Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases
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Abstract
Abstract Gaining insight into the downstream consequences of non-coding variants is an essential step towards the identification of therapeutic targets from genome-wide association study (GWAS) findings. Here we have harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets. This sample size enabled us to perform both cis - and trans -expression quantitative locus (eQTL) mapping. Upon comparing the brain cortex cis -eQTLs (for 12,307 unique genes at FDR<0.05) with a large blood cis- eQTL analysis (n=31,684 samples), we observed that brain eQTLs are more tissue specific than previously assumed. We inferred the brain cell type for 1,515 cis -eQTLs by using cell type proportion information. We conducted Mendelian Randomization on 31 brain-related traits using cis -eQTLs as instruments and found 159 significant findings that also passed colocalization. Furthermore, two multiple sclerosis (MS) findings had cell type specific signals, a neuron-specific cis- eQTL for CYP24A1 and a macrophage specific cis -eQTL for CLECL1 . To further interpret GWAS hits, we performed trans -eQTL analysis. We identified 2,589 trans -eQTLs (at FDR<0.05) for 373 unique SNPs, affecting 1,263 unique genes, and 21 replicated significantly using single-nucleus RNA-seq data from excitatory neurons. We also generated a brain-specific gene-coregulation network that we used to predict which genes have brain-specific functions, and to perform a novel network analysis of Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinson’s disease (PD) GWAS data. This resulted in the identification of distinct sets of genes that show significantly enriched co-regulation with genes inside the associated GWAS loci, and which might reflect drivers of these diseases.
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The authors analyzed several studies involving human samples from European and Asian origin and did not find any evidence of rs79570848 being reported as a cis-eQTL related to brain diseases.
“We have analyzed several studies involving human samples from European and Asian origin and did not find any evidence of rs79570848 being reported as a cis-eQTL (defined as variants within 1 Mb of the transcription start site of the regulated gene) related to brain diseases (Walker et al. 2020; de Klein et al. 2023).”
From The Human Accelerated Region HAR202 Controls <i>NPAS3</i> Expression in the Developing Forebrain Displaying Differential Enhancer Activity Between Modern and Archaic Human Sequences (2024) · cites this paper as de Klein et al. 2023
The Results sections on cis-eQTL discovery and ancestry comparisons describe brain cis-eQTL analyses, define the cis window as within 1 Mb of a gene’s transcription start site, and compare European- and East Asian-ancestry datasets. But our copy does not report a finding about rs79570848 or state that it was not identified as a brain-disease-related cis-eQTL; that negative finding is the citing authors’ own conclusion. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAnalyses of transcript levels from blood and brain among other tissues have generated eQTLs, identifying potentially causal genes for various diseases.
“Analyses of transcript levels from blood and brain among other tissues have generated expression QTLs (eQTLs) (3–5), identifying potentially causal genes for various diseases.”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [3]
The CSF variant was not significant in any plasma pQTL dataset or in brain/blood eQTL analyses.
“The CSF variant was not significant in any plasma pQTL dataset (9–11) or in brain/blood eQTL analyses (3–5).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [3]
The Methods section on eQTL analysis describes how variants were tested, and the Results section on tissue-specific genetic effects compares brain and blood findings. Neither identifies the CSF variant or reports that it was nonsignificant in brain or blood eQTL analyses. Searches for CSF and plasma pQTL results also found no passage establishing the claimed negative results; the plasma pQTL portion is attributed to other citations in the sentence. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Self-citation
Flagged first, then by citations
Cited by
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SingleBrain: A Meta-Analysis of Single-Nucleus eQTLs Linking Genetic Risk to Brain Disorders
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A genetic and proteomic comparison of key AD biomarkers across tissues
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High false sign rates in transcriptome-wide association studies
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- Authors
- de Klein, N., Tsai, E. A., Vochteloo, M., Baird, D., Huang, Y., Chen, C.-Y., van Dam, S., Oelen, R., Deelen, P., Bakker, O. B., El Garwany, O., Ouyang, Z., Marshall, E. E., Zavodszky, M. I., van Rheenen, W., Bakker, M. K., Veldink, J., Gaunt, T. R., Runz, H., Franke, L., & Westra, H.-J.
- Affiliations
- Biogen (United States) (US)
- University Medical Center Groningen (NL)
- Utrecht University (NL)
- University of Bristol (GB)
- Corresponding author
- Runz, Heiko
- Franke, Lude
- Westra, Harm-Jan
- ORCID
- Venue
- Nature Genetics
- Published
- 2023-02-23
- Volume
- 55
- Issue
- 3
- Pages
- 377-388
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC10011140
- OpenAlex
- W4321611595
- Cited by
- 272
- References
- 145
- Citations by year
- 2026: 62 · 2025: 72 · 2024: 72 · 2023: 31 · 2022: 26 · 2021: 9
- FWCI
- 40.96
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Funders
- Mayo Clinic (U24 NS072026, U01 AG046139, P30 AG19610, P50 AG016574, R01 AG032990, U01 AG006786, R01 NS080820, 211002)
- Illinois Department of Public Health (P30AG10161, R01AG15819, R01AG17917, R01AG30146)
- Van Andel Research Institute
- Biogen
- University of Miami
- University of Pennsylvania
- F. Hoffmann-La Roche (U01MH103392, P50MH084053S1, R01MH097276, R01MH093725, R37MH057881, MH06692, P50M096891, AG02219, RO1-MH-075916, R01MH085542, AG05138, HHSN271201300031C, P50MH066392, P50MH080405)
- Mayo Foundation for Medical Education and Research (U24 NS072026, AG016574)
- Harvard University
- Arizona Department of Health Services (HHSN271201300031C, P30 AG19610, P50 AG016574, U24 NS072026, 211002, Contract 211002, R01 AG032990)
- North Carolina State University
- University of Pittsburgh
- CurePSP
- Rush University (P30AG10161, R01 AG15819, R01AG30146)
- Target ALS
- Broad Institute
- Tow Foundation
- Leidos
- Translational Genomics Research Institute
- European Commission (HORIZON2020, 772376)
- University of Bristol (MC_UU_00011/4)
- Rijksuniversiteit Groningen
- ZonMw (917.14.374, 09150182010019)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (917.14.374, 637640, ZonMW-VIDI 917.14.374)
- Università degli Studi di Trento
- Universitair Medisch Centrum Groningen
- Université de Genève
- National Institutes of Health (U24NS07-2026, MH06692, P50M096891, 211002, HHSN271201300031C, AG016574, P50MH080405, AG046139, AG05138, AG19610, AG017216, AG02219, P50MH084053S1, AG003949, AG025711)
- Medical Research Council (MC_UU_00011/4, U01 AG006786, MC_UU_00011)
- National Institute on Aging (211002, R01AG017917, U01-AG006576, P30-AG 10161, P30-AG-19610, U01 AG32984, R01AG030146, R01 AG30146, AG-02219, R01‐AG17917, R01 AG003949, R01 AG15819, AG046139, R01-AG36836, R01AG018023, AG016574, R01AG036836, AG 05138, AG-19610, U01AG046152, U01AG006786, AG-006786, R01-AG025711, R01AG015819, P30AG010161, U01AG032984, AG025711, R01AG032990, P30AG019610, U01AG046139, AG017216, U01‐AG46152, AG003949, R01-AG017216, P50AG016574)
- National Heart, Lung, and Blood Institute (R01 AG003949, R01 AG025711, R01 AG017216, U01 AG006576, R01AG17917)
- National Institute of Mental Health (HHSN271201300031C, R01MH093725, P50MH066392, R01MH109677, 211002, R01MH075916, R01MH085542, R37MH057881, R01MH109897, R01MH097276, U01MH103392, R01MH110921, P30 AG19610)
- National Institute on Drug Abuse (HHSN271201300031C)
- National Human Genome Research Institute
- NIH Office of the Director
- National Cancer Institute
- National Institute of Neurological Disorders and Stroke (R01NS080820, NS072026, 211002, NS080820, U24NS072026, U24 NS072026 National Brain, P30 AG19610)
- Center for Information Technology
- Common Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- brain transcriptome, cis-eQTL, trans-eQTL, central nervous system disorders, multiple sclerosis, CYP24A1, Mendelian randomization, colocalization, drug target prioritization, tissue-specific gene expression
- MeSH terms
- Brain, Brain Diseases, Humans, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Gene Regulatory Networks, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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