Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

TL;DR

This paper introduces Findr, a fast and accurate method for causal inference in gene expression studies that accounts for hidden confounders, outperforming existing methods in accuracy and speed.

Contribution

The paper presents a novel causal inference method, Findr, which improves accuracy by modeling hidden confounders and is significantly faster than previous approaches.

Findings

Outperformed existing methods on DREAM5 challenge

Accurately predicted microRNA and transcription factor targets

Achieved nearly a million times faster computation

Abstract

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 https://github.com/lingfeiwang/findr