Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no b server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap beten GWAS and disease mechanism study by generating hypothesis of SNP ! gene ! pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/.
['Kunlin Zhang', 'Suhua Chang', 'Sijia Cui', 'Liyuan Guo', 'Liuyan Zhang', 'Jing Wang']
I am writing a research paper on a idea ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework
[{"from": "human", "value": "I am writing a research paper on a idea ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Genome-wide association study (GWAS) is widely utilized to identify genes involved in human complex disease or some other trait. One key challenge for GWAS data interpretation is to identify causal SNPs and provide profound evidence on how they affect the trait. Currently, researches are focusing on identification of candidate causal variants from the most significant SNPs of GWAS, while there is lack of support on biological mechanisms as represented by pathways. Although pathway-based analysis (PBA) has been designed to identify disease-related pathways by analyzing the full list of SNPs from GWAS, it does not emphasize on interpreting causal SNPs. To our knowledge, so far there is no b server available to solve the challenge for GWAS data interpretation within one analytical framework. ICSNPathway is developed to identify candidate causal SNPs and their corresponding candidate causal pathways from GWAS by integrating linkage disequilibrium (LD) analysis, functional SNP annotation and PBA. ICSNPathway provides a feasible solution to bridge the gap beten GWAS and disease mechanism study by generating hypothesis of SNP ! gene ! pathway(s). The ICSNPathway server is freely available at http://icsnpathway.psych.ac.cn/."}]