Translational cardiometabolic genomic medicine

TRANSLATIONAL CARDIOMETABOLIC GENOMIC MEDICINE Edited by ANNABELLE RODRIGUEZ-OQUENDO AMSTERDAM • BOSTON • CAMBRIDGE • HEIDELBERG LONDON • NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, UK 525 B Street, Suite 1800, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, UK Copyright © 2016 Elsevier Inc All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/ permissions This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein) Notices Knowledge and best practice in this field are constantly changing As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein ISBN: 978-0-12-799961-6 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress For information on all Academic Press publications visit our website at http://store.elsevier.com/ Typeset by TNQ Books and Journals www.tnq.co.in Printed and bound in the United States of America Contributors Rodrigo Alonso Obesity and Lipid Units, Department of Nutrition, Clı´nica Las Condes, Santiago de Chile, Chile Sahar Al Seesi Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA; Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT, USA Veronica Alvarez Obesity and Lipid Units, Department of Nutrition, Clı´nica Las Condes, Santiago de Chile, Chile Thomas E Cheatham, III Department of Medicinal Chemistry, L.S Skaggs Pharmacy Institute, University of Utah, Salt Lake City, UT, USA Ada Cuevas Obesity and Lipid Units, Department of Nutrition, Clı´nica Las Condes, Santiago de Chile, Chile Anthony M DeAngelis CT, USA University of Connecticut Health Center, Farmington, Fei Duan Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT, USA Charles R Farber Departments of Public Health Sciences and Biochemistry and Molecular Genetics, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA Magdalena Farı´as Obesity and Lipid Units, Department of Nutrition, Clı´nica Las Condes, Santiago de Chile, Chile Alexis C Frazier-Wood Department of Pediatrics, USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA Lita A Freeman Lipoprotein Metabolism Section, Cardiopulmonary Branch, National Heart, Lung and Blood Institute, Bethesda, MD, USA Rodrigo Galindo-Murillo Department of Medicinal Chemistry, L.S Skaggs Pharmacy Institute, University of Utah, Salt Lake City, UT, USA David Herrington Department of Internal Medicine, Section of Cardiology, Wake Forest School of Medicine, Winston-Salem, NC, USA Angela Kueck Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT, USA Ion I Mandoiu Department of Computer Science & Engineering, University of Connecticut, Storrs, CT, USA Gareth J McKay Centre for Public Health, Queen’s University Belfast, Belfast, Northern Ireland, UK xi xii CONTRIBUTORS Larry D Mesner Departments of Public Health Sciences and Biochemistry and Molecular Genetics, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA Steven Myint Center for Enterprise and Development, Duke-NUS Medical School, Singapore; Nanyang Business School, Nanyang Technological University, Singapore; Inex Private Ltd, Singapore; Plexpress Oy, Finland Adam Naj Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Waqas Qureshi Department of Internal Medicine, Section of Cardiology, Wake Forest School of Medicine, Winston-Salem, NC, USA Theodore P Rasmussen Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, USA; University of Connecticut Stem Cell Institute, University of Connecticut, Storrs, CT, USA; Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, USA Alan T Remaley Lipoprotein Metabolism Section, Cardiopulmonary Branch, National Heart, Lung and Blood Institute, Bethesda, MD, USA Stephen S Rich Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA Annabelle Rodriguez-Oquendo Farmington, CT, USA University of Connecticut Health Center, Meaghan Roy-O’Reilly University of Connecticut Health Center, Farmington, CT, USA Pramod K Srivastava Department of Immunology, Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT, USA; Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT, USA Kasey C Vickers Department of Medicine, Vanderbilt University, School of Medicine, Nashville, TN, USA C H A P T E R Metabolomics and Cardiovascular Medicine Waqas Qureshi, David Herrington Department of Internal Medicine, Section of Cardiology, Wake Forest School of Medicine, Winston-Salem, NC, USA INTRODUCTION Cardiovascular disease is a complex, multifactorial disease that currently affects 1.5 billion people worldwide [1] Many causative pathways through which risk factors act are still unclear Metabolomics has recently emerged as a promising field that may help elucidate intricate details of the relationships between changes in human biology and complex cardiovascular disease phenotypes Metabolites are the end-products of metabolic processes occurring in cellular organelles These are molecules smaller than kDa (1 Da ¼ 1.66 Â 10À27 kg) Historically, studies of metabolism have typically focused on a narrow range of metabolites or specific metabolic pathways In contrast, metabolomics generally involves a more comprehensive assessment of many metabolites and pathways The Metabolomics Society defines metabolomics as “comprehensive characterization of the small molecule metabolites in biological systems It can provide an overview of the metabolic status and global biochemical events associated with a cellular or biological system” [2] Similarly, Nicholson [3] defined metabolomics as a “quantitative measurement of the dynamic multi-parametric metabolic response of living systems to pathophysiologic stimuli or genetic modification.” [3] The term “metabolome” was first coined by Oliver et al [4] and Tweeddale et al [5] in 1998 Fiehn et al used the term “metabolomics” for the first time in 2001 [6] Although the term was initially used in the literature pertaining to plants and agricultural research, with advancement in analytical tools and sample extraction methods, human and animal medical fields have also embraced these terms and approaches Translational Cardiometabolic Genomic Medicine http://dx.doi.org/10.1016/B978-0-12-799961-6.00001-9 Copyright © 2016 Elsevier Inc All rights reserved METABOLOMICS AND CARDIOVASCULAR MEDICINE A major advantage of studying the metabolome is that metabolites are generally considered to be molecular phenotypes much closer to clinical traits of interestdreflecting the integrated effects of both upstream molecular signals (e.g., genome, epigenome, transcriptome, and proteome) and environmental factors (e.g., diet, psychological stressors, microbiome, and environmental exposuresdcollectively referred to as the exposome) This concept was built on central dogma from the 1950s that there is a linear and unidirectional flow of information from genome to phenome More recently, this concept has evolved into a complex network-based