Evidence map›Paper›PMID 24092820›Full record

ArticleGenome research2014

Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals.

Alexis Battle, Sara Mostafavi, Xiaowei Zhu, James B Potash, Myrna M Weissman, Courtney McCormick, Christian D Haudenschild, Kenneth B Beckman, Jianxin Shi, Rui Mei and 4 more

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 380 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
380citing papers in PubMed, 9 pooled it
–field-weighted citation impact
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

380 citing papers in PubMed, 9 syntheses or guidelines pooled it, 641 citations in OpenAlex.

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320 more citing papers are in PubMed but not listed here.

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

14 authors at 8 institutions in 1 country.

Alexis BattleDepartment of Computer Science, Stanford University, Stanford, California 94305, USA;
Sara Mostafavi
Xiaowei Zhu
James B Potash
Myrna M Weissman
Courtney McCormick
Christian D Haudenschild
Kenneth B Beckman
Jianxin Shi
Rui Mei
Alexander E Urban
Stephen B Montgomery
Douglas F Levinson
Daphne Koller
Stanford University · USFluxion Biosciences (United States) · USIllumina (United States) · USNational Cancer Institute · USNew York State Psychiatric InstitutePersonalis (United States) · USUniversity of Iowa Hospitals and Clinics · USUniversity of Minnesota · US

Funding

Depression susceptibility genes and networks: expression, eQTL and GWAS analysisRC2MH089916 · NIMH · STANFORD UNIVERSITY · PI LEVINSON, DOUGLAS FREDERICK · 2009 to 2010
$3.4M
Methods for high-resolution analysis of genetic effects on gene expressionR01MH090941 · NIMH · UNIVERSITY OF GENEVA · PI DERMITZAKIS, EMMANOUIL, GUIGO, RODERIC · 2010 to 2012
$863k
NIMH NIH HHS R01 MH090941NIMH NIH HHS R01MH090941NIMH NIH HHS RC2 MH089916NIMH NIH HHS RC2MH089916
6 · The paper itself

Abstract

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.

Indexed as

Genetic VariationQuantitative Trait LociSequence Analysis, RNATranscriptomeBayes TheoremChromosomes, HumanGenome, HumanGenotypeHumansPhenotypePolymorphism, Single NucleotideRegulatory Sequences, Ribonucleic AcidRegulatory Sequences, Ribonucleic Acid

Identifiers

PMID24092820
PMCPMC3875855
OpenAlexW2144961623

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.