In the post-genomic era, and especially within the past decade, genome-wide association studies (GWAS) have largely contributed in accelerating the identification of genetic variants across the genomes of millions of individuals, significantly associated with various disease phenotypes including cancer. For common diseases, e.g. type 2 diabetes mellitus, coronary artery disease, schizophrenia, Crohn’s disease, breast and lung cancer, etc., thousands of susceptibility genetic loci have been determined, the analysis of which may reveal valid functional information about the disorders. However, the effect size of each individual variant associated with a multifactorial disease is relatively small, the overall disease risk corresponds to the cumulative effect size resulting from multiple genes and, most importantly, it is enhanced due to gene interactions. The information content of GWAS data can be upgraded in the context of biomolecular interaction networks, e.g. protein interaction, and/or gene regulatory networks, individually or in combination. Such strategies have successfully been used for the evaluation of GWAS combined with relevant gene expression data to (a) prioritize groups of core susceptibility genes with respect to their position in the biomolecular network, (b) reveal disease modules, pathways, and disease subgroups, (c) discover novel, and extent currently known disease-associated biological mechanisms, (d) identify additional risk genes based on the network architecture of the disease, and (e) suggest repurposing or development of new drugs to plausible targets through disease comorbidity assessment.
Presently, most GWAS are based on SNP arrays, because it is an accurate and relative inexpensive methodology. Whole genome or exome sequencing or single-cell omics, gradually becoming affordable, would increase the number of identified risk genetic variants, thus decrease part of the still missing heritability, widen the risk allele frequency spectrum, and, even, identify rare disease causal gene mutants. Whole genome analysis alternatives integrated with genome-wide transcriptomics data subjected to biomolecular network and pathway analysis would permit the quantitative functional profiling of genetic variants and may suggest novel disease-associated biological mechanisms and regulatory structures.
In this Research Topic, we welcome manuscript formats including original research, full-length or mini-reviews emphasizing on the upgrade of the information content of genome-wide associated data for common multigenic or oligogenic diseases, generated from any level of genetic reference, through their analysis in the context of biomolecular interaction networks. Integrated with relevant functional information, this analysis will enhance the biological interpretation of network architecture into disease mechanisms.
Areas of interest for this research topic within the context of biomolecular interaction network analysis, may include but are not limited to:
- Evaluation of GWAS combined with relevant gene expression data
- Integration of GWAS with transcriptome and/or proteome-wide association studies (TWAS, PWAS) to identify gene-disease associations and prioritize genes and pathways relevant to disease
- Identification of “modifier” genes associated with phenotypic variation of oligogenic or monogenic disorders by evaluating various genome-wide approaches through biomolecular interaction network information.
- Detect disease functional modules for understanding complex disease biology
- Analyze disease comorbidity for drug repurposing
In the post-genomic era, and especially within the past decade, genome-wide association studies (GWAS) have largely contributed in accelerating the identification of genetic variants across the genomes of millions of individuals, significantly associated with various disease phenotypes including cancer. For common diseases, e.g. type 2 diabetes mellitus, coronary artery disease, schizophrenia, Crohn’s disease, breast and lung cancer, etc., thousands of susceptibility genetic loci have been determined, the analysis of which may reveal valid functional information about the disorders. However, the effect size of each individual variant associated with a multifactorial disease is relatively small, the overall disease risk corresponds to the cumulative effect size resulting from multiple genes and, most importantly, it is enhanced due to gene interactions. The information content of GWAS data can be upgraded in the context of biomolecular interaction networks, e.g. protein interaction, and/or gene regulatory networks, individually or in combination. Such strategies have successfully been used for the evaluation of GWAS combined with relevant gene expression data to (a) prioritize groups of core susceptibility genes with respect to their position in the biomolecular network, (b) reveal disease modules, pathways, and disease subgroups, (c) discover novel, and extent currently known disease-associated biological mechanisms, (d) identify additional risk genes based on the network architecture of the disease, and (e) suggest repurposing or development of new drugs to plausible targets through disease comorbidity assessment.
Presently, most GWAS are based on SNP arrays, because it is an accurate and relative inexpensive methodology. Whole genome or exome sequencing or single-cell omics, gradually becoming affordable, would increase the number of identified risk genetic variants, thus decrease part of the still missing heritability, widen the risk allele frequency spectrum, and, even, identify rare disease causal gene mutants. Whole genome analysis alternatives integrated with genome-wide transcriptomics data subjected to biomolecular network and pathway analysis would permit the quantitative functional profiling of genetic variants and may suggest novel disease-associated biological mechanisms and regulatory structures.
In this Research Topic, we welcome manuscript formats including original research, full-length or mini-reviews emphasizing on the upgrade of the information content of genome-wide associated data for common multigenic or oligogenic diseases, generated from any level of genetic reference, through their analysis in the context of biomolecular interaction networks. Integrated with relevant functional information, this analysis will enhance the biological interpretation of network architecture into disease mechanisms.
Areas of interest for this research topic within the context of biomolecular interaction network analysis, may include but are not limited to:
- Evaluation of GWAS combined with relevant gene expression data
- Integration of GWAS with transcriptome and/or proteome-wide association studies (TWAS, PWAS) to identify gene-disease associations and prioritize genes and pathways relevant to disease
- Identification of “modifier” genes associated with phenotypic variation of oligogenic or monogenic disorders by evaluating various genome-wide approaches through biomolecular interaction network information.
- Detect disease functional modules for understanding complex disease biology
- Analyze disease comorbidity for drug repurposing
