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

Lingfei Wang,Tom Michoel,Jennifer Listgarten

doi:10.1371/journal.pcbi.1005703

Lingfei Wang, Tom Michoel + Show 1 more

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https://doi.org/10.1371/journal.pcbi.1005703

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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 consideration 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.

Highlights

Genetic variation in non-coding genomic regions, including at loci associated with complex traits and diseases identified by genome-wide association studies (GWAS), predominantly plays a gene-regulatory role [1]
We developed a highly efficient, scalable software package Findr (Fast Inference of Networks from Directed Regulations) implementing novel and existing causal inference tests
Application of Findr on real and simulated genome and transcriptome variation data showed that our novel tests, which account for weak secondary linkage and hidden confounders at the potential cost of an increased number of false positives, resulted in a significantly improved performance to predict known gene regulatory interactions compared to existing methods, traditional methods based on conditional independence tests, which had highly elevated false negative rates

Summary

Introduction

Genetic variation in non-coding genomic regions, including at loci associated with complex traits and diseases identified by genome-wide association studies (GWAS), predominantly plays a gene-regulatory role [1]. The number and size of studies mapping genome and transcriptome variation has surged in recent years due to the advent of high-throughput sequencing technologies, and ever more expansive catalogues of expression-associated DNA variants, termed expression quantitative trait loci (eQTLs), are being mapped in humans, model organisms, crops and other species [1, 3,4,5]. Existing statistical models rely on a conditional independence test which assumes that no hidden confounding factors affect the coexpression of causally related gene pairs. It is known that the conditional independence test is susceptible to variations in relative measurement errors between genes [8, 9, 18], an inherent feature of both microarray and RNA-seq based expression data [19]

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