Gene-environment interaction analysis : methods in bioinformatics and computational biology / edited by Sumiko Anno. -- Singapore : Pan Stanford Publishing, c2016. – (58.1486/G326)

Contents

1. Understanding Skin Color Variations as an Adaptation by Detecting Gene-Environment Interactions

1.1    Introduction

1.2    A Linkage Disequilibrium-Based Statistical Approach to Detect Interactions between SNP Alleles at Multiple Loci That Contribute to Skin Pigmentation Variation between Human Populations

1.3    SNP Analyses Reveal Natural Selection of Pigmentation Candidate Genes from Haplotypes

1.4    Elucidation of the UVR-Induced Selective Genetic Mechanisms Influencing Variations in Human Skin Pigmentation

1.5    Conclusions

2. Information Theoretic Methods for Gene-Environment Interaction Analysis

2.1    Introduction

2.2    Information Theoretic Metrics and Searching for GEIs

2.3    How and Why Do the KWII and the Pal Measure Statistical Interaction?

2.4    Performance of KWII and PAl Search Algorithms on Simulated Data

2.5    Algorithms

2.6    Applications of Information Theory to Interaction Analysis

2.7    Critiques of Information Theory Approaches to Interaction Analysis

2.8    Conclusions

3. Approaches for Gene-Environment Interaction Analysis: Practice of Regional Epidemiological Study

3.1    Introduction

3.2    Community Investigation and Setting of the Research Objective

3.3    Study Design and Methods

3.4    Statistical Analysis

3.5    Later Surveys

3.6    Conclusion

4. Use of Bioinformatics in Revealing the Identity of Nature's Products with Minimum Genetic Variation: The Sibling Species

4.1    Cryptic Species: An Introduction

4.2    Computer Power in Taxonomy of Sibling Species

4.3    Automated Species Identification

4.4    Species Complex among Insect Vectors in Sri Lanka: Two Case Studies Where Bioinformatic Approaches Have Solved Taxonomic Problems

4.5    Conclusion

5. Integrated Bioinformatics, Biostatistics, and Molecular Epidemiologic Approaches to Study How the Environment and Genes Work Together to Influence the Oevelopment of Complex Chronic Oiseases

5.1    Introduction

5.2    Tools for Identifying Genetic, Environmental, and Stochastic Factors Relevant in Complex Human Diseases

5.3    Integration of Functional Genomic, Epigenetic, and Environmental Data of Molecular Epidemiological Studies Using Bioinformatics and Biostatistics Approaches

5.4    Predictive Analysis of Lifetime Risk of Developing Disease

5.5    Technical Challenges

5.6    Conclusion

5.7    Online Resources