The SNP-SIG meeting is broadly divided in two sessions (“SNPs as markers: evolution, populations, GWAS” and “SNPs as effectors: function, structure, and regulation”) that encompass the four major research topics of the field:

Databases, data mining algorithms and visualization tools for SNP analysis.
Current genomic databases 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. This year we will focus explicitly on two subsection of this topic: the development of SNP-related ontologies and methods for the automatic extraction of SNP data from literature.

Methods for predicting regulatory/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 regulatory, functional and structural effects of SNPs in silica. This topic encompasses the variants with protein and RNA effects, as well as variants in regulatory and functionally un-annotated regions. This year we would like to explicitly encourage submission relating to the studies of regulatory SNPs and genetic variation in drug response.

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 silica in concert with other evidence (e.g. meta-analysis of a few GWAS or epistasis analysis within a single GWAS). In particular we will encourage the submissions addressing the detection of causative and marker SNPs and those tracking the possible relationships between different region of the genome.

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. We particularly encourage submissions discussing SNPs in animal and bacterial models, as well as metagenomic studies.