Author

Daniel Krefl

SIB Swiss Institute of BioinformaticsORCID 0000-0001-8554-2498Unclaimed 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 Approach:statistical model· 3 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Sven BergmanniD· 3 shared Mattia TomasoniiD· 2 shared Alexander L. ButtoniD· 1 shared Bita KhaliliiD· 1 shared Ciara BerginiD· 1 shared Eleonora PorcuiD· 1 shared Hana AbouzeidiD· 1 shared Lazaros KonstantinidisiD· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersGenetic & Genomic Medicine

Sven BergmannORCID

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3 papers

Recent·Most cited

GWAS of Retinal Vessel Tortuosity Identifies 173 Novel Loci Revealing Genes and Pathways Associated with Vascular Pathomechanics and Cardiometabolic Diseases

Mattia Tomasoni, Michael J. Beyeler, Sofía Ortín Vela, Ninon Mounier, Eleonora Porcu, Tanguy Corre, Daniel Krefl, Alexander L. Button, Hana Abouzeid, Lazaros Konstantinidis, Murielle Bochud, Reinier O. Schlingemann, Ciara Bergin, Sven Bergmann

This study performs a GWAS on retinal vessel tortuosity, identifying 173 novel loci and linking them to vascular pathomechanics and cardiometabolic diseases.

Ophthalmology Science · Feb 16, 2023Peer-reviewedGenetic & Genomic Medicine

Cross-GWAS coherence test at the gene and pathway level

Daniel Krefl, Sven Bergmann

This paper introduces a method to detect genes with coherent association signals across two GWAS traits, finding shared genetic links between severe COVID-19 and several medical conditions.

PLoS Computational Biology · Sep 26, 2022Peer-reviewedGenetic & Genomic Medicine

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

Bita Khalili, Mattia Tomasoni, Mirjam Mattei, Roger Mallol, Reyhan Sönmez, Daniel Krefl, Rico Rueedi, Sven Bergmann

The paper evaluates three NMR analysis tools for metabolite identification in human urine, finding ISA and ACP robustly identify metabolites while PCA fails.

Journal of Proteome Research · Jul 18, 2019Peer-reviewedBioinformatics