Polymorphic Cis- and Trans-Regulation of Human Gene Expression

Cheung, Vivian G.; Nayak, Renuka R.; Wang, Isabel X.; Elwyn, Susannah; Cousins, Sarah M.; Morley, Michael; Spielman, Richard S.

doi:10.1371/journal.pbio.1000480

Cited by 150 publications

(164 citation statements)

References 72 publications

(98 reference statements)

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“…We observed good agreement between expression values for biological replicates used between the studies (average Pearson correlation (r) between expression values ¼ 0.87). Despite the much smaller size of the Cheung et al 33 data set, the association between rDNA dosage and GE were significantly correlated between studies (correlations between the strength of the association uncovered with expressed data from the Cheung et al 33 study and from the Montgomery et al 24 study: 18S:…”

Section: Resultsmentioning

confidence: 96%

“…These observations indicate that the major functional associations can be replicated independently within each population. Second, we performed association analysis on a subset of 27 overlapping CEU individuals for which RNA-seq data were generated from another study and processed through the same analysis pipeline 33,34 . We observed good agreement between expression values for biological replicates used between the studies (average Pearson correlation (r) between expression values ¼ 0.87).…”

Section: Resultsmentioning

confidence: 99%

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Ribosomal DNA copy number is coupled with gene expression variation and mitochondrial abundance in humans

et al. 2014

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Ribosomes are essential intracellular machines composed of proteins and RNA molecules. The DNA sequences (rDNA) encoding ribosomal RNAs (rRNAs) are tandemly repeated and give origin to the nucleolus. Here we develop a computational method for estimating rDNA dosage (copy number) and mitochondrial DNA abundance using whole-genome short-read DNA sequencing. We estimate these attributes across hundreds of human genomes and their association with global gene expression. The analyses uncover abundant variation in rDNA dosage that is coupled with the expression of hundreds of functionally coherent gene sets. These include associations with genes coding for chromatin components that target the nucleolus, including CTCF and HP1b. Finally, the data show an inverse association between rDNA dosage and mitochondrial DNA abundance that is manifested across genotypes. Our findings uncover a novel and cryptic source of hypervariable genomic diversity with global regulatory consequences (ribosomal eQTL) in humans. The variation provides a mechanism for cellular homeostasis and for rapid and reversible adaptation.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Ribosomal DNA copy number is coupled with gene expression variation and mitochondrial abundance in humans

et al. 2014

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“…We use three publicly available data sets, the Bottomly data (Bottomly et al (2011)), the Cheung data (Cheung et al (2010)), and the Pickrell/Montgomery ("MontPick") data (Pickrell et al (2010), Montgomery et al (2010)), as obtained from the recount site (http://bowtie-bio.sourceforge.net/recount/). The Bottomly data consist of RNA-Seq results from 21 samples of gene expression in the striatum in two strains of mice, 10 from the C7BL/6J strain and 11 from the DBA/2J strain.…”

Section: Resampling From Existing Data Setsmentioning

confidence: 99%

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Rocke

Ruan

Zhang

et al. 2015

Preprint

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Motivation: An important property of a valid method for testing for differential expression is that the false positive rate should at least roughly correspond to the p-value cutoff, so that if 10,000 genes are tested at a p-value cutoff of 10 −4 , and if all the null hypotheses are true, then there should be only about 1 gene declared to be significantly differentially expressed. We tested this by resampling from existing RNA-Seq data sets and also by matched negative binomial simulations.Results: Methods we examined, which rely strongly on a negative binomial model, such as edgeR, DESeq, and DESeq2, show large numbers of false positives in both the resampled real-data case and in the simulated negative binomial case. This also occurs with a negative binomial generalized linear model function in R. Methods that use only the variance function, such as limma-voom, do not show excessive false positives, as is also the case with a variance stabilizing transformation followed by linear model analysis with limma. The excess false positives are likely caused by apparently small biases in estimation of negative binomial dispersion and, perhaps surprisingly, occur mostly when the mean and/or the dispersion is high, rather than for low-count genes. Contact:dmrocke@ucdavis.edu, lruan@ucdavis.edu, yilzhang@ucdavis.edu, gt4636b@gatech.edu, bpdurbin@ucdavis.edu, saviran@ucdavis.edu.Supplementary Information: The computational tools developed for this study are freely available via our website http://dmrocke.ucdavis.edu/software.html. They can be downloaded as R code or run directly through an interactive web-based shiny application to reproduce the analysis presented here per a user's choice of dataset and the methods to be evaluated.

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“…It is currently unclear to what extent eQTL common between cell types and tissues operate in cis-compared with trans-, as low power to detect trans-eQTL makes comparisons difficult (Gilad et al 2008;Ding et al 2010). Recently, several studies mapping cis and trans eQTL have suggested that considerable proportions of regulatory variation act in trans (Price et al 2008(Price et al , 2011Cheung et al 2010;Montgomery et al 2010;Pickrell et al 2010). This should lead us to reexamine the inferences drawn from comparing cis-eQTL overlap in multiple tissues.…”

mentioning

confidence: 99%

Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent

Powell

Henders²,

McRae³

et al. 2011

Genome Res.

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The degree to which the level of genetic variation for gene expression is shared across multiple tissues has important implications for research investigating the role of expression on the etiology of complex human traits and diseases. In the last few years, several studies have been published reporting the extent of overlap in expression quantitative trait loci (eQTL) identified in multiple tissues or cell types. Although these studies provide important information on the regulatory control of genes across tissues, their limited power means that they can typically only explain a small proportion of genetic variation for gene expression. Here, using expression data from monozygotic twins (MZ), we investigate the genetic control of gene expression in lymphoblastoid cell lines (LCL) and whole blood (WB). We estimate the genetic correlation that represents the combined effects of all causal loci across the whole genome and is a measure of the level of common genetic control of gene expression between the two RNA sources. Our results show that, when averaged across the genome, mean levels of genetic correlation for gene expression in LCL and WB samples are close to zero. We support our results with evidence from gene expression in an independent sample of LCL, T-cells, and fibroblasts. In addition, we provide evidence that housekeeping genes, which maintain basic cellular functions, are more likely to have high genetic correlations between the RNA sources than non-housekeeping genes, implying a relationship between the transcript function and the degree to which a gene has tissue-specific genetic regulatory control.

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Polymorphic Cis- and Trans-Regulation of Human Gene Expression

Abstract: Using genetic and molecular analyses, we identified over 1,000 polymorphic regulators that regulate expression levels of human genes.

Cited by 150 publications

References 72 publications

Ribosomal DNA copy number is coupled with gene expression variation and mitochondrial abundance in humans

Ribosomal DNA copy number is coupled with gene expression variation and mitochondrial abundance in humans

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent

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