Author

Barbara Rakitsch

European Bioinformatics Institute

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Oliver StegleiD· 2 shared Benjamin M. Neale· 1 shared Carl Johann Simon-Gabriel· 1 shared Christoph Lippert· 1 shared Daniel Howrigan· 1 shared Danilo HortaiD· 1 shared Francesco Paolo CasaleiD· 1 shared Franceso Paolo Casale· 1 shared

Activity by Year

2014-2017 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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2 shared papersGenetics

Oliver StegleORCID

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3 papers

Recent·Most cited

Joint genetic analysis using variant sets reveals polygenic gene-context interactions

Francesco Paolo Casale, Danilo Horta, Barbara Rakitsch, Oliver Stegle

The paper introduces iSet, a method for detecting gene-context interactions using linear mixed models, and finds it identifies more interactions than alternative methods, including a gene-sex interaction for C-reactive protein.

PLoS Genetics · Apr 20, 2017Peer-reviewedGenetics

Bootstrat: Population Informed Bootstrapping for Rare Variant Tests

Hailiang Huang, Gina M. Peloso, Daniel Howrigan, Barbara Rakitsch, Carl Johann Simon-Gabriel, Jacqueline I. Goldstein, Mark J. Daly, Karsten Borgwardt, Benjamin M. Neale

Bootstrat corrects population stratification in rare variant tests by using genetic distance to create phenotype probabilities, maintaining statistical power while controlling type I error.

bioRxiv · Aug 11, 2016 · PreprintGenomics

LIMIX: genetic analysis of multiple traits

Christoph Lippert, Franceso Paolo Casale, Barbara Rakitsch, Oliver Stegle

LIMIX improves GWAS power and phenotype prediction by enabling flexible multi-trait mixed models with multi-locus regression and handling large trait numbers.

bioRxiv · May 22, 2014 · PreprintGenetics