Genotype Imputation Methods and Their Effects on Genomic Predictions in Cattle
Bibliographic record
Abstract
In this study, we reviewed six imputation methods (Impute 2, FImpute 2.2, Beagle 4.1, Beagle 3.3.2, MaCH, and Bimbam) and evaluated the accuracy of imputation from simulated 6K bovine SNPs to 50K SNPs with 1800 beef cattle from two purebred and four crossbred populations and the impact of imputed genotypes on performance of genomic predictions for residual feed intake (RFI) in beef cattle. Accuracy of imputation was reported in both concordance rate (CR) and allelic $$r^{2}$$ and assessed via fivefold cross-validations. Running times of different methods were compared. Impute 2, FImpute and Beagle 4.1 yielded the most accurate imputation results (with CR > 91%). FImpute was the fastest and had advantages over all other methods in imputing rare variants. Minor allele frequency (MAF) and genetic relatedness between individuals in reference and validation populations can affect accuracy of imputation. For all methods, imputation accuracy for genotypes carrying the minor allele increases as the MAF increases. Impute 2 outperformed all other methods on MAF > 5% and onwards. FImpute and Impute 2 that adopted the nearest neighbour scheme coped better with individuals of distant relativeness. Bimbam yielded the poorest CR (76%) due to admixed reference panels. Imputed genotypes and actual 50K/6K genotypes were employed to predict genomic breeding values (GEBVs) of RFI using a Bayesian method and GBLUP. Accuracies of GEBV were similar using actual 50K genotypes or imputed genotypes, except those from Bimbam, and the imputation errors had minimal impact on the genomic predictions.
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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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".