framework to account for multiple interconnected factors at all levels that determine a clinical phenotype Nevertheless, the metabolome remains at the intersection of many factors that ultimately reflect or determine the health of an individual (Figure 1) Thus, it is essential to understand both the metabolome and its interactions with other -omics to apply metabolomics to practice The application of this concept in clinical and population research is still relatively new However, already many important insights about the relationship between the metabolome and clinical cardiovascular disease have emerged, and several studies have identified key genomeemetabolome associations that better characterize the molecular signals contributing to pathogenesis of atherosclerosis, hypertension, and other cardiovascular conditions In this chapter, we provide an overview FIGURE System biology network Adapted from Adamski et al Curr Opin Biotechnol 2013; 24:39e47 METABOLOMIC METHODS of metabolomics trends and techniques, and review current understanding of the relationships among the metabolome, the genome, and the cardiovascular disease METABOLOMIC METHODS A metabolomic study is composed of several discrete steps including bio-sampling, separation, metabolite detection, and data analysis (Figure 2) Each step is critically important to ensure valid and interpretable results 2.1 Bio-specimen Selection and Handling Particular attention should be given to the type of bio-sample and sampling techniques, as these determine the types of metabolites that need to be studied (Table 1) While collecting bio-samples, care must be taken to avoid degradation and contamination of collected sample Some necessary considerations for sampling include timing (e.g., 24-h sampling versus collection after a 12- to14-h fasting period), sudden dietary changes (potentially interfere with the metabolome for over a week), bacterial or fungal contamination (especially for urine samples), medication intake (metabolites of medications may change the metabolome), diurnal variation in the hormones (anabolic and catabolic hormones vary during the day and may alter the metabolome), randomization, and transport and storage conditions of samples Serum is generally the preferred blood bio-specimen for metabolomic studies This is because plasma retains fibrinogen and other coagulation cascade proteolytic enzymes that can continue to be active ex vivo and alter the metabolome or interfere with metabolite detection Furthermore, ethylenediaminetetraacetic acid is typically used as the anticoagulant in plasma samples and can denature chromatograms and proteins in a way that may influence results Serum has fewer potentially interfering coagulation cascade enzymes and proteins However, even in serum there remain active enzymes that can continue to modify the metabolome ex vivo To counteract these enzymatic processes, serum can be collected on ice It is recommended to use samples stored at À20  C within days and samples stored at À80  C within month However, in one report, changes in the metabolome were negligible after storage for 2.5 years [7] In the case of repeated usage, fewer than three freezeethaw cycles is advisable [7] Until now, urine has been the most common sample for human metabolomics studies It has many advantages, such as a noninvasive method of collection, lack of special preparation for collection, almost no METABOLOMICS AND CARDIOVASCULAR MEDICINE FIGURE Schematic view of steps of a metabolomic study Adapted from Want et al Nature Protoc 2013; 8:17e32 METABOLOMIC METHODS TABLE Sample Types Used in Metabolomics Studies Approximate volume Sample Advantage Disadvantage Plasma/ serum Minimally invasive, readily available, provides information of metabolic footprint of many metabolic reactions May not reflect specific tissue level changes (e.g., cardiac changes) Tissue level metabolic fluxes cannot be measured Requires deproteination of mass spectrometer Anticoagulant used for plasma collection may interfere with the endogenous metabolites 150e550 mL Urine Noninvasive, readily available, provides information of metabolic footprint of many metabolic reactions May not reflect specific tissue level changes (e.g., cardiac changes) Tissue level metabolic fluxes cannot be measured Contains high amount of urea that can damage mass spectrometer Differences in pH of urine leads to difficulty in evaluation of various metabolites especially when nuclear magnetic resonance spectroscopy was used 500e1000 mL Primary cell culture Tissue level information can be obtained, metabolic fluxes can be studied Takes time, expensive, technically demanding, and requires complex procedures, phenotype may change over time 100e600 mL Tissue Tissue level information can be obtained, metabolic fluxes can be studied, accurate description of phenotype Takes time, expensive, technically demanding, and requires complex procedures, invasive for some of the specific tissues >20 mg sample pretreatment, and lower protein content, which helps to increase the sensitivity of identifying other metabolites [8] Urine, like blood or plasma, provides a metabolomic “footprint” of the whole body, not necessarily a single organ Usually a 24-h urine sample is collected for metabolomics studies; this method requires that participants receive METABOLOMICS AND CARDIOVASCULAR MEDICINE certain instructions and follow specific procedures Improper procedures may lead to bacterial overgrowth secondary to either contamination or infection that can affect the urine metabolome Many other types of samples have also been used in metabolomics research (e.g., cell lysates and spinal fluid), although these types of bio-samples are less amenable to large-scale clinical and population research Prior to sample separation, typical sample preparation steps are required Standardization of these sample preparation steps is pivotal to avoid random or, even worse, systematic bias in the results For example, alcohol-based extraction is a common procedure employed in sample extraction However, the pyruvate pathway is up-regulated when boiling ethanol or freezeethaw extraction is used, but down-regulated when cold methanol is used [9] The ideal method should be highly reproducible, simple, rapid, unselective, and include a metabolism-quenching step The latter is not frequently used in human studies, but is important because many active enzymes may influence metabolites in the collected sample (especially in plasma or serum samples) If the enzymes continue to remain active, they may change the concentrations of metabolites of interest Special attention should be paid to this matter when evaluating metabolites that are affected by rate-limiting steps, for example, glucose6-phosphate or ATP 2.2 Metabolite Separation