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 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 and gene-gene interactions. Unfortunately, standard methods used to test for association with single common genetic variants are underpowered for detection of rare variants and genetic interactions.

This special issue will be dedicated to presenting state-of-the-art statistical and computational methods for finding missing heritability underlying complex traits with massive genetic data including GWAS, next-generation sequencing, and DNA microarray data, as well as other multiomics data. The main focus of this special issue will be on data mining and machine learning for revealing hidden association structure of rare variant-phenotype relationship. This special issue will provide a platform to the researchers with expertise in data mining to discuss recent advancements in analytic approach of rare variant association and genetic interaction in the field of statistics and bioinformatics.

Potential topics include, but are 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