Contingency table for a single SNP and a phenotype .

<p>Contingency table for a single SNP and a phenotype .</p

Notations for the bounds.

<p>Notations for the bounds.</p

Pruning SNP-pairs in using the upper bound.

<p>Pruning SNP-pairs in using the upper bound.</p

The index array for efficient retrieval of the candidate SNP-pairs.

<p>The index array for efficient retrieval of the candidate SNP-pairs.</p

Grouping of by .

<p>Grouping of by .</p

Chapter 10: Mining Genome-Wide Genetic Markers

<div><p>Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3) The limitations of current approaches and future directions.</p> </div

Notations used in the derivation of the upper bound for two-locus Chi-square test.

<p>Notations used in the derivation of the upper bound for two-locus Chi-square test.</p

GO enrichment analysis of the gene sets identified by NCA and our model.

<p>GO enrichment analysis of the gene sets identified by NCA and our model.</p

GO enrichment analysis of the gene sets associated with hidden variables.

<p>GO enrichment analysis of the gene sets associated with hidden variables.</p

Histogram of sizes of the gene sets associated with known and putative regulators.

<p>Histogram of sizes of the gene sets associated with known and putative regulators.</p