Methods Chromatography is the principal separation technique used to obtain fine resolution of metabolites for metabolomics studies It plays a key role in obtaining analytical data needed in metabolic profiling The separation of metabolites is achieved by interactions of analytes with solvent and a variety of stationary phases (e.g., solid or liquid chromatography columns and so ondsee below) The basic theory of chromatography was first described in 1903 when a chromatogram made up of calcium carbonate (stationary phase) was inoculated with a leaf extract (sample) mixed with ethanol (mobile phase) For metabolomics research, a sample (or sample extract) is dissolved in a mobile phase (liquid or gas) This mobile phase is then forced through an immiscible stationary phase The component of sample that has an affinity for the stationary phase will travel slowly, whereas the component that repels or does not dissolve in the stationary phase is the first one to reach the other end of chromatography column By making changes in these phases, metabolites can be separated with a high degree of resolution, and a high-quality chromatogram can be obtained There are a number of definitions and key concepts that are useful to keep in mind when considering metabolomic studies 326 INDEX CEU See Centre d’Etude du Polymorphism Humain collection (CEU) CFB region See CFH factor B region (CFB region) CFH See Chronic inflammation including factor H (CFH); Complement factor H (CFH) CFH component (CC2), 286–287 CFH factor B region (CFB region), 286–287 CHAC1 See Cation transport regulatorlike protein (CHAC1) CHB See Han Chinese population in Beijing, China (CHB) CHD See Coronary heart disease (CHD) Chemotherapy drugs, 238–239 Cholesterol, 252 within blood, 100–102 enzymatic steps in conversion of acetate to, 262f homeostasis, 100f and infertility, 252–253 levels in CVD, 288–289 modeling cholesterol homeostasis, 105–109 Cholesterol metabolism, 251–253 cellular cholesterol supply mechanisms, 254f cellular uptake of lipoprotein cholesterol, 253–254 endometriosis, 270–272 genetic alterations effect cholesterol mobilization, 260–261 cholesterol uptake, 256–260 de novo cholesterol synthesis, 261–266 human fertility disorder, 266–267 intracellular cholesterol, 255 PCOS, 267–269 POF, 269–270 SR-BI, 253, 255f Cholesterol uptake, genetic alterations effect on ApoE, 256–257 LDLR, 257–258 lipoprotein particles, 256 SR-BI, 258–260 Cholesteryl ester transfer protein (CETP), 119, 127–128, 287, 291 Cholesteryl esters (CEs), 253 ChoRE See Carbohydrate response element (ChoRE) chromatin-associated smRNAs (CAsRNA), 48–49 Chromatin-modifying genes, 147–148 Chromatography, Chromosome 10q26, 287 Chronic inflammation, 267, 294 Chronic inflammation including factor H (CFH), 286–287 cis-acting siRNAs (casiRNA), 47–48 CITED2 loci, 147–148 CK See Creatine kinase (CK) Classic genetic disorders HDL-modifying genes identification, 123 familial hyperalphalipoproteinemia, 127–129 familial hypoalphalipoproteinemia, 123–127 Clinical Laboratory Improvement Amendments (CLIA), 318 Clinical trials, 238–239 Clinical utility, 316 Clinical validity, 316 Cloned live-born offspring, 109–110 Closely-related phenotypes, 184 CMS See Centers for Medicare and Medicaid Services (CMS) CNR1 See Cannabinoid receptor (CNR1) CNS See Central nervous system (CNS) CNVs See Copy number variants (CNVs) Cocaine-and amphetamine–related transcript (CART), 163f, 164 “Commercial know-how” secret, 315 Commercialization considerations in commercializing research, 311–312 of gene-based tests, 309–310 model, 312–313 Companion diagnostics, 309 Complement factor H (CFH), 294 Y402H variant in, 295–296 Complement pathway, 293–296 Component (C3), 286–287 Composition of matter, 314 Compound-specific databases, 15 Computed tomography (CT), 197 Conformite Europeene Mark certification (CE Mark certification), 316–317 Conformity assessment procedure, 318–319 Copy number variants (CNVs), 169–170 INDEX Copyright, 313 Coronary artery disease (CAD), 17–18, 67 HDL-modifying genes associated with, 149–150 Coronary heart disease (CHD), 76, 211–212 Creatine kinase (CK), 308 Crowdfunding, 313 CRP See C-reactive protein (CRP) CSL-112, 126–127 CT See Computed tomography (CT) CVD See Cardiovascular disease (CVD) Cyp19a1 See Cytochrome P450 aromatase (Cyp19a1) CYP51A1 gene, 263–264 CYP7A1 See Cytochrome P450 7A1 (CYP7A1) Cysteine (Cys), 289–290 Cytochrome P450 7A1 (CYP7A1), 102–103 Cytochrome P450 aromatase (Cyp19a1), 256–257 D dA See Deoxyadenosine (dA) DAGLB loci, 149 DAI See Differential agretopic index (DAI) Danish familial dementia, 195 DAPK1 See Death-associated protein kinase (DAPK1) Database of Interacting Protein (DIP), 67 DBP See Diastolic blood pressure (DBP) dC See Deoxycytocine (dC) De novo cholesterol synthesis, 261 7-Dehydrocholesterol, 266 desmosterol, 264–265 enzymatic steps in conversion of acetate to cholesterol, 262f HMGCR, 261–263 lanosterol, 263–264 Death-associated protein kinase (DAPK1), 184–185 7-Dehydrocholesterol, 266 Dementia postapoplexy, 179–180 DENND1A gene, 268–269 Deoxyadenosine (dA), 87 Deoxycytocine (dC), 87 Deoxyguanosine (dG), 87 Deoxythymidine (dT), 87 Depression, 183 DESI See Desorption electrospray ionization (DESI) Desmosterol, 264–265 327 Desmosterolosis, 265 Desorption electrospray ionization (DESI), 13 Desorption/ionization on silicon (DIOS), 13 dG See Deoxyguanosine (dG) DGAT2 loci, 136 DHCR24 gene See 3b-hydroxysteroid-D24 reductase gene (DHCR24 gene) DHCR7 gene See 3b-hydroxysterol D7 cholesterol reductase gene (DHCR7 gene) Diabetes, 20–22, 145–146 Diastolic blood pressure (DBP), 146 Diet, 182 Dietary assessment methods, 210 Differential agretopic index (DAI), 246–247 DIMINUTO/DWARF1 gene, 264 DIOS See Desorption/ionization on silicon (DIOS) DIP See Database of Interacting Protein (DIP) Directional sequencing, 240–241 “Disease in a dish” models, 113–114 Disease module, 64 DNA breathing, 93 characteristics, 94t naming convention, 86f sequence-based deformability and timescale, 92–94 sequence-specific structure and dynamics, 84 solvation effects in, 91 structure and characterization, 86–87 dihedral torsions, 88 double-stranded canonical DNA, 87 helicoidal parameters representation, 89f structures of duplex DNA, 90t DNA sequencing methods (DNAseq methods), 39–40 Dolly the Sheep See Cloned live-born offspring dT See Deoxythymidine (dT) Dyslipidemia, 109, 136–145, 252 DZ See Non-identical twins (DZ) E Early-stage AMD, 285–286 ECs See Endothelial cells (ECs) 328 INDEX EDTA See Ethylenediaminetetraacetic acid (EDTA) EFTA See European Free Trade Association (EFTA) EI See Electron ionization (EI) EIF2AK2 See Eukaryotic translation initiation factor 2-alpha kinase (EIF2AK2) Electron impact, 13 Electron ionization (EI), 13 Electrospray ionization (ESI), 13 elongation