A sequence of methodological changes due to sequencing
Bibliographic record
Abstract
PURPOSE OF REVIEW: During the past 2 years, next-generation sequencing studies have revolutionized the field of genetic association studies. We review the concomitant evolution of statistical methods. RECENT FINDINGS: As much of the genetic variability identified with sequencing is extremely rare, many new methods have been developed for rare variant association studies. Sequencing data available as a result of large public projects are also being integrated with genome-wide association study (GWAS) chip data to improve genotype imputation. A further trend in recent methodological development has been the use of the linear mixed effect model (LMM). LMMs are used for rare variant association to handle effect heterogeneity. They are also used more generally in GWAS to account for population structure. SUMMARY: Many rare variant association tests have been developed to analyze the genetic variation discovered with large-scale DNA sequencing; however, no single approach outperforms others under all disease models and power tends to be low. Sequencing data are also contributing to improved imputation of uncommon genetic variants, although imputation of rare variants remains a challenge. The appropriate correction for population structure in rare variant analyses remains unclear; specialized adjustment techniques may be necessary.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".