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Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes

Molecular Psychiatry volumeĀ 30,Ā pages 3478ā€“3490 (2025)Cite this article

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Abstract

Genomic studies of molecular traits have provided mechanistic insights into complex disease, though these lag behind for brain-related traits due to the inaccessibility of brain tissue. We leveraged cerebrospinal fluid (CSF) to study neurobiological mechanisms in vivo, measuring 5543 CSF metabolites, the largest panel in CSF to date, in 977 individuals of European ancestry. Individuals originated from two separate cohorts including cognitively healthy subjects (nā€‰=ā€‰490) and a well-characterized memory clinic sample, the Amsterdam Dementia Cohort (ADC, nā€‰=ā€‰487). We performed metabolite quantitative trait loci (mQTL) mapping on CSF metabolomics and found 126 significant mQTLs, representing 65 unique CSF metabolites across 51 independent loci. To better understand the role of CSF mQTLs in brain-related disorders we integrated our CSF mQTL results with pre-existing summary statistics on brain traits, identifying 34 genetic associations between CSF metabolites and brain traits. Over 90% of significant mQTLs demonstrated colocalized associations with brain-specific gene expression, unveiling potential neurobiological pathways.

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Fig. 1: Overview of theĀ CSF mQTL mapping study.
Fig. 2: Manhattan plot and genetic architecture of mQTL associations.
Fig. 3: Heatmap of Z scores of CSF metabolites with at least one study-wide significant metabolome-wide significant association with one of the brain-related traits.
Fig. 4: Phosphatidylcholine QTL colocalization with FADS1 locus.

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Data availability

GWAS summary statistics on all CSF metabolite levels that were generated as part of this study have been deposited to the European Bioinformatics Institute GWAS Catalog (https://www.ebi.ac.uk/gwas/) under accession no. GCST90286909-GCST90292451. GENCODE v.41 basic gene annotation GTF file from https://www.gencodegenes.org/human/stats_41.html UKBB LD reference panel available from Amazon S3 bucket s3://broad-alkesgroup-ukbb-ld/UKBB_LD/. UKBB functional annotations available from Amazon S3 bucket https://broad-alkesgroup-ukbb-ld.s3.amazonaws.com/UKBB_LD/baselineLF_v2.2.UKB.polyfun.tar.gz. PsychENCODE TWAS weights are available from http://resource.psychencode.org/. PsychENCODE isoTWAS weights are available from https://zenodo.org/record/6795947#.Y8mi2-zMLBI. Summary statistics on Alzheimerā€™s disease [41], dementia with Lewy bodies [42] and amyotrophic lateral sclerosis [44] are publicly available at the European Bioinformatics Institute GWAS Catalog under accession no. GCST90027158, GCST90001390 and GCST90027163, respectively. Summary statistics on bipolar disorder [45], schizophrenia [46], major depressive disorder [47], attention deficit hyperactivity disorder (ADHD) [48] and alcohol abuse disorder [50] are publicly available on the psychiatric genomics consortium (PGC) website (https://www.med.unc.edu/pgc/results-and-downloads). The MEGASTROKE consortium, launched by the International Stroke Genetics Consortium, has published the stroke [43] summary statistics at https://www.megastroke.org. Full insomnia [49] summary statistics for UKB and the top 10,000 SNPs for 23andMe are available at https://ctg.cncr.nl/software/summary_statistics/.

Change history

  • 16 October 2025

    The accession number GCST90286909-GCST90292451 was incorrectly given as GCP000726 and has been corrected. The original article has been corrected.

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Acknowledgements

We are greatly appreciative to those individuals who donated the CSF samples on which this study was based. We appreciate data acquisition and reporting by the UC Davis West Coast Metabolomics Center. We thank Dr. Evan Hurlow for his insightful discussions on the organic chemistry involved in this study. We would also like to thank Nicole Zelster for her contributions in the early stages of the project. We also thank Dr. Jurjen Luykx for his help with the recruitment of the cognitively healthy controls. This work used computational and storage services associated with the Hoffman2 Cluster which is operated by the UCLA Office of Advanced Research Computingā€™s Research Technology Group. Research of Alzheimer Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc Steun Alzheimercentrum Amsterdam. The clinical database structure was developed with funding from Stichting Dioraphte. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant number 115372) and Stichting Dioraphte. Genotyping of the Dutch case-control samples was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW project number 733051061). This project was funded by the NIH National Institute on Aging (NIA) grant RF1AG058484 and the National Institute of Mental Health (NIMH) grant R01MH115676 to RAO. LMR was funded by the Memorabel fellowship (ZonMW projectnumber: 10510022110012). TB was supported by the NIH (grant no. 5T32HG002536). SJL is recipient of funding from ZonMW (#733050512), Health~Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). Stichting Dioraphte, the Edwin Bouw Fonds and Stichting VUmc fonds. WMF, SJL, CT are recipients of ABOARD, which is a public-private partnership receiving funding from ZonMW (#73305095007) and Health~Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). More than 30 partners participate in ABOARD (www.aboard-project.nl). ABOARD also receives funding from de Hersenstichting, Edwin Bouw Fonds and Gieskes-Strijbisfonds. The MEGASTROKE project received funding from sources specified at http://www.megastroke.org/acknowledgments.html.

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Author notes

  1. These authors contributed equally: Lianne M. Reus, Toni Boltz.

Authors and Affiliations

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

    Lianne M. Reus,Ā Toni Boltz,Ā Marcelo Francia,Ā Merel Bot,Ā Naren Ramesh,Ā Loes Olde LoohuisĀ &Ā Roel A. Ophoff

  2. Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands

    Lianne M. Reus,Ā Yolande A. L. Pijnenburg,Ā Anouk den Braber,Ā Wiesje M. van der Flier,Ā Pieter Jelle Visser,Ā Sven J. van der LeeĀ &Ā Betty M. Tijms

  3. Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands

    Lianne M. Reus,Ā Yolande A. L. Pijnenburg,Ā Anouk den Braber,Ā Wiesje M. van der Flier,Ā Pieter Jelle Visser,Ā Sven J. van der LeeĀ &Ā Betty M. Tijms

  4. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Toni Boltz,Ā Loes Olde LoohuisĀ &Ā Roel A. Ophoff

  5. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Maria Koromina

  6. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Maria Koromina

  7. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA

    Maria Koromina

  8. Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Anouk den Braber

  9. Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands

    Wiesje M. van der Flier

  10. Department of Psychiatry, Maastricht University, Maastricht, The Netherlands

    Pieter Jelle Visser

  11. Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, Sweden

    Pieter Jelle Visser

  12. Section Genomics of Neurodegenerative Diseases and Aging, Department of Human Genetics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands

    Sven J. van der Lee

  13. Neurochemistry Lab, Department of Laboratory Medicine, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Charlotte E. Teunissen

  14. Department of Computational Medicine, University of California, Los Angeles, CA, USA

    Loes Olde Loohuis

  15. Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands

    Roel A. Ophoff

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  1. Lianne M. Reus
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Contributions

LMR and TB led and performed the main analyses and wrote the main draft of the manuscript. MF, NR, MK aided in data QC and analysis. MB prepared and maintained cognitively healthy CSF and genotype samples for processing. YALP, AB, WMF, PJV, SJL, BMT, and CET provided the Amsterdam Dementia Cohort clinical data and sample collection, and contributed to discussion of results. LOL and RAO contributed to the conception, supervised analyses, and the discussion of the results. All authors have reviewed and approved the manuscript.

Corresponding authors

Correspondence to Lianne M. Reus, Toni Boltz or Roel A. Ophoff.

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Reus, L.M., Boltz, T., Francia, M. et al. Quantitative trait loci mapping of circulating metabolites in cerebrospinal fluid to uncover biological mechanisms involved in brain-related phenotypes. Mol Psychiatry 30, 3478ā€“3490 (2025). https://doi.org/10.1038/s41380-025-02934-0

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