factors (elFs), 43–44 ELSI See Ethical, legal, and social implications (ELSI) Elucidating gene function, 74–75 EMA See European Medicines Agency (EMA) Embryonic stem cells, 106 Embryonic viability, genetic alterations affecting, 261–266 Enabling disclosure, 314 Endog See Endonuclease G (Endog) Endogenous siRNAs, 47–48 Endometriosis, 266–267, 270–272 Endonuclease G (Endog), 74–75 Endophenotypes, 184 Endoplasmic reticulum (ER), 255 Endothelial cells (ECs), 70–71 EOC See Epithelial ovarian cancer (EOC) EPIC See Prospective Investigation into Cancer and Nutrition (EPIC) Epidemiology ethnicity impact on CVD, 211 all-cause CVD, 211–212 CHD, 212 stroke, 212 VaD, 180–183 Epigenetics, 26–30 Epithelial ovarian cancer (EOC), 237 See also Next-generation sequencing (NGS) early detection, 238 immune response, 239–240 implications for genomics-driven immunotherapy, 246–247 pipeline for phase clinical trial testing, 247f risk factors, 237–238 sample analysis in TCGA database, 244–246 treatment for patients, 238–239 EPO See European Patent Office (EPO) eQTL See expression QTL (eQTL) Equilibrium constant, ER See Endoplasmic reticulum (ER) ESI See Electrospray ionization (ESI) ESP See Exome sequencing project (ESP) ESR1 See Estrogen receptor a (ESR1) EST See Expressed sequence tags (EST) Estrogen, 256–257, 270–272 Estrogen receptor a (ESR1), 270 Ethical, legal, and social implications (ELSI), 316 Ethnic differences in biomarkers, 215 lipids, 216–217 lipoproteins, 218 markers of inflammation, 215–216 in CVD risk factors, 212–213 obesity, 213–214 T2D, 214 Ethylenediaminetetraacetic acid (EDTA), ETS variant gene (ETV5), 168–169 EU See European Union (EU) Eukaryotic translation initiation factor 2-alpha kinase (EIF2AK2), 184–185 European Free Trade Association (EFTA), 317 European Medicines Agency (EMA), 310, 316 European Patent Office (EPO), 314 European Union (EU), 317 Excess abdominal fat, 293–294 See also Obesity Exclusive licenses, 312 Exome, 226–227 Exome sequencing project (ESP), 226–227, 241–243 array hybridization exome extraction protocol, 242f in-solution hybridization exome extraction protocol, 242f Exposome, Expressed sequence tags (EST), 39–40 expression QTL (eQTL), 74 F F2-isoprostane, 18 Familial cerebral amyloid angiopathy (FCAA), 195 Familial hyperalphalipoproteinemia, 127 CETP, 127–128 SR-BI, 128–129 INDEX Familial hypercholesterolemia (FH), 103–104 Familial hypoalphalipoproteinemia ABCA1 transporter, 123–124 apoA-I, 125–127 LCAT, 124–125 Fas Cell Surface Death Receptor (FAS Death Receptor), 147 Fat mass and obesity gene (FTO gene), 146, 166–168 FCAA See Familial cerebral amyloid angiopathy (FCAA) FDA See US Food and Drug Administration (FDA) FF-MAF See Follicular fluid meiosis activating factor (FF-MAF) FGB See b-fibrinogen gene (FGB) FGFs See Fibroblast growth factors (FGFs) FH See Familial hypercholesterolemia (FH) Fibroblast growth factors (FGFs), 107–108 Flow-injection analysis, 11 Foam cells, 100–102 Follicular fluid meiosis activating factor (FF-MAF), 263–264 FPKM See Fragments per kilobase per million reads (FPKM) Fragments per kilobase per million reads (FPKM), 244 FSHR gene, 268 FTO gene See Fat mass and obesity gene (FTO gene) Functional modules, 62–64 G G-protein coupled receptor 120 (GPR120), 166 G-quadruplexes, 88–89 GA See Geographic atrophy (GA) GAB2 See GRB-associated binding protein (GAB2) Gas chromatography (GC), Gas chromatography-mass spectrometry (GC-MS), 8–9, 265–266 GC See Gas chromatography (GC) GC-MS See Gas chromatography-mass spectrometry (GC-MS) GCC See Gulf Cooperation Council (GCC) GCKR See Glucose kinase receptor gene (GCKR) 329 GEI See Gene–environment interaction (GEI) Gene discovery, 73–74 Gene Module Association Study (GMAS), 77 Gene–environment interaction (GEI), 170–171 Genetic analysis, 71 See also Networks Genetic disease modeling, iPSCs for, 105–109 Genetic Investigation of Anthropometric Traits consortium (GIANT consortium), 168–169 Genetic(s) of AMD, 286–287 mapping, 72 mutations, 251–252 testing, 172 VaD, 183 candidate genes, 184–185 genomic variants in candidate gene studies, 186t–187t monogenic forms of VaD, 191–196 polygenic contributors to VaD, 190–191 Genome-wide association studies (GWAS), 23, 72, 74, 83–84, 105, 119, 130t–134t, 162–163, 183, 190, 266–267, 271, 286–287, 291, 294 data network analysis, 75–76 epigenetics, 26–30 GWAS-identified HDL-associated genes, 149 HDL-modifying genes identification, 129 functions, 136, 137t–138t meta-analysis, 135 overlap of loci, 135f interactions between-omics and scenarios, 29f IPs, 24f metabolic phenotypes, 23–25 metabolomics, 26–30 associations ideogram, 27f metabolomics and, 23 network view of genetic and metabolomic associations, 28f NMR spectroscopy, 26f novel metabolic pathway identification in, 25–26 single nucleotide polymorphisms, 167t 330 INDEX Genome-wide linkage studies, 162–163, 184 Genomic(s) of human fertility disorders, 266–267 research translation to marketplace cardiovascular genomics marketplace, 307–310 commercial potential of human genome, 307 commercialization model, 312–313 considerations in commercializing research, 311–312 intellectual property, 313–315 marketing and sales development, 320 product development, 315–317 regulatory considerations, 317–319 Genomics-guided immunotherapy EOC, 237–239 immune response, 239–240 implications for genomics-driven immunotherapy, 246–247 sample analysis in TCGA database, 244–246 NGS in cancer genomics, 240–244 Genotype–environment interaction, 111–112 Genotype–phenotype map, 72–73 Geographic atrophy (GA), 283–284 GIANT consortium See Genetic Investigation of Anthropometric Traits consortium (GIANT consortium) GIH See Gujarati Indians in Houston, Texas (GIH) Global genetic risk score (GRS), 171 Glucosamine-6-phosphate deaminase (GNPDA2), 168–169 Glucose kinase receptor gene (GCKR), 25–26 Glybera, 307 Glycerol-3-phosphate acyltransferase (GPAM), 41–43 Glycine, 20–22 Gm6484 See LOC55908 loci GMAS See Gene Module Association Study (GMAS) GNPDA2 See Glucosamine-6-phosphate deaminase (GNPDA2) GOLDEN study, 171 Golgi membrane protein (GOLM1), 184–185 Government technology transfer, 312 GPAM See Glycerol-3-phosphate acyltransferase (GPAM) GPR120 See G-protein coupled receptor 120 (GPR120) Gradient mobile phase, GRB-associated binding protein (GAB2), 184–185 GRS See Global genetic risk score (GRS) GTF2A1L gene, 268 “Guilt-by-association” approach, 74 Gujarati Indians in Houston, Texas (GIH), 223–224 Gulf Cooperation Council (GCC), 314 GWAS See Genome-wide association studies (GWAS) H HAAS See HonolulueAsia Aging Study (HAAS) Han Chinese population in Beijing, China (CHB), 223–224 HapMap, 223–224 HAS1 See Hyaluron synthase (HAS1) HCG See Human chorionic gonadotropin (HCG) HCHWA-D See Hereditary cerebral hemorrhage with amyloidosisDutch type (HCHWA-D) HDL