Gene Environment Interaction And Extension To Empirical Hierarchical Bayes Models In Genome Wide Association Studies

Gene-environment Interaction and Extension to Empirical Hierarchical Bayes Models in Genome-wide Association Studies

There are over 100,000 human diseases of which only around 10,000 are known to be monogenic, resulting from modification in a single gene. Many multifactorial diseases, such as cancer and lung cancer in particular, are outcomes of the interplay between genetic and environmental factors. It is well known that smoking is the major environmental risk factor in lung cancer. In recent years, great progress in genotyping technology and cost control has enabled researchers to perform large-scale association studies, involving thousands of individuals genotyped on millions of markers. To date, geno...

Cancer Research

The Empirical Hierarchical Bayes Approach for Pathway Integration and Gene-environment Interactions in Genome-wide Association Studies

Complex diseases such as cancer result from a complicated interplay of multiple genetic and environmental factors. To unveil their genetic component, the simple analysis of single-nucleotide polymorphisms (SNP) as done in genome-wide association studies (GWAS) is not sufficient. Complementary approaches considering the complexity of diseases, such as the incorporation of biological pathway information or detection of gene-environment interaction, are necessary. In this thesis we focus on an empirical hierarchical Bayes model proposed for the integration of external information into genome-w ...