Genetic effects on migration behaviour contribute to increasing spatial differentiation at trait-associated loci in Estonia
Bibliographic record
Abstract
Abstract There is emerging evidence that migration behaviour can be selective with respect to individuals’ genotypes. As a result, it can generate genotype-environment correlations at migration-associated loci that cannot be corrected by standard methods used in genetic association studies. Here, we explore this phenomenon by looking into the dynamics of the spatial distribution of polygenic scores (PGSs) in the Estonian Biobank. We show that contemporary migrations intensify inter-regional differences in PGSs for many traits, with the effect of educational attainment (EA) PGS being the strongest and, to a large extent, explaining inter-regional differences in other PGSs. This differentiation is mainly driven by the migration of individuals with relatively high PGS for EA to the two largest cities from the rest of the country. Importantly, we replicate this pattern within families: individuals migrating to the major cities have, on average, higher PGS for EA relative to their siblings residing in other regions of Estonia. This trend is observed for the period starting from the mid-20th century to the present, despite substantial changes in Estonian society during this period. Thus, we show that there is increasing genetic differentiation at trait-associated loci between most urbanized regions of Estonia versus the rest of the country, leading to genotype-environment correlations that cannot be fully corrected using standard approaches. We also provide evidence for direct genetic effects on migration behaviour based on sibling analysis and discuss potential links between migration and EA.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".