Traditionally in genetics, researchers would identify a remarkable phenotype and then work to uncover the associated genotype; this is called 'forward genetics'. More recently, new techniques have made it possible to systematically make an enormous number of changes to a genome and, for each such change individually, observe the phenotypic consequences. This is what I mean by 'high-throughput reverse genetics' and the best example is the yeast deletion set, a collection of ~6K yeast strains, each of which is characterized by the deletion (or knockout) of a single gene. In other organisms, such as worms and human cells, similar approaches are possible by inhibiting the expression of specific genes, generally through RNA interference (RNAi). I will present some statistical methods appropriate for the analysis of data from high-throughput reverse genetics studies, with some coverage of low-level issues, such as normalization, and high-level analyses, such as clustering and growth curve modelling on a large scale. This talk will probably appeal most to people with an existing background or interest in genomics, particularly gene expression data, and who are interested in hearing about other platforms for the genome-scale study of gene function.
Group for Research in Decision Analysis