Author

Jade Yu Cheng

University of CopenhagenORCID 0000-0002-8994-1581Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:human· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:genome assembly· 1 Research Focus:GWAS / association mapping· 1 Research Focus:polygenic risk· 1 Research Focus:variant calling· 1

Frequent Co-Authors

Rasmus NielseniD· 3 shared Fernando RacimoiD· 2 shared Aaron J. SterniD· 1 shared Alexandra BuzhilovaiD· 1 shared Allison Fox· 1 shared Anders AlbrechtseniD· 1 shared Andrés IngasoniD· 1 shared Anna K. FotakisiD· 1 shared

Activity by Year

2017-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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3 shared papersBioinformatics

Rasmus NielsenORCID

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3 papers

Recent·Most cited

BioinformaticsMolecular Biology and EvolutionPeer-reviewedOct 7, 2021

Ohana: detecting selection in multiple populations by modelling ancestral admixture components

Jade Yu Cheng, Aaron J. Stern, Fernando Racimo, Rasmus Nielsen

Ohana detects selection in multiple populations by modeling ancestral admixture components, identifying loci with extreme genetic differentiation and new candidate regions in admixed populations.

66 citationstop 3%

humanwhole-genome sequencing

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GeneticsNaturePeer-reviewedSep 16, 2020

Population genomics of the Viking world

Ashot Margaryan, Daniel John Lawson, Martin Sikora…·Eske Willerslev

This study analyzes ancient genomes from the Viking Age to understand genetic structure and migration patterns, finding significant foreign gene flow and population differentiation.

343 citationstop 1%

humanwhole-genome sequencing

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BioinformaticsBioinformaticsPeer-reviewedJan 17, 2017

Ohana, a tool set for population genetic analyses of admixture components

Jade Yu Cheng, Thomas Mailund, Rasmus Nielsen

Ohana improves admixture analysis by introducing a new optimization algorithm and Gaussian-based tree estimation, showing better likelihood results than existing methods.

DOI 10.1093/bioinformatics/btx098

statistical modelbenchmarking

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