Leonard C. Schalkwyk

20.1k total citations · 2 hit papers
152 papers, 9.1k citations indexed

About

Leonard C. Schalkwyk is a scholar working on Molecular Biology, Genetics and Physiology. According to data from OpenAlex, Leonard C. Schalkwyk has authored 152 papers receiving a total of 9.1k indexed citations (citations by other indexed papers that have themselves been cited), including 104 papers in Molecular Biology, 76 papers in Genetics and 18 papers in Physiology. Recurrent topics in Leonard C. Schalkwyk's work include Epigenetics and DNA Methylation (49 papers), Genetic Syndromes and Imprinting (26 papers) and Genetic Associations and Epidemiology (21 papers). Leonard C. Schalkwyk is often cited by papers focused on Epigenetics and DNA Methylation (49 papers), Genetic Syndromes and Imprinting (26 papers) and Genetic Associations and Epidemiology (21 papers). Leonard C. Schalkwyk collaborates with scholars based in United Kingdom, United States and Germany. Leonard C. Schalkwyk's co-authors include Jonathan Mill, Katie Lunnon, Eilís Hannon, Cathy Fernandes, Ruth Pidsley, Chloe C. Y. Wong, Robert Plomin, Emma L. Meaburn, Manuela Volta and W. Ford Doolittle and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Leonard C. Schalkwyk

151 papers receiving 9.0k 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 data-driven approach to preprocessing Illumina 450K methylation array data

2013 671 citations Chloe C. Y. Wong, Katie Lunnon et al. BMC Genomics profile →
Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood

2012 494 citations Katie Lunnon, Claire Troakes et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Leonard C. Schalkwyk United Kingdom	54	5.7k	3.0k	1.1k	793	777	152	9.1k
Artūras Petronis Canada	43	4.1k 0.7×	2.6k 0.9×	919 0.8×	437 0.6×	522 0.7×	105	7.3k
Isabelle M. Mansuy Switzerland	48	4.4k 0.8×	1.3k 0.4×	1.5k 1.3×	1.0k 1.3×	953 1.2×	117	8.7k
Guoping Fan United States	54	10.9k 1.9×	3.7k 1.2×	1.1k 0.9×	1.0k 1.3×	967 1.2×	130	15.7k
Bertram Müller‐Myhsok Germany	50	2.0k 0.3×	1.7k 0.6×	533 0.5×	729 0.9×	848 1.1×	197	8.7k
James B. Potash United States	46	3.5k 0.6×	2.3k 0.8×	528 0.5×	350 0.4×	721 0.9×	136	8.4k
Kazuya Iwamoto Japan	45	3.8k 0.7×	2.0k 0.7×	313 0.3×	497 0.6×	582 0.7×	160	7.1k
Allison E. Ashley‐Koch United States	48	2.2k 0.4×	1.8k 0.6×	802 0.7×	531 0.7×	1.6k 2.1×	210	8.3k
Arvind Kumar India	34	4.1k 0.7×	1.5k 0.5×	475 0.4×	860 1.1×	836 1.1×	107	8.4k
Matthew Suderman United Kingdom	48	4.3k 0.7×	1.1k 0.4×	2.5k 2.3×	714 0.9×	348 0.4×	164	8.2k
Antony W. Braithwaite New Zealand	37	3.1k 0.5×	1.7k 0.6×	496 0.4×	570 0.7×	869 1.1×	111	10.0k

Countries citing papers authored by Leonard C. Schalkwyk

Since Specialization

Citations

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

Fields of papers citing papers by Leonard C. Schalkwyk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Leonard C. Schalkwyk. 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 Leonard C. Schalkwyk. The network helps show where Leonard C. Schalkwyk may publish in the future.

Co-authorship network of co-authors of Leonard C. Schalkwyk

This figure shows the co-authorship network connecting the top 25 collaborators of Leonard C. Schalkwyk. A scholar is included among the top collaborators of Leonard C. Schalkwyk 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 Leonard C. Schalkwyk. Leonard C. Schalkwyk 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

Walker, Emma, Emma Dempster, Barry A. Chioza, et al.. (2025). Guidance for the design and analysis of cell-type-specific DNA methylation epidemiology studies. Briefings in Bioinformatics. 26(4). 1 indexed citations

Hannon, Eilís, Emma Dempster, Jonathan Davies, et al.. (2024). Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles. BMC Biology. 22(1). 17–17. 7 indexed citations

Flynn, Robert J., Aaron R. Jeffries, Gemma Shireby, et al.. (2022). Evaluation of nanopore sequencing for epigenetic epidemiology: a comparison with DNA methylation microarrays. Human Molecular Genetics. 31(18). 3181–3190. 8 indexed citations

