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Markus Scholz

Bio: Markus Scholz is an academic researcher from Leipzig University. The author has contributed to research in topics: Genome-wide association study & Population. The author has an hindex of 44, co-authored 268 publications receiving 10119 citations. Previous affiliations of Markus Scholz include Humboldt University of Berlin & Life University.


Papers
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Journal ArticleDOI
Majid Nikpay1, Anuj Goel2, Won H-H.3, Leanne M. Hall4  +164 moreInstitutions (60)
TL;DR: This article conducted a meta-analysis of coronary artery disease (CAD) cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 millions low-frequency (0.005 < MAF < 0.5) variants.
Abstract: Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.

1,839 citations

Journal ArticleDOI
TL;DR: These findings identify circANRIL as a prototype of a circRNA regulating ribosome biogenesis and conferring atheroprotection, thereby showing that circularization of long non-coding RNAs may alter RNA function and protect from human disease.
Abstract: Circular RNAs (circRNAs) are broadly expressed in eukaryotic cells, but their molecular mechanism in human disease remains obscure. Here we show that circular antisense non-coding RNA in the INK4 locus (circANRIL), which is transcribed at a locus of atherosclerotic cardiovascular disease on chromosome 9p21, confers atheroprotection by controlling ribosomal RNA (rRNA) maturation and modulating pathways of atherogenesis. CircANRIL binds to pescadillo homologue 1 (PES1), an essential 60S-preribosomal assembly factor, thereby impairing exonuclease-mediated pre-rRNA processing and ribosome biogenesis in vascular smooth muscle cells and macrophages. As a consequence, circANRIL induces nucleolar stress and p53 activation, resulting in the induction of apoptosis and inhibition of proliferation, which are key cell functions in atherosclerosis. Collectively, these findings identify circANRIL as a prototype of a circRNA regulating ribosome biogenesis and conferring atheroprotection, thereby showing that circularization of long non-coding RNAs may alter RNA function and protect from human disease.

808 citations

23 Oct 2015
TL;DR: A GWAS meta-analysis of CAD cases and controls provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.
Abstract: Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.

797 citations

Posted ContentDOI
Urmo Võsa, Annique Claringbould, Harm-Jan Westra, Marc Jan Bonder, Patrick Deelen, Biao Zeng1, Holger Kirsten2, Ashis Saha3, Roman Kreuzhuber4, Silva Kasela5, Natalia Pervjakova5, Alvaes I6, Marie-Julie Favé6, Mawusse Agbessi6, Mark W. Christiansen7, Rick Jansen8, Ilkka Seppälä, Lin Tong9, Alexander Teumer10, Katharina Schramm, Gibran Hemani11, Joost Verlouw12, Hanieh Yaghootkar13, Reyhan Sonmez14, Andrew A. Brown15, Andrew A. Brown16, Kukushkina5, Anette Kalnapenkis5, Sina Rüeger14, Eleonora Porcu14, Jaanika Kronberg-Guzman5, Jarno Kettunen17, Joseph E. Powell18, Bernett Lee19, Futao Zhang20, Wibowo Arindrarto21, Frank Beutner2, Harm Brugge, Dmitreva J22, Mahmoud Elansary22, Benjamin P. Fairfax23, Michel Georges22, Bastiaan T. Heijmans21, Mika Kähönen24, Yungil Kim3, Julian C. Knight23, Peter Kovacs2, Knut Krohn2, Shuang Li, Markus Loeffler2, Urko M. Marigorta1, Hailiang Mei21, Yukihide Momozawa22, Martina Müller-Nurasyid, Matthias Nauck10, Michel G. Nivard8, Brenda W.J.H. Penninx8, Jonathan K. Pritchard25, Olli T. Raitakari26, Rotzchke O19, Eline Slagboom21, Coen D.A. Stehouwer27, Michael Stumvoll2, Patrick F. Sullivan28, Peter A C 't Hoen29, Joachim Thiery2, Anke Tönjes2, van Dongen J2, van Iterson M2, Jan H. Veldink30, Uwe Völker10, C Wijmenga, Morris A. Swertz, Anand Kumar Andiappan19, Grant W. Montgomery20, Samuli Ripatti17, Markus Perola17, Z. Kutalik14, Emmanouil T. Dermitzakis15, Sven Bergmann14, Timothy M. Frayling13, van Meurs J14, Holger Prokisch, Habibul Ahsan9, Brandon L. Pierce9, Terho Lehtimäki24, D.I. Boomsma8, Bruce M. Psaty7, Sina A. Gharib7, Philip Awadalla6, Lili Milani5, Willem H. Ouwehand4, Kate Downes4, Oliver Stegle31, Alexis Battle3, Jian Yang20, Peter M. Visscher20, Markus Scholz2, Greg Gibson1, Tõnu Esko5, Lude Franke 
19 Oct 2018-bioRxiv
TL;DR: It is observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting the ability to use cis- eZTLs to pinpoint causal genes within susceptibility loci.
Abstract: While many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear. To identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium. We observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci. In contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology. We observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

