Deconstructing the GWAS library: next-generation GWAS
Bibliographic record
Abstract
Genome-wide association studies (GWAS) have identified numerous common genetic variants associated with cardiovascular traits and diseases. These studies have increased our understanding of the genetic architecture of cardiac diseases and have facilitated the identification of genetic risk factors in patients. Furthermore, they have spurred the development of novel effective therapies by targeting the causal disease pathways. Despite the demonstrated clinical utility of GWAS, the mechanism of action of many of these variants remains unstudied, and this has hindered the full potential of GWAS. Various high-throughput screening and machine-learning technologies have been developed to assist with predicting and prioritizing pathogenic variants for experimental validation. These technologies can potentially unravel novel pathways in disease pathogenesis and accelerate the development of new therapies. In this review, we provide an overview of the various GWAS performed in heart disease and describe the various methods employed to prioritize disease-relevant variants from these studies, including bioinformatic and experimental approaches. We highlight relevant examples that have applied these tools to identify the specific variants in each identified locus and how some of these variants have spurred novel therapies. Finally, we discuss the outstanding challenges facing research in this field and how they can be addressed.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".