Non-uniform genetic effect sizes of variants associated with refractive error suggests gene-gene or gene-environment interactions are pervasive
Bibliographic record
Abstract
Abstract Refractive error is a complex ocular trait controlled by genetic and environmental factors. Genome-wide association studies (GWAS) have identified approximately 150 genetic variants associated with refractive error. Among the known environmental factors, education, near-work and time spent outdoors have been demonstrated to have the strongest associations. Currently, the extent of gene-environment or gene-gene interactions in myopia is unknown. Here we show that the majority of genetic variants associated with refractive error show evidence of effect size heterogeneity, which is a hallmark feature of genetic interactions. Using conditional quantile regression, we observed that 88% of genetic variants associated with refractive error have at least nominally-significant non-uniform, non-linear profiles across the refractive error distribution. SNP effects tend to be strongest at the phenotype extremes and have weaker effects in emmetropes. A parsimonious explanation for these findings is that gene-environment or gene-gene interactions in refractive error are pervasive. Author summary The prevalence of myopia (nearsightedness) in the United States and East Asia has almost doubled in the past 30 years. Such a rapid rise in prevalence cannot be explained by genetics, which implies that environmental (lifestyle) risk factors play a major role. Nevertheless, diverse approaches have suggested that genetics is also important, and indeed approximately 150 distinct genetic risk loci for myopia have been discovered to date. One attractive explanation for the evidence implicating both genes and environment in myopia is gene-environment (GxE) interaction (a difference in genetic effect in individuals exposed to a high vs. low level of an environmental risk factor). Past studies aiming to discover GxE interactions in myopia have met with limited success, perhaps because information on lifestyle exposures during childhood has rarely been available. Here we used an agnostic approach that does not require information about specific lifestyle exposures in order to detect ‘signatures’ of GxE interaction. We found compelling evidence for widespread genetic interactions in myopia, with 88% of 150 known myopia genetic susceptibility loci showing an interaction signature. These findings suggest that GxE interactions in myopia are pervasive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".