Natural genetic variation between individuals in a population leads to variations in gene expression that are informative for the inference of gene regulatory networks. 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. A systematic evaluation using simulated data from the DREAM5 Systems Genetics challenge demonstrated that the novel tests outperformed existing causal inference methods as well as all challenge submissions. We confirmed these results using siRNA silencing, ChIP-sequencing and microRNA target data to validate causal gene-gene and microRNA-gene interactions inferred from genotype, microRNA and mRNA sequencing data of nearly 400 human individuals from the Geuvadis study. Findr provides the community with the first efficient and accurate causal inference tool for modern datasets of tens of thousands of RNA expression traits and genotypes from hundreds or more human individuals . Findr is publicly available at URL
Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors , which improves current methods by taking into account hidden confounders and weak regulations . Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster . Findr is publicly available at URL
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Natural genetic variation between individuals in a population leads to variations in gene expression that are informative for the inference of gene regulatory networks. Particularly, genome-wide genotype and transcriptome data from the same samples allow for
Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate
R
the DNA variations in cis-regulatory regions
cis-regulatory DNA variations
R
. 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
, which improves current methods by taking into account hidden confounders
R
, 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. A systematic evaluation using simulated data from
. Findr outperformed existing methods on
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demonstrated that the novel tests outperformed existing causal inference methods as well as all challenge submissions. We confirmed these results using siRNA silencing, ChIP-sequencing and microRNA target data to validate causal gene-gene
R
microRNA-gene interactions inferred from genotype, microRNA and mRNA sequencing data of nearly 400 human individuals from the Geuvadis study. Findr provides the community with the first efficient and accurate causal inference tool for modern datasets of tens of thousands of RNA expression traits and genotypes from hundreds or more human individuals
on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster