GENERAL DISCUSSION 223 10 elements) near four migraine GWAS risk loci.147 Different types of data can be combined as was shown by the same Danish group who combines RNA-seq data with WGS data of more than hundred migraine families in a so called ‘systems genetics approach’.148 With this approach they implicated a “visual cortex module” in migraine pathophysiology.148 Their approach also suggested the involvement of glutamate, serotonin, G-protein signalling pathways and hormonal pathways. Other ways of integrating both common and rare variants is by deep resequencing of GWAS loci or performing whole-genome sequencing (WGS). However, thus far we have mainly focussed on the protein coding components of the genome which only make up about 1% of the whole genome.Thus analysis and interpretation of WGS data has still a long way to go. Future of data integration Multiple -omics fields have arisen over the last years, from genomics, epigenomics, transcriptomics to proteomics and metabolomics. GWAS is considered part of the most mature omics field, namely genomics and as presented its data can be utilised to assess the correlation between different traits, assess causality and calculate PRS. Large amounts of biological data are generated with analysing omics with high-throughput technologies. Each field of omics can be depicted as a layer, where there is a connection within different components of each layer (intra-layer relationship), for example different metabolites relate to each other within the metabolomics layer. In addition the different omics field layers also have a biological effect on each other (inter-layer relationships), so the epigenomics layer influences which genes are transcribed (Figure 1). All these omics relationships give new possibilities to further investigate the pathophysiology of disease. Studying isolated biological components/layers is not enough to understand biological systems. Integrating this multi-omics data can identify novel biological pathways that are not necessarily distinguishable in the individual omics layers. It has already been shown that integrating DNA copy number, loss of heterozygosity, and methylation led to an increase in explained gene expression changes in breast cancer that would otherwise have been overlooked in single layer analysis.149 Integrating these layers has the potential to give a more comprehensive and deeper understanding of biological systems, thus a more holistic view on the systems biology.150 The field of multi-omics integration is relatively new field with a lot of new developments. From SNP to gene Over the last two decades GWAS has identified thousands of loci for a large number of human diseases. However, a major short coming of GWAS is that the associated loci rarely reflect the causal variants, target genes, cell types and biological functions. The majority of risk loci in GWAS fall into non-coding regions of the genome. Even when they are located in a coding region it is often unclear whether this is a functional variant or a variant in LD with the functional variant. Genetic variants can modulate transcription of target genes up to several megabase pairs (Mbp)
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