Gene Environment Interaction Analysis. Methods in Bioinformatics and Computational Biology. edited by. Sumiko Anno

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1 Gene Environment Interaction Analysis Methods in Bioinformatics and Computational Biology edited by Sumiko Anno

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3 Gene Environment Interaction Analysis

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5 Gene Environment Interaction Analysis Methods in Bioinformatics and Computational Biology edited by Sumiko Anno

6 Published by Pan Stanford Publishing Pte. Ltd. Penthouse Level, Suntec Tower 3 8 Temasek Boulevard Singapore editorial@panstanford.com Web: British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Gene Environment Interaction Analysis: Methods in Bioinformatics and Computational Biology Copyright 2016 by Pan Stanford Publishing Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. ISBN (Hardcover) ISBN (ebook) Printed in the USA

7 Contents Preface xiii 1. Understanding Skin Color Variations as an Adaptation by Detecting Gene Environment Interactions 1 Sumiko Anno, Kazuhiko Ohshima, and Takashi Abe 1.1 Introduction Human Skin Color as an Environmental Adaptation Mechanism of Melanin Formation Skin Color Diversity due to DNA Polymorphism Objectives A Linkage Disequilibrium Based Statistical Approach to Detect Interactions between SNP Alleles at Multiple Loci That Contribute to Skin Pigmentation Variation between Human Populations SNP Analysis of European and East Asian Cohorts Genotype and Allele Frequencies for the 20 SNPs in the European and East Asian Population Groups Cluster Analysis Linkage Disequilibrium Generated by Gene Gene Interactions Contributes to Differences between Racial Groups SNP Analyses Reveal Natural Selection of Pigmentation Candidate Genes from Haplotypes Background Detecting Natural Selection in the Human Genome on the Basis of the Haplotype Structure 14

8 vi Contents Discussion Future Investigations of Natural Selection and Environmental Adaptability Regarding Skin Pigmentation Elucidation of the UVR-Induced Selective Genetic Mechanisms Influencing Variations in Human Skin Pigmentation Influence of UVR Levels on Skin Color Variation Statistical Methods of Clarifying Gene Environment Interactions, Viewing Skin Pigmentation as a Complex Trait New Methods for Detecting Gene Environment Interactions for Human Skin Pigmentation Variation RS Data Processing and Spatial Analysis in GIS Gene Environment Interaction Analysis Results Discussion Conclusions Information Theoretic Methods for Gene Environment Interaction Analysis 39 Jonathan Knights and Murali Ramanathan 2.1 Introduction Information Theoretic Metrics and Searching for GEIs Entropy and Mutual Information Entropy Mutual information The k-way interaction information Total correlation information Phenotype-associated information 45

9 Contents vii 2.3 How and Why Do the KWII and the PAI Measure Statistical Interaction? Performance of KWII and PAI Search Algorithms on Simulated Data KWII as an Interaction Metric PAI removes the effects of LD on TCI Algorithms Noninformation Theoretic Methods Information Theoretic Algorithms Applications of Information Theory to Interaction Analysis Critiques of Information Theory Approaches to Interaction Analysis Conclusions Approaches for Gene Environment Interaction Analysis: Practice of Regional Epidemiological Study 73 Mio Nakazato and Takahiro Maeda 3.1 Introduction Community Investigation and Setting of the Research Objective Study Background and Purpose: Homocysteine and Arteriosclerosis Study Design and Methods Survey Items Sample Size Sample Collection and Storage Explanation of the community research survey to each organization and request for cooperation Methods and flow of the research survey Collection and storage of blood samples Measurement and Analysis of Samples Data Management Ethics Committee 86

10 viii Contents Preparation of the Field of Regional Epidemiological Study Study Limitations Statistical Analysis Preparation of Analytical Sheets Close Investigation of Data Selection of Statistical Analysis Methods Practice of Analysis Later Surveys Homocysteine and Folic Acid Adiponectin Polymorphism and Arteriosclerosis Leukocyte Count and Arteriosclerosis Current Study Purpose in Our Regional Epidemiological Research Field Other Studies Conclusion Use of Bioinformatics in Revealing the Identity of Nature s Products with Minimum Genetic Variation: The Sibling Species 121 K. Gajapathy, A. Ramanan, S. L. Goodacre, R. Ramasamy, and S. N. Surendran 4.1 Cryptic Species: An Introduction Computer Power in Taxonomy of Sibling Species DNA and Protein as Tools in Taxonomy: Alternatives or Auxiliary? Protein-Based Assays Karyotyping and Chromosome Banding Pattern Automated Species Identification Species Complex among Insect Vectors in Sri Lanka: Two Case Studies Where Bioinformatic Approaches Have Solved Taxonomic Problems Conclusion 141

11 Contents ix 5. Integrated Bioinformatics, Biostatistics, and Molecular Epidemiologic Approaches to Study How the Environment and Genes Work Together to Influence the Development of Complex Chronic Diseases 151 Alok Deoraj, Changwon Yoo, and Deodutta Roy 5.1 Introduction Tools for Identifying Genetic, Environmental, and Stochastic Factors Relevant in Complex Human Diseases Candidate Gene Copy Number Variations Single-Nucleotide Polymorphism Haplotype Mapping Genome-Wide Association Screen Epigenetic Changes and Variation in Noncoding DNA Elements Variations, Mutations, and Damages in the Mitochondrial Genome Chronic Disease Bionetwork through Interactome, Phenome, and Systems Approaches Transcriptomics Proteomics Interactomics Metabolomics Phenomics Integration of Functional Genomic, Epigenetic, and Environmental Data of Molecular Epidemiological Studies Using Bioinformatics and Biostatistics Approaches Molecular Epidemiologic Study Designs for G G and GEI Family-Based Studies Studies of Unrelated Individuals Retrospective Design Prospective Design Case-Only Design 171

12 x Contents Identification of Mutations, SNPs, Changes in CNVs, and Variations in a Significant Set of Over- and Underexpressed Genes Enrichment Analysis of Significant Genes and Proteins Analysis of Combined Effects of Modified Gene Expressions, CNVs, Mutations, and Molecular Interactions Influencing Biological Pathways and Network(s) That Contribute to the Susceptibility to, Resistance to, or Development of Complex Diseases Multifactor Dimensionality Reduction Bayesian Networks Validation of Key Causal Genes/ Proteins/Molecules/Environmental and Stochastic Factors Predicted to Be Involved in the Development of Disease by Statistical and Other Approaches Validation by Statistical Methods Literature-Based Validation of Key Causal Genes/ Proteins/Molecules/ Environmental and Stochastic Factors Involved in the Etiology of Chronic Diseases Using Models Generated by Empirical Data Predictive Analysis of Lifetime Risk of Developing Disease Technical Challenges Sample Size Complex Mixture of Covariates Coordination in Data Collection and Their Meta-Analysis 184

13 Contents xi Lack of Computational Power Conclusion Online Resources 187 Index 193

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15 Preface Gene environment (G E) interactions contribute to the development of complex diseases and phenotypic variation. They are a hot topic in human genetics, and analyses of G E interactions are expected to have many potential applications. Despite the importance of G E interactions in the etiology of complex diseases and phenotypic variation, insufficient attention has been paid to developing models for detecting these interactions. This textbook introduces different models of G E interactions for use in the determination of human disease and phenotypic variation. Applying models of the complex interactions between genes and environment will lead to novel methods of disease detection and prevention, as well as new interventions in various fields. I would like to thank the publisher for bringing me along on this adventure and for doing an excellent job. I am particularly grateful to Stanford Chong, Sarabjeet Garcha, and Shivani Sharma. Sumiko Anno February 2016

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