Author

Joline W. J. Beulens

University Medical Center UtrechtORCID 0000-0002-4181-0937Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 3 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Assay:bulk RNA-seq· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Research Focus:GWAS / association mapping· 2 Assay:GWAS summary statistics· 2

Frequent Co-Authors

Leen M. ‘t HartiD· 4 shared Adem Y. DawediD· 3 shared Andrea MariiD· 3 shared Andrew Tym HattersleyiD· 3 shared Angus G. JonesiD· 3 shared Anubha MahajaniD· 3 shared Giuseppe Nicola GiordanoiD· 3 shared Hartmut Ruetten· 3 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersGenetic & Genomic Medicine

Leen M. ‘t HartORCID

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4 papers

Recent·Most cited

Genetic & Genomic MedicineNature CommunicationsPeer-reviewedAug 21, 2023

Genetic analysis of blood molecular phenotypes reveals regulatory networks affecting complex traits: a DIRECT study.

Andrew Anand Brown, Juan J. Fernandez-Tajes, Mun‐Gwan Hong…·Ulrik Plesner Jacobsen

The paper identifies shared genetic regulation across mRNA, proteins, and metabolites in blood, revealing networks and mediators of GWAS variants.

44 citationstop 2%

humanbulk RNA-seq

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Genetic & Genomic MedicineThe Lancet Diabetes & EndocrinologyPeer-reviewedDec 15, 2022

Pharmacogenomics of GLP-1 Receptor Agonists: A genome-wide analysis of observational data and large randomized controlled trials

Adem Y. Dawed, Andrea Mari, Andrew Anand Brown…·A. Jonsson

This study identifies genetic variants associated with glycaemic response to GLP-1 receptor agonists using observational and trial data, finding variants in GLP-1R and ARRB1 linked to HbA1c reduction.

137 citationstop 1%

humanGWAS summary statistics

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Genetic & Genomic MedicinePLoS MedicinePeer-reviewedJun 19, 2020

Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT study

Naeimeh Atabaki‐Pasdar, Mattias Ohlsson, Ana Viñuela…·Paul W. Franks

This study develops machine learning models to predict liver fat content using multi-omic and clinical data from NAFLD patients, achieving higher accuracy than existing tools.

97 citationstop 1%

humanmetabolomics

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GenomicsbioRxivPreprintMar 27, 2020

Understanding functional consequences of type 2 diabetes risk loci using the universal data integration and visualization R package CONQUER

Gerard A. Bouland, Joline W. J. Beulens, Joey Nap…·Roderick C. Slieker

The paper introduces the R-package CONQUER to analyze type 2 diabetes risk loci by integrating genomic and epigenomic data, revealing tissue-shared effects on metabolic and HLA pathways.

1 citation

humanGWAS summary statistics

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