Autosomal haplotypes as markers for the histories and structures of human populations
Bibliographic record
Abstract
The demographic history of humans is very complex. Populations have undergone bottlenecks, isolation, migration, admixture and expansions. All of these have added to the complexity of what makes a population and how that population has changed over time. A record of these events can be found in our DNA. This project used autosomal DNA to trace the histories and structures of human populations by using a combination of a SNP (single nucleotide polymorphism) and an STR (short tandem repeat) – SNPSTR (Mountain et al. 2002). Forensic STRs formed the basis for the SNPSTR systems because their allelic diversity is well characterised, their mutation rates have been reliably measured and they are robust in PCR amplification. Four SNPSTR systems were found, using SNPs which had been verified by HapMap and/or Perlegen and which were < 500 base pairs away from the forensic STRs. These SNPSTRs were typed on DNAs from the HapMap project, the CEPH-HGDP, Cornwall, UK African Caribbeans, Danes and Greenland Inuit. They were analysed using an ABI3100 and GeneMapper software. Data from the combined SNPSTRs allowed inferences to be made about population structures, and also enabled the calculation of the TMRCA of the derived SNPs associated with the forensic STRs. Population structure was evident in the MDS plots where rudimentary population groupings could be seen. The Americas were outliers, reflecting their later peopling some 15,000 years ago (Jobling et al. 2003). Haplogroup analysis highlighted population isolates, such as the Surui in Brazil. The STRUCTURE analysis of the SNPSTR data has also provided some insights into the admixed nature of the autosomal DNA in known admixed populations such as the Greenland Inuit and to some extent, the African Caribbeans. One SNPSTR was expanded into a larger haplotype block - a PHAX - Phylogeographically informative Haplotypes on the Autosomes and seX chromosomes, by means of a SNaPshot reaction. The preliminary data from this suggested that this would allow us to gain a more complete insight into the histories and structures of human populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".