Data mining from genomic variants and its application to genome-wide analysis 2015

in conjunction with
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2015


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 complex genetic architecture of common diseases. The previously associated variants through GWAS only explain a small portion of the genetic factors in complex diseases. This rather limited finding is partly ascribed to the lack of intensive analysis on undiscovered genetic determinants such as rare variants. Unfortunately, standard methods used to test for association with single common genetic variants are underpowered for rare variants unless sample sizes or effect sizes are very large. In this context, the analysis strategy of association studies has been stepped from common variant approach toward rare variant approach for understanding the complexity of genotype-phenotype association. Considering the current investment in sequencing-based association studies, with unprecedented amount of newly discovered rare variants and high costs, a more complete examination of the resulting data is warranted by using rare variant analysis. Although trait-associated rare variant 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 rare variant association analysis. Here we mainly focus on statistical and computational methods for data mining and machine learning for revealing hidden association structure of rare variant-phenotype relationship. This workshop will provide a platform to the researchers with expertise in data mining to discuss recent advancements in analytic approach of rare variant association in field of statistics and bioinformatics. Topics of interest include but not limited to:

  • Data mining of GWAS and rare variant association results
  • Knowledge based prioritizing analysis of rare variant analysis
  • Constructing biological network from GWAS and rare variant association
  • Biological interpretation and visualization of GWAS and rare variant association
  • Gene-Gene interaction analysis for rare variant association
  • Gene-Environment interaction for rare variant association
  • Multiple-gene based analysis for rare variant association
  • Pathway/Gene-set based test for rare variant analysis
  • Integration analysis with genomic variants
  • Rare variant analysis with family-based design


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).

Important dates

Sep 10, 2015: Due date for full workshop papers submission (at least two reviews for each paper)
Sep 30, 2015: Notification of paper acceptance to authors
Oct 17, 2015: Camera-ready of accepted papers
Nov 9-12, 2015: Workshops and Conference

Program chair

Taesung Park, Seoul National University, Seoul, Korea.

Program committee members (tentative)

Kwangmi Ahn, National Institutes of Health /NIMH
Min-Seok Kwon, Harvard University
Seungyeoun Lee, Sejong University, Korea
Xiang-Yang Lou, University of Alabama, USA
Minsun Song, National Institutes of Health/ NCI
Heejong Sung, National Institutes of Health
Sungho Won, Seoul National University, Korea