Longitudinal Data Analysis in Genome‐Wide Association Studies
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.
Bibliographic record
Abstract
Genome-wide association studies have led to the discovery of thousands of susceptibility genetic variants (typically single-nucleotide polymorphisms [SNPs]) for a wide range of complex diseases and traits commonly measured at a single point in time. Although many novel genotype-phenotype associations have been identified and successfully replicated using cross-sectionally measured phenotypes, there is growing interest in the study of longitudinally measured phenotypes because these allow for the study of the natural trajectory of traits and disease progression. However, there are several challenges with analysis and interpretation of longitudinal data. Here, we summarize the methods and strategies proposed and applied in genome-wide association studies of blood pressure related phenotypes made available through Genetic Analysis Workshop 18 (GAW18). The investigators considered methods that incorporated correlation across time points and familial relatedness among the individuals into their studies and compared their approaches with single-time-point analysis using baseline data. Some of the studies used unrelated individuals; some also used the simulated data provided by the GAW18 organizers to assess type I error and power of their approach in detecting true associations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it