See High-density lipoproteins (HDL) HDL-C See High-density lipoproteincholesterol (HDL-C) Health disparities, 209–210, 219 Health technology assessments (HTA), 310 Heart failure, 19–20, 21t Hepatic glycogen-targeting protein phosphatase See PPP1R3B loci Hepatic lipase gene (LIPC gene), 225–226, 287, 291 Hepatocyte, 99–100 LDL functions, 99–103 production from pluripotent stem cells, 108 stem cell-derived, 114–115 Hepatocyte growth factor (HGF), 108 Hepatocyte-like cells (HLCs), 107–109 Hereditary cerebral hemorrhage with amyloidosis-Dutch type (HCHWA-D), 195 INDEX Heritability of BMI, 221–222 CVD, 220–222 missing, 169–170 hESCs See Human embryonic stem cells (hESCs) HGF See Hepatocyte growth factor (HGF) High-density lipoprotein-cholesterol (HDL-C), 288–289, 292 High-density lipoproteins (HDL), 119, 216–217, 257, 287 See also Low density lipoprotein (LDL) chromatin-modifying genes, 147–148 composition, 120 diabetes, 145–146 dyslipidemia and lipid metabolism, 136–145 function, 121–122 GWAS-identified HDL-associated genes, 149 HDL-modifying genes identification CAD, 149–150 classic genetic disorders, 121–122 GWAS, 129–136 lipid trafficking-related genes, 146–147 obesity, 145–146 RCT pathway, 121f signal transduction-related genes, 148–149 High-performance liquid chromatography (HPLC), High-throughput RNA sequencing, 39–40 HILIC See Hydrophilic interaction chromatography (HILIC) hiPSCs See Human induced pluripotent stem cells (hiPSCs) HLCs See Hepatocyte-like cells (HLCs) HMDB See Human Metabolome Database (HMDB) HMG-CoA reductase See 3-hydroxy3-methylglutaryl coenzyme A reductase (HMG-CoA reductase) HMGCoA reductase (HMGCR), 99, 102–103, 261–263 Homocysteine, 269–270 HonolulueAsia Aging Study (HAAS), 182 Hormone-sensitive lipase (HSL), 253, 260–261 HPLC See High-performance liquid chromatography (HPLC) HPRD See Human Protein Reference Database (HPRD) 331 HSL See Hormone-sensitive lipase (HSL) HSP157 gene, 272 HSPC157 gene, 272 HTA See Health technology assessments (HTA) Hub nodes, 64–65 Human chorionic gonadotropin (HCG), 268 Human disease network, 66–67 Human embryonic stem cells (hESCs), 106–107 Human endometrial tissue, 270–271 Human fertility See also Infertility; Polycystic ovary syndrome (PCOS) disorder, 266–267 genetic alterations effect cholesterol mobilization, 260–261 cholesterol uptake, 256–260 Human genomic findings related to LDL dysfunction, 105 Human induced pluripotent stem cells (hiPSCs), 106 Human Metabolome Database (HMDB), 15 Human Protein Reference Database (HPRD), 67 Human single gene genetic disorders, 103–104 Hyaluron synthase (HAS1), 145–146 Hydrophilic interaction chromatography (HILIC), 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA reductase), 255 Hyperalphalipoproteinemia, 123, 259–260 Hyperglycemia, 182 Hyperlipidemia, 182 Hypertension, 18–19, 182 Hypoalphalipoproteinemia, 123 Hypoglycemia, 182 Hypothalamus, 164 hypothalamic control of appetite and energy regulation, 163–164 I I/D polymorphism See Insertion/ deletion polymorphism (I/D polymorphism) ICH See International Conference on Harmonisation (ICH) ICM See Inner cell mass (ICM) Identical twins (MZ), 219 332 IDIN See IRF7-driven inflammatory gene network (IDIN) IDL See Intermediate-density lipoproteins (IDL) IkB kinase activity (IKK activity), 146 Immunoprecipitation (IP), 67 In vitro fertilization (IVF), 259–260 In vitro stem cell approaches, modeling cholesterol homeostasis using, 105–109 In-vitro diagnostic medical devices (IVDD), 318–319 CE Mark certification for, 316–317 In-vitro diagnostics (IVD), 308–309 market share, 309f indels See Insertion–deletion mutations (indels) Induced pluripotent stem cells (iPSCs), 109 for genetic disease modeling, 105–109 Infertility, 251 See also Human fertilityddisorder; Polycystic ovary syndrome (PCOS) cholesterol and, 252–253 SR-BI deficiency and, 257–258 Inflammation, 293–296 markers of, 215–216 Inflammatory cytokines, 267 Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), 196 Inner cell mass (ICM), 107 Insertion/deletion polymorphism (I/D polymorphism), 185 Insertion–deletion mutations (indels), 112–113 INSIG1 protein, 255 INSR gene See Insulin receptor gene (INSR gene) Insulin, 267–268 Insulin receptor (IR), 145, 149–150 Insulin receptor gene (INSR gene), 267–268 Insulin receptor substrate (IRSI 1), 171 IntAct See Protein interaction database (IntAct) Intellectual property (IP), 313–315 Interferon Regulatory Transcription Factor (Irf7), 74 Intermediate phenotypes (IPs), 24f Intermediate-density lipoproteins (IDL), 100–102, 256 INDEX International Classification of Good and services (Nice agreement), 313–314 International Conference on Harmonisation (ICH), 310 International Organization for Standards (ISO), 316 Intracranial atherosclerosis, 183 Invention summary, 314 IP See Immunoprecipitation (IP); Intellectual property (IP) IPs See Intermediate phenotypes (IPs) iPSCs See Induced pluripotent stem cells (iPSCs) IQCODE See Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) IR See Insulin receptor (IR) IRF7-driven inflammatory gene network (IDIN), 74 Irf See Interferon Regulatory Transcription Factor (Irf7) IRSI See Insulin receptor substrate (IRSI 1) ISO See International Organization for Standards (ISO) IVD See In-vitro diagnostics (IVD) IVDD See In-vitro diagnostic medical devices (IVDD) IVDD Directive 98/79/EC, 318–319 IVF See In vitro fertilization (IVF) J Japanese in Tokyo, Japan (JPT), 223–224 K K(lysine) acetyltransferase (KAT5), 147–148 KAT5 See K(lysine) acetyltransferase (KAT5) KCTD15 See Potassium channel tetramerization domain containing 15 (KCTD15) KEGG See Kyoto Encyclopedia of Genes and Genomes (KEGG) Kickstarter, 313 KLF14 transcription factor, 145, 147–148 Kooperative Gesundheitsforschung in der Region Augsburg study (KORA study), 20–22 Kyoto Encyclopedia of Genes and Genomes (KEGG), 15 INDEX L Laboratory-derived tests (LDTs), 308–309 Lacuna, 239–240 Lanosterol, 263–264 LC See Liquid chromatography (LC) LCAT See Lecithin:cholesterol acyltransferase (LCAT) LCAT-knockout mice (LCAT-KO mice), 125 LCAT-KO mice See LCAT-knockout mice (LCAT-KO mice) LDL See Low density lipoprotein (LDL) LDL-C See Low density lipoprotein cholesterol (LDL-C) LDLR See Low density lipoprotein receptor (LDLR) LDTs See Laboratory-derived tests (LDTs) Lecithin:cholesterol acyltransferase (LCAT), 120, 124–125 Left ventricular mass (LVM), 66, 74–75 LEP gene See Leptin gene (LEP gene) LEPR gene See Leptin receptor gene (LEPR gene) Leptin, 163–164 Leptin gene (LEP gene), 164–165 Leptin receptor gene (LEPR gene), 163–165 Leukocyte immunoglobulin-like receptor (LILRA3), 149 LHCGR gene, 268 Licensing, 312 LILRA3 See Leukocyte immunoglobulinlike receptor (LILRA3) LINE See Long interspersed elements (LINE) Lipasin See LOC55908 loci LIPC gene See Hepatic lipase gene (LIPC