Joint meeting with Manchester University's Biostats Group

Theme: Analysis challenges arising from post-genomic high-dimensional datasets

Genome wide association studies have been very successful in identification of disease susceptibility loci for various complex human diseases in recent 2-3 years. The Wellcome Trust Case Control Consortium (WTCCC) is the best example of this development, where seven common complex diseases, including rheumatoid arthritis (RA), have been investigated and a number of disease susceptibility loci have been uncovered. Before genome-wide association was applied to RA, there were only two genetic loci known to be associated with RA. Now more than ten novel loci have been identified. Despite these successes, all these genetic factors identified so far can only explain about 10% of the disease susceptibility variance, leaving wide open where is the rest of genetic contribution and how to find each element.

In this talk, RA will also be used as an example to illustrate the classical relationships between sample size, power, effect size, and type I errors. Challenges in localization of causal variants and detection of gene-gene interactions will also be discussed.

Modern biotechnology is providing a wealth of new data sets that are high-dimensional but comparatively low sample size. This presents challenges for traditional methods of analysis. Understanding the properties of sample covariance matrices under such conditions has become a recent research focus. In this talk I will outline new findings and my own research in this area.

David's slides

Neil Lawrence (School of Computer Science, University of Manchester)
Inference in Ordinary Differential Equations with Latent Functions through Gaussian Processes

Neil's slides

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