Wang, Yucheng, et al.. (2022). Characterising sex differences of autosomal DNA methylation in whole blood using the Illumina EPIC array. Clinical Epigenetics. 14(1). 48 indexed citations

Wang, Yucheng, et al.. (2022). InterpolatedXY: a two-step strategy to normalize DNA methylation microarray data avoiding sex bias. Bioinformatics. 38(16). 3950–3957. 9 indexed citations

Shireby, Gemma, Emma Dempster, Joe Burrage, et al.. (2022). Uncertainty quantification of reference-based cellular deconvolution algorithms. Epigenetics. 18(1). 2137659–2137659. 8 indexed citations

Leung, Szi Kay, Aaron R. Jeffries, Isabel Castanho, et al.. (2021). Full-length transcript sequencing of human and mouse cerebral cortex identifies widespread isoform diversity and alternative splicing. Cell Reports. 37(7). 110022–110022. 84 indexed citations

Shireby, Gemma, Jonathan Davies, Paul T. Francis, et al.. (2020). Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex. Brain. 143(12). 3763–3775. 110 indexed citations

Wong, Chloe C. Y., Rebecca G. Smith, Eilís Hannon, et al.. (2019). Genome-wide DNA methylation profiling identifies convergent molecular signatures associated with idiopathic and syndromic autism in post-mortem human brain tissue. Human Molecular Genetics. 28(13). 2201–2211. 59 indexed citations

10.

Mansell, Georgina, T.J. Gorrie-Stone, Yanchun Bao, et al.. (2019). Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genomics. 20(1). 366–366. 180 indexed citations

11.

Malki, Karim, Ebba Du Rietz, Wim E. Crusio, et al.. (2016). Transcriptome analysis of genes and gene networks involved in aggressive behavior in mouse and zebrafish. American Journal of Medical Genetics Part B Neuropsychiatric Genetics. 171(6). 827–838. 31 indexed citations

12.

Smith, Adam R., Rebecca G. Smith, Eilís Hannon, et al.. (2016). Increased DNA methylation near TREM2 is consistently seen in the superior temporal gyrus in Alzheimer's disease brain. Neurobiology of Aging. 47. 35–40. 67 indexed citations

13.

Malki, Karim, Oliver Pain, Maria Grazia Tosto, et al.. (2015). Identification of genes and gene pathways associated with major depressive disorder by integrative brain analysis of rat and human prefrontal cortex transcriptomes. Translational Psychiatry. 5(3). e519–e519. 42 indexed citations

14.

Boks, Marco P., Bart P. F. Rutten, TRDJ Radstake, et al.. (2015). Longitudinal Changes of Telomere Length and Epigenetic Age Related to Traumatic Stress and Post-traumatic Stress Disorder. Biological Psychiatry. 77(9). 1 indexed citations

15.

Sütt, Silva, Sulev Kõks, Leonard C. Schalkwyk, et al.. (2014). Effect of Chronic Valproic Acid Treatment on Hepatic Gene Expression Profile inWfs1Knockout Mouse. PPAR Research. 2014. 1–11. 8 indexed citations

16.

Malki, Karim, Maria Grazia Tosto, Anbarasu Lourdusamy, et al.. (2013). Integrative Mouse and Human mRNA Studies Using WGCNA Nominates Novel Candidate Genes Involved in the Pathogenesis of Major Depressive Disorder. Pharmacogenomics. 14(16). 1979–1990. 46 indexed citations

17.

Powell, Timothy R., Rebecca G. Smith, Sophie Hackinger, et al.. (2013). DNA methylation in interleukin-11 predicts clinical response to antidepressants in GENDEP. Translational Psychiatry. 3(9). e300–e300. 60 indexed citations

18.

Kõks, Sulev, Agne Velthut‐Meikas, Signe Altmäe, et al.. (2009). The differential transcriptome and ontology profiles of floating and cumulus granulosa cells in stimulated human antral follicles. Molecular Human Reproduction. 16(4). 229–240. 56 indexed citations

19.

Caspi, Avshalom, Benjamin Williams, Julia Kim‐Cohen, et al.. (2007). Moderation of breastfeeding effects on the IQ by genetic variation in fatty acid metabolism. Proceedings of the National Academy of Sciences. 104(47). 18860–18865. 258 indexed citations

20.

Butcher, Lee M, Emma L. Meaburn, Linzy Hill, Robert Plomin, & Leonard C. Schalkwyk. (2002). How many non-synonymous SNPS are available on public databases?. American Journal of Medical Genetics Part A. 114(7). 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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