Author

Tea K. E. Uggen

Oslo University Hospital

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Biology Disciplines

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Genetics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Alexey A. ShadriniD· 1 shared Anders Martin DaleiD· 1 shared Dominic HollandiD· 1 shared Francesco BettellaiD· 1 shared Kevin Sean O’ConnelliD· 1 shared Olav Bjerkehagen SmelandiD· 1 shared Ole A. AndreasseniD· 1 shared Oleksandr FreiiD· 1 shared

Activity by Year

2020-2020 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperGenetics

Alexey A. ShadrinORCID

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1 paper

Recent·Most cited

Annotation-Informed Causal Mixture Modeling (AI-MiXeR) reveals phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories

Alexey A. Shadrin, Oleksandr Frei, Olav Bjerkehagen Smeland, Francesco Bettella, Kevin Sean O’Connell, Osman Gani, Shahram Bahrami, Tea K. E. Uggen, Srdjan Djurovic, Dominic Holland, Ole A. Andreassen, Anders Martin Dale

The paper introduces AI-MiXeR, a method to estimate variant counts and effect sizes across functional annotations using GWAS summary statistics, revealing phenotype-specific polygenicity and effect size differences.

Bioinformatics · Jun 9, 2020Peer-reviewedGenetics