M
Martijn Vochteloo
Researcher at University Medical Center Groningen
Publications - 3
Citations - 51
Martijn Vochteloo is an academic researcher from University Medical Center Groningen. The author has contributed to research in topics: Expression quantitative trait loci & Gene. The author has an hindex of 1, co-authored 1 publications receiving 9 citations. Previous affiliations of Martijn Vochteloo include Hanze University of Applied Sciences.
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Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases
Niek de Klein,Niek de Klein,Ellen A. Tsai,Martijn Vochteloo,Martijn Vochteloo,Denis Baird,Denis Baird,Yunfeng Huang,Chia-Yen Chen,Sipko van Dam,Patrick Deelen,Olivier B. Bakker,Omar El Garwany,Omar El Garwany,Zhengyu Ouyang,Eric Marshall,Maria I. Zavodszky,Wouter van Rheenen,Mark K Bakker,Jan H. Veldink,Tom R. Gaunt,Heiko Runz,Lude Franke,Harm-Jan Westra +23 more
TL;DR: In this article, the authors harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets and performed both cis-and trans-expression quantitative locus (eQTL) mapping.
Journal ArticleDOI
Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure
Roy Oelen,Dylan H. de Vries,Harm Brugge,M. Grace Gordon,Martijn Vochteloo,Chun Ye,Harm-Jan Westra,Lude Franke,Monique G. P. van der Wijst +8 more
TL;DR: In this paper , a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens was provided.
Posted ContentDOI
Unbiased identification of unknown cellular and environmental factors that mediate eQTLs using principal interaction component analysis
Martijn Vochteloo,P.M. Van Deelen,Britt Vink,Ellen A. Tsai,Heiko Runz,Sergio Andreu-Sánchez,Jingyuan Fu,Alexandra Zhernakova,Harm-Jan Westra,Lude Franke +9 more
TL;DR: PICALO is robust, works well with heterogeneous datasets, yields reproducible interaction components, and identifies eQTL interactions and contexts that would have been missed when using cell counts or expression based principal components.