168 L.K. Goh, R. Metlapally and T. Young

believe new approaches such as integrating genomic information with quantitative analyses to prioritize loci or genes of interest will streamline the gene discovery process and improve statistical power. Two approaches are reviewed in this chapter: genomic convergence and pathway analysis.

Genomic Convergence Using Genomic Content

Genomic convergence refers to the integration of genomic information with quantitative analyses to prioritize loci or genes of interest.41,42 Quantitatively, it gives weight to the role of biological relevance of the loci, taking into consideration the genomic properties, functionality, gene expression as well as evidence of association reported in literature. It is a multi-factorial, multi-step approach toward discovery of candidate susceptibility genes for diseases. The idea was first applied on a linkage study for Parkinson disease (PD).41 In the study, gene expression using serial analysis of gene expression (SAGE) was integrated or mapped with loci from linkage analyses. As fewer than 10% of linkage regions are actually expressed, the approach resulted in significant wet laboratory effort. While GWAS are powerful for identifying susceptible loci associated with diseases, the number of candidate loci presented and the high false positives pose challenges in discovery of genes that are involved in the disease. By complementing this with genomic information available in databases and literature, and converging this information using a quantitative approach, a comprehensive analysis of the significant role candidate loci can play in the biological pathway of the disease can be developed.

One of the challenges in genomic convergence is the mapping or integration of different sources of information. The most common mapping framework exists in the genome databases that have been set up by several key genome groups such as the University of California Santa Cruz (UCSC), National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/), and European Bioinformatics Institute (EBI) (http://www.ebi.ac.uk/). The genome databases that have been developed to collate information in a structured and easily visualized framework to aid researchers have been instrumental in new discovery. Three most popular genome databases are the UCSC Genome Browser ((http://genome.ucsc.edu/), EBI Ensembl Genome Browser (http://www. ebi.ac.uk/ensembl/), and NCBI MapViewer (http://www.ncbi.nlm.nih.gov/ projects/mapview/). All browsers allow multiple and simultaneous