Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/30846
Title: Depression symptom-specific genetic associations in clinically diagnosed and proxy case Alzheimer’s disease
Authors: Gilchrist, L
Spargo, TP
Green, RE
Coleman, JRI
Howard, DM
Thorp, JG
Adey, BN
Lord, J
Davies, HL
Mundy, J
ter Kuile, AR
Davies, MR
Hübel, C
Bristow, S
Lee, SH
Rogers, H
Curtis, C
Kakar, S
Malouf, CM
Kalsi, G
Arathimos, R
Corbett, A
Ballard, C
Brooker, H
Creese, B
Aarsland, D
Hampshire, A
Velayudhan, L
Eley, TC
Breen, G
Iacoangeli, A
Kõks, S
Lewis, CM
Proitsi, P
Keywords: genetic predisposition to disease;genome-wide association studies
Issue Date: 15-Jan-2025
Publisher: Springer Nature
Citation: Gilchrist, L. et al. (2025) 'Depression symptom-specific genetic associations in clinically diagnosed and proxy case Alzheimer’s disease', Nature Mental Health, 3, pp. 212 - 228. doi: 10.1038/s44220-024-00369-0.
Abstract: Depression is a risk factor for the later development of Alzheimer’s disease (AD), but evidence for the genetic relationship is mixed. Assessing depression symptom-specific genetic associations may better clarify this relationship. To address this, we conducted genome-wide meta-analysis (a genome-wide association study, GWAS) of the nine depression symptom items, plus their sum score, on the Patient Health Questionnaire (PHQ-9) (GWAS-equivalent N: 224,535–308,421) using data from UK Biobank, the GLAD study and PROTECT, identifying 37 genomic risk loci. Using six AD GWASs with varying proportions of clinical and proxy (family history) case ascertainment, we identified 20 significant genetic correlations with depression/depression symptoms. However, only one of these was identified with a clinical AD GWAS. Local genetic correlations were detected in 14 regions. No statistical colocalization was identified in these regions. However, the region of the transmembrane protein 106B gene (TMEM106B) showed colocalization between multiple depression phenotypes and both clinical-only and clinical + proxy AD. Mendelian randomization and polygenic risk score analyses did not yield significant results after multiple testing correction in either direction. Our findings do not demonstrate a causal role of depression/depression symptoms on AD and suggest that previous evidence of genetic overlap between depression and AD may be driven by the inclusion of family history-based proxy cases/controls. However, colocalization at TMEM106B warrants further investigation.
Description: Data availability: All GWAS summary statistics generated in the process of conducting this study have been deposited on Zenodo at https://doi.org/10.5281/zenodo.13828101 (ref. 121). Individual-level data from UK Biobank, GLAD and PROTECT are subject to restrictions. Data are available on reasonable request from UK Biobank (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/contact-us) through application to the NIHR BioResource for GLAD (https://bioresource.nihr.ac.uk/using-our-bioresource/academic-and-clinical-researchers/apply-for-bioresource-data/) and through the PROTECT data team (https://medicine.exeter.ac.uk/clinical-biomedical/research/protect/). The Alzheimer’s disease GWAS summary statistics used in this study are publically available through the GWAS catalog (https://www.ebi.ac.uk/gwas/efotraits/MONDO_0004975). GWAS summary statistics for the Wightman et al. GWAS excluding the UK Biobank are available at https://vu.data.surfsara.nl/index.php/s/LGjeIk6phQ6zw8I. For clinical and broad depression, summary statistics are available through the Psychiatric Genomic Consortium (https://pgc.unc.edu). eQTL summary datasets used in SMR analysis from Lloyd-Jones et al.100 and PsychENCODE101 can be obtained from the website of the Yang laboratory (https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata). This study has been pre-registered on the Open Science Framework (https://osf.io/94q35/?view_only=e77f72d4100d47eea7f3ef07dfa9c059).
Code availability; Code for performing these analyses has been deposited on GitHub (https://github.com/lpgilchrist/PHQ-9_AD_genetic_overlap_project). This study made use of the following publicly available analysis software: CAUSE (https://jean997.github.io/cause/index.html); coloc (https://chr1swallace.github.io/coloc/); COLOC-reporter (https://github.com/ThomasPSpargo/COLOC-reporter); FUMA GWAS (https://fuma.ctglab.nl); HDL (https://github.com/zhenin/HDL); LAVA (https://github.com/josefin-werme/LAVA); LDSC (https://github.com/bulik/ldsc); MegaPRS (https://dougspeed.com/megaprs/); METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation); MTAG (https://github.com/JonJala/mtag); MungeSumstats (https://github.com/Al-Murphy/MungeSumstats); REGENIE (https://rgcgithub.github.io/regenie/); SMR (https://yanglab.westlake.edu.cn/software/smr/); susieR (https://stephenslab.github.io/susieR/index.html); TwoSampleMR (https://mrcieu.github.io/TwoSampleMR/).
Supplementary information is available online at: https://www.nature.com/articles/s44220-024-00369-0#Sec33 .
For the purposes of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any accepted author manuscript version arising from this submission.
URI: https://bura.brunel.ac.uk/handle/2438/30846
DOI: https://doi.org/10.1038/s44220-024-00369-0
Other Identifiers: ORCiD: Lachlan Gilchrist https://orcid.org/0009-0005-6484-2105
ORCiD: Thomas P. Spargo https://orcid.org/0000-0003-4297-6418
ORCiD: Rebecca E. Green https://orcid.org/0000-0002-0513-5188
ORCiD: Jonathan R. I. Coleman https://orcid.org/0000-0002-6759-0944
ORCiD: David M. Howard https://orcid.org/0000-0002-6005-1972
ORCiD: Jackson G. Thorp https://orcid.org/0000-0002-9461-6417
ORCiD: Brett N. Adey https://orcid.org/0000-0003-4356-4079
ORCiD: Jodie Lord https://orcid.org/0000-0003-2955-7809
ORCiD: Helena L. Davies https://orcid.org/0000-0002-9419-1009
ORCiD: Jessica Mundy https://orcid.org/0000-0001-5513-8902
ORCiD: Abigail R. ter Kuile https://orcid.org/0000-0002-7869-3754
ORCiD: Molly R. Davies https://orcid.org/0000-0003-3483-9907
ORCiD: Christopher Hübel https://orcid.org/0000-0002-1267-8287
ORCiD: Shannon Bristow https://orcid.org/0000-0002-0896-781X
ORCiD: Sang Hyuck Lee https://orcid.org/0000-0002-5610-033X
ORCiD: Henry Rogers https://orcid.org/0000-0003-2531-7496
ORCiD: Charles Curtis https://orcid.org/0000-0002-2992-1941
ORCiD: Saakshi Kakar https://orcid.org/0000-0003-1677-1857
ORCiD: Chelsea M. Malouf https://orcid.org/0000-0002-5564-7464
ORCiD: Gursharan Kalsi https://orcid.org/0000-0002-5156-7176
ORCiD: Ryan Arathimos https://orcid.org/0000-0002-0473-500X
ORCiD: Anne Corbett https://orcid.org/0000-0003-2015-0316
ORCiD: Clive Ballard https://orcid.org/0000-0003-0022-5632
ORCiD: Helen Brooker https://orcid.org/0000-0002-7908-263X
ORCiD: Byron Creese https://orcid.org/0000-0001-6490-6037
ORCiD: Dag Aarsland https://orcid.org/0000-0001-6314-216X
ORCiD: Adam Hampshire https://orcid.org/0000-0002-5176-5420
ORCiD: Latha Velayudhan https://orcid.org/0000-0002-7712-930X
ORCiD: Thalia C. Eley https://orcid.org/0000-0001-6458-0700
ORCiD: Gerome Breen https://orcid.org/0000-0003-2053-1792
ORCiD: Alfredo Iacoangeli https://orcid.org/0000-0002-5280-5017
ORCiD: Sulev Kõks https://orcid.org/0000-0001-6087-6643
ORCiD: Cathryn M. Lewis https://orcid.org/0000-0002-8249-8476
ORCiD: Petroula Proitsi https://orcid.org/0000-0002-2553-6974
Appears in Collections:Dept of Life Sciences Research Papers

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