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

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

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 u...

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Bibliographic Details
Main Authors: Lingfei Wang, Tom Michoel
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-08-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1005703
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spelling doaj-77d961da8c484a2884994ba911a502ec2021-04-21T15:44:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-08-01138e100570310.1371/journal.pcbi.1005703Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.Lingfei WangTom MichoelMapping 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.https://doi.org/10.1371/journal.pcbi.1005703
collection DOAJ
language English
format Article
sources DOAJ
author Lingfei Wang
Tom Michoel
spellingShingle Lingfei Wang
Tom Michoel
Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.
PLoS Computational Biology
author_facet Lingfei Wang
Tom Michoel
author_sort Lingfei Wang
title Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.
title_short Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.
title_full Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.
title_fullStr Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.
title_full_unstemmed Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.
title_sort efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2017-08-01
description 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.
url https://doi.org/10.1371/journal.pcbi.1005703
work_keys_str_mv AT lingfeiwang efficientandaccuratecausalinferencewithhiddenconfoundersfromgenometranscriptomevariationdata
AT tommichoel efficientandaccuratecausalinferencewithhiddenconfoundersfromgenometranscriptomevariationdata
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