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Most genetic variations are in the form of single nucleotide polymorphisms (SNPs). Most SNP discovery methods focus on coding regions, as the predicted relative risk of a non-synonymous substitution in the protein product of a gene is very high. Since the overall frequency of such events in the genome is low, a re-prioritization of where to look may be appropriate. The 'impact' (roughly equivalent to the expectation value that a SNP is phenotypically interesting) can be defined in terms of the product of the expected frequency of a given type of variant with the predicted relative risk of a deleterious phenotype. Ranking the types of variant in terms of impact suggests that focusing on other types of variation (such as those likely to affect splicing or expression level) may be worthwhile strategies.

We are particularly interested in non-coding regions of the genome and are currently investigating how variation in microRNA genes and their corresponding targets may play a role in human disease. As part of this effort we have developed a computational method of predicting microRNA targets, which we use in conjunction with an assay for interaction between a microRNA and a predicted target, and a microarray-based method of measuring microRNA expression profiles.

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