iPheGWAS: an intelligent computational framework to integrate and visualise genome-phenome wide association results
biorxiv(2022)
摘要
Estimating the genetic correlations by LDSC is computationally demanding and visualising multiple GWAS results along with their genetic relationships is restricted. This study developed iPheGWAS, a novel approach which applied hierarchical clustering to GWAS summary statistics to (i) calculate their genetic relatedness, and (ii) enable three-dimensional visualisation of multiple ordered GWAS plots. Simulation and real-world data analysis demonstrated that when investigating genetic relationships among multiple phenotypes, iPheGWAS can deliver comparable results with LDSC but with 8 times faster computational speed. It can also provide novel findings in studying genetically-correlated comorbidities, such as mental illness and rheumatoid arthritis.
### Competing Interest Statement
The authors have declared no competing interest.
更多查看译文
关键词
wide association studies,intelligent computational framework,genome-phenome
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要