16<sup>th</sup> IHIW: Analysis of <scp>HLA</scp> Population Data, with updated results for 1996 to 2012 workshop data (<scp>AHPD</scp> project report)
Bibliographic record
Abstract
We present here the results of the Analysis of HLA Population Data (AHPD) project of the 16th International HLA and Immunogenetics Workshop (16IHIW) held in Liverpool in May-June 2012. Thanks to the collaboration of 25 laboratories from 18 different countries, HLA genotypic data for 59 new population samples (either well-defined populations or donor registry samples) were gathered and 55 were analysed statistically following HLA-NET recommendations. The new data included, among others, large sets of well-defined populations from north-east Europe and West Asia, as well as many donor registry data from European countries. The Gene[rate] computer tools were combined to create a Gene[rate] computer pipeline to automatically (i) estimate allele frequencies by an expectation-maximization algorithm accommodating ambiguities, (ii) estimate heterozygosity, (iii) test for Hardy-Weinberg equilibrium (HWE), (iv) test for selective neutrality, (v) generate frequency graphs and summary statistics for each sample at each locus and (vi) plot multidimensional scaling (MDS) analyses comparing the new samples with previous IHIW data. Intrapopulation analyses show that HWE is rarely rejected, while neutrality tests often indicate a significant excess of heterozygotes compared with neutral expectations. The comparison of the 16IHIW AHPD data with data collected during previous workshops (12th-15th) shows that geography is an excellent predictor of HLA genetic differentiations for HLA-A, -B and -DRB1 loci but not for HLA-DQ, whose patterns are probably more influenced by natural selection. In Europe, HLA genetic variation clearly follows a north to south-east axis despite a low level of differentiation between European, North African and West Asian populations. Pacific populations are genetically close to Austronesian-speaking South-East Asian and Taiwanese populations, in agreement with current theories on the peopling of Oceania. Thanks to this project, HLA genetic variation is more clearly defined worldwide and better interpreted in relation to human peopling history and HLA molecular evolution.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".