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Predictive Power of Polygenic Risk Scores for Intraocular Pressure or Vertical Cup-Disc Ratio

2024· letter· en· W4404586544 on OpenAlexaboutno aff
Weixiong He, Samantha Sze‐Yee Lee, Santiago Díaz‐Torres, Xikun Han, Puya Gharahkhani, Michael Hunter, Chandrakumar Balartnasingam, Jamie E. Craig, Alex W. Hewitt, David A. Mackey, Stuart MacGregor

Bibliographic record

VenueJAMA Ophthalmology · 2024
Typeletter
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlaucomaIntraocular pressureOphthalmologyOptic nerveGenome-wide association studyOptic diskOpen angle glaucomaOcular hypertensionOptic discOptometrySingle-nucleotide polymorphismGenotypeGenetics

Abstract

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Importance: Early detection of glaucoma is essential to timely monitoring and treatment, and primary open-angle glaucoma risk can be assessed by measuring intraocular pressure (IOP) or optic nerve head vertical cup-disc ratio (VCDR). Polygenic risk scores (PRSs) could provide a link between genetic effects estimated from genome-wide association studies (GWASs) and clinical applications to provide estimates of an individual's genetic risk by combining many identified variants into a score. Objective: To construct IOP and VCDR PRSs with clinically relevant predictive power. Design, Setting, and Participants: This genetic association study evaluated the PRSs for 6959 of 51 338 individuals in the Canadian Longitudinal Study on Aging (CLSA; 2010 to 2015 with data from 11 centers in Canada) and 4960 of 5107 individuals the community-based Busselton Healthy Aging Study (BHAS; 2010 to 2015 in Busselton, Western Australia) with an artificial intelligence grading approach used to obtain precise VCDR estimates for the CLSA dataset. Data for approximately 500 000 individuals in UK Biobank from 2006 to 2010 were used to validate the power of the PRS. Data were analyzed from June to November 2023. Main Outcomes and Measures: IOP and VCDR PRSs and phenotypic variance (R2) explained by each PRS. Results: Participants in CLSA were aged 45 to 85 years; those in BHAS, 46 to 64 years; and those in UK Biobank, 40 to 69 years. The VCDR PRS explained 22.0% (95% CI, 20.1-23.9) and 19.7% (95% CI, 16.3-23.3) of the phenotypic variance in VCDR in CLSA and BHAS, respectively, while the IOP PRS explained 12.9% (95% CI, 11.3-14.6) and 9.6% (95% CI, 8.1-11.2) of phenotypic variance in CLSA and BHAS IOP measurements. The VCDR PRS variance explained 5.2% (95% CI, 3.6-7.1), 12.1% (95% CI, 7.5-17.5), and 14.3% (95% CI, 9.3-19.9), and the IOP PRS variance explained 2.3% (95% CI, 1.5-3.3), 3.2% (95% CI, 1.3-5.8), and 7.5% (95% CI, 6.2-8.9) (P < .001) across African, East Asian, and South Asian populations, respectively. Conclusions and Relevance: VCDR and IOP PRSs derived using a large recently published multitrait GWAS exhibited validity across independent cohorts. The findings suggest that an IOP PRS has the potential to identify individuals who may benefit from more intensive IOP-lowering treatments, which could be crucial in managing glaucoma risk more effectively. Individuals with a high VCDR PRS may be at risk of developing glaucoma even if their IOP measures fall within the normal range, suggesting that these PRSs could help in early detection and intervention, particularly among those who might otherwise be considered at low risk based on IOP alone.

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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)0.010
metaresearch head score (Gemma)0.046
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 score0.021
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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".

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Citations6
Published2024
Admission routes1
Has abstractyes

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