Looking at the root of fine-scale genetic structure in founder populations
Bibliographic record
Abstract
The genome sequencing revolution over the past few decades has generated data from increasingly large cohorts of individuals. The analysis of these data has allowed researchers to identify patterns in genetic variation within and between human populations. Differences in allele frequencies across diverse groups of individuals are commonly accounted for in genome wide association studies to avoid spurious associations. As a result, continental population structure observed in diverse cohorts has been well studied, and has led to many advances in our understanding of deep human history. However, the study of fine-scale structure within populations has only recently become possible as sample sizes of individuals belonging to the same population continue to increase. To this point, genomic data from founder populations has played an important role in investigating demographic factors that can lead to the formation of population structure. The work presented here investigates genetic signatures observed in founder populations as case studies to identify factors that can lead to the formation of fine-scale structure.First we consider a mutational signature observed in the Japanese cohort of the 1000 Genomes Project. Differences in mutational signatures across continental populations have been reported in multiple cohorts. These differences be- tween populations are measured as an enrichment in certain types of mutations. Over time, these mutational signatures can lead to the observation of genetic population structure. The source of these mutational signatures have been hypothesized to be the result of environmental factors or mutator phenotypes. However, we determined that the signature observed in the Japanese population of the 1000 Genomes Project was the result of a technical artefact resulting from sequencing technology batch effects. We developed new statistical methods that enabled us to identify suspicious variants in the Japanese cohort as well as the rest of the 1000 Genomes Project cohort. We also identified a number of publications whose results will have to be revisited in the light of our findings.Moving beyond technical artefacts, we turn our attention to another well studied founder population : the French- Canadian (FC) population of Quebec. First, by comparing the genomes of 2,276 French and 20,451 FC individuals, we find the structure observed in the FC population is independent of ancestral French population structure. Then, we generalized the msprime software to perform genome-wide coalescent simulations conditioning on the known pedigree of the FC population and provide a freely accessible simulated whole-genome sequence dataset with spatiotemporal metadata for 1,426,749 individuals reflecting intricate FC population structure. Furthermore, we detail how topography and historical events shaped the present day population of FC. We find enrichments for migration rates, genetic and genealogical relatedness within river networks across Quebec. We expect this high-resolution model of human populations will provide new opportunities to investigate population genetics at an unprecedented scale
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".