Before revision:  Particularly, genome-wide genotype and transcriptome data from the same samples allow for causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors . However, existing causal inference programs are not efficient enough for contemporary datasets, and unrealistically assume the absence of hidden confounders affecting the coexpression of causally related gene pairs. Here we propose alternative statistical tests to infer causal effects in the presence of confounding and weak regulations , and implemented both the novel and the traditional causal inference tests in the software package Findr (Fast Inference of Networks from Directed Regulations), achieving thousands to millions of times of speedup due to analytical false positive rate estimation and implementational optimizations.
After revision:  Particularly, genome-wide genotype and transcriptome data from the same samples allow for causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors , which improves current methods by taking into account hidden confounders and weak regulations , and implemented both the novel and the traditional causal inference tests in the software package Findr (Fast Inference of Networks from Directed Regulations), achieving thousands to millions of times of speedup due to analytical false positive rate estimation and implementational optimizations.