A0615
Title: Inferring causal direction between two traits using R-squared with application to transcriptome-wide association studies
Authors: Huiling Liao - University of Minnesota (United States)
Haoran Xue - City University of Hong Kong (Hong Kong) [presenting]
Wei Pan - University of Minnesota (United States)
Abstract: In the framework of Mendelian randomization, two single SNP-trait Pearsons correlation-based methods have been developed to infer the causal direction between an exposure (e.g. a gene) and an outcome (e.g. a trait), including the widely used MR Steiger's method and its recent extension called causal direction-ratio (CD-Ratio). Steiger's method uses a single SNP as an instrumental variable (IV) for inference, while CD-Ratio combines the results from each of multiple SNPs. An approach is proposed based on R-squared, the coefficient of determination, to simultaneously combine information from multiple SNPs to infer the presence and direction of a causal relationship between an exposure and an outcome. The proposed method can be regarded as a generalization of Steiger's method from using a single SNP to multiple SNPs as IVs. It is especially useful in transcriptome-wide association studies (TWAS) with typically small sample sizes for gene expression data, providing a more flexible and powerful approach to inferring causal directions. It can be applied to GWAS summary data with a reference panel. Its potential robustness is also discussed to invalid IVs. The performance of TWAS, Steiger's method, CD-Ratio, and the new R-squared-based method is compared in simulations and real data analysis to demonstrate some advantages of the proposed method.
