations

14.

Rooij, Eva van, Daniel Quiat, Brett A. Johnson, et al.. (2009). A Family of microRNAs Encoded by Myosin Genes Governs Myosin Expression and Muscle Performance. Developmental Cell. 17(5). 662–673. 784 indexed citations breakdown →

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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