12:30-2:00 PM
a pizza lunch will be served
Professor of Community and Family Medicine
Professor of Genetics
Associate Director for Population Sciences, Norris Cotton Cancer Center
Dartmouth Geisel School of Medicine
Modeling Genome Wide Effects on Cancer Risk
Genome wide association studies (GWAS) have been a highly effective tool for exploring genetic contributions to complex diseases, but have failed to explain very much of the heritability or total genetic risk associated with cancer development. In this talk
I describe ongoing efforts to characterize the features of single nucleotide polymorphisms that are associated with successful replication of findings during GWAS as a measure of the particular attributes that should be considered when designing studies and
particularly for associating weights for evaluating findings from GWAS and large scale sequencing studies. Finally I describe new methods for seeking to identify further components of missing heritability that reflect gene-gene and gene-environment interactions.
These interactions are modeled using a Bayesian approach. Results from simulation studies and application to data from association studies of lung cancer show that a model that weakly constrains the prior probabilities that interactions are included in a model
generally outperformed, as reflected by root mean squared error and posterior probabilities, either models with stricter constraints or that did not impose constraints.