In the foyer of the Ryan Institute Lecture Theatre, see map

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Norma Coffey (NUIG)

Time-course microarray analyses involve measuring the expression levels of thousands of genes repeatedly through time. Multivariate clustering methods such as principal components analysis, k-means clustering, finite mixture models etc.  have difficulties handling missing values, require uniform sampling for all genes, fail to account for the correlation between measurements made on the same gene or do not facilitate the removal of noise from the measured data thus ignoring any smoothness that may be evident in the expression profiles. This talk proposes the use of curve-based clustering, which can handle the latter issues. We use the linear mixed effects model representation of penalized spline smoothing to estimate the gene expression curves which provides a framework for simultaneously determining a smooth estimate of the mean expression profile in each cluster, determining estimates of the gene-specific expression profiles within a cluster through the use of additional random effects and clustering expression profiles using mixtures of mixed effects models.
 

Raymond J. Carroll (Texas A & M University, USA)

We consider population-based case-control studies of gene-environment interactions using prospective logistic regression models. Data sets like this arise when studying pathways based on haplotypes as well as in multistage genome wide association studies (GWAS). In a typical case-control study, logistic regression is used and there is little power for detecting interactions. However, in many cases it is reasonable to assume that, for example, genotype and environment are independent in the population, possibly conditional on factors to account for population stratification. In such as case, we have developed an extremely statistically powerful semiparametric approach for this problem, showing that it leads to much more efficient estimates of gene-environment interaction parameters and the gene main effect than the standard approach: decreases of standard errors for the former are often by factors of 50% and more. 

I am going to review case-control studies, and show a new look at them that allows new methods to handle gene-environment interactions. Illustrations with various data sets will be made.