CHAPTER 10 224 10 away, when the variants are located in regulatory elements. Many statistical methods have been developed to prioritize the risk loci by integrating functional evidence. These computational tools often use the genomic structure, eQTLs, transcription factor binding sites, histone modifications and others for gene prioritization. In this thesis we have mapped the risk variants to causal genes and identified relevant gene sets using a variety of such tools, DEPICT, eQTL mapping, MAGMA, FOCUS, transcriptome-wide association studies (TWAS).103, 151-155 Figure 1 Multi-layer network In this multi-layer network, each network represents an independent layer. A group of molecules with similar chemical properties, such as genome, transcriptome, proteome, and metabolome, is called an ‘omic’ layer, which can be measured by next-generation sequencers (NGS), microarray, mass spectrometry, and nuclear magnetic resonance (1H-NMR) spectroscopy. The common nodes, or identified metabolites, proteins, genes etc., are connected to each other across different layers by inter-layer edges. The edges within individual layers and between them can help to understand biological systems. Pathway analysis Pathway analysis is also known as functional enrichment analysis. The main purpose is to give biological context to the lists of loci, genes or other biological data generated by high-throughput pipelines. The vast amount of data generated makes it impossible to model by a naïve approach.
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