Author

Jarkko Toivonen

Finnish Red CrossORCID 0000-0002-6843-5831Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:GWAS / association mapping· 2 Assay:GWAS summary statistics· 2 Organism:human· 2 Research Focus:benchmarking· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Mikko ArvasiD· 2 shared Alexandre P. FranciscoiD· 1 shared Blood Service Biobank· 1 shared David M. EvansiD· 1 shared Dominik KempaiD· 1 shared Esko UkkoneniD· 1 shared FinnGeniD· 1 shared Gabriel Cuéllar-PartidaiD· 1 shared

Activity by Year

2017-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Mikko ArvasORCID

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3 papers

Recent·Most cited

Genetic & Genomic MedicineEuropean Journal of Human GeneticsPeer-reviewedApr 27, 2026

Genome-Wide Association Study Identifies Protective Genetic Factors in Active Blood Donors Against Multiple Diseases

Jonna Clancy, Jarkko Toivonen, Jouni Lauronen…·Jarmo Ritari

This study identifies 2,973 genetic loci associated with health outcomes in blood donors, suggesting genetic factors contribute to the healthier donor effect.

3 citations

humanGWAS summary statistics

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Genetic & Genomic MedicineNature CommunicationsPeer-reviewedOct 26, 2024

Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects

Liang‐Dar Hwang, Gabriel Cuéllar-Partida, Loïc Yengo…·David M. Evans

DINGO improves locus discovery in GWAS of perinatal traits by combining maternal and fetal GWAS data through meta-analysis and novel statistical tests, identifying more genetic loci than separate analyses.

10 citations

humanGWAS summary statistics

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BioinformaticsbioRxivPreprintMay 27, 2017

An Experiment in Learning the Language of Sequence Motifs: Sequence Logos vs. Finite-State Machines

Alexandre P. Francisco, Travis Gagie, Dominik Kempa…·Esko Ukkonen

The paper compares finite-state machines and position weight matrices for modeling sequence motifs, finding FSMs provide better discrimination of positive and negative sequences.

DOI 10.1101/143024

DNA methylationstatistical model

𝕏 PostBluesky