Author

Mark W. Christiansen

University of Washington

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· 4 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 5 Organism:human· 4 Assay:GWAS summary statistics· 3 Assay:bulk RNA-seq· 2 Research Focus:eQTL / molecular QTL mapping· 2 Research Focus:GWAS / association mapping· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Alexander TeumeriD· 5 shared Anand Kumar AndiappaniD· 5 shared Anette KalnapenkisiD· 5 shared Anke TönjesiD· 5 shared Annique ClaringbouldiD· 5 shared Ashis SahaiD· 5 shared Bernett Teck Kwong LeeiD· 5 shared Biao ZengiD· 5 shared

Activity by Year

2018-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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5 shared papersGenetics

Alexander TeumerORCID

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Author profile →

5 papers

Recent·Most cited

OTTERS: A powerful TWAS framework leveraging summary-level reference data

Qile Dai, Geyu Zhou, Hongyu Zhao, Urmo Võsa, Lude Franke, Alexis J. Battle, Alexander Teumer, Terho J. Lehtimäki, Olli Tuomas Raitakari, Tõnu Esko, Mawussé D. T. Agbessi, Habibul Ahsan, Isabel Alves, Anand Kumar Andiappan, Wibowo Arindrarto, , Jingjing Yang

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

Nature Communications · Mar 7, 2023Peer-reviewedGenetics

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

Urmo Võsa, Annique Claringbould, Harm-Jan Westra, Marc Jan Bonder, Patrick Deelen, Biao Zeng, Holger Kirsten, Ashis Saha, Roman Kreuzhuber, Seyhan Yazar, Harm Brugge, Roy Oelen, Dylan H. de Vries, Monique G.P. van der Wijst, Silva Kasela, , Bruce M. Psaty

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

Nature Genetics · Sep 1, 2021Peer-reviewedGenomics

Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Eleonora Porcu, Sina Rüeger, Kaido Lepik, Mawussé D. T. Agbessi, Habibul Ahsan, Isabel Alves, Anand Kumar Andiappan, Wibowo Arindrarto, Philip Awadalla, Alexis J. Battle, Frank Beutner, Marc Jan Bonder, Dorret Irene Boomsma, Mark W. Christiansen, Annique Claringbould, , René Luijk

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

Nature Communications · Jul 24, 2019Peer-reviewedGenetics

Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

Angli Xue, Yang Wu, Zhihong Zhu, Futao Zhang, Kathryn E. Kemper, Zhili Zheng, Loïc Yengo, Luke R. Lloyd‐Jones, Julia Sidorenko, Yeda Wu, Mawussé D. T. Agbessi, Habibul Ahsan, Isabel Alves, Anand Kumar Andiappan, Philip Awadalla, , Jian Yang

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

Nature Communications · Jul 23, 2018Peer-reviewedGenetics

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Ting Qi, Yang Wu, Jian Zeng, Futao Zhang, Angli Xue, Longda Jiang, Zhihong Zhu, Kathryn E. Kemper, Loïc Yengo, Zhili Zheng, Mawussé D. T. Agbessi, Habibul Ahsan, Isabel Alves, Anand Kumar Andiappan, Philip Awadalla, , Jian Yang

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

Nature Communications · Jun 5, 2018Peer-reviewedGenetics