The Role of Different Linkage Disequilibrium Patterns in Genomic Prediction: The gBULP Based Exploratory Method in Tehran Cardiometabolic Genetic Study
Bibliographic record
Abstract
Abstract Background: Current GWAS discoveries have discovered novel clinical improvements in recent decades, such as estimating whole-genome risk. Genetic prediction of traits has substantial impacts on public health care and disease prevention. This study aimed to investigate the effects of different linkage disequilibrium (LD) patterns on genomic prediction accuracy and SNP-based heritability estimation for four lipid profile traits.Results: This family-based study included 11,798 individuals ranging from 3 to 80 ys, extracted from Tehran Cardiometabolic Genetic Study (TCGS). LD patterns were considered on different thresholds (0.01, 0.03, 0.05, 0.07, 0.09, 0.1, 0.2, 0.3, 0.5, 0.6, 0.7, 0.8, and 0.9) to create subsets of SNPs. We have compared the prediction accuracy and SNP-based heritability estimation of the selected SNPs within these patterns as well as randomly selected SNPs with equal sizes. Subsets of SNPs selected based on LD patterns had a higher prediction accuracy level than subsets of SNPs selected randomly, and when the LD threshold increases, the difference tends to zero. The results were consistent when the prediction accuracy of subsets were adjusted for their SNP numbers in all traits. For all traits, when the number of SNPs was adjusted, between LD threshold 0.01 and 0.2, both prediction accuracy and SNP-based heritability have a dramatic rise. After substantial growth, there was a steady decline, and they reach a peak at an LD threshold between 0.2 and 0.3.Conclusions: This research indicated that having selected subsets of SNPs based on the LD threshold always outperform randomly selected SNPs for prediction objectives. However, determining the specific LD threshold for prediction purposes might be controversial since achieving the highest level of prediction accuracy, when the number of SNPs is adjusted, prompts different results (in our case, 0.3 when the SNP number was adjusted and 0.9 when the SNP number is not adjusted). Finally, we concluded that choosing the LD threshold as a tool to boost genetic prediction accuracy should be used with intense care.
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 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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".