Molecular Epidemiology of Chronic Diseases

by Wild, Chris; Vineis, Paolo; Garte, Seymour

• ISBN13: 9780470027431
• ISBN10: 0470027436
• Edition: 1st
• Format: Hardcover
• Copyright: 2008-06-09
• Publisher: WILEY

Summary

With the sequencing of the human genome and the mapping of millions of single nucleotide polymorphisms, epidemiology has moved into the molecular domain. Scientists can now use molecular markers to track disease-associated genes in populations, enabling them to study complex chronic diseases that might result from the weak interactions of many genes with the environment. Use of these laboratory generated biomarker data and an understanding of disease mechanisms are increasingly important in elucidating disease aetiology.Molecular Epidemiology of Disease crosses the disciplinary boundaries between laboratory scientists, epidemiologists, clinical researchers and biostatisticians and is accessible to all these relevant research communities in focusing on practical issues of application, rather than reviews of current areas of research. Covers categories of biomarkers of exposure, susceptibility and disease Includes chapters on novel technologies: genomics, transcriptomics, proteomics and metabonomics, which are increasingly finding application in population studies Emphasizes new statistical and bioinformatics approaches necessitated by the large data sets generated using these new methodologies Demonstrates the potential applications of laboratory techniques in tackling epidemiological problems while considering their limitations, including the sources of uncertainty and inaccuracy Discusses issues such as reliability (compared to traditional epidemiological methods) and the timing of exposure Explores practical elements of conducting population studies, including biological repositories and ethicsMolecular Epidemiology of Disease provides an easy-to-use, clearly presented handbook that allows epidemiologists to understand the specifics of research involving biomarkers, and laboratory scientists to understand the main issues of epidemiological study design and analysis. It also provides a useful tool for courses on molecular epidemiology, using many examples from population studies to illustrate key concepts and principles.

Author Biography

Professor Chris Wild. Molecular Epidemiology Unit, Head, Centre for Epidemiology and Biostatistics, Faculty of Medicine and Health, The LIGHT Laboratories, University of Leeds, UK.

Professor Paolo Vineis. Chair in Environmental Epidemiology, Div of Primary Care and Population Health Sciences, Medicine, Imperial College, London, UK.

Professor Seymour Garte. Professor of Environmental and Community Medicine, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey; Scientific Director, Genetics Research Institute, Milan, Italy. Center for Environmental Oncology, University of Pittsburgh Cancer Institute, Pittsburgh,?PA,?USA.

Table of Contents

Contributors

Artist statement

Acknowledgements

Introduction: why molecular epidemiology

References

Study design

Introduction: study design at square one

Epidemiological measures

Bias

Bias in screening practices

More on confounding

Specificities of molecular epidemiology design

Special designs in molecular epidemiology

Bias

Selection bias related to sample collection

Confounding and population admixture

Mendelian randomization

Conclusions

References

Essential reading

Molecular epidemiological studies that can be nested within cohorts

Introduction

Case-cohort studies

Design and calculable measures of effect

Case-cohort designs offer flexibility

Analytical complexity

Nested case-control studies

Design and calculable measures of effect

Matching

Counter-matching

Individuals may be included in the analyses multiple times

Considerations regarding biomarker analyses in case-cohort and nested case-control studies

Batch effects

Batch effects and case-cohort studies

Batch effects and nested case-control studies

Storage effects

Storage effects and case-cohort studies

Storage effects and nested case-control studies

Freeze-thaw cycles

Freeze-thaw cycles and case-cohort studies

Freeze-thaw cycles and nested case-control studies

Conclusion

References

Family studies, haplotypes and gene association studies

Introduction

Family studies

Is there an increased risk of disease in relatives of cases?

Is the familial aggregation due to genes or environment?

What is the genetic mechanism?

Where is the gene?

Linkage analysis

Example

Genetic association studies

Genetic case-control studies

Bias and confounding

Family-based study designs

Haplotypes

Reconstructing haplotypes

Association studies with haplotypes

SNP selection

Whole-genome association studies

Discussion

References

Individual susceptibility and gene-environment interaction

Individual susceptibility

Genetic susceptibility

Metabolic susceptibility genes

Study designs

Gene-environment interaction

Exposure dose effects in gene-environment interactions

Mutational effects of gene-environment interactions

Conclusions

References

Biomarker validation

Validity and reliability

Biomarker variability

Measurement of variation

Other issues of validation

Measurement error

Sources of laboratory measurement error

Blood collection for biomarkers

Validation of high-throughput techniques

References

Exposure assessment

Introduction

Initial considerations of an exposure assessment strategy

Exposure pathways and routes

Exposure dimensions

Exposure classification, measurement or modelling

Retrospective exposure assessment

Validation studies

Quality control issues

References

Carcinogen metabolites as biomarkers

Introduction

Overview of carcinogen metabolism

Examples of carcinogen metabolite biomarkers

Total NNAL (NNAL plus its glucuron

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