Genome-Wide Association Study (GWAS) has been widely used as a powerful methodology to identify risk loci for disease since it could scan the genome for the associating millions of loci (genomic variants) with disease without any priori hypothesis. Over the past 15 years, more and more GWAS summary level as well as individual level data are available, especially that a lot of GWAS summary statistics are open to the public and several user-friendly web databases have allowed biologists to query GWAS data easily. Accompanying the rapid increase of the GWAS data, many techniques were proposed to solve the outstanding questions of GWAS, for example, the majority of disease-associated loci lie in non-coding regions of the genome and it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. Transcriptome-Wide Association Study (TWAS) has emerged as a very powerful technique in investigating disease-associated genes since 2015. Unlike conventional GWAS, TWAS investigates the association of disease risk with the expression level of a given gene using aggregated information from multiple cis-genetic variants, thus may have higher statistical power and facilitate identification of novel association signals that are missed in GWAS. It is noted that the topic not involves researches about disease modifying gene or gene influencing disease. GWAS and TWAS have been applied by a lot of studies to explore the biological mechanisms of disease in recent years, this research topic involves the development of new GWAS and TWAS methodology and the novel significant genes validation by GWAS and TWAS. The meta-analysis using GWAS and TWAS identified genes are also very welcome.
This collection welcomes, but is not limited to, the following subtopics:
GWAS method/ software/ database
TWAS method/ software/ database
GWAS/ TWAS
Expression quantitative trait loci analysis (eQTLs)
Methylation quantitative trait locus (mQTLs)
Meta-analysis using GWAS/ TWAS identified genes
Inspiring perspectives or opinions for GWAS/ TWAS
Reviews of statistical methods or resources
Original Research, Reviews, Methods, Technology and Code, Data Reports and, Commentary Articles are all welcome.
Keywords: genome-wide association studies, transcriptome-wide association study, linear mixed models, Bayesian models, pathway analysis, statistical methodology, meta-analysis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Genome-Wide Association Study (GWAS) has been widely used as a powerful methodology to identify risk loci for disease since it could scan the genome for the associating millions of loci (genomic variants) with disease without any priori hypothesis. Over the past 15 years, more and more GWAS summary level as well as individual level data are available, especially that a lot of GWAS summary statistics are open to the public and several user-friendly web databases have allowed biologists to query GWAS data easily. Accompanying the rapid increase of the GWAS data, many techniques were proposed to solve the outstanding questions of GWAS, for example, the majority of disease-associated loci lie in non-coding regions of the genome and it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. Transcriptome-Wide Association Study (TWAS) has emerged as a very powerful technique in investigating disease-associated genes since 2015. Unlike conventional GWAS, TWAS investigates the association of disease risk with the expression level of a given gene using aggregated information from multiple cis-genetic variants, thus may have higher statistical power and facilitate identification of novel association signals that are missed in GWAS. It is noted that the topic not involves researches about disease modifying gene or gene influencing disease. GWAS and TWAS have been applied by a lot of studies to explore the biological mechanisms of disease in recent years, this research topic involves the development of new GWAS and TWAS methodology and the novel significant genes validation by GWAS and TWAS. The meta-analysis using GWAS and TWAS identified genes are also very welcome.
This collection welcomes, but is not limited to, the following subtopics:
GWAS method/ software/ database
TWAS method/ software/ database
GWAS/ TWAS
Expression quantitative trait loci analysis (eQTLs)
Methylation quantitative trait locus (mQTLs)
Meta-analysis using GWAS/ TWAS identified genes
Inspiring perspectives or opinions for GWAS/ TWAS
Reviews of statistical methods or resources
Original Research, Reviews, Methods, Technology and Code, Data Reports and, Commentary Articles are all welcome.
Keywords: genome-wide association studies, transcriptome-wide association study, linear mixed models, Bayesian models, pathway analysis, statistical methodology, meta-analysis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
