Inheritance of rare functional GCKR variants and their contribution to triglyceride levels in families
Bibliographic record
Abstract
Significant resources have been invested in sequencing studies to investigate the role of rare variants in complex disease etiology. However, the diagnostic interpretation of individual rare variants remains a major challenge, and may require accurate variant functional classification and the collection of large numbers of variant carriers. Utilizing sequence data from 458 individuals with hypertriglyceridemia and 333 controls with normal plasma triglyceride levels, we investigated these issues using GCKR, encoding glucokinase regulatory protein. Eighteen rare non-synonymous GCKR variants identified in these 791 individuals were comprehensively characterized by a range of biochemical and cell biological assays, including a novel high-throughput-screening-based approach capable of measuring all variant proteins simultaneously. Functionally deleterious variants were collectively associated with hypertriglyceridemia, but a range of in silico prediction algorithms showed little consistency between algorithms and poor agreement with functional data. We extended our study by obtaining sequence data on family members; however, functional variants did not co-segregate with triglyceride levels. Therefore, despite evidence for their collective functional and clinical relevance, our results emphasize the low predictive value of rare GCKR variants in individuals and the complex heritability of lipid traits.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".