Background:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limite...Background:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limited statistical power and difficulties in biological interpretation.With the recent progress in expression quantitative trait loci(eQTL)studies,transcriptome-wide association studies(TWAS)provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies.Results:In this review,we will introduce the general framework of TWAS,the relevant resources,and the computational tools.Extensions of the original TWAS methods will also be discussed.Furthermore,we will briefly introduce methods that are closely related to TWAS,including MR-based methods and colocalization approaches.Connection and difference between these approaches will be discussed.Conclusion:Finally,we will summarize strengths,limitations,and potential directions for TWAS.展开更多
基金National Natural Science Foundation of China(No.11601259)Shanghai Municipal Science and Technology Major Project(No.2017SHZDZX01).Y.X.and N.S.were supported in part by the China Scholarship Council,and H.Z.was supported in part by NIH grant R01GM122078,NSF grants DMS 1713120 and DMS 1902903.
文摘Background:Genome-wide association studies(GWAS)have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade,however,they are still hampered by limited statistical power and difficulties in biological interpretation.With the recent progress in expression quantitative trait loci(eQTL)studies,transcriptome-wide association studies(TWAS)provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies.Results:In this review,we will introduce the general framework of TWAS,the relevant resources,and the computational tools.Extensions of the original TWAS methods will also be discussed.Furthermore,we will briefly introduce methods that are closely related to TWAS,including MR-based methods and colocalization approaches.Connection and difference between these approaches will be discussed.Conclusion:Finally,we will summarize strengths,limitations,and potential directions for TWAS.