gene) Lipid(s), 216–217 metabolism, 136–145 trafficking-related genes, 146–147 Lipoprotein lipase (LPL), 100–102 Lipoprotein-mediated cholesterol uptake, 256 Lipoproteins, 218 Liquid chromatography (LC), 8, 10t lncRNAs See Long non-coding RNAs (lncRNAs) LOC55908 loci, 136–145 Long interspersed elements (LINE), 47–48 Long Leptin receptor (ObRb), 163f Long non-coding RNAs (lncRNAs), 41 Long tandem repeats (LTR), 47–48 333 Low density lipoprotein (LDL), 100–102, 218 See also High-density lipoproteins (HDL) functions of hepatocyte, 99–103 future directions, 115–116 human genomic findings related to LDL dysfunction, 105 human single gene genetic disorders, 103–104 using iPSCs for genetic disease modeling, 109–114 induced pluripotency to model human genetic disorders, 113f modeling cholesterol homeostasis, 105–109 personalized medicine research, 109–114 pool, 283 production and uptake, 101f using stem cell-derived hepatocytes, 114–115 Low density lipoprotein cholesterol (LDL-C), 216–217 Low density lipoprotein receptor (LDLR), 102–104, 253–254, 257–258 recycling, 103f Low-grade inflammation score, 216 LPL See Lipoprotein lipase (LPL) LTR See Long tandem repeats (LTR) Luhya in Webuye, Kenya (LWK), 223–224 LVM See Left ventricular mass (LVM) LWK See Luhya in Webuye, Kenya (LWK) Lynch syndrome, 237–238 M Maasai in Kinyawa, Kenya (MKK), 223–224 MAC See Membrane attack complex (MAC) Macrophage-enriched metabolic network (MEMN), 73 Macrophages, 122 Macular pigment (MP), 291–292 MAF See Minor allele frequencies (MAF) Magnetic resonance imaging (MRI), 197 Major vault protein (MVP), 47 MALDI See Matrix-assisted laser desorption ionization (MALDI) MARCH8 actin-binding protein, 147 Marketing and sales development, 320 Matrix-assisted laser desorption ionization (MALDI), 12–13 334 INDEX MC4R See Melanocortin-4 receptor (MC4R) MCI See Mild cognitive impairment (MCI) MD simulations See Molecular dynamics simulations (MD simulations) Medical device, 317–318 Mediterranean-type dietary pattern, 182 Melanocortin-4 receptor (MC4R), 146, 163f, 164 Membrane attack complex (MAC), 294–295 MEMN See Macrophage-enriched metabolic network (MEMN) MESA See Multi-Ethnic Study of Atherosclerosis (MESA) Meso-zeaxanthin (meso-Z), 291–292 Messenger ribonucleic acid (mRNA), 295 meta-genome-wide association studies (mGWAS), 25–26 Metabolic pathway databases, 15 Metabolic profiling, Metabolic syndrome, 182 Metabolite quantitative trait loci (mQTLs), 24–25 Metabolite(s), detection ionization techniques, 13 MS, 12–13 NMR spectroscopy, 11 time-consuming process, 13 two-dimensional NMR experiments, 12t separation methods, peak capacity, 8–11 resolution, 7–8 retention time, solvent strength, Metabolomics, advantage, clinical and population-based studies, 17–22 data analysis, 13–15 databases, 16t and GWAS, 23–30 in heart failure, 21t informatics resources, 15 methods bio-specimen selection and handling, 3–6 metabolite detection, 11–13 metabolite separation methods, 6–11 sample types, 5t steps, 4f system biology network, 2f Metabolomics Society, Metabolomics-wide association studies (MWAS), 23 See also Genome-wide association studies (GWAS) Metalloproteinase (TIMP3), 287 Methylenetetrahydrofolate reductase gene (MTHFR gene), 184–185, 269–270 Mexican ancestry in Los Angeles, California (MXL), 223–224 mGWAS See meta-genome-wide association studies (mGWAS) MI See Myocardial infarction (MI) MicroRNAs (miRNAs), 41–43 Microsomal triglyceride transfer protein (MTP), 100–102 MID See Multi-infarct dementia (MID) Mild cognitive impairment (MCI), 180 Minor allele frequencies (MAF), 263–264 MINT See Molecular Interaction database (MINT) miRNAs See MicroRNAs (miRNAs) Missing heritability, 169–170 Mitochondrial carrier (MTCH2), 168–169 Mitochondrial dysfunction, 287 MKK See Maasai in Kinyawa, Kenya (MKK) MLN64 See STARD3 gene product Modularity, 62–64 Module discovery, 73–74 MOGAT2 loci, 136 Molecular diagnostics market, 308 Molecular dynamics simulations (MD simulations), 85–86 Molecular Interaction database (MINT), 67 Monogenic diseases, 195–196 Monogenic obesity, 164–165 genes associated with human monogenic obesity, 165t MP See Macular pigment (MP) MPO See Myeloperoxidase (MPO) mQTLs See Metabolite quantitative trait loci (mQTLs) MRI See Magnetic resonance imaging (MRI) mRNA See Messenger ribonucleic acid (mRNA) MS/MS See Tandem mass spectrometry (MS/MS) INDEX MTCH2 See Mitochondrial carrier (MTCH2) MTHFR gene See Methylenetetrahydrofolate reductase gene (MTHFR gene) MTP See Microsomal triglyceride transfer protein (MTP) Multi-Ethnic Study of Atherosclerosis (MESA), 216 Multi-infarct dementia (MID), 179–180 Multihit “threshold” model of AMD, 288f Multiple genome-wide studies, 22 MVP See Major vault protein (MVP) MWAS See Metabolomics-wide association studies (MWAS) MXL See Mexican ancestry in Los Angeles, California (MXL) Myeloperoxidase (MPO), 308 Myocardial infarction (MI), 293, 308 MZ See Identical twins (MZ) N National Health and Nutrition Examination Survey (NHANES), 213–216 National Health Interview Survey (NHIS), 213–214 National Institute of Health and Clinical Excellence (NICE), 310 natural antisense transcript-derived siRNAs (natsiRNA), 47–48 NEGR1 See Neuronal growth regulator (NEGR1) Neovascular AMD (nvAMD), 283–284 Network biology, 77 Network theory, 71 Network-based systems genetics studies elucidating gene function, 74–75 gene discovery, 73–74 GWAS data network analysis, 75–76 module discovery, 73–74 Networks, 60–61 integrating genetics and, 71 current state of CVD genetics, 71–72 systems genetics, 72–73 network-based systems genetics studies, 73–76 organizing principles directed vs undirected, 65 hub nodes, 64–65 modularity, 62–64 scale-free topology, 61–62 335 topological and functional network modules, 63f types, 65 co-expression networks, 68–71 phenotype/disease networks, 66–67 PPI networks, 67–68 WGCNA, 69f Neuronal growth regulator (NEGR1), 168–169 Neuropeptide Y (NPY), 164 Neuropeptide Y (NYY), 163f Next-generation sequencing (NGS), 39–40, 240 See also Epithelial ovarian cancer (EOC) bioinformatics analysis, 243–244 exome sequencing, 241–243 RNA-Seq, 240–241, 241f NGS See Next-generation sequencing (NGS) NHANES See National Health and Nutrition Examination Survey (NHANES) NHIS See National Health Interview Survey (NHIS) NHW See Non-Hispanic whites (NHW) NICE See National Institute of Health and Clinical Excellence (NICE) NMR spectroscopy See Nuclear magnetic resonance spectroscopy (NMR spectroscopy) Non-exclusive licenses, 312 Non-Hispanic whites (NHW), 209–210 Non-identical twins (DZ), 219 Non-polar solvents, Novel drugs, 119 NPY See Neuropeptide Y (NPY) nts See Nucleotides (nts) Nuclear magnetic resonance spectroscopy (NMR spectroscopy), 10, 84 Nucleobases, 87 Nucleotides (nts), 41 Nutritional influences, 291–293 nvAMD See