Mixed-effects models for joint modeling of sequence data in longitudinal studies
Bibliographic record
Abstract
In this paper, we propose a novel mixed-effects model for longitudinal changes of systolic blood pressure (SBP) over time that can estimate the joint effect of multiple sequence variants on SBP after accounting for familial correlation and the time dependencies within individuals. First we carried out agenome-wide association study (GWAS) using chromosome 3 single-nucleotide polymorphisms(SNPs) to identify regions associated with SBP levels. In a second step, we examined the sequence data to fine-map additional variants in these regions. Four SNPs from two intergenic regions (PLXNA1-TPRA1, BPESC1-PISTR1) and one gene (NLGN1) were detected to be significantly associated with SBP after adjusting for multiple testing. These SNPs were used to capture the multilocus genotype diversity in the regions. The multilocus genotypes derived from these four variants were then treated as random effects in the mixed-effects model, and the corresponding confidence intervals (Cis) were built to assess the significance of the joint effect of the sequence variants on SBP. We found that multilocus genotypes (GG,TT,AG,GG), (GG,TT,GG,GG), and (GG,TT,AA,AG) are associated with higher SBPand (GG,CT,AA,AA), (AA,TT,AA,AA), and (AG,CT,AA,AG) are associated with lower SBP. The linear mixed-effects models provide a powerful tool for GWAS and the analysis of joint modeling of multilocus genotypes.
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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 | 0.060 | 0.083 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".