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

2024· letter· en· W4404586544 on OpenAlex
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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
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 head0.014
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