DMGVAGA 2012 - 2012 3rd Workshop on Data mining from genomic variants and its application to genome-wide analysis

With the recent development of high-throughput DNA microarray and next-generation sequencing techniques for detecting various genomic variants (SNVs, CNVs, INDELs etc.), genome-wide association studies (GWAS) have became a popular strategy to discover genetic factors affecting common complex diseases. Many GWAS have successfully identified genetic risk factors associated with common diseases and have achieved substantial success in unveiling genomic regions responsible for the various aspects of phenotypes.
However, identifying the underlying mechanism of disease susceptible loci has proven to be difficult due to the polygenic and multiple-pathway nature of complex diseases. The newly identified genes from GWAS only explain a small portion of the genetic factors in complex diseases. This rather limited finding is partly ascribed to the lack of multiple gene-based analysis. In this context, the analysis strategy of GWAS has been stepped from single variant approach toward multiple variants approach for understanding the complexity of genotype-phenotype association considering gene-gene and gene-environment interaction.
Unfortunately, multiple gene-based analysis is complicated owing to computational burden, the large number of comparisons implied by even bivariate analysis, and a limited set of appropriate analysis tools.
Considering the current investment in GWAS, with large sample sizes and high costs, a more complete examination of the resulting data is warranted by using multiple gene-based analysis. Although multiple gene-based analysis is generally recognized as one solution to discover additional genetic factors and understand complex genetic components affecting disease susceptibility, several issues remain to be further investigated. The goal of this workshop is to identify and discuss the most challenging issues in analytic approach of GWAS such as multiple gene-based analysis. Here we mainly focus on statistical and computational methods for data mining and machine learning for revealing hidden association network of genotype-phenotype relationship. This workshop will provide a platform to the researchers with expertise in data mining and GWAS to discuss recent advancements in analytic approach of GWAS in field of statistics and bioinformatics.
Topics of interest include but not limited to:
Data mining of GWAS results
Knowledge based analysis of GWAS
Constructing biological network from GWAS results
Biological interpretation and visualization of GWAS results
Gene-Gene interaction analysis for GWAS
Gene-Environment interaction for GWAS
Multiple-gene based analysis for GWAS
Multiple-pathway based test for GWAS
Integration analysis with genomic variants
Genome-wide applications to CNVs, INDELs and other structural variants
PUBLICATION:
The workshop proceeding will be made available online. Selected extended papers from the workshop will be invited for consideration for publication in a special issue of International Journal of Data Mining and Bioinformatics (SCI indexed) .