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Multi-Trait Analysis of GWAS and Biological Insights Into Cognition: A Response to Hill (2018)
- Max Lam, Joey W. Trampush, Jin Yu, Emma Knowles, Srdjan Djurovic, Ingrid Melle, Kjetil Sundet, Andrea Christoforou, Ivar Reinvang, Pamela DeRosse, Astri J. Lundervold, Vidar M. Steen, Thomas Espeseth, Katri Räikkönen, Elisabeth Widen, Aarno Palotie, Johan G. Eriksson, Ina Giegling, Bettina Konte, Panos Roussos, Stella Giakoumaki, Katherine E. Burdick, Antony Payton, William Ollier, Ornit Chiba-Falek, Deborah K. Attix, Anna C. Need, Elizabeth T. Cirulli, Aristotle N. Voineskos, Nikos C. Stefanis, Dimitrios Avramopoulos, Alex Hatzimanolis, Dan E. Arking, Nikolaos Smyrnis, Robert M. Bilder, Nelson A. Freimer, Tyrone D. Cannon, Edythe London, Russell A. Poldrack, Fred W. Sabb, Eliza Congdon, Emily Drabant Conley, Matthew A. Scult, Dwight Dickinson, Richard E. Straub, Gary Donohoe, Derek Morris, Aiden Corvin, Michael Gill, Ahmad R. Hariri, Daniel R. Weinberger, Neil Pendleton, Panos Bitsios, Dan Rujescu, Jari Lahti, Stephanie Le Hellard, Matthew C. Keller, Ole A. Andreassen, David C. Glahn, Anil K. Malhotra, Todd Lencz
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- Journal:
- Twin Research and Human Genetics / Volume 21 / Issue 5 / October 2018
- Published online by Cambridge University Press:
- 13 July 2018, pp. 394-397
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- Article
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Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88) presented a critique of our recently published paper in Cell Reports entitled ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ (Lam et al., Cell Reports, Vol. 21, 2017, 2597–2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229–237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from ‘inflation in the FDR [false discovery rate]’, as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84–88), and are not ‘more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence’.
4 - Genetics, genomics and proteomics in sudden cardiac death
- from Part II - Basic science
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- By Lesley A. Kane, Department of Biological Chemistry, Johns Hopkins University, Baltimore, USA, Silvia G. Priori, Molecular Cardiology, IRCCS Fondazione Salvatore Maugeri, Pavia, Italy and; Department of Cardiology, University of Pavia, Pavia, Italy, Carlo Napolitano, Molecular Cardiology, IRCCS Fondazione Salvatore Maugeri, Pavia, Italy, Dan E. Arking, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, USA, Jennifer E. Van Eyk, Department of Biological Chemistry, Johns Hopkins University, Baltimore, USA; Department of Medicine and Department of Biomedical Engineering, Johns Hopkins Universtiy, Baltimore, USA
- Edited by Norman A. Paradis, University of Colorado, Denver, Henry R. Halperin, The Johns Hopkins University School of Medicine, Karl B. Kern, University of Arizona, Volker Wenzel, Douglas A. Chamberlain, Cardiff University
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- Book:
- Cardiac Arrest
- Published online:
- 06 January 2010
- Print publication:
- 18 October 2007, pp 70-89
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Summary
Introduction
Sudden cardiac death (SCD) is an enigma: despite an overall decrease in cardiac mortality, SCD rates appear to be rising along with the concomitant increase in prevalence of coronary disease and heart failure. Even with decades of research, the underlying cellular mechanisms and stimulus/triggers are not well understood. This chapter addresses the application of large scale “omic” strategies to this critical clinical problem. First, is a discussion of the steps currently underway using genetic strategies to characterize several inherited arrhythmogenic diseases. The final two sections focus on two newer strategies, the technologies of genomics and proteomics.
Genetics, genomics and proteomics are complementary technologies. Figure 4.1 shows the flow from genes to proteins and emphasizes the increasing complexity at each step. Genetics strategies concentrate on identifying and characterizing a small number of candidate genes, informed by our understanding of the relevant biology, and are largely focused on analyzing sequence variants.Genomics looks more globally, with new approaches using unbiased whole-genome scans to examine both sequence variants and other genomic alterations, such as copy number polymorphism. Analysis of expressed genes, mRNA, is performed using the technologies of transcriptomics. Finally, the expressed proteins are studied using proteomics. This includes potential mutations (seen as amino acid changes) as well as post-translational modification (such as glycosylation or phosphorylation). It is only through the combined application of these technologies that we will be able to elucidate the underlying mechanisms of SCD, with the ultimate goal of both predicting individual risk and improving therapeutic intervention.