Common Regulatory Variation Impacts Gene Expression in a Cell Type–Dependent Manner

Dimas, Antigone S.; Deutsch, Samuel; Stranger, Barbara Elaine; Montgomery, Stephen B.; Borel, Christelle; Attar-Cohen, Homa; Ingle, Catherine; Beazley, Claude; Arcelus, Maria Gutierrez; Sekowska, Magdalena; Gagnebin, Marilyne; Nisbett, James; Deloukas, Panos; Dermitzakis, Emmanouil T.; Antonarakis, Stylianos E.

doi:10.1126/science.1174148

Cited by 682 publications

(729 citation statements)

References 27 publications

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“…To validate our results, we analyzed the ASE of these 5 biallelic genes in 179 bulk samples from GenCord primary fibroblasts. 31 All genes were detectable and displayed an unambiguous biallelic expression in fibroblast cell types, thus confirming our initial finding with the single-cell based method (Figure 3). Moreover, we examined in detail the 42 imprinted genes that were recently detected in 1,582 human primary tissue samples.…”

Section: Characterization Of Known Imprinted Genes In Singlecell Fibrsupporting

confidence: 84%

“…To empirically validate the single-cell RNA-seq approach for its sensitivity to detect more genes than standard RNA-seq from bulk cell populations, we compared the number of detected genes between 165 bulk samples obtained from the GenCord fibroblast collection 31 and 165 single-cell fibroblasts from one individual from the same collection (Figure 1). The number of detected genes reached Figure 1).…”

Section: Single Cells Reveal Transcripts Of Low Abundancementioning

confidence: 99%

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Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

Santoni

Stamoulis

Garieri

et al. 2017

The American Journal of Human Genetics

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Genomic imprinting results in parental-specific gene expression. Imprinted genes are involved in the etiology of rare syndromes and have been associated with common diseases such as diabetes and cancer. Standard RNA bulk cell sequencing applied to whole-tissue samples has been used to detect imprinted genes in human and mouse models. However, lowly expressed genes cannot be detected by using RNA bulk approaches. Here, we report an original and robust method that combines single-cell RNA-seq and whole-genome sequencing into an optimized statistical framework to analyze genomic imprinting in specific cell types and in different individuals. Using samples from the probands of 2 family trios and 3 unrelated individuals, 1,084 individual primary fibroblasts were RNA sequenced and more than 700,000 informative heterozygous single-nucleotide variations (SNVs) were genotyped. The allele-specific coverage per gene of each SNV in each single cell was used to fit a beta-binomial distribution to model the likelihood of a gene being expressed from one and the same allele. Genes presenting a significant aggregate allelic ratio (between 0.9 and 1) were retained to identify of the allelic parent of origin. Our approach allowed us to validate the imprinting status of all of the known imprinted genes expressed in fibroblasts and the discovery of nine putative imprinted genes, thereby demonstrating the advantages of single-cell over bulk RNA-seq to identify imprinted genes. The proposed single-cell methodology is a powerful tool for establishing a cell type-specific map of genomic imprinting.

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Section: Characterization Of Known Imprinted Genes In Singlecell Fibrsupporting

confidence: 84%

Section: Single Cells Reveal Transcripts Of Low Abundancementioning

confidence: 99%

Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

Santoni

Stamoulis

Garieri

et al. 2017

The American Journal of Human Genetics

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View full text Add to dashboard Cite

show abstract

“…To examine if quantification of expression levels of exons and eQTL discoveries are driven by mapping bias we incorporated the information obtained by our simulations with RNA-seq data from lymphoblastoid cells lines of 185 individuals from the Gencord project [20,21]. In the original analysis of this dataset, 49 bp paired-end reads (median 39 million per individual) were mapped to hg19 reference genome [25] with BWA [17].…”

Section: Methodsmentioning

confidence: 99%

Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies

et al. 2014

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Background: RNA sequencing (RNA-seq) is the current gold-standard method to quantify gene expression for expression quantitative trait locus (eQTL) studies. However, a potential caveat in these studies is that RNA-seq reads carrying the non-reference allele of variant loci can have lower probability to map correctly to the reference genome, which could bias gene quantifications and cause false positive eQTL associations. In this study, we analyze the effect of this allelic mapping bias in eQTL discovery.

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“…15 It is recognized that cis-eQTLs are highly tissue-specific. 16,17 However, the acquisition of the appropriate tissue type is logistically challenging and the majority of eQTL studies were based on lymphoblastoid cell lines (LCLs). Recently, Grisanzio et al 18 reported five cis-eQTLs in prostate tissue (both normal and tumor) that alter expression of nearby genes.…”

Section: Introductionmentioning

confidence: 99%

Variants at IRX4 as prostate cancer expression quantitative trait loci

Hussain

Vijai

et al. 2013

Eur J Hum Genet

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Genome-wide association studies (GWAS) have identified numerous prostate cancer-associated risk loci. Some variants at these loci may be regulatory and influence expression of nearby genes. Such loci are known as cis-expression quantitative trait loci (cis-eQTL). As cis-eQTLs are highly tissue-specific, we asked if GWAS-identified prostate cancer risk loci are cis-eQTLs in human prostate tumor tissues. We investigated 50 prostate cancer samples for their genotype at 59 prostate cancer riskassociated single-nucleotide polymorphisms (SNPs) and performed cis-eQTL analysis of transcripts from paired primary tumors within two megabase windows. We tested 586 transcript-genotype associations, of which 27 were significant (false discovery rate r10%). An equivalent eQTL analysis of the same prostate cancer risk loci in lymphoblastoid cell lines did not result in any significant associations. The top-ranked cis-eQTL involved the IRX4 (Iroquois homeobox protein 4) transcript and rs12653946, tagged by rs10866528 in our study (P ¼ 4.91 Â 10 À5 ). Replication studies, linkage disequilibrium, and imputation analyses highlight population specificity at this locus. We independently validated IRX4 as a potential prostate cancer risk gene through cis-eQTL analysis of prostate cancer risk variants. Cis-eQTL analysis in relevant tissues, even with a small sample size, can be a powerful method to expedite functional follow-up of GWAS.

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Common Regulatory Variation Impacts Gene Expression in a Cell Type–Dependent Manner

Cited by 682 publications

References 27 publications

Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies

Variants at IRX4 as prostate cancer expression quantitative trait loci

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