A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs
Bibliographic record
Abstract
Motivation: Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation. Results: We present Big-LD, a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. Big-LD uses an agglomerative approach that starts by identifying small communities of SNPs, i.e. the SNPs in each LD bin region, and proceeds by merging these communities. We determine the number of blocks using a method to find maximum-weight independent set. Big-LD produces larger LD blocks compared to existing methods such as MATILDE, Haploview, MIG ++, or S-MIG ++ and the LD blocks better agree with recombination hotspot locations determined by sperm-typing experiments. The observed average runtime of Big-LD for 13 288 240 non-monomorphic SNPs from 1000 Genomes Project autosome data (286 East Asians) is about 5.83 h, which is a significant improvement over the existing methods. Availability and implementation: Source code and documentation are available for download at http://github.com/sunnyeesl/BigLD. Contact: yyoo@snu.ac.kr. Supplementary information: Supplementary data are available at Bioinformatics online.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".