Håkon Håkonarson

158.8k total citations · 7 hit papers
587 papers, 34.7k citations indexed

About

Håkon Håkonarson is a scholar working on Genetics, Molecular Biology and Immunology. According to data from OpenAlex, Håkon Håkonarson has authored 587 papers receiving a total of 34.7k indexed citations (citations by other indexed papers that have themselves been cited), including 258 papers in Genetics, 217 papers in Molecular Biology and 84 papers in Immunology. Recurrent topics in Håkon Håkonarson's work include Genetic Associations and Epidemiology (80 papers), Genomic variations and chromosomal abnormalities (72 papers) and Genetics and Neurodevelopmental Disorders (50 papers). Håkon Håkonarson is often cited by papers focused on Genetic Associations and Epidemiology (80 papers), Genomic variations and chromosomal abnormalities (72 papers) and Genetics and Neurodevelopmental Disorders (50 papers). Håkon Håkonarson collaborates with scholars based in United States, China and Iceland. Håkon Håkonarson's co-authors include Kai Wang, Man Li, Raquel E. Gur, Ruben C. Gur, Joseph Glessner, Theodore D. Satterthwaite, Kosha Ruparel, Struan F.A. Grant, Mingyao Li and Daniel H. Wolf and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Håkon Håkonarson

560 papers receiving 34.2k citations

Hit Papers

ANNOVAR: functional annotation of genetic variants from h... 2007 2026 2013 2019 2010 2012 2007 2012 2013 2.5k 5.0k 7.5k
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. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
    2010 9.0k citations Kai Wang, Håkon Håkonarson et al. profile →
  2. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data
    2012 1.3k citations Håkon Håkonarson et al. profile →
  3. PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data
    2007 1.2k citations Kai Wang, Joseph Glessner et al. profile →
  4. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth
    2012 869 citations Håkon Håkonarson et al. profile →
  5. Sex differences in the structural connectome of the human brain
    2013 778 citations Håkon Håkonarson et al. Proceedings of the National Academy of Sciences profile →
  6. Rare Variants Create Synthetic Genome-Wide Associations
    2010 611 citations Kai Wang, Håkon Håkonarson et al. profile →
  7. Analysing biological pathways in genome-wide association studies
    2010 566 citations Kai Wang, Håkon Håkonarson 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
Håkon Håkonarson United States 76 12.4k 11.6k 5.5k 3.7k 3.2k 587 34.7k
Pak C. Sham Hong Kong 97 16.4k 1.3× 24.3k 2.1× 6.0k 1.1× 3.1k 0.9× 3.1k 1.0× 695 62.0k
Shaun Purcell United States 53 11.7k 0.9× 22.3k 1.9× 2.1k 0.4× 2.7k 0.7× 1.7k 0.5× 136 39.1k
Benjamin M. Neale United States 55 12.4k 1.0× 24.7k 2.1× 2.0k 0.4× 2.7k 0.7× 2.2k 0.7× 165 42.0k
Neil Risch United States 96 10.4k 0.8× 18.8k 1.6× 2.7k 0.5× 1.9k 0.5× 2.1k 0.7× 318 40.0k
Michael J. Owen United Kingdom 107 16.4k 1.3× 11.7k 1.0× 5.7k 1.0× 1.6k 0.4× 799 0.2× 615 40.9k
Hideyuki Okano Japan 122 32.0k 2.6× 5.0k 0.4× 2.3k 0.4× 4.7k 1.3× 5.3k 1.6× 1.2k 60.9k
John Blangero United States 81 7.2k 0.6× 9.8k 0.8× 3.5k 0.6× 1.0k 0.3× 2.0k 0.6× 644 28.6k
Uta Francke United States 99 24.1k 1.9× 14.5k 1.2× 2.6k 0.5× 2.5k 0.7× 2.4k 0.8× 503 41.9k
Nicholas J. Schork United States 94 10.9k 0.9× 10.9k 0.9× 1.7k 0.3× 1.4k 0.4× 1.9k 0.6× 460 32.3k
Julian Maller United States 14 11.0k 0.9× 17.9k 1.5× 1.1k 0.2× 2.7k 0.7× 1.9k 0.6× 15 35.6k

Countries citing papers authored by Håkon Håkonarson

Since Specialization
Citations

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

Fields of papers citing papers by Håkon Håkonarson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Håkon Håkonarson

This figure shows the co-authorship network connecting the top 25 collaborators of Håkon Håkonarson. A scholar is included among the top collaborators of Håkon Håkonarson 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 Håkon Håkonarson. Håkon Håkonarson 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.
Qu, Hui‐Qi, Joseph Glessner, Charlly Kao, & Håkon Håkonarson. (2025). Data-informed insights into sex differences in peripheral blood mononuclear cells from single-cell transcriptomics. Genes & Diseases. 12(5). 101525–101525.
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Liu, Yichuan, Hui‐Qi Qu, Xiao Chang, et al.. (2025). Deep learning algorithms reveal genomic markers for anxiety disorder in a large cohort of children with down syndrome. Molecular Psychiatry. 30(10). 4710–4719. 1 indexed citations
4.
Burrows, Kimberley, Anni Heiskala, Jonathan P. Bradfield, et al.. (2024). A framework for conducting GWAS using repeated measures data with an application to childhood BMI. Nature Communications. 15(1). 10067–10067.
5.
Qu, Hui‐Qi, Chethan Gangireddy, Anjali Vaidya, et al.. (2024). Rare variants in cardiomyopathy genes predispose to cardiac injury in severe COVID-19 patients of African or Hispanic ancestry. Journal of Molecular Medicine. 103(2). 175–185. 1 indexed citations
6.
Yang, Jie, Dandan Ji, Xinyi Meng, et al.. (2024). NASP gene contributes to autism by epigenetic dysregulation of neural and immune pathways. Journal of Medical Genetics. 61(7). 677–688. 2 indexed citations
7.
Qu, Hui‐Qi, Joseph Glessner, Yichuan Liu, et al.. (2023). High Comorbidity of Pediatric Cancers in Patients with Birth Defects: Insights from Whole Genome Sequencing Analysis of Copy Number Variations. Translational research. 266. 49–56.
8.
Lin, Xiang, Tian Tian, Zhi Wei, & Håkon Håkonarson. (2022). Clustering of single-cell multi-omics data with a multimodal deep learning method. Nature Communications. 13(1). 7705–7705. 62 indexed citations
9.
Qu, Hui‐Qi, James W. Snyder, Joseph Glessner, et al.. (2022). Circulating LIGHT (TNFSF14) and Interleukin-18 Levels in Sepsis-Induced Multi-Organ Injuries. Biomedicines. 10(2). 264–264. 11 indexed citations
10.
Strong, Alanna, Jisun Kim, Deborah Watson, et al.. (2022). TBX6 as a cause of a combined skeletal‐kidney dysplasia syndrome. American Journal of Medical Genetics Part A. 188(12). 3469–3481. 8 indexed citations
11.
Hwang, Daniel, Larissa Lumi Watanabe Ishikawa, Alexandra Boehm, et al.. (2022). CSF-1 maintains pathogenic but not homeostatic myeloid cells in the central nervous system during autoimmune neuroinflammation. Proceedings of the National Academy of Sciences. 119(14). e2111804119–e2111804119. 17 indexed citations
12.
Li, Jin, Zhi Wei, Jie Zhang, Håkon Håkonarson, & Scott D. Cook‐Sather. (2019). Candidate gene analyses for acute pain and morphine analgesia after pediatric day surgery: African American versus European Caucasian ancestry and dose prediction limits. The Pharmacogenomics Journal. 19(6). 570–581. 20 indexed citations
13.
Li, Junyi, Michael March, Yan V. Sun, et al.. (2019). Identification of Target Genes at Juvenile Idiopathic Arthritis GWAS Loci in Human Neutrophils. Frontiers in Genetics. 10. 181–181. 6 indexed citations
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Capasso, Mario, Sharon J. Diskin, Flora Cimmino, et al.. (2014). Common Genetic Variants in NEFL Influence Gene Expression and Neuroblastoma Risk. Cancer Research. 74(23). 6913–6924. 79 indexed citations
16.
Li, Qiaoli, Jill L. Brodsky, Laura K. Conlin, et al.. (2013). Mutations in the ABCC6 Gene as a Cause of Generalized Arterial Calcification of Infancy: Genotypic Overlap with Pseudoxanthoma Elasticum. Journal of Investigative Dermatology. 134(3). 658–665. 59 indexed citations
17.
Glessner, Joseph, Kai Wang, Patrick Sleiman, et al.. (2010). Duplication of the SLIT3 Locus on 5q35.1 Predisposes to Major Depressive Disorder. PLoS ONE. 5(12). e15463–e15463. 44 indexed citations
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Håkonarson, Håkon, Unnur Steina Björnsdóttir, Elínborg Ostermann, et al.. (2001). Allelic Frequencies and Patterns of Single-nucleotide Polymorphisms in Candidate Genes for Asthma and Atopy in Iceland. American Journal of Respiratory and Critical Care Medicine. 164(11). 2036–2044. 79 indexed citations
20.
Håkonarson, Håkon & Michael Grunstein. (1998). Regulation of Second Messengers Associated with Airway Smooth Muscle Contraction and Relaxation. American Journal of Respiratory and Critical Care Medicine. 158(Supplement_2). S115–S122. 51 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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