Imputing genotypes using regularized generalized linear regression models
Bibliographic record
Abstract
As genomic sequencing technologies continue to advance, researchers are furthering their understanding of the relationships between genetic variants and expressed traits. However, missing data can significantly limit the power of a genetic study. Here, the use of a regularized generalized linear model, denoted by GLMNET, is proposed to impute missing genotypes. The method aims to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly the specification of the number of neighboring SNPs to be included for imputing the missing genotype. The performance of GLMNET-based method is compared to the conventional multinomial regression method and two phase-based methods: fastPHASE and BEAGLE. Two simulation scenarios are evaluated: a sparse-missing model, and a small-panel expansion model. The sparse-missing model simulates a scenario where SNPs were missing in a random fashion across the genome. In the small-panel expansion model, a set of individuals is only genotyped at a subset of the SNPs of the large panel. Each imputation method is tested in the context of two data-sets: Canadian Holstein cattle data and human HapMap CEU data. Results show that the proposed GLMNET method outperforms the other methods in the small panel expansion scenario and fastPHASE performs slightly better than the GLMNET method in the sparse-missing scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".