The SNP-SIG meeting is broadly divided in two sessions (“Prediction and annotation of structural/functional impact of SNPs” and “SNPs and Personal Genomics: GWAS, population and phylogenetic analysis”) that encompass the four major research topics of the field:

Databases, data mining algorithms and visualization tools for SNP analysis.
Current genomic databases already contain millions of SNPs and vast amounts of related annotation data. Information continues to flow into these resources at ever increasing speeds. Making biologically relevant sense of this data deluge requires the development of better-defined data collection and access strategies. This topic is of particular interest to researchers working on storage, retrieval, and visualization of SNP related data, as well as to those looking to apply the available tools.

Methods for predicting structural/functional impacts of SNPs.
Experimental study of functional effects of SNPs is complicated by a number of factors (e.g. compounding effects of variation in other genetic regions including linkage disequilibrium, problems with crystallization, expression related changes in phenotype, etc) and generally results in a very expensive and not very accurate estimation of real effect. A number of tools have been recently developed to evaluate functional and structural effects of SNPs in silico. This topic of research encompasses both the amino-acid-sequence (coding non-synonymous) and the nucleotide-sequence (non-coding regulatory, coding synonymous) related side of SNP analysis. The research in this field overlaps directly with the next topic of interest – prioritization of SNPs from the GWAS studies for further detailed analysis.

Personal Genomics, GWAS studies and SNP prioritization.
The recent development in sequencing technologies has moved personal genomics (and thereby personalized medicine) much closer to reality. Genome wide association studies have become increasingly relied upon for disease-gene discoveries. Yet, a number of studies show that the discovered disorder associated SNPs do not account fully for the observed genetic risk. Thus, the GWAS should ideally provide preliminary genetic information, which could then be analyzed in silico in concert with other evidence (e.g. meta-analysis of a few GWAS or epistasis analysis within a single GWAS). The results of this type of analyses can then be used in combination with the pathway knowledge in guiding judgment of individual disease risks.

Population genomics and phylogenetic analysis.
Variation is the driving force of evolution. SNPs are the most common form of genetic variation. SNPs have been shown to be very useful for typing and resolving relationships between organisms of different species. This research topic would encompass the discussion of advantages and limitations of SNP based algorithms for the analysis of population genomic data and phylogenetically conserved genomic regions.