Detecting SNP Combinations Discriminating Human Populations From HapMap Data
Bibliographic record
Abstract
The genomes of different human beings are similar. There are only a relatively small number of genetic differences between people. The genetic differences between people are very worthy of study. Researchers have proposed the fixation index FST measurement to find the single nucleotide polymorphisms (SNPs) which can reflect human population differences. However, most SNPs have interactions and they work together, which leads to the differences among human populations. The number of all possible m-locus combinations chosen from n SNPs grows exponentially. Most methods concern on 2-locus interactions. In this paper, we propose a novel method to find a new coordinate system under which the energy distributions of different populations are quite different. We select out candidate SNPs from n SNPs by using the information of the axes in the coordinate system. The number of candidate SNPs is small, thus SNP-SNP interactions can be searched efficiently. The method can also find interactions of more than two loci. These interactions should be able to reflect the evolution of human populations from another way. The numbers of SNP-SNP interactions are regarded as the differences between pairwise populations and a hierarchical clustering algorithm is used to construct the evolutionary tree. In the experiments, we apply the method to SNP data of four chromosomes separately and the trees constructed on these four chromosomes are highly consistent. Furthermore, the trees are also consistent with previous studies, which indicates that evolutionary information is well mined. The method provides a new insight to analyze the human population differences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".