Author

Willemijn J. Jansen

Maastricht UniversityORCID 0000-0001-9081-1496Unclaimed 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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Organism:human· 3 Assay:GWAS summary statistics· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Barbara FrankeiD· 2 shared Geert PoelmansiD· 2 shared I. Hyun RuischiD· 2 shared Janita BralteniD· 2 shared Nina Roth MotaiD· 2 shared A. Serretti· 1 shared Alam K. A.· 1 shared Alessandro SerrettiiD· 1 shared

Activity by Year

2017-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Barbara FrankeORCID

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3 papers

Recent·Most cited

Genetic & Genomic MedicineEuropean NeuropsychopharmacologyPeer-reviewedOct 1, 2026

The multivariate genetic architecture of psychiatric and insulin resistance multimorbidity

Nina Roth Mota, G. Fanelli, I. Erdogan…·Janita Bralten

This paper investigates the shared genetic architecture of psychiatric and insulin resistance conditions using multivariate GWAS and identifies potential biological mechanisms and drug candidates.

DOI 10.1016/j.euroneuro.2026.112906

humanGWAS summary statistics

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Genetic & Genomic MedicineTranslational PsychiatryPeer-reviewedFeb 14, 2022

Insulinopathies of the brain? Genetic overlap between somatic insulin-related and neuropsychiatric disorders

Giuseppe Fanelli, Barbara Franke, Ward De Witte…·Janita Bralten

The paper investigates genetic overlaps between neuropsychiatric disorders and insulin-related diseases, finding significant genetic correlations and suggesting potential clusters of disorders with divergent pleiotropic effects.

87 citationstop 1%

humanGWAS summary statistics

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BioinformaticsbioRxivPreprintAug 10, 2017

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

Nicola Voyle, Willemijn J. Jansen, Aoife Keohane…·EDAR and DESCRIPA study groups

This study uses Bayesian logistic regression to assess if genetic risk factors predict amyloid burden in non-demented individuals, finding that known factors like age and APOE are more predictive than polygenic risk scores.

1 citation

humanproteomics

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