500 citations

Journal ArticleDOI
Gail Davies1, Max Lam, Sarah E. Harris1, Joey W. Trampush2  +254 moreInstitutions (79)
TL;DR: In this paper, the authors combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci associated with general cognitive function.
Abstract: General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.

421 citations


Cited by
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Book
01 Jan 2009

8,216 citations

Journal ArticleDOI
TL;DR: This year's edition of the Statistical Update includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association’s 2020 Impact Goals.
Abstract: Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovas...

5,078 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

Journal ArticleDOI
François Mach, Colin Baigent, Alberico L. Catapano, Konstantinos C. Koskinas1, Manuela Casula, Lina Badimon1, M. John Chapman, Guy De Backer, Victoria Delgado, Brian A. Ference, Ian D. Graham, Alison Halliday, Ulf Landmesser, Borislava Mihaylova, Terje R. Pedersen, Gabriele Riccardi, Dimitrios J. Richter, Marc S. Sabatine, Marja-Riitta Taskinen, Lale Tokgozoglu, Olov Wiklund, Christian Mueller, Heinz Drexel, Victor Aboyans, Alberto Corsini, Wolfram Doehner, Michel Farnier, Bruna Gigante, Meral Kayıkçıoğlu, Goran Krstacic, Ekaterini Lambrinou, Basil S. Lewis, Josep Masip, Philippe Moulin, Steffen E. Petersen, Anna Sonia Petronio, Massimo F Piepoli, Xavier Pintó, Lorenz Räber, Kausik K. Ray, Željko Reiner, Walter F Riesen, Marco Roffi, Jean-Paul Schmid, Evgeny Shlyakhto, Iain A. Simpson, Erik S.G. Stroes, Isabella Sudano, Alexandros D Tselepis, Margus Viigimaa, Cecile Vindis, Alexander Vonbank, Michal Vrablik, Mislav Vrsalovic, José Luis Zamorano, Jean-Philippe Collet, Stephan Windecker, Veronica Dean, Donna Fitzsimons, Chris P Gale, Diederick E. Grobbee, Sigrun Halvorsen, Gerhard Hindricks, Bernard Iung, Peter Jüni, Hugo A. Katus, Christophe Leclercq, Maddalena Lettino, Béla Merkely, Miguel Sousa-Uva, Rhian M. Touyz, Djamaleddine Nibouche, Parounak H. Zelveian, Peter Siostrzonek, Ruslan Najafov, Philippe van de Borne, Belma Pojskic, Arman Postadzhiyan, Lambros Kypris, Jindřich Špinar, Mogens Lytken Larsen, Hesham Salah Eldin, Timo E. Strandberg, Jean Ferrières, Rusudan Agladze, Ulrich Laufs, Loukianos S. Rallidis, Laszlo Bajnok, Thorbjorn Gudjonsson, Vincent Maher, Yaakov Henkin, Michele Massimo Gulizia, Aisulu Mussagaliyeva, Gani Bajraktari, Alina Kerimkulova, Gustavs Latkovskis, Omar Hamoui, Rimvydas Šlapikas, Laurent Visser, P. Dingli, Victoria Ivanov, Aneta Boskovic, Mbarek Nazzi, Frank L.J. Visseren, Irena Mitevska, Kjetil Retterstøl, Piotr Jankowski, Ricardo Fontes-Carvalho, Dan Gaita, Marat V. Ezhov, Marina Foscoli, Vojislav Giga, Daniel Pella, Zlatko Fras, Leopoldo Pérez de Isla, Emil Hagström, Roger Lehmann, Leila Abid, Oner Ozdogan, Olena Mitchenko, Riyaz S. Patel 

4,069 citations

Journal ArticleDOI
12 Oct 2017-Nature
TL;DR: It is found that local genetic variation affects gene expression levels for the majority of genes, and inter-chromosomal genetic effects for 93 genes and 112 loci are identified, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
Abstract: Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

3,289 citations