Edinburgh Research Explorer

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

Research output: Contribution to journalArticle

Related Edinburgh Organisations

Access status

Open

Documents

Original languageEnglish
Pages (from-to)e1005703
JournalPLoS Computational Biology
Volume13
Issue number8
DOIs
StatePublished - 18 Aug 2017

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.

Research areas

  • Journal Article

ID: 42385616