Genotype Imputation and Reference Panel: A Systematic Evaluation
Bibliographic record
Abstract
Abstract Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that the imputation accuracy could always benefit from the increased panel size when the reference panel was population-specific, the results brought two new thoughts as follows. First, when the haplotype size of reference panel was fixed, the imputation accuracy of common and low-frequency variants (MAF>0.5%) decreased while the population-diversity of reference panel increased, but for rare variants (MAF<0.5%), a fraction of diversity (<20%) of panel could improve the imputation accuracy. Second, when the haplotype size of reference panel was increased with extra population-diverse samples, the imputation accuracy of common variants (MAF>5%) for European population could always benefit from the expanding sample size. But for Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of extra diverse sample (15%∼21%). In addition, we evaluated the existing reference panels such as the HRC and 1000G Phase3 and CONVERGE. For European population, HRC was the best reference panel. For Han Chinese population, we proposed an optimum constituent ratio for the Han Chinese imputation if researchers would like to customize their own sequenced reference panel, but a high quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome, a tool was provided to investigate other populations of interest ( https://github.com/Abyss-bai/reference-panel-reconstruction ). Highlights (Key points) A total of 394 reference panels were designed and customized by three strategies, and large-scale genotype imputations were performed with these panels for systematic evaluation in Han Chinese and European populations. The accuracy of imputed variants reached the highest when reference panel contains a fraction of extra diverse sample (15%∼21%) for Han Chinese population, if the haplotype size of the reference panel was increased with extra samples, which is the most common cases. The imputation accuracy showed the different trends between Han Chinese and European populations. In a sense, the European genome may more diverse than Han Chinese genome by itself. Existing reference panels were not the best choice for Chinese imputation, a high quality and large-scale Chinese reference panel was still needed.
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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 | 0.218 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".