Evidence map›Paper›PMID 28190458›Full record

ArticleAmerican journal of human genetics2017

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

Federico A Santoni, Georgios Stamoulis, Marco Garieri, Emilie Falconnet, Pascale Ribaux, Christelle Borel, Stylianos E Antonarakis

Open access · bronzeAbstract readTwin Study
In one paragraph

Article in American journal of human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
6.8field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed, 91 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Federico A SantoniDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland; University Hospitals of Geneva, Geneva 1211, Switzerland. Electronic address: federico.santoni@unige.ch.
Georgios StamoulisDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland.
Marco GarieriDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland.
Emilie FalconnetDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland.
Pascale RibauxDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland.
Christelle BorelDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland.
Stylianos E AntonarakisDepartment of Genetic Medicine and Development, University of Geneva Medical School, Geneva 1211, Switzerland; University Hospitals of Geneva, Geneva 1211, Switzerland; Institute of Genetics and Genomics of Geneva, Geneva 1211, Switzerland. Electronic address: stylianos.antonarakis@unige.ch.
University of Geneva · CHUniversity Hospital of Geneva · CH

Funding

European Research Council 249968
6 · The paper itself

Abstract

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.

Indexed as

AllelesGene ExpressionGenomic ImprintingCell LineFibroblastsGenetic Association StudiesGenotyping TechniquesHeterozygoteHumansModels, TheoreticalPolymorphism, Single NucleotideSequence Analysis, RNASingle-Cell AnalysisTranscriptomeallele-specific expressionimprintingsingle cell

Identifiers

PMID28190458
PMCPMC5339288
OpenAlexW2586474818

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.