Dataset Information

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.

ABSTRACT: Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples ([Formula: see text] for cis-eQTLs and [Formula: see text] for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n =?526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n?=?1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.

SUBMITTER: Qi T

PROVIDER: S-EPMC5995828 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.

Qi Ting T Wu Yang Y Zeng Jian J Zhang Futao F Xue Angli A Jiang Longda L Zhu Zhihong Z Kemper Kathryn K Yengo Loic L Zheng Zhili Z Marioni Riccardo E RE Montgomery Grant W GW Deary Ian J IJ Wray Naomi R NR Visscher Peter M PM McRae Allan F AF Yang Jian J

Nature communications 20180611 1

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r <sub>b</sub> ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between in ...[more]

PMID: 29891976

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Project description:BackgroundTranscription factors (TFs) regulate downstream genes in response to environmental stresses in plants. Identification of TF target genes can provide insight on molecular mechanisms of stress response systems, which can lead to practical applications such as engineering crops that thrive in challenging environments. Despite various computational techniques that have been developed for identifying TF targets, it remains a challenge to make best use of available experimental data, especially from time-series transcriptome profiling data, for improving TF target identification.ResultsIn this study, we used a novel approach that combined kinetic modelling of gene expression with a statistical meta-analysis to predict targets of 757 TFs using expression data of 14,905 genes in Arabidopsis exposed to different durations and types of abiotic stresses. Using a kinetic model for the time delay between the expression of a TF gene and its potential targets, we shifted a TF's expression profile to make an interacting pair coherent. We found that partitioning the expression data by tissue and developmental stage improved correlation between TFs and their targets. We identified consensus pairs of correlated profiles between a TF and all other genes among partitioned datasets. We applied this approach to predict novel targets of known TFs. Some of these putative targets were validated from the literature, for E2F's targets in particular, while others provide explicit genes as hypotheses for future studies.ConclusionOur method provides a general framework for TF target prediction with consideration of the time lag between initiation of a TF and activation of its targets. The framework helps make significant inferences by reducing the effects of independent noises in different experiments and by identifying recurring regulatory relationships under various biological conditions. Our TF target predictions may shed some light on common regulatory networks in abiotic stress responses.

Dataset Information

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.

Publications

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.

Similar Datasets

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