Neovascular AMD (nvAMD) NYY See Neuropeptide Y (NYY) O Obesity, 22, 145–146, 161, 213–214, 221, 293–294 candidate gene approach, 162 common, 165–169 GEI studies, 170–171 genetic testing, 172 336 Obesity (Continued) genome-wide linkage studies, 162–163 leptin and hypothalamic control of appetite and energy regulation, 163–164 missing heritability, 169–170 monogenic, 164–165 pathogenesis, 161–162 SNPs, 167t “Obesogenic” environment, 165–166 ObRb See Long Leptin receptor (ObRb) OMIM See Online Mendelian Inheritance in Man (OMIM) Online Mendelian Inheritance in Man (OMIM), 66–67 OPLS See Orthogonal projections to latent structures (OPLS) Orphan snoRNAs, 44–45 Orthogonal projections to latent structures (OPLS), 14–15 Ovarian cancer, 238–239 Oxidative stress, 18, 287 P Parkin (PARK2), 136–145 paRNA See Promoter-associated RNAs (paRNA) Partial least squares regression (PLS regression), 14–15 Partition coefficient, Patent Cooperation Treaty (PCT), 314 Patenting DNA technology, impetus for, 315 Patents, 313–314 PCKS9 protein, 104 PCOS See Polycystic ovary syndrome (PCOS) PCSK1 See Proprotein convertase (PCSK1) PCSK9 See Proprotein Convertase Subtilisin/Kexin type (PCSK9) PCT See Patent Cooperation Treaty (PCT) Peak capacity (Pc), capillary electrophoresis, 10 flow-injection analysis, 11 NMR, 10 relationship of genome, epigenome, transcriptome, exome, proteome, metabolome, and phenome, 9f solvents, Peptide YY (PYY), 163f Peripheral vascular disease (PVD), 183 INDEX Peroxisome proliferator-activated receptor gamma (PPARg), 41–43, 166 Personalized medicine, 219–220 research, 109–114 PGS1 See Phosphatidylglycerophosphate synthase (PGS1) Phenotype/disease networks, 66–67 Phenylalanine, 25–26 Phosphatidylglycerophosphate synthase (PGS1), 136–145 Physical activity, 182 piggyBac transposons, 110–111 PIWI-interacting RNAs (piRNA), 47–48 Platinum sensitive, 238–239 PLS regression See Partial least squares regression (PLS regression) Pluripotent cells, 107–108 PMA See Premarket application (PMA); Premarket Approval (PMA) POF See Premature ovarian failure (POF) Polar solvents, Polycystic ovary syndrome (PCOS), 252, 266–269 See also Human fertilityddisorder; Infertility Polygenic contributors to VaD, 190–191 Polymorphisms, 269–270 POMC See Proopiomelanocortin (POMC) Population genetics, formula of, 112 Potassium channel tetramerization domain containing 15 (KCTD15), 168–169 PPARg See Peroxisome proliferatoractivated receptor gamma (PPARg) PPI network See Protein–protein interaction network (PPI network) PPP1R3B loci, 145–146 Premarket application (PMA), 318 Premarket Approval (PMA), 318 Premature ovarian failure (POF), 252, 266–267, 269–270 Preterm delivery (PTD), 263 Principal component analysis, 14 Product development, 315–317 PROMoter uPstream Transcripts (PROMPTs), 48–49 Promoter-associated RNAs (paRNA), 48–49 PROMPTs See PROMoter uPstream Transcripts (PROMPTs) Proopiomelanocortin (POMC), 163f, 164 Proprotein convertase (PCSK1), 163f, 164 INDEX Proprotein Convertase Subtilisin/Kexin type (PCSK9), 102–103 Prospective Investigation into Cancer and Nutrition (EPIC), 20–22 Protein interaction database (IntAct), 67 Protein–protein interaction network (PPI network), 61, 64, 67–68 Proteins abnormal extracellular deposition, 285 functional alterations in, 251–252 PTD See Preterm delivery (PTD) Purine–purine steps (RR steps), 92–93 Purnell equation, PVD See Peripheral vascular disease (PVD) Pyrimidine–purine steps (YR steps), 92–93 PYROXD2 locus variant, 24–25 PYY See Peptide YY (PYY) 337 Quantitative trait locus (QTL), 73 RNA-Seq, 240–241, 241f RNAseq methods See RNA sequencing methods (RNAseq methods) RNH1 See Ribonuclease/angiogenin inhibitor (RNH1) RNP See Ro ribonucleoprotein complex (RNP) Ro ribonucleoprotein complex (RNP), 46–47 RPE See Retinal pigment epithelium (RPE) RPLC See Reversed-phase chromatography (RPLC) rRNA See ribosomal RNAs (rRNA) rRNA-derived smRNAs (rsRNA), 49 rs12007229 variant, 190 rs9939609 variant, 167–168 RSPO3 gene, 149 rsRNA See rRNA-derived smRNAs (rsRNA) RVX-208 loci, 126 R S RASGRF2 gene, 190 RCT See Reverse cholesterol transport (RCT) Recombinant inbred strains (RI strains), 66 Regulators, 310 Regulatory considerations, 317–319 Reimbursement, 308–309, 320 Resolution (R), 7–8 Retention factor, Retention time (TR), Retinal pigment epithelium (RPE), 283–284 Reverse cholesterol transport (RCT), 102, 122, 287–289, 292 Reversed-phase chromatography (RPLC), RI strains See Recombinant inbred strains (RI strains) Ribonuclease/angiogenin inhibitor (RNH1), 43–44 ribosomal RNAs (rRNA), 41, 44–45, 49 RIFL See LOC55908 loci RISC See RNA-induced silencing complex (RISC) RNA sequencing methods (RNAseq methods), 39–40 advantages, 40–41 RNA-induced silencing complex (RISC), 41–43, 46–47 SACGHS See Secretary’s Advisory Committee on Genetics, Health, and Society (SACGHS) SBNO1 See Strawberry Notch Homolog (SBNO1) SBP See Systolic blood pressure (SBP) Scale-free network, 64 topology, 61–62 SCAP See SREBP cleavage activating protein (SCAP) SCARB1 gene, 292 scaRNA See snoRNAs and Cajal bodyspecific RNAs (scaRNA) Scavenger receptor class B type I (SR-BI), 122, 128–129, 253, 255f, 258–260 deficiency, 257–258 SDMA See Symmetrical dimethylarginine (SDMA) sdRNA See snoRNA-derived smRNAs (sdRNA) Secretary’s Advisory Committee on Genetics, Health, and Society (SACGHS), 310 Seladin-1 See Selective Alzheimer’s disease indicator (Seladin-1) Selective Alzheimer’s disease indicator (Seladin-1), 264 Selectivity factor, Q 338 INDEX Sequence-based deformability, 92–94 Serum, H-NMR data, 17–18 cholesterol, 99 targeted serum metabolomics studies, 22 SES See Socioeconomic status (SES) SH2B adaptor protein (SH2B1), 168–169 Shared constituents, 285 Short interspersed elements (SINE), 47–48 Signal transduction-related genes, 148–149 SINE See Short interspersed elements (SINE) Single nucleotide variations (SNVs), 244 Single-nucleotide polymorphisms (SNPs), 22, 72, 83–84, 112–113, 123–124, 167t, 184–185, 222, 224, 268, 287 “Six degrees of separation” theory, 61–62 Skin fibroblasts, 112–113 SLC39A8 zinc transporter, 146 SLCO1B1 gene, 308–309 SLE See Systemic lupus erythematosus (SLE) SLOS See Smith–Lemli–Opitz syndrome (SLOS) Small non-coding RNAs (sncRNAs), 41 CAsRNA, 48–49 endogenous siRNAs, 47–48 miRNAs, 41–43 rRNA-derived smRNAs, 49 snRNA, 44–46 tDRs, 43–44 vault RNA-derived smRNAs, 47 Y RNA-derived miRNAs, 46–47 Small nuclear RNAs (snRNA), 41, 44–46 Small nucleolar RNAs (snoRNA), 41 Small RNAseq (smRNAseq), 39–40 Small vessel ischemic disease (SVID), 182 Smith–Lemli–Opitz syndrome (SLOS), 266 Smoking, 182 smRNAs-derived from vault RNA (svRNA), 47 smRNAseq See Small RNAseq (smRNAseq) sncRNAs See Small non-coding RNAs (sncRNAs) SNORA See ACA-box snoRNAs (SNORA) snoRNA See Small nucleolar RNAs (snoRNA) snoRNA-derived smRNAs (sdRNA), 