Evidence map›Paper›PMID 37293101›Full record

ArticlebioRxiv : the preprint server for biology2023

Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders.

Toni Boltz, Tommer Schwarz, Merel Bot, Kangcheng Hou, Christa Caggiano, Sandra Lapinska, Chenda Duan, Marco P Boks, Rene S Kahn, Noah Zaitlen and 2 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 2 countries.

Toni BoltzDepartment of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Tommer SchwarzBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Merel BotCenter for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA.
Kangcheng HouBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Christa CaggianoBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Sandra LapinskaBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Chenda DuanDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
Marco P BoksDepartment of Psychiatry, Brain Center University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands.
Rene S KahnDepartment of Psychiatry, Brain Center University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands.
Noah ZaitlenBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Bogdan PasaniucDepartment of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Roel OphoffDepartment of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
University of California, Los Angeles · USUtrecht University · NL

Funding

Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
Joint genomic and statistical analyses of schizophrenia and bipolar to decipher genetic susceptibilityR01MH115676 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Roel A Ophoff, Bogdan Pasaniuc · 2018 to 2026
$5.9M
NHGRI NIH HHS T32 HG002536NIMH NIH HHS R01 MH115676
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have uncovered susceptibility loci associated with psychiatric disorders like bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome with unknown causal mechanisms of the link between genetic variation and disease risk. Expression quantitative trait loci (eQTL) analysis of bulk tissue is a common approach to decipher underlying mechanisms, though this can obscure cell-type specific signals thus masking trait-relevant mechanisms. While single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell type proportions and cell type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-Seq from 1,730 samples derived from whole blood in a cohort ascertained for individuals with BP and SCZ this study estimated cell type proportions and their relation with disease status and medication. We found between 2,875 and 4,629 eGenes for each cell type, including 1,211 eGenes that are not found using bulk expression alone. We performed a colocalization test between cell type eQTLs and various traits and identified hundreds of associations between cell type eQTLs and GWAS loci that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on cell type expression regulation and found examples of genes that are differentially regulated dependent on lithium use. Our study suggests that computational methods can be applied to large bulk RNA-Seq datasets of non-brain tissue to identify disease-relevant, cell type specific biology of psychiatric disorders and psychiatric medication.

Identifiers

PMID37293101
PMCPMC10245943
OpenAlexW4378212635

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.