Between the Lines of Genetic Code

Acknowledgments

List of Contributors

Introduction

Interaction May be Confused with Many Different Things

Genetics in Human Diseases

Complex Human Diseases

Genome-Wide Association Studies as a Source of Information

References

Part One: Methodology

Chapter One. Terminology and Definitions for Interaction Studies

1.1 Introduction

1.2 Regression Models

1.3 The Linear Regression Model

1.4 Assumptions of Linear Regression Models

1.5 Estimation

1.6 Interaction on the Linear Scale

1.7 Logistic Regression Models

1.8 Other Nonlinear Regression Models

1.9 The Hazard Function and the Survival Function

1.10 Hazard Ratio

1.11 Assumptions

1.12 Interaction on the Logistic Scale

1.13 Estimating Multiplicative Interaction

1.14 Estimating Additive Interaction

1.15 Genetic Models

1.16 Summary

References

Chapter Two. Reducing Dimensionality in the Search for Gene–Gene Interactions

2.1 Introduction

2.2 Challenges in Searching for Gene–Gene Interactions

2.3 Data Mining Approaches for Gene–Gene Interactions

2.4 Example of a Data Mining Method: MDR

2.5 Strategies to Improve Power of Data Mining Methods

2.6 Computational Optimizations

2.7 Future of Gene–Gene Interaction Modeling

2.8 Conclusions

References

Chapter Three. Study Design for Interaction Analyses

3.1 Introduction

3.2 Study Base

3.3 Cohort Studies

3.4 Case-Control Studies

3.5 Systematic and Random Errors

3.6 Example of Gene–Environment Interaction

3.7 Summary

References

Chapter Four. High-Throughput Genetic Interaction Study

4.1 Introduction

4.2 Gene–Gene Interactions

4.3 Gene–Environment Interactions

4.4 Novel Statistic for Genome-Wide Interaction Analysis

4.5 Conclusions

References

Appendix 4.1 The Algorithm of the Forest-Based Haplotype Approach

Appendix 4.2 The FEPI-MB algorithm

Appendix 4.3 The bNEAT Algorithm

Appendix 4.4 The GEIRA Algorithm

Part 1. Conclusions

Reference

Part Two: Experimental Data

Chapter Five. Gene–Gene and Gene–Environment Interaction in Rheumatoid Arthritis

5.1 Introduction

5.2 Phenotypic Heterogeneity of Rheumatoid Arthritis: ACPAs

5.3 Genetic Risk Factors for Rheumatoid Arthritis

5.4 Nongenetic Factors in Risk for Rheumatoid Arthritis (Table 5.2)

5.5 Gene–Environment and Gene–Gene Interactions in Rheumatoid Arthritis (Table 5.3)

Conclusions

References

Chapter Six. Genetic and Environmental Risk Factors for Multiple Sclerosis—A Role for Interaction Analysis

6.1 Genetics of Multiple Sclerosis

6.2 Environmental Risk Factors for Multiple Sclerosis

6.3 Study of Interactions in Multiple Sclerosis

6.4 Gene–Gene Interactions

6.5 Gene–Environment Interactions

6.6 Conclusions: What Have We Learnt?

References

Chapter Seven. Gene–Gene and Gene–Environment Interactions in Defining Risk and Spectrum of Phenotypes in Idiopathic Inflammatory Myopathies

7.1 Clinical Introduction

7.2 Early HLA Results in Idiopathic Inflammatory Myopathies

7.3 HLA-Related Differences in PM/DM

7.4 The Association Between HLA Genes, Class II Haplotypes, and MSAs/MAAs

7.5 Contribution of HLA-DRB1 Gene Dose to Disease Phenotype and Severity in IBM

7.6 Remarkable Lessons from Statin-Induced Myositis

7.7 Cancer-Associated Myositis (CAM), HLA, and Anti-155/140 Autoantibodies

7.8 Relationship Between Smoking, HLA-DRB1*03, and Anti-Jo-1 in IIM

7.9 Possible Pathogenic Role of HLA and Autoantibodies

References

Part 2. Conclusions

References

Part Three: Future Avenues

Chapter Eight. Functional Studies of Gene–Gene Interaction of Autoimmune Diseases

8.1 Introduction

8.2 Attribution of Genetic Variation to Gene Function

8.3 Examples of Experimental Studies of Gene–Gene Interaction in Autoimmune Diseases

8.4 Conclusions

References

Chapter Nine. Genetic Vectors Approach in a Study of Fine Structure of Interaction Between Risk Haplotype of HTR2A and HLA-DRB1 Shared Epitope Alleles in Rheumatoid Arthritis

9.1 Introduction

9.2 Methods

9.3 Results

9.4 Discussion

References

Appendix Standard Correlations and Statistical Interaction

Chapter Ten. Network Biology Empowering Detection and Understanding of Interactions Between Genetic Factors in Development of Complex Phenotypes

10.1 Rise of Big Data, Computing, and Prediction

10.2 Using Genetic Variants as Independent Features is Not Sufficient for Realizing P4 Medicine

10.3 Network Biology—A Framework for Detecting and Interpreting Genetic Interactions

10.4 Inferring Genetic Interactions or Edges From Data is a Special Case of a Feature Selection Problem

10.5 Network Biology—A Framework for Detecting Genetic Interactions

Acknowledgments

References

Part 3. Conclusions

Index