44–45 snoRNAs and Cajal body-specific RNAs (scaRNA), 44–45 SNPs See Single-nucleotide polymorphisms (SNPs) snRNA See Small nuclear RNAs (snRNA) SNVs See Single nucleotide variations (SNVs) SNX13 See Sorting nexin 13 (SNX13) Socioeconomic status (SES), 209–210 Solvation effects in DNA, 91 Solvent strength, Somatic variants, 243 Sorting nexin 13 (SNX13), 147 Spine of hydration, 91 Splice-site associated smRNAs (spliRNA), 48–49 spliRNA See Splice-site associated smRNAs (spliRNA) SR-BI See Scavenger receptor class B type I (SR-BI) SREBP cleavage activating protein (SCAP), 255 SREBPs See Sterol regulatory elementbinding proteins (SREBPs) STAB1 See Stabilin (STAB1) Stabilin (STAB1), 146–147 STARD3 gene product, 146–147 Stem cell-derived hepatocytes, 114–115 Sterol regulatory element-binding proteins (SREBPs), 255 Strawberry Notch Homolog (SBNO1), 149 Stroke, 181, 184–185, 191–195, 212 SVID See Small vessel ischemic disease (SVID) svRNA See smRNAs-derived from vault RNA (svRNA) Symmetrical dimethylarginine (SDMA), 17 Systemic chemotherapy, 238–239 Systemic lupus erythematosus (SLE), 46–47 Systems biology, 23 Systems genetics, 72–73 Systolic blood pressure (SBP), 146 T T1D See Type diabetes (T1D) Tandem mass spectrometry (MS/MS), 23–24 Tangier disease, 123 TaqI polymorphism, 127 INDEX tasiRNA See trans-acting siRNAs (tasiRNA) tDRs See tRNA-derived smRNAs (tDRs) Technical disclosure, 314 Technology transfer organizations (TTOs), 311 tel-sRNA See Telomere-specific smRNAs (tel-sRNA) Telemorase-associated protein (TEP1), 47 Telomere-specific smRNAs (tel-sRNA), 48 TEP1 See Telemorase-associated protein (TEP1) Tetratricopeptide repeat (TPR), 136 TG See Triglyceride (TG) “Thrifty gene” hypothesis, 161–162 Time of flight mass spectroscopy (TOFMS), 12–13 Time to pregnancy (TTP), 252 Timescale, 92–94 TIMP3 See Metalloproteinase (TIMP3) tiRNAs See Transcription initiation RNAs (tiRNAs) TMAO See Trimethylamine-N-oxide (TMAO) TMEM18 See Transmembrane protein 18 (TMEM18) TOF-MS See Time of flight mass spectroscopy (TOF-MS) TOM See Topological overlap measure (TOM) Topological module, 62–64 Topological overlap measure (TOM), 70 Torcetrapib, 128 Toscani in Italia (TSI), 223–224 TPR See Tetratricopeptide repeat (TPR) Trade Related Aspects of IP Rights standards (TRIPS standards), 314 Trade secret, 314 Trademark, 313–314 trans-acting siRNAs (tasiRNA), 47–48 Transcription factor-driven gene networks, 74 Transcription initiation RNAs (tiRNAs), 48–49 Transcriptional start sites (TSSs), 41–43 Transcriptome, 39–40, 49–50 sequencing, 240 Transcriptomics, 39–40 See also Small non-coding RNAs (sncRNAs) Transfer RNAs (tRNA), 41 Transmembrane protein 18 (TMEM18), 168–169 339 tRFs See tRNAderived fragments (tRFs) tRHs See tRNA-derived halves (tRHs) TRIB1 See Tribbles pseudokinase (TRIB1) Tribbles pseudokinase (TRIB1), 148–149 Triglyceride (TG), 216–217 Trimethylamine-N-oxide (TMAO), 18 TRIPS standards See Trade Related Aspects of IP Rights standards (TRIPS standards) tRNA See Transfer RNAs (tRNA) tRNA-derived halves (tRHs), 43–44 tRNA-derived smRNAs (tDRs), 43–44 tRNAderived fragments (tRFs), 43–44 TSI See Toscani in Italia (TSI) TSSs See Transcriptional start sites (TSSs) TTOs See Technology transfer organizations (TTOs) TTP See Time to pregnancy (TTP) 2p21 loci, 268–269 Type diabetes (T1D), 74, 212–213 Type diabetes mellitus (T2DM) See Type-2 diabetes (T2D) Type-2 diabetes (T2D), 145, 166–167, 211–214, 221 Tyrosine kinase 2, 145 U UBASH3B See Ubiquitin associated and SH3 domain containing B (UBASH3B) UBCH7 See Ubiquitin-conjugating enzyme E2L3 (UBE2L3) UBE2L3 See Ubiquitin-conjugating enzyme E2L3 (UBE2L3) Ubiquitin associated and SH3 domain containing B (UBASH3B), 149 Ubiquitin-conjugating enzyme E2L3 (UBE2L3), 136–145 UC See Unesterified cholesterol (UC) UC:TC See Unesterified cholesterol to total cholesterol (UC:TC) Ultra-performance liquid chromatography (UPLC), Unesterified cholesterol (UC), 253–254 Unesterified cholesterol to total cholesterol (UC:TC), 258–259 Unfolded protein response (UPR), 70–71 3’untranslated regions (3’ UTR), 41–43 UPLC See Ultra-performance liquid chromatography (UPLC) 340 INDEX UPR See Unfolded protein response (UPR) US Federal law, 318 US Food and Drug Administration (FDA), 317–318 US Patent and Trademark office (USPTO), 315 USPTO See US Patent and Trademark office (USPTO) 3’ UTR See 3’untranslated regions (3’ UTR) V VaD See Vascular dementia (VaD) VaD-precipitating diseases, 192t–194t Van Deemter plot, 8, 8f Variable number tandem repeats (VNTRs), 112–113 Vascular cognitive impairment (VCI), 179–180 non-modifiable risk factors, 181 Vascular dementia (VaD), 179 epidemiology, 180–183 future of genetic studies limitations, 196–197 opportunities, 197 genetics, 183 candidate genes, 184–185 genomic variants in candidate gene studies, 186t–187t monogenic forms of VaD, 191–196 polygenic contributors to VaD, 190–191 identifications, 179–180 monogenic causes of VaD, 192t–194t VaD-precipitating diseases, 192t–194t Vascular endothelial growth factor (VEGF), 296–297 Vascular smooth muscle cells (VSMCs), 191–195 Vault poly (ADP-ribose) polymerase (VPRAP), 47 Vault RNA-derived smRNAs, 47 VCI See Vascular cognitive impairment (VCI) VEGF See Vascular endothelial growth factor (VEGF) VEGFA signal transduction gene, 149 Venture Capital, 313 Very low-density lipoproteins (VLDL), 100–102, 122, 256 VLDL See Very low-density lipoproteins (VLDL) VNTRs See Variable number tandem repeats (VNTRs) VPRAP See Vault poly (ADP-ribose) polymerase (VPRAP) VSMCs See Vascular smooth muscle cells (VSMCs) W Watson–Crick pairing (WC pairing), 87 WC pairing See Watson–Crick pairing (WC pairing) Weighted Gene Co-expression Network Analysis (WGCNA), 68, 69f, 70 WES See Whole exome sequencing (WES) WGCNA See Weighted Gene Co-expression Network Analysis (WGCNA) WGS See Whole genome sequencing (WGS) WHI See Women’s Health Initiative (WHI) White matter hyperintensity (WMH), 190–191 White matter lesions (WMLs), 181, 184 Whole exome sequencing (WES), 197 Whole genome sequencing (WGS), 197 WMH See White matter hyperintensity (WMH) WMLs See White matter lesions (WMLs) WNT4 gene, 271–272 Women’s Health Initiative (WHI), 215 World Trade Organization (WTO), 314 Y Y RNA-derived miRNAs, 46–47 Yoruba in Ibadan, Nigeria (YRI), 223–224 ... approaches Translational Cardiometabolic Genomic Medicine http://dx.doi.org/10.1016/B978-0-12-799961-6.00001-9 Copyright © 2016 Elsevier Inc All rights reserved 2 METABOLOMICS AND CARDIOVASCULAR MEDICINE. .. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Waqas Qureshi Department of Internal Medicine, Section of Cardiology, Wake Forest School of Medicine, Winston-Salem,... School of Medicine, Farmington, CT, USA; Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, CT, USA Kasey C Vickers Department of Medicine,