Author

Bjarni Vilhjalmsson

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 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Research Focus:GWAS / association mapping· 3 Approach:statistical model· 3 Assay:GWAS summary statistics· 2 Organism:human· 2 Approach:deep learning· 1 Research Focus:polygenic risk· 1

Frequent Co-Authors

Alexander GuseviD· 2 shared Jian YangiD· 2 shared Noah ZaitleniD· 2 shared Po-Ru LohiD· 2 shared Alkes Price· 1 shared Benjamin Neale· 1 shared Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study the Discovery· 1 shared Bogdan Pasaniuc· 1 shared

Activity by Year

2015-2016 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 32 mutual co-author links

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2 shared papersBioinformatics

Alexander GusevORCID

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Author profile →

3 papers

Recent·Most cited

Playing Musical Chairs in Big Data to Reveal Variables Associations

Hugues Aschard, Bjarni Vilhjalmsson, Chirag Patel, David Skurnik, Jimmy Yu, Brian Wolpin, Peter Kraft, Noah Zaitlen

The paper introduces a method to enhance statistical power in detecting weak associations by leveraging correlated variables, showing equivalent power gains to increasing sample size.

bioRxiv · Jun 5, 2016 · PreprintGenetics

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Bjarni Vilhjalmsson, Jian Yang, Hilary Kiyo Finucane, Alexander Gusev, Sara Lindstrom, Stephan Ripke, Giulio Genovese, Po-Ru Loh, Gaurav Bhatia, Ron Do, Tristian Hayeck, Hong-Hee Won, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study the Discovery, Sekar Kathiresan, , Nick J Patte

This paper introduces LDpred, a method that improves polygenic risk score accuracy by modeling linkage disequilibrium, showing increased R2 values in schizophrenia and multiple sclerosis datasets.

The American Journal of Human Genetics · Oct 1, 2015Peer-reviewedBioinformatics

Mixed Model with Correction for Case-Control Ascertainment Increases Association Power

tristan hayeck, Noah Zaitlen, Po-Ru Loh, Bjarni Vilhjalmsson, Samuela Pollack, Alexander Gusev, Jian Yang, Guo-Bo Chen, Michael E. Goddard, Peter M. Visscher, Nick Patterson, Alkes Price

The paper introduces a new mixed model method for case-control studies that improves association power while controlling false positives, showing gains in power on real and simulated data.

The American Journal of Human Genetics · May 1, 2015Peer-reviewedGenetics