Author

Zhana Kuncheva

University of EdinburghORCID 0000-0003-4057-6525Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 3 Research Focus:GWAS / association mapping· 2 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Assay:GWAS summary statistics· 1 Assay:proteomics· 1

Frequent Co-Authors

Benjamin B. SuniD· 3 shared Christopher N. FoleyiD· 3 shared Riccardo Emilio MarioniiD· 3 shared Heiko RunziD· 2 shared Anne RichmondiD· 1 shared Biogen Biobank Team· 1 shared Catalina A. VallejosiD· 1 shared Daniel L. McCartneyiD· 1 shared

Activity by Year

2023-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersGenetics

Benjamin B. SunORCID

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3 papers

Recent·Most cited

GeneticsbioRxivPreprintSep 5, 2024

Imputed DNA methylation outperforms measured loci associations with smoking and chronological age

Anne Richmond, Jure Mur, Sarah Elizabeth Harris…·Riccardo Emilio Marioni

The paper shows that imputing DNA methylation loci from epigenome-wide data provides stronger associations with smoking and age than directly measured CpGs.

1 citation

humanDNA methylation

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Genetic & Genomic MedicineNature CommunicationsPeer-reviewedAug 26, 2024

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank

Robert Francis Hillary, Danni A. Gadd, Zhana Kuncheva…·Benjamin B. Sun

This study identifies gene-environment interactions affecting plasma proteins in UK Biobank, discovering 683 vQTLs and over 1,400 GEIs across 571 proteins.

19 citationstop 3%

humanproteomics

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GeneticsbioRxivPreprintJul 21, 2023

isGWAS: ultra-high-throughput, scalable and equitable inference of genetic associations with disease

Christopher N. Foley, Zhana Kuncheva, Riccardo Emilio Marioni…·Benjamin B. Sun

isGWAS enables fast and scalable genetic association analysis using cohort-level summary data, achieving speeds up to 1500 times faster than existing methods.

1 citation

humanGWAS summary statistics

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