SnpRecode: A Versatile and Fast Genotype Recoding and Correlation Function
Bibliographic record
Abstract
Abstract Genotype imputation is an essential tool used in genomic selection in plants and animals. A popular imputation tool used in animal genomics is FImpute. FImpute, however, accepts a specific genotype format and produces dosages whose conversion to VCF or Plink format requires multiple software packages in a pipeline with a large amount of processing time. We have developed SnpRecode as a helper tool that bridges the gap between regular genotype files and the FImpute imputation software by allowing for fast and seamless conversion of genotypes to-and-from FImpute format. SnpRecode also implements a fast genotype correlation function to estimate and plot the imputation accuracy. We run tests on 6,000 samples with a step of 1,000 to determine the performance of SnpRecode on various sample sizes and runtime and memory usage used as performance measures. The performance of SnpRecode was modest at 10sec/1,000 samples. Written in Python programming language, SnpRecode provides users with great flexibility in implementation with other software packages in a pipeline.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".