Harm-Jan Westra

33.2k total citations · 1 hit paper
38 papers, 1.9k citations indexed

About

Harm-Jan Westra is a scholar working on Molecular Biology, Genetics and Immunology. According to data from OpenAlex, Harm-Jan Westra has authored 38 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 20 papers in Genetics and 10 papers in Immunology. Recurrent topics in Harm-Jan Westra's work include Genetic Associations and Epidemiology (13 papers), Bioinformatics and Genomic Networks (10 papers) and RNA modifications and cancer (5 papers). Harm-Jan Westra is often cited by papers focused on Genetic Associations and Epidemiology (13 papers), Bioinformatics and Genomic Networks (10 papers) and RNA modifications and cancer (5 papers). Harm-Jan Westra collaborates with scholars based in Netherlands, United States and United Kingdom. Harm-Jan Westra's co-authors include Lude Franke, Rudolf S.N. Fehrmann, Cisca Wijmenga, Juha Karjalainen, Tōnu Esko, Soumya Raychaudhuri, Tune H. Pers, Joel N. Hirschhorn, Ritsert C. Jansen and Gerard J. te Meerman and has published in prestigious journals such as Nature Communications, Nature Genetics and Bioinformatics.

In The Last Decade

Harm-Jan Westra

38 papers receiving 1.9k 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.

Biological interpretation of genome-wide association studies using predicted gene functions

2015 359 citations Tune H. Pers, Juha Karjalainen et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Harm-Jan Westra Netherlands	23	1.0k	825	371	312	135	38	1.9k
Antigone S. Dimas United Kingdom	11	1.4k 1.3×	1.2k 1.5×	265 0.7×	327 1.0×	68 0.5×	17	2.3k
Marc A. Schaub United States	8	1.5k 1.4×	1.1k 1.3×	302 0.8×	341 1.1×	132 1.0×	9	2.6k
Maya Kasowski United States	10	2.0k 1.9×	1.1k 1.4×	285 0.8×	448 1.4×	127 0.9×	14	3.0k
Kathryn G. Ewens United States	22	1.5k 1.4×	1.1k 1.3×	276 0.7×	254 0.8×	109 0.8×	33	2.8k
Avinash Abhyankar United States	20	959 0.9×	650 0.8×	358 1.0×	132 0.4×	186 1.4×	30	1.9k
Kevin S. Smith United States	27	1.8k 1.8×	543 0.7×	415 1.1×	225 0.7×	203 1.5×	61	3.1k
Ayellet V. Segrè United States	15	1.6k 1.5×	839 1.0×	189 0.5×	722 2.3×	182 1.3×	33	2.5k
Chad Garner United States	27	1.1k 1.0×	615 0.7×	161 0.4×	302 1.0×	188 1.4×	62	2.7k
Yosuke Kawai Japan	24	1.0k 1.0×	359 0.4×	179 0.5×	187 0.6×	149 1.1×	116	2.0k
Francesco Vallania United States	21	1.0k 1.0×	428 0.5×	427 1.2×	226 0.7×	244 1.8×	32	1.9k

Countries citing papers authored by Harm-Jan Westra

Since Specialization

Citations

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

Fields of papers citing papers by Harm-Jan Westra

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Harm-Jan Westra. 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 Harm-Jan Westra. The network helps show where Harm-Jan Westra may publish in the future.

Co-authorship network of co-authors of Harm-Jan Westra

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

All Works

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20 of 20 papers shown

Bakker, Olivier B., Annique Claringbould, Harm-Jan Westra, et al.. (2024). Identification of rare disease genes as drivers of common diseases through tissue-specific gene regulatory networks. Scientific Reports. 14(1). 30206–30206. 1 indexed citations

Kim, Taehyeung, Marta Martínez‐Bonet, Qiang Wang, et al.. (2024). Non-coding autoimmune risk variant defines role for ICOS in T peripheral helper cell development. Nature Communications. 15(1). 2150–2150. 8 indexed citations

Vochteloo, Martijn, Patrick Deelen, Ellen Tsai, et al.. (2024). PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs. Genome biology. 25(1). 29–29. 1 indexed citations

Bakker, Olivier B., Annique Claringbould, Jeroen de Ridder, et al.. (2024). Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes. BMC Medical Genomics. 17(1). 186–186. 1 indexed citations

Claringbould, Annique, Dorret I. Boomsma, M. Arfan Ikram, et al.. (2021). Correction for both common and rare cell types in blood is important to identify genes that correlate with age. BMC Genomics. 22(1). 184–184. 3 indexed citations

Graaf, Adriaan van der, Annique Claringbould, Antoine Rimbert, et al.. (2020). Mendelian randomization while jointly modeling cis genetics identifies causal relationships between gene expression and lipids. Nature Communications. 11(1). 4930–4930. 27 indexed citations

Vries, Dylan H. de, Vasiliki Matzaraki, Olivier B. Bakker, et al.. (2020). Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response. PLoS Pathogens. 16(4). e1008408–e1008408. 14 indexed citations

Lu, Xueling, Eliza Fraszczyk, Thomas P. van der Meer, et al.. (2020). An epigenome-wide association study identifies multiple DNA methylation markers of exposure to endocrine disruptors. Environment International. 144. 106016–106016. 31 indexed citations

Davenport, Emma E., Tiffany Amariuta, María Gutiérrez‐Arcelus, et al.. (2018). Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial. Genome biology. 19(1). 168–168. 27 indexed citations

10.

Li, Gang, Marta Martínez‐Bonet, Di Wu, et al.. (2018). High-throughput identification of noncoding functional SNPs via type IIS enzyme restriction. Nature Genetics. 50(8). 1180–1188. 24 indexed citations

11.

Wijst, Monique G.P. van der, Dylan H. de Vries, Harm Brugge, Harm-Jan Westra, & Lude Franke. (2018). An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Medicine. 10(1). 96–96. 55 indexed citations

12.

Zych, Konrad, Basten L. Snoek, Mark Elvin, et al.. (2017). reGenotyper: Detecting mislabeled samples in genetic data. PLoS ONE. 12(2). e0171324–e0171324. 11 indexed citations

13.

Kasela, Silva, Kai Kisand, Liina Tserel, et al.. (2017). Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells. PLoS Genetics. 13(3). e1006643–e1006643. 62 indexed citations

14.

Fehrmann, Rudolf S.N., Juha Karjalainen, Małgorzata Krajewska, et al.. (2015). Gene expression analysis identifies global gene dosage sensitivity in cancer. Nature Genetics. 47(2). 115–125. 209 indexed citations

15.

Pers, Tune H., Juha Karjalainen, Yingleong Chan, et al.. (2015). Biological interpretation of genome-wide association studies using predicted gene functions. Nature Communications. 6(1). 5890–5890. 359 indexed citations breakdown →

16.

Westra, Harm-Jan & Lude Franke. (2014). From genome to function by studying eQTLs. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1842(10). 1896–1902. 110 indexed citations

17.

Kogelman, Lisette J. A., Daria V. Zhernakova, Harm-Jan Westra, et al.. (2014). Systems Genetics Analysis of Obesity using RNA-Seq Data in an F2 Pig Resource Population. Research at the University of Copenhagen (University of Copenhagen). 127. 1 indexed citations

18.

Deelen, Patrick, Marc Jan Bonder, Harm-Jan Westra, et al.. (2014). Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration. BMC Research Notes. 7(1). 901–901. 76 indexed citations

19.

Kumar, Vinod, Harm-Jan Westra, Juha Karjalainen, et al.. (2013). Human Disease-Associated Genetic Variation Impacts Large Intergenic Non-Coding RNA Expression. PLoS Genetics. 9(1). e1003201–e1003201. 216 indexed citations

20.

Boer, Rudolf A. de, Niek Verweij, Dirk J. van Veldhuisen, et al.. (2012). A Genome-Wide Association Study of Circulating Galectin-3. PLoS ONE. 7(10). e47385–e47385. 39 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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