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Record W3203936566 · doi:10.1038/s41386-021-01189-x

Transcriptome-based polygenic score links depression-related corticolimbic gene expression changes to sex-specific brain morphology and depression risk

2021· article· en· W3203936566 on OpenAlexafffund
Amy Miles, Fernanda C. Dos Santos, Enda M. Byrne, Miguel E. Rentería, Andrew M. McIntosh, Mark J. Adams, Giorgio Pistis, Enrique Castelao, Martin Preisig, Bernhard T. Baune, Klaus Oliver Schubert, Cathryn M. Lewis, Lisa Jones, Ian Jones, Rudolf Uher, Jordan W. Smoller, Roy H. Perlis, Douglas F. Levinson, James B. Potash, Myrna M. Weissman, Jianxin Shi, Glyn Lewis, Brenda W.J.H. Penninx, Dorret I. Boomsma, Steven P. Hamilton, Etienne Sibille, Ahmad R. Hariri, Yuliya S. Nikolova

Bibliographic record

VenueNeuropsychopharmacology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of TorontoDalhousie UniversityCentre for Addiction and Mental Health
FundersNational Institute on Drug AbuseNational Institute on AgingNational Institute of Mental HealthMedical Research CouncilCentre for Addiction and Mental Health FoundationNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Alliance for Research on Schizophrenia and Depression
KeywordsDepression (economics)TranscriptomePolygenic risk scoreBrain morphometryPsychologyGene expressionGeneClinical psychologyNeuroscienceInternal medicineBiologyMedicineGeneticsSingle-nucleotide polymorphismMagnetic resonance imagingGenotype

Abstract

fetched live from OpenAlex

Studies in post-mortem human brain tissue have associated major depressive disorder (MDD) with cortical transcriptomic changes, whose potential in vivo impact remains unexplored. To address this translational gap, we recently developed a transcriptome-based polygenic risk score (T-PRS) based on common functional variants capturing ‘depression-like’ shifts in cortical gene expression. Here, we used a non-clinical sample of young adults ( n = 482, Duke Neurogenetics Study: 53% women; aged 19.8 ± 1.2 years) to map T-PRS onto brain morphology measures, including Freesurfer-derived subcortical volume, cortical thickness, surface area, and local gyrification index, as well as broad MDD risk, indexed by self-reported family history of depression. We conducted side-by-side comparisons with a PRS independently derived from a Psychiatric Genomics Consortium (PGC) MDD GWAS (PGC-PRS), and sought to link T-PRS with diagnosis and symptom severity directly in PGC-MDD participants ( n = 29,340, 59% women; 12,923 MDD cases, 16,417 controls). T-PRS was associated with smaller amygdala volume in women ( t = −3.478, p = 0.001) and lower prefrontal gyrification across sexes. In men, T-PRS was associated with hypergyrification in temporal and occipital regions. Prefrontal hypogyrification mediated a male-specific indirect link between T-PRS and familial depression ( b = 0.005, p = 0.029). PGC-PRS was similarly associated with lower amygdala volume and cortical gyrification; however, both effects were male-specific and hypogyrification emerged in distinct parietal and temporo-occipital regions, unassociated with familial depression. In PGC-MDD, T-PRS did not predict diagnosis (OR = 1.007, 95% CI = [0.997–1.018]) but correlated with symptom severity in men (rho = 0.175, p = 7.957 × 10 −4 ) in one cohort ( N = 762, 48% men). Depression-like shifts in cortical gene expression have sex-specific effects on brain morphology and may contribute to broad depression vulnerability in men.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.298
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2021
Admission routes2
Has abstractyes

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