Introduction

With the recent development of high-throughput DNA microarray and next-generation sequencing (NGS) techniques for detecting various genomic variants (SNVs, CNVs, INDELs etc.), genome-wide association studies (GWAS) have become 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 NGS-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 NGS data
• Knowledge based prioritizing analysis of NGS data
• Constructing biological network from GWAS and NGS data
• Biological interpretation and visualization of GWAS and NGS data
• Gene-Gene interaction analysis for NGS data
• Gene-Environment interaction for NGS data
• Multiple-gene based analysis for NGS data
• Pathway/Gene-set based test for NGS data
• Integration analysis with genomic variants
• Rare variant analysis with family-based design

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).

Important dates

Sept 20, 2019: Due date for full workshop papers submission
Oct 15, 2019: Notification of paper acceptance to authors
Nov. 1, 2019: Camera-ready of accepted papers
Nov. 18-21, 2019: Workshops and Conference

Program chair

Taesung Park, Seoul National University, Seoul, Korea.

Program committee members (tentative)

Taejin Ahn, Handong University, Korea
Yujin Chung, Kyungki University, USA
Iksoo Huh, Seoul National University, Korea
Youngchul Kim, Moffit Cancer Center, USA
Young Jin Kim, Center for Genome Science, Korean National Institute of Health, Korea
Min-Sek Kwon, Harvard University, USA
Seungyeoun Lee, Sejong University, Korea
Seungyoon Nam, Gachon University, Korea
Mira Park, Eulji University, Korea
Sungho Won, Seoul National University, Korea