A comparison of different algorithms for phasing haplotypes using Holstein cattle genotypes and pedigree data
Bibliographic record
Abstract
Phasing genotypes to haplotypes is becoming increasingly important due to its applications in the study of diseases, population and evolutionary genetics, imputation, and so on. Several studies have focused on the development of computational methods that infer haplotype phase from population genotype data. The aim of this study was to compare phasing algorithms implemented in Beagle, Findhap, FImpute, Impute2, and ShapeIt2 software using 50k and 777k (HD) genotyping data. Six scenarios were considered: no-parents, sire-progeny pairs, sire-dam-progeny trios, each with and without pedigree information in Holstein cattle. Algorithms were compared with respect to their phasing accuracy and computational efficiency. In the studied population, Beagle and FImpute were more accurate than other phasing algorithms. Across scenarios, phasing accuracies for Beagle and FImpute were 99.49-99.90% and 99.44-99.99% for 50k, respectively, and 99.90-99.99% and 99.87-99.99% for HD, respectively. Generally, FImpute resulted in higher accuracy when genotypic information of at least one parent was available. In the absence of parental genotypes and pedigree information, Beagle and Impute2 (with double the default number of states) were slightly more accurate than FImpute. Findhap gave high phasing accuracy when parents' genotypes and pedigree information were available. In terms of computing time, Findhap was the fastest algorithm followed by FImpute. FImpute was 30 to 131, 87 to 786, and 353 to 1,400 times faster across scenarios than Beagle, ShapeIt2, and Impute2, respectively. In summary, FImpute and Beagle were the most accurate phasing algorithms. Moreover, the low computational requirement of FImpute makes it an attractive algorithm for phasing genotypes of large livestock populations.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".