Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders.

cell type deconvolution eQTL gene expression neuropsychiatric

Journal

American journal of human genetics
ISSN: 1537-6605
Titre abrégé: Am J Hum Genet
Pays: United States
ID NLM: 0370475

Informations de publication

Date de publication:
01 Feb 2024
Historique:
received: 05 06 2023
revised: 12 12 2023
accepted: 13 12 2023
medline: 3 2 2024
pubmed: 3 2 2024
entrez: 2 2 2024
Statut: ppublish

Résumé

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although 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 from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.

Identifiants

DOI: 10.1016/j.ajhg.2023.12.018 PMID: 38306997
pubmed: 38306997
pii: S0002-9297(23)00450-0
doi: 10.1016/j.ajhg.2023.12.018
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

323-337

Informations de copyright

Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of interests Tommer Schwarz currently is employed at Cytoreason in Tel Aviv, Israel. The remaining authors declare no competing interests.

Auteurs

Toni Boltz (T)

Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. Electronic address: tboltz@g.ucla.edu.

Tommer Schwarz (T)

Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.

Merel Bot (M)

Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.

Kangcheng Hou (K)

Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.

Christa Caggiano (C)

Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.

Sandra Lapinska (S)

Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.

Chenda Duan (C)

Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.

Marco P Boks (MP)

Department of Psychiatry, Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands.

Rene S Kahn (RS)

Department of Psychiatry, Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA.

Noah Zaitlen (N)

Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Neurology, University of California Los Angeles, Los Angeles, Los Angeles, CA, USA.

Bogdan Pasaniuc (B)

Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.

Roel Ophoff (R)

Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA; Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands. Electronic address: ophoff@ucla.edu.

